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Sem van der Hoeven
2021-05-26 15:12:05 +02:00
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.gitignore vendored Normal file
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.DS_Store
.floydexpt
.floydignore
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
env/
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
*.egg-info/
.installed.cfg
*.egg
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*,cover
.hypothesis/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# IPython Notebook
.ipynb_checkpoints
# pyenv
.python-version
# celery beat schedule file
celerybeat-schedule
# dotenv
.env
# virtualenv
venv/
ENV/
# Spyder project settings
.spyderproject
# Rope project settings
.ropeproject

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dist: xenial
language: python
python:
- "3.7"
install:
- sudo apt-get update
# We do this conditionally because it saves us some downloading if the
# version is the same.
- wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
- bash miniconda.sh -b -p $HOME/miniconda
- export PATH="$HOME/miniconda/bin:$PATH"
- hash -r
- conda config --set always_yes yes --set changeps1 no
- conda update -q conda
# Useful for debugging any issues with conda
- conda info -a
- conda env create -q -n test-environment python=$TRAVIS_PYTHON_VERSION -f environment.yml
- source activate test-environment
script:
- travis_wait 30 py.test -v

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COPYRIGHT
All contributions by Francesco Mosconi:
Copyright (c) 2017, Francesco Mosconi.
All rights reserved.
All contributions by Catalit LLC:
Copyright (c) 2017, Catalit LLC.
All rights reserved.
All other contributions:
Copyright (c) 2015, the respective contributors.
All rights reserved.
Each contributor holds copyright over their respective contributions.
The project versioning (Git) records all such contribution source information.
MIT License
Copyright (c) 2017
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Check Environment\n",
"This notebook checks that you have correctly created the environment and that all packages needed are installed."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Environment\n",
"\n",
"The next command should return a line like (Mac/Linux):\n",
"\n",
" /<YOUR-HOME-FOLDER>/anaconda/envs/ztdl/bin/python\n",
"\n",
"or like (Windows 10):\n",
"\n",
" C:\\\\<YOUR-HOME-FOLDER>\\\\Anaconda3\\\\envs\\\\ztdl\\\\python.exe\n",
"\n",
"In particular you should make sure that you are using the python executable from within the course environment.\n",
"\n",
"If that's not the case do this:\n",
"\n",
"1. close this notebook\n",
"2. go to the terminal and stop jupyer notebook\n",
"3. make sure that you have activated the environment, you should see a prompt like:\n",
"\n",
" (ztdl) $\n",
"4. (optional) if you don't see that prompt activate the environment:\n",
" - mac/linux:\n",
" \n",
" conda activate ztdl\n",
"\n",
" - windows:\n",
"\n",
" activate ztdl\n",
"5. restart jupyter notebook"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"sys.executable"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Python 3.7\n",
"\n",
"The next line should say that you're using Python 3.7.x from Anaconda. At the time of publication it looks like this (Mac/Linux):\n",
"\n",
" Python 3.7.3 (default, Mar 27 2019, 22:11:17)\n",
" [GCC 7.3.0] :: Anaconda, Inc. on linux\n",
" Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n",
"\n",
"or like this (Windows 10):\n",
"\n",
" Python 3.7.3 (default, Apr 24 2019, 15:29:51) [MSC v.1915 64 bit (AMD64)] :: Anaconda, Inc. on win32\n",
" Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n",
"\n",
"but date and exact version of GCC may change in the future.\n",
"\n",
"If you see a different version of python, go back to the previous step and make sure you created and activated the environment correctly."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.version"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Jupyter\n",
"\n",
"Check that Jupyter is running from within the environment. The next line should look like (Mac/Linux):\n",
"\n",
" /<YOUR-HOME-FOLDER>/anaconda/envs/ztdl/lib/python3.6/site-packages/jupyter.py'\n",
"\n",
"or like this (Windows 10):\n",
"\n",
" C:\\\\Users\\\\<YOUR-USER>\\\\Anaconda3\\\\envs\\\\ztdl\\\\lib\\\\site-packages\\\\jupyter.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import jupyter\n",
"jupyter.__file__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Other packages\n",
"\n",
"Here we will check that all the packages are installed and have the correct versions. If everything is ok you should see:\n",
" \n",
" Using TensorFlow backend.\n",
" \n",
" Houston we are go!\n",
"\n",
"If there's any issue here please make sure you have checked the previous steps and if it's all good please send us a question in the Q&A forum."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pip\n",
"import numpy\n",
"import jupyter\n",
"import matplotlib\n",
"import sklearn\n",
"import scipy\n",
"import pandas\n",
"import PIL\n",
"import seaborn\n",
"import tensorflow\n",
"\n",
"\n",
"def check_version(pkg, version):\n",
" actual = pkg.__version__.split('.')\n",
" if len(actual) == 3:\n",
" actual_major = '.'.join(actual[:2])\n",
" elif len(actual) == 2:\n",
" actual_major = '.'.join(actual)\n",
" else:\n",
" raise NotImplementedError(pkg.__name__ +\n",
" \"actual version :\"+\n",
" pkg.__version__)\n",
" try:\n",
" assert(actual_major == version)\n",
" except Exception as ex:\n",
" print(\"{} {}\\t=> {}\".format(pkg.__name__,\n",
" version,\n",
" pkg.__version__))\n",
" raise ex\n",
"\n",
"check_version(pip, '21.0')\n",
"check_version(numpy, '1.19')\n",
"check_version(matplotlib, '3.3')\n",
"check_version(sklearn, '0.24')\n",
"check_version(scipy, '1.6')\n",
"check_version(pandas, '1.2')\n",
"check_version(PIL, '8.2')\n",
"check_version(seaborn, '0.11')\n",
"check_version(tensorflow, '2.5')\n",
"\n",
"print(\"Houston we are go!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# First Deep Learning Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np # import the numpy library and assign the name np to it\n",
"%matplotlib inline # magic function that sets the backend of matplotlib to the inline backend\n",
"import matplotlib.pyplot as plt # import the matplotlib.pyplot and assign the name plt to it"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import make_circles"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X, y = make_circles(n_samples=1000,\n",
" noise=0.1,\n",
" factor=0.2,\n",
" random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(5, 5))\n",
"plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
"plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
"plt.xlim(-1.5, 1.5)\n",
"plt.ylim(-1.5, 1.5)\n",
"plt.legend(['0', '1'])\n",
"plt.title(\"Blue circles and Red crosses\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense\n",
"from tensorflow.keras.optimizers import SGD"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.add(Dense(4, input_shape=(2,), activation='tanh'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.add(Dense(1, activation='sigmoid'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.compile(SGD(learning_rate=0.5), 'binary_crossentropy', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X, y, epochs=20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"hticks = np.linspace(-1.5, 1.5, 101)\n",
"vticks = np.linspace(-1.5, 1.5, 101)\n",
"aa, bb = np.meshgrid(hticks, vticks)\n",
"ab = np.c_[aa.ravel(), bb.ravel()]\n",
"c = model.predict(ab)\n",
"cc = c.reshape(aa.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(5, 5))\n",
"plt.contourf(aa, bb, cc, cmap='bwr', alpha=0.2)\n",
"plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
"plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
"plt.xlim(-1.5, 1.5)\n",
"plt.ylim(-1.5, 1.5)\n",
"plt.legend(['0', '1'])\n",
"plt.title(\"Blue circles and Red crosses\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# First Deep Learning Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import make_circles"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X, y = make_circles(n_samples=1000,\n",
" noise=0.1,\n",
" factor=0.2,\n",
" random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(5, 5))\n",
"plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
"plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
"plt.xlim(-1.5, 1.5)\n",
"plt.ylim(-1.5, 1.5)\n",
"plt.legend(['0', '1'])\n",
"plt.title(\"Blue circles and Red crosses\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense\n",
"from tensorflow.keras.optimizers import SGD"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.add(Dense(4, input_shape=(2,), activation='tanh'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.add(Dense(1, activation='sigmoid'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.compile(SGD(learning_rate=0.5), 'binary_crossentropy', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X, y, epochs=20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"hticks = np.linspace(-1.5, 1.5, 101)\n",
"vticks = np.linspace(-1.5, 1.5, 101)\n",
"aa, bb = np.meshgrid(hticks, vticks)\n",
"ab = np.c_[aa.ravel(), bb.ravel()]\n",
"c = model.predict(ab)\n",
"cc = c.reshape(aa.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(5, 5))\n",
"plt.contourf(aa, bb, cc, cmap='bwr', alpha=0.2)\n",
"plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
"plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
"plt.xlim(-1.5, 1.5)\n",
"plt.ylim(-1.5, 1.5)\n",
"plt.legend(['0', '1'])\n",
"plt.title(\"Blue circles and Red crosses\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deep Learning Intro"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Shallow and Deep Networks"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import make_moons\n",
"\n",
"X, y = make_moons(n_samples=1000, noise=0.1, random_state=0)\n",
"plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
"plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
"plt.legend(['0', '1'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
" test_size=0.3,\n",
" random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense\n",
"from tensorflow.keras.optimizers import SGD, Adam"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Shallow Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Dense(1, input_shape=(2,), activation='sigmoid'))\n",
"model.compile(Adam(learning_rate=0.05), 'binary_crossentropy', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train, y_train, epochs=200, verbose=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"results = model.evaluate(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"The Accuracy score on the Train set is:\\t{:0.3f}\".format(results[1]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def plot_decision_boundary(model, X, y):\n",
" amin, bmin = X.min(axis=0) - 0.1\n",
" amax, bmax = X.max(axis=0) + 0.1\n",
" hticks = np.linspace(amin, amax, 101)\n",
" vticks = np.linspace(bmin, bmax, 101)\n",
" \n",
" aa, bb = np.meshgrid(hticks, vticks)\n",
" ab = np.c_[aa.ravel(), bb.ravel()]\n",
" \n",
" c = model.predict(ab)\n",
" cc = c.reshape(aa.shape)\n",
"\n",
" plt.figure(figsize=(12, 8))\n",
" plt.contourf(aa, bb, cc, cmap='bwr', alpha=0.2)\n",
" plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
" plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
" plt.legend(['0', '1'])\n",
" \n",
"plot_decision_boundary(model, X, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deep model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Dense(4, input_shape=(2,), activation='tanh'))\n",
"model.add(Dense(2, activation='tanh'))\n",
"model.add(Dense(1, activation='sigmoid'))\n",
"model.compile(Adam(learning_rate=0.05), 'binary_crossentropy', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train, y_train, epochs=100, verbose=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.evaluate(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import accuracy_score, confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_train_pred = model.predict_classes(X_train)\n",
"y_test_pred = model.predict_classes(X_test)\n",
"\n",
"print(\"The Accuracy score on the Train set is:\\t{:0.3f}\".format(accuracy_score(y_train, y_train_pred)))\n",
"print(\"The Accuracy score on the Test set is:\\t{:0.3f}\".format(accuracy_score(y_test, y_test_pred)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot_decision_boundary(model, X, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multiclass classification\n",
"\n",
"### The Iris dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('../data/iris.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"sns.pairplot(df, hue=\"species\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X = df.drop('species', axis=1)\n",
"X.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"target_names = df['species'].unique()\n",
"target_names"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"target_dict = {n:i for i, n in enumerate(target_names)}\n",
"target_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y= df['species'].map(target_dict)\n",
"y.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.utils import to_categorical"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_cat = to_categorical(y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_cat[:10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X.values, y_cat,\n",
" test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Dense(3, input_shape=(4,), activation='softmax'))\n",
"model.compile(Adam(learning_rate=0.1),\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train, y_train, epochs=20, validation_split=0.1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred[:5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_test_class = np.argmax(y_test, axis=1)\n",
"y_pred_class = np.argmax(y_pred, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import classification_report"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(classification_report(y_test_class, y_pred_class))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"confusion_matrix(y_test_class, y_pred_class)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"The [Pima Indians dataset](https://archive.ics.uci.edu/ml/datasets/diabetes) is a very famous dataset distributed by UCI and originally collected from the National Institute of Diabetes and Digestive and Kidney Diseases. It contains data from clinical exams for women age 21 and above of Pima indian origins. The objective is to predict based on diagnostic measurements whether a patient has diabetes.\n",
"\n",
"It has the following features:\n",
"\n",
"- Pregnancies: Number of times pregnant\n",
"- Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n",
"- BloodPressure: Diastolic blood pressure (mm Hg)\n",
"- SkinThickness: Triceps skin fold thickness (mm)\n",
"- Insulin: 2-Hour serum insulin (mu U/ml)\n",
"- BMI: Body mass index (weight in kg/(height in m)^2)\n",
"- DiabetesPedigreeFunction: Diabetes pedigree function\n",
"- Age: Age (years)\n",
"\n",
"The last colum is the outcome, and it is a binary variable.\n",
"\n",
"In this first exercise we will explore it through the following steps:\n",
"\n",
"1. Load the ..data/diabetes.csv dataset, use pandas to explore the range of each feature\n",
"- For each feature draw a histogram. Bonus points if you draw all the histograms in the same figure.\n",
"- Explore correlations of features with the outcome column. You can do this in several ways, for example using the `sns.pairplot` we used above or drawing a heatmap of the correlations.\n",
"- Do features need standardization? If so what stardardization technique will you use? MinMax? Standard?\n",
"- Prepare your final `X` and `y` variables to be used by a ML model. Make sure you define your target variable well. Will you need dummy columns?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 2"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Build a fully connected NN model that predicts diabetes. Follow these steps:\n",
"\n",
"1. Split your data in a train/test with a test size of 20% and a `random_state = 22`\n",
"- define a sequential model with at least one inner layer. You will have to make choices for the following things:\n",
" - what is the size of the input?\n",
" - how many nodes will you use in each layer?\n",
" - what is the size of the output?\n",
" - what activation functions will you use in the inner layers?\n",
" - what activation function will you use at output?\n",
" - what loss function will you use?\n",
" - what optimizer will you use?\n",
"- fit your model on the training set, using a validation_split of 0.1\n",
"- test your trained model on the test data from the train/test split\n",
"- check the accuracy score, the confusion matrix and the classification report"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 3\n",
"Compare your work with the results presented in [this notebook](https://www.kaggle.com/futurist/d/uciml/pima-indians-diabetes-database/pima-data-visualisation-and-machine-learning). Are your Neural Network results better or worse than the results obtained by traditional Machine Learning techniques?\n",
"\n",
"- Try training a Support Vector Machine or a Random Forest model on the exact same train/test split. Is the performance better or worse?\n",
"- Try restricting your features to only 4 features like in the suggested notebook. How does model performance change?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 4\n",
"\n",
"[Tensorflow playground](http://playground.tensorflow.org/) is a web based neural network demo. It is really useful to develop an intuition about what happens when you change architecture, activation function or other parameters. Try playing with it for a few minutes. You don't need do understand the meaning of every knob and button in the page, just get a sense for what happens if you change something. In the next chapter we'll explore these things in more detail.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Convolutional Neural Networks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Machine learning on images"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### MNIST"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.datasets import mnist"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(X_train, y_train), (X_test, y_test) = mnist.load_data('/tmp/mnist.npz')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_test.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.imshow(X_train[0], cmap='gray')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train = X_train.reshape(-1, 28*28)\n",
"X_test = X_test.reshape(-1, 28*28)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train = X_train.astype('float32')\n",
"X_test = X_test.astype('float32')\n",
"X_train /= 255.0\n",
"X_test /= 255.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.utils import to_categorical"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_train_cat = to_categorical(y_train)\n",
"y_test_cat = to_categorical(y_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_train[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_train_cat[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_train_cat.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_test_cat.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fully connected on images"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense\n",
"import tensorflow.keras.backend as K\n",
"\n",
"# K.clear_session()\n",
"\n",
"model = Sequential()\n",
"model.add(Dense(512, input_dim=28*28, activation='relu'))\n",
"model.add(Dense(256, activation='relu'))\n",
"model.add(Dense(128, activation='relu'))\n",
"model.add(Dense(32, activation='relu'))\n",
"model.add(Dense(10, activation='softmax'))\n",
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='rmsprop',\n",
" metrics=['accuracy'])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"h = model.fit(X_train, y_train_cat, batch_size=128, epochs=10, verbose=1, validation_split=0.3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.plot(h.history['accuracy'])\n",
"plt.plot(h.history['val_accuracy'])\n",
"plt.legend(['Training', 'Validation'])\n",
"plt.title('Accuracy')\n",
"plt.xlabel('Epochs');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"test_accuracy = model.evaluate(X_test, y_test_cat)[1]\n",
"test_accuracy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tensor Math"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"A = np.random.randint(10, size=(2, 3, 4, 5))\n",
"B = np.random.randint(10, size=(2, 3))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"A"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"A[0, 1, 0, 3]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"B"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### A random colored image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img = np.random.randint(255, size=(4, 4, 3), dtype='uint8')\n",
"img"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(5, 5))\n",
"plt.subplot(221)\n",
"plt.imshow(img)\n",
"plt.title(\"All Channels combined\")\n",
"\n",
"plt.subplot(222)\n",
"plt.imshow(img[:, : , 0], cmap='Reds')\n",
"plt.title(\"Red channel\")\n",
"\n",
"plt.subplot(223)\n",
"plt.imshow(img[:, : , 1], cmap='Greens')\n",
"plt.title(\"Green channel\")\n",
"\n",
"plt.subplot(224)\n",
"plt.imshow(img[:, : , 2], cmap='Blues')\n",
"plt.title(\"Blue channel\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tensor operations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"2 * A"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"A + A"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"A.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"B.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.tensordot(A, B, axes=([0, 1], [0, 1]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.tensordot(A, B, axes=([0], [0])).shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1D convolution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], dtype='float32')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b = np.array([-1, 1], dtype='float32')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"c = np.convolve(a, b)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"c"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.subplot(211)\n",
"plt.plot(a, 'o-')\n",
"\n",
"plt.subplot(212)\n",
"plt.plot(c, 'o-');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Image filters with convolutions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from scipy.ndimage.filters import convolve\n",
"from scipy.signal import convolve2d\n",
"from scipy import misc"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img = misc.ascent()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.imshow(img, cmap='gray');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"h_kernel = np.array([[ 1, 2, 1],\n",
" [ 0, 0, 0],\n",
" [-1, -2, -1]])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"plt.imshow(h_kernel, cmap='gray');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"res = convolve2d(img, h_kernel)\n",
"\n",
"plt.imshow(res, cmap='gray');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Convolutional neural networks"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import Conv2D"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(5, 5))\n",
"plt.imshow(img, cmap='gray');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img_tensor = img.reshape((1, 512, 512, 1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Conv2D(1, (3, 3), strides=(2,1), input_shape=(512, 512, 1)))\n",
"model.compile('adam', 'mse')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img_pred_tensor = model.predict(img_tensor)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img_pred_tensor.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img_pred = img_pred_tensor[0, :, :, 0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.imshow(img_pred, cmap='gray');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"weights = model.get_weights()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"weights[0].shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.imshow(weights[0][:, :, 0, 0], cmap='gray');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"weights[0] = np.ones(weights[0].shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.set_weights(weights)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img_pred_tensor = model.predict(img_tensor)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img_pred = img_pred_tensor[0, :, :, 0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.imshow(img_pred, cmap='gray');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Conv2D(1, (3, 3), input_shape=(512, 512, 1), padding='same'))\n",
"model.compile('adam', 'mse')\n",
"\n",
"img_pred_tensor = model.predict(img_tensor)\n",
"\n",
"\n",
"img_pred_tensor.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pooling layers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import MaxPool2D, AvgPool2D"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(MaxPool2D((5, 5), input_shape=(512, 512, 1)))\n",
"model.compile('adam', 'mse')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img_pred = model.predict(img_tensor)[0, :, :, 0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.imshow(img_pred, cmap='gray')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(AvgPool2D((5, 5), input_shape=(512, 512, 1)))\n",
"model.compile('adam', 'mse')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img_pred = model.predict(img_tensor)[0, :, :, 0]\n",
"plt.imshow(img_pred, cmap='gray');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Final architecture"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train = X_train.reshape(-1, 28, 28, 1)\n",
"X_test = X_test.reshape(-1, 28, 28, 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import Flatten, Activation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"K.clear_session()\n",
"\n",
"model = Sequential()\n",
"\n",
"model.add(Conv2D(32, (3, 3), input_shape=(28, 28, 1)))\n",
"model.add(MaxPool2D(pool_size=(2, 2)))\n",
"model.add(Activation('relu'))\n",
"\n",
"model.add(Flatten())\n",
"\n",
"model.add(Dense(128, activation='relu'))\n",
"\n",
"model.add(Dense(10, activation='softmax'))\n",
"\n",
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='rmsprop',\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train, y_train_cat, batch_size=128,\n",
" epochs=2, verbose=1, validation_split=0.3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.evaluate(X_test, y_test_cat)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Exercise 1\n",
"You've been hired by a shipping company to overhaul the way they route mail, parcels and packages. They want to build an image recognition system capable of recognizing the digits in the zipcode on a package, so that it can be automatically routed to the correct location.\n",
"You are tasked to build the digit recognition system. Luckily, you can rely on the MNIST dataset for the intial training of your model!\n",
"\n",
"Build a deep convolutional neural network with at least two convolutional and two pooling layers before the fully connected layer.\n",
"\n",
"- Start from the network we have just built\n",
"- Insert a `Conv2D` layer after the first `MaxPool2D`, give it 64 filters.\n",
"- Insert a `MaxPool2D` after that one\n",
"- Insert an `Activation` layer\n",
"- retrain the model\n",
"- does performance improve?\n",
"- how many parameters does this new model have? More or less than the previous model? Why?\n",
"- how long did this second model take to train? Longer or shorter than the previous model? Why?\n",
"- did it perform better or worse than the previous model?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 2\n",
"\n",
"Pleased with your performance with the digits recognition task, your boss decides to challenge you with a harder task. Their online branch allows people to upload images to a website that generates and prints a postcard that is shipped to destination. Your boss would like to know what images people are loading on the site in order to provide targeted advertising on the same page, so he asks you to build an image recognition system capable of recognizing a few objects. Luckily for you, there's a dataset ready made with a collection of labeled images. This is the [Cifar 10 Dataset](http://www.cs.toronto.edu/~kriz/cifar.html), a very famous dataset that contains images for 10 different categories:\n",
"\n",
"- airplane \t\t\t\t\t\t\t\t\t\t\n",
"- automobile \t\t\t\t\t\t\t\t\t\t\n",
"- bird \t\t\t\t\t\t\t\t\t\t\n",
"- cat \t\t\t\t\t\t\t\t\t\t\n",
"- deer \t\t\t\t\t\t\t\t\t\t\n",
"- dog \t\t\t\t\t\t\t\t\t\t\n",
"- frog \t\t\t\t\t\t\t\t\t\t\n",
"- horse \t\t\t\t\t\t\t\t\t\t\n",
"- ship \t\t\t\t\t\t\t\t\t\t\n",
"- truck\n",
"\n",
"In this exercise we will reach the limit of what you can achieve on your laptop and get ready for the next session on cloud GPUs.\n",
"\n",
"Here's what you have to do:\n",
"- load the cifar10 dataset using `keras.datasets.cifar10.load_data()`\n",
"- display a few images, see how hard/easy it is for you to recognize an object with such low resolution\n",
"- check the shape of X_train, does it need reshape?\n",
"- check the scale of X_train, does it need rescaling?\n",
"- check the shape of y_train, does it need reshape?\n",
"- build a model with the following architecture, and choose the parameters and activation functions for each of the layers:\n",
" - conv2d\n",
" - conv2d\n",
" - maxpool\n",
" - conv2d\n",
" - conv2d\n",
" - maxpool\n",
" - flatten\n",
" - dense\n",
" - output\n",
"- compile the model and check the number of parameters\n",
"- attempt to train the model with the optimizer of your choice. How fast does training proceed?\n",
"- If training is too slow (as expected) stop the execution and move to the next session!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.datasets import cifar10"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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@@ -0,0 +1,565 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Recurrent Neural Networks"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Time series forecasting"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('../data/cansim-0800020-eng-6674700030567901031.csv',\n",
" skiprows=6, skipfooter=9,\n",
" engine='python')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pandas.tseries.offsets import MonthEnd"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df['Adjustments'] = pd.to_datetime(df['Adjustments']) + MonthEnd(1)\n",
"df = df.set_index('Adjustments')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"split_date = pd.Timestamp('01-01-2011')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train = df.loc[:split_date, ['Unadjusted']]\n",
"test = df.loc[split_date:, ['Unadjusted']]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ax = train.plot()\n",
"test.plot(ax=ax)\n",
"plt.legend(['train', 'test'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import MinMaxScaler\n",
"\n",
"sc = MinMaxScaler()\n",
"\n",
"train_sc = sc.fit_transform(train)\n",
"test_sc = sc.transform(test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_sc[:4]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train = train_sc[:-1]\n",
"y_train = train_sc[1:]\n",
"\n",
"X_test = test_sc[:-1]\n",
"y_test = test_sc[1:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fully connected predictor"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense\n",
"import tensorflow.keras.backend as K\n",
"from tensorflow.keras.callbacks import EarlyStopping"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"K.clear_session()\n",
"\n",
"model = Sequential()\n",
"model.add(Dense(12, input_dim=1, activation='relu'))\n",
"model.add(Dense(1))\n",
"model.compile(loss='mean_squared_error', optimizer='adam')\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"early_stop = EarlyStopping(monitor='loss', patience=1, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train, y_train, epochs=200,\n",
" batch_size=2, verbose=1,\n",
" callbacks=[early_stop])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.plot(y_test)\n",
"plt.plot(y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Recurrent predictor"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import LSTM"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#3D tensor with shape (batch_size, timesteps, input_dim)\n",
"X_train[:, None].shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train_t = X_train[:, None]\n",
"X_test_t = X_test[:, None]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"K.clear_session()\n",
"model = Sequential()\n",
"\n",
"model.add(LSTM(6, input_shape=(1, 1)))\n",
"\n",
"model.add(Dense(1))\n",
"\n",
"model.compile(loss='mean_squared_error', optimizer='adam')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train_t, y_train,\n",
" epochs=100, batch_size=1, verbose=1,\n",
" callbacks=[early_stop])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(X_test_t)\n",
"plt.plot(y_test)\n",
"plt.plot(y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Windows"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_sc.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_sc_df = pd.DataFrame(train_sc, columns=['Scaled'], index=train.index)\n",
"test_sc_df = pd.DataFrame(test_sc, columns=['Scaled'], index=test.index)\n",
"train_sc_df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for s in range(1, 13):\n",
" train_sc_df['shift_{}'.format(s)] = train_sc_df['Scaled'].shift(s)\n",
" test_sc_df['shift_{}'.format(s)] = test_sc_df['Scaled'].shift(s)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_sc_df.head(13)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train = train_sc_df.dropna().drop('Scaled', axis=1)\n",
"y_train = train_sc_df.dropna()[['Scaled']]\n",
"\n",
"X_test = test_sc_df.dropna().drop('Scaled', axis=1)\n",
"y_test = test_sc_df.dropna()[['Scaled']]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train = X_train.values\n",
"X_test= X_test.values\n",
"\n",
"y_train = y_train.values\n",
"y_test = y_test.values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fully Connected on Windows"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"K.clear_session()\n",
"\n",
"model = Sequential()\n",
"model.add(Dense(12, input_dim=12, activation='relu'))\n",
"model.add(Dense(1))\n",
"model.compile(loss='mean_squared_error', optimizer='adam')\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train, y_train, epochs=200,\n",
" batch_size=1, verbose=1, callbacks=[early_stop])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(X_test)\n",
"plt.plot(y_test)\n",
"plt.plot(y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### LSTM on Windows"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train_t = X_train.reshape(X_train.shape[0], 1, 12)\n",
"X_test_t = X_test.reshape(X_test.shape[0], 1, 12)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train_t.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"K.clear_session()\n",
"model = Sequential()\n",
"\n",
"model.add(LSTM(6, input_shape=(1, 12)))\n",
"\n",
"model.add(Dense(1))\n",
"\n",
"model.compile(loss='mean_squared_error', optimizer='adam')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train_t, y_train, epochs=100,\n",
" batch_size=1, verbose=1, callbacks=[early_stop])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(X_test_t)\n",
"plt.plot(y_test)\n",
"plt.plot(y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 1\n",
"\n",
"In the model above we reshaped the input shape to: `(num_samples, 1, 12)`, i.e. we treated a window of 12 months as a vector of 12 coordinates that we simultaneously passed to all the LSTM nodes. An alternative way to look at the problem is to reshape the input to `(num_samples, 12, 1)`. This means we consider each input window as a sequence of 12 values that we will pass in sequence to the LSTM. In principle this looks like a more accurate description of our situation. But does it yield better predictions? Let's check it.\n",
"\n",
"- Reshape `X_train` and `X_test` so that they represent a set of univariate sequences\n",
"- retrain the same LSTM(6) model, you'll have to adapt the `input_shape`\n",
"- check the performance of this new model, is it better at predicting the test data?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Exercise 2\n",
"\n",
"RNN models can be applied to images too. In general we can apply them to any data where there's a connnection between nearby units. Let's see how we can easily build a model that works with images.\n",
"\n",
"- Load the MNIST data, by now you should be able to do it blindfolded :)\n",
"- reshape it so that an image looks like a long sequence of pixels\n",
"- create a recurrent model and train it on the training data\n",
"- how does it perform compared to a fully connected? How does it compare to Convolutional Neural Networks?\n",
"\n",
"(feel free to run this exercise on a cloud GPU if it's too slow on your laptop)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

872
course/9 Improving performance.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 9 Improving performance"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Learning curves"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import load_digits"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"digits = load_digits()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X, y = digits.data, digits.target"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for i in range(8):\n",
" plt.subplot(1,8,i+1)\n",
" plt.imshow(X.reshape(-1, 8, 8)[i], cmap='gray')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense\n",
"from tensorflow.keras.utils import to_categorical\n",
"import tensorflow.keras.backend as K\n",
"from tensorflow.keras.callbacks import EarlyStopping"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"K.clear_session()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Dense(16, input_shape=(64,), activation='relu'))\n",
"model.add(Dense(10, activation='softmax'))\n",
"model.compile('adam', 'categorical_crossentropy', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# store the initial random weights\n",
"initial_weights = model.get_weights()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_cat = to_categorical(y, 10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y_cat,\n",
" test_size=0.3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_sizes = (len(X_train) * np.linspace(0.1, 0.999, 4)).astype(int)\n",
"train_sizes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_scores = []\n",
"test_scores = []\n",
"\n",
"for train_size in train_sizes:\n",
" X_train_frac, _, y_train_frac, _ = \\\n",
" train_test_split(X_train, y_train, train_size=train_size)\n",
" \n",
" # at each iteration reset the weights of the model\n",
" # to the initial random weights\n",
" model.set_weights(initial_weights)\n",
" \n",
" h = model.fit(X_train_frac, y_train_frac,\n",
" verbose=0,\n",
" epochs=300,\n",
" callbacks=[EarlyStopping(monitor='loss', patience=1)])\n",
"\n",
" r = model.evaluate(X_train_frac, y_train_frac, verbose=0)\n",
" train_scores.append(r[-1])\n",
" \n",
" e = model.evaluate(X_test, y_test, verbose=0)\n",
" test_scores.append(e[-1])\n",
" \n",
" print(\"Done size: \", train_size)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.plot(train_sizes, train_scores, 'o-', label=\"Training score\")\n",
"plt.plot(train_sizes, test_scores, 'o-', label=\"Test score\")\n",
"plt.legend(loc=\"best\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Batch Normalization"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import BatchNormalization"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def repeated_training(X_train,\n",
" y_train,\n",
" X_test,\n",
" y_test,\n",
" units=512,\n",
" activation='sigmoid',\n",
" optimizer='sgd',\n",
" do_bn=False,\n",
" epochs=10,\n",
" repeats=3):\n",
" histories = []\n",
" \n",
" for repeat in range(repeats):\n",
" K.clear_session()\n",
"\n",
" model = Sequential()\n",
" \n",
" # first fully connected layer\n",
" model.add(Dense(units,\n",
" input_shape=X_train.shape[1:],\n",
" kernel_initializer='normal',\n",
" activation=activation))\n",
" if do_bn:\n",
" model.add(BatchNormalization())\n",
"\n",
" # second fully connected layer\n",
" model.add(Dense(units,\n",
" kernel_initializer='normal',\n",
" activation=activation))\n",
" if do_bn:\n",
" model.add(BatchNormalization())\n",
"\n",
" # third fully connected layer\n",
" model.add(Dense(units,\n",
" kernel_initializer='normal',\n",
" activation=activation))\n",
" if do_bn:\n",
" model.add(BatchNormalization())\n",
"\n",
" # output layer\n",
" model.add(Dense(10, activation='softmax'))\n",
" \n",
" model.compile(optimizer,\n",
" 'categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
" h = model.fit(X_train, y_train,\n",
" validation_data=(X_test, y_test),\n",
" epochs=epochs,\n",
" verbose=0)\n",
" histories.append([h.history['accuracy'], h.history['val_accuracy']])\n",
" print(repeat, end=' ')\n",
"\n",
" histories = np.array(histories)\n",
" \n",
" # calculate mean and standard deviation across repeats:\n",
" mean_acc = histories.mean(axis=0)\n",
" std_acc = histories.std(axis=0)\n",
" print()\n",
" \n",
" return mean_acc[0], std_acc[0], mean_acc[1], std_acc[1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mean_acc, std_acc, mean_acc_val, std_acc_val = \\\n",
" repeated_training(X_train, y_train, X_test, y_test, do_bn=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mean_acc_bn, std_acc_bn, mean_acc_val_bn, std_acc_val_bn = \\\n",
" repeated_training(X_train, y_train, X_test, y_test, do_bn=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def plot_mean_std(m, s):\n",
" plt.plot(m)\n",
" plt.fill_between(range(len(m)), m-s, m+s, alpha=0.1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot_mean_std(mean_acc, std_acc)\n",
"plot_mean_std(mean_acc_val, std_acc_val)\n",
"plot_mean_std(mean_acc_bn, std_acc_bn)\n",
"plot_mean_std(mean_acc_val_bn, std_acc_val_bn)\n",
"plt.ylim(0, 1.01)\n",
"plt.title(\"Batch Normalization Accuracy\")\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Accuracy')\n",
"plt.legend(['Train', 'Test', 'Train with Batch Normalization', 'Test with Batch Normalization'], loc='best');\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Weight Regularization & Dropout"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import Dropout"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Dropout(0.2, input_shape=X_train.shape[1:]))\n",
"# first fully connected layer\n",
"model.add(Dense(512, kernel_initializer='normal',\n",
" kernel_regularizer='l2', activation='sigmoid'))\n",
"model.add(Dropout(0.4))\n",
"model.add(Dense(10, activation='softmax'))\n",
"\n",
"model.compile('sgd',\n",
" 'categorical_crossentropy',\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data augmentation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"\n",
"generator = ImageDataGenerator(rescale = 1./255,\n",
" width_shift_range=0.1,\n",
" height_shift_range=0.1,\n",
" rotation_range = 20,\n",
" shear_range = 0.3,\n",
" zoom_range = 0.3,\n",
" horizontal_flip = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train = generator.flow_from_directory('../data/generator',\n",
" target_size = (128, 128),\n",
" batch_size = 32,\n",
" class_mode = 'binary')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(12, 12))\n",
"for i in range(16):\n",
" img, label = train.next()\n",
" plt.subplot(4, 4, i+1)\n",
" plt.imshow(img[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Embeddings"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import Embedding"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Embedding(input_dim=100, output_dim=2))\n",
"model.compile(loss='binary_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"emb = model.predict(np.array([[81, 1, 96, 79],\n",
" [17, 47, 69, 50],\n",
" [49, 3, 12, 88]]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"emb.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"emb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sentiment prediction on movie Reviews"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.datasets import imdb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(X_train, y_train), (X_test, y_test) = imdb.load_data('/tmp/imdb.npz',\n",
" num_words=None,\n",
" skip_top=0,\n",
" maxlen=None,\n",
" start_char=1,\n",
" oov_char=2,\n",
" index_from=3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train[1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"idx = imdb.get_word_index()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"max(idx.values())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"idx"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rev_idx = {v+3:k for k,v in idx.items()}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rev_idx"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rev_idx[0] = 'padding_char'\n",
"rev_idx[1] = 'start_char'\n",
"rev_idx[2] = 'oov_char'\n",
"rev_idx[3] = 'unk_char'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rev_idx[3]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_train[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"example_review = ' '.join([rev_idx[word] for word in X_train[0]])\n",
"example_review"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(X_train[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(X_train[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(X_train[2])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(X_train[3])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
"from tensorflow.keras.layers import LSTM"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"maxlen = 100\n",
"\n",
"X_train_pad = pad_sequences(X_train, maxlen=maxlen)\n",
"X_test_pad = pad_sequences(X_test, maxlen=maxlen)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train_pad.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train_pad[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"max_features = max([max(x) for x in X_train_pad] + \n",
" [max(x) for x in X_test_pad]) + 1\n",
"max_features"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_train"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Embedding(max_features, 128))\n",
"model.add(LSTM(64, dropout=0.2, recurrent_dropout=0.2))\n",
"model.add(Dense(1, activation='sigmoid'))\n",
"\n",
"model.compile(loss='binary_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train_pad, y_train,\n",
" batch_size=32,\n",
" epochs=2,\n",
" validation_split=0.3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"score, acc = model.evaluate(X_test_pad, y_test)\n",
"print('Test score:', score)\n",
"print('Test accuracy:', acc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 1\n",
"\n",
"- Reload the IMDB data keeping only the first 20000 most common words\n",
"- pad the reviews to a shorter length (eg. 70 or 80), this time make sure you keep the first part of the review if it's longer than the maximum length\n",
"- re run the model (remember to set max_features correctly)\n",
"- does it train faster this time?\n",
"- do you get a better performance?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 2\n",
"\n",
"- Reload the digits data as above\n",
"- define a function repeated_training_reg_dropout that adds regularization and dropout to a fully connected network\n",
"- compare the performance with/witouth dropout and regularization like we did for batch normalization\n",
"- do you get a better performance?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 3\n",
"\n",
"This is a very long and complex exercise, that should give you an idea of a real world scenario. Feel free to look at the solution if you feel lost. Also, feel free to run this on Floyd with a GPU, in which case you don't need to download the data.\n",
"\n",
"If you are running this locally, download and unpack the male/female pictures from [here](https://www.dropbox.com/s/nov493om2jmh2gp/male_female.tgz?dl=0). These images and labels were obtained from [Crowdflower](https://www.crowdflower.com/data-for-everyone/).\n",
"\n",
"Your goal is to build an image classifier that will recognize the gender of a person from pictures.\n",
"\n",
"- Have a look at the directory structure and inspect a couple of pictures\n",
"- Design a model that will take a color image of size 64x64 as input and return a binary output (female=0/male=1)\n",
"- Feel free to introduce any regularization technique in your model (Dropout, Batch Normalization, Weight Regularization)\n",
"- Compile your model with an optimizer of your choice\n",
"- Using `ImageDataGenerator`, define a train generator that will augment your images with some geometric transformations. Feel free to choose the parameters that make sense to you.\n",
"- Define also a test generator, whose only purpose is to rescale the pixels by 1./255\n",
"- use the function `flow_from_directory` to generate batches from the train and test folders. Make sure you set the `target_size` to 64x64.\n",
"- Use the `model.fit_generator` function to fit the model on the batches generated from the ImageDataGenerator. Since you are streaming and augmenting the data in real time you will have to decide how many batches make an epoch and how many epochs you want to run\n",
"- Train your model (you should get to at least 85% accuracy)\n",
"- Once you are satisfied with your training, check a few of the misclassified pictures. Are those sensible errors?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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"Table 080-0020 Retail trade, sales by the North American Industry Classification System (NAICS), monthly (dollars x 1,000)(2,3,4,5,6)"
Survey or program details:
Retail Trade Survey (Monthly) - 2406
Monthly Retail Trade Survey (Department Store Organizations) - 2408
Geography,Canada,Canada
North American Industry Classification System (NAICS),Retail trade [44-45] ,Retail trade [44-45]
Adjustments,Unadjusted,Seasonally adjusted
Jan-1991,12588862,15026890
Feb-1991,12154321,15304585
Mar-1991,14337072,15413591
Apr-1991,15108570,15293409
May-1991,17225734,15676083
Jun-1991,16342833,15507931
Jul-1991,15996243,15556313
Aug-1991,16064910,15430645
Sep-1991,15015317,15427313
Oct-1991,15606864,15410250
Nov-1991,16237366,15662790
Dec-1991,18381340,15349625
Jan-1992,13084963,15477875
Feb-1992,12773972,15513022
Mar-1992,14198775,15527933
Apr-1992,15558390,15708556
May-1992,16776396,15642000
Jun-1992,16716231,15823989
Jul-1992,16637483,15869453
Aug-1992,15842075,15844631
Sep-1992,15812400,15983239
Oct-1992,16562268,16125835
Nov-1992,16015869,16049478
Dec-1992,19682921,16095727
Jan-1993,13672727,16408864
Feb-1993,12900733,16239039
Mar-1993,15211859,16314960
Apr-1993,16642246,16577426
May-1993,17442405,16472045
Jun-1993,17444074,16351907
Jul-1993,17610326,16712914
Aug-1993,16645660,16703413
Sep-1993,16790330,16755338
Oct-1993,16921755,16819382
Nov-1993,17124609,16958202
Dec-1993,20928208,17021436
Jan-1994,14005058,17076164
Feb-1994,13799079,17393150
Mar-1994,16865149,17890903
Apr-1994,17494589,17507688
May-1994,18739509,17775079
Jun-1994,19323481,17882069
Jul-1994,18297834,17785800
Aug-1994,18101290,17881976
Sep-1994,18161417,17952647
Oct-1994,17998875,18193703
Nov-1994,18516766,18264676
Dec-1994,22688647,18387840
Jan-1995,14927996,18337565
Feb-1995,14520623,18259470
Mar-1995,17457477,18225708
Apr-1995,17774107,18217661
May-1995,19740889,18333051
Jun-1995,20319460,18503481
Jul-1995,18747299,18407254
Aug-1995,19280525,18720783
Sep-1995,18860566,18628735
Oct-1995,18177152,18412692
Nov-1995,18962903,18506305
Dec-1995,22308880,18525162
Jan-1996,15379086,18531426
Feb-1996,15521981,18657652
Mar-1996,17613469,18774049
Apr-1996,18421405,18739023
May-1996,20624568,18758009
Jun-1996,20099348,18977805
Jul-1996,19423284,18914063
Aug-1996,19889359,19071178
Sep-1996,18589571,19019991
Oct-1996,19686383,19488074
Nov-1996,20293165,19820074
Dec-1996,22897980,19688254
Jan-1997,16882321,19857365
Feb-1997,16033605,20141489
Mar-1997,18225453,20056949
Apr-1997,20432272,20215340
May-1997,22594727,20386953
Jun-1997,21577744,20466883
Jul-1997,21570145,20681230
Aug-1997,21065784,20605349
Sep-1997,20532806,20646564
Oct-1997,21491163,21037021
Nov-1997,20904746,20973561
Dec-1997,25507180,21749250
Jan-1998,17736224,20776214
Feb-1998,16797018,21153779
Mar-1998,19408883,21041225
Apr-1998,21501677,21504619
May-1998,23312947,21504262
Jun-1998,22654803,21247311
Jul-1998,22594775,21385620
Aug-1998,21512734,21335980
Sep-1998,21645562,21660645
Oct-1998,21994089,21565457
Nov-1998,21461344,21714061
Dec-1998,25874332,21605209
Jan-1999,18438151,22074043
Feb-1999,17658952,22286260
Mar-1999,21082603,22402680
Apr-1999,22587382,22389229
May-1999,23892100,22300484
Jun-1999,24036828,22450487
Jul-1999,23994614,22614164
Aug-1999,22926469,22806183
Sep-1999,22984278,22817165
Oct-1999,22813633,22967565
Nov-1999,22972959,23036527
Dec-1999,28143999,23387176
Jan-2000,19324692,23434451
Feb-2000,19140440,23378327
Mar-2000,22918829,23813646
Apr-2000,22914155,23537859
May-2000,25659687,23644020
Jun-2000,25945400,23841011
Jul-2000,24821347,24204193
Aug-2000,25102965,24266358
Sep-2000,24710257,24495699
Oct-2000,23687124,24330740
Nov-2000,24556357,24373656
Dec-2000,29057176,24518477
Jan-2001,20607642,24640517
Feb-2001,19444855,24477976
Mar-2001,23652255,24583988
Apr-2001,24370700,24944482
May-2001,27585889,25143416
Jun-2001,27243919,25190078
Jul-2001,25507932,24813831
Aug-2001,26322941,25017925
Sep-2001,24263969,24703734
Oct-2001,24917747,25149772
Nov-2001,26048646,25657476
Dec-2001,30481412,26124705
Jan-2002,22361219,26496729
Feb-2002,20787209,26105990
Mar-2002,24642692,26072633
Apr-2002,26405170,26657565
May-2002,29087583,26165998
Jun-2002,28363263,26768924
Jul-2002,27912328,26620588
Aug-2002,28202300,26767279
Sep-2002,26054411,26620259
Oct-2002,27131743,27061844
Nov-2002,27276942,27015215
Dec-2002,31300554,27172388
Jan-2003,23301871,27137545
Feb-2003,21980804,27622538
Mar-2003,25468203,27275170
Apr-2003,27059495,27176306
May-2003,30417563,27484615
Jun-2003,28912102,27569750
Jul-2003,29492832,27707861
Aug-2003,29102135,28020755
Sep-2003,27467571,27841344
Oct-2003,28223631,27825024
Nov-2003,27391422,27777531
Dec-2003,32325789,27704978
Jan-2004,23778728,27935993
Feb-2004,23008594,28719948
Mar-2004,26967793,28689514
Apr-2004,28592026,28254086
May-2004,30479247,28554094
Jun-2004,30711705,28550528
Jul-2004,30898334,28616168
Aug-2004,29535183,28836665
Sep-2004,29245397,29243662
Oct-2004,29445711,29561177
Nov-2004,29232659,29901036
Dec-2004,34561103,29593609
Jan-2005,24498615,29888781
Feb-2005,24028226,30460620
Mar-2005,28600602,30006264
Apr-2005,30600811,29940271
May-2005,31948565,29935878
Jun-2005,32967426,30590992
Jul-2005,32620077,30800241
Aug-2005,32025283,30647393
Sep-2005,30914826,30600008
Oct-2005,30241532,30887481
Nov-2005,30828069,31012789
Dec-2005,36726743,31230056
Jan-2006,25993203,31747679
Feb-2006,25128165,31744450
Mar-2006,30760061,31871717
Apr-2006,32106585,32354405
May-2006,34894460,32048264
Jun-2006,35049112,32242363
Jul-2006,34341547,33040218
Aug-2006,35045180,33007575
Sep-2006,33056559,32492838
Oct-2006,31830349,32595465
Nov-2006,32663281,32814138
Dec-2006,38605976,33515367
Jan-2007,27777968,33221023
Feb-2007,26548520,33466188
Mar-2007,32818504,33910296
Apr-2007,33621240,34385868
May-2007,38434319,34789277
Jun-2007,37555708,34436278
Jul-2007,35635889,34430726
Aug-2007,36978090,34725483
Sep-2007,34057842,34341226
Oct-2007,34070363,34305870
Nov-2007,35091406,34958995
Dec-2007,40006665,35625285
Jan-2008,30525699,35933156
Feb-2008,29418898,35526909
Mar-2008,32925876,35530142
Apr-2008,36272111,35914167
May-2008,39778972,36006888
Jun-2008,37842321,36400129
Jul-2008,38632038,36403867
Aug-2008,37775417,36137537
Sep-2008,36138751,36390521
Oct-2008,36158245,35747571
Nov-2008,34230901,34661690
Dec-2008,38256712,33303365
Jan-2009,29192654,33747849
Feb-2009,26804723,33869426
Mar-2009,31356949,33894022
Apr-2009,33942769,33930673
May-2009,37316515,34345160
Jun-2009,36865690,34735113
Jul-2009,37191480,34755444
Aug-2009,36049418,35039739
Sep-2009,35537357,35239195
Oct-2009,36133694,35330869
Nov-2009,34354756,35250896
Dec-2009,40969765,35577384
Jan-2010,30668321,36080033
Feb-2010,28632551,36051781
Mar-2010,34967182,37001843
Apr-2010,36469949,36148495
May-2010,38424455,36041318
Jun-2010,38973462,36350588
Jul-2010,38932294,36295314
Aug-2010,37395330,36515170
Sep-2010,36923390,36632898
Oct-2010,37014326,36879707
Nov-2010,37408825,37568029
Dec-2010,43147947,37392857
Jan-2011,31191594,37392259
Feb-2011,29797949,37437926
Mar-2011,36099866,37617167
Apr-2011,38035760,37755408
May-2011,40046516,37723958
Jun-2011,40839556,38228307
Jul-2011,39832282,37925826
Aug-2011,39541248,37976798
Sep-2011,38877263,38181654
Oct-2011,38203872,38623692
Nov-2011,39174736,38779553
Dec-2011,45089701,39087795
Jan-2012,32361808,39102435
Feb-2012,32087072,38968001
Mar-2012,37933733,39201228
Apr-2012,37775805,38920526
May-2012,42584571,38841267
Jun-2012,41789242,38773515
Jul-2012,40130908,38854126
Aug-2012,41321526,38854279
Sep-2012,39069513,39058649
Oct-2012,39487597,39277317
Nov-2012,40095933,39224805
Dec-2012,43489091,39050651
Jan-2013,33574671,39523536
Feb-2013,31636843,39710038
Mar-2013,37561378,39811962
Apr-2013,39401295,39655045
May-2013,44577490,40295930
Jun-2013,42169145,39992542
Jul-2013,42417829,40388278
Aug-2013,43237460,40660890
Sep-2013,40170270,40631164
Oct-2013,41560987,40813306
Nov-2013,41893714,40798569
Dec-2013,44796794,40716614
Jan-2014,34980327,40976155
Feb-2014,32905708,41256280
Mar-2014,38460091,41242344
Apr-2014,41809373,41852467
May-2014,46379543,41906455
Jun-2014,44178750,42457216
Jul-2014,45285331,42562972
Aug-2014,44359733,42456214
Sep-2014,43017529,42685882
Oct-2014,43775478,42690228
Nov-2014,42968326,42603501
Dec-2014,46887481,42317955
Jan-2015,34820395,40971992
Feb-2015,33174923,41801906
Mar-2015,39444291,42420253
Apr-2015,42297319,42331926
May-2015,46670930,42721761
Jun-2015,45584849,42989280
Jul-2015,46295664,43154020
Aug-2015,44793347,43309509
Sep-2015,43999627,43303889
Oct-2015,44507776,43378904
Nov-2015,43696305,43921767
Dec-2015,48097829,43078048
Jan-2016,36415115,43977584
Feb-2016,35649450,44205540
Mar-2016,41403762,43839996
Apr-2016,44881587,44181416
May-2016,47337082,44176591
Jun-2016,47399117,44162244
Jul-2016,46321314,44110862
Aug-2016,46201453,44216280
Sep-2016,45528702,44534797
Oct-2016,44770113,45061618
Nov-2016,46285062,45141762
Dec-2016,50016137,44943929
Jan-2017,37628452,45952103
Footnotes:
2,The total for retail trade excludes North American Industry Classification System (NAICS) 454.
3,"This CANSIM table replaces archived table 80-0014, 80-0015 and 80-0017."
4,"Quality indicator: Code A=Excellent. Code B=Very good. Code C=Good. Code D=Acceptable. Code E=Poor, use with caution. Code F=Unreliable (data not published)."
5,"Data for Northwest Territories includes Nunavut, from 1991-01 to 1998-12."
6,"In April 2013, data from 2004 onwards will be based on the 2012 North American Industry Classification System (NAICS). Data prior to 2004 will continue to be based on the 2007 North American Industry Classification System (NAICS)."
Source:
"Statistics Canada. Table 080-0020 - Retail trade, sales by the North American Industry Classification System (NAICS), monthly (dollars)"
"(accessed: April 19, 2017)"
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Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
6,148,72,35,0,33.6,0.627,50,1
1,85,66,29,0,26.6,0.351,31,0
8,183,64,0,0,23.3,0.672,32,1
1,89,66,23,94,28.1,0.167,21,0
0,137,40,35,168,43.1,2.288,33,1
5,116,74,0,0,25.6,0.201,30,0
3,78,50,32,88,31,0.248,26,1
10,115,0,0,0,35.3,0.134,29,0
2,197,70,45,543,30.5,0.158,53,1
8,125,96,0,0,0,0.232,54,1
4,110,92,0,0,37.6,0.191,30,0
10,168,74,0,0,38,0.537,34,1
10,139,80,0,0,27.1,1.441,57,0
1,189,60,23,846,30.1,0.398,59,1
5,166,72,19,175,25.8,0.587,51,1
7,100,0,0,0,30,0.484,32,1
0,118,84,47,230,45.8,0.551,31,1
7,107,74,0,0,29.6,0.254,31,1
1,103,30,38,83,43.3,0.183,33,0
1,115,70,30,96,34.6,0.529,32,1
3,126,88,41,235,39.3,0.704,27,0
8,99,84,0,0,35.4,0.388,50,0
7,196,90,0,0,39.8,0.451,41,1
9,119,80,35,0,29,0.263,29,1
11,143,94,33,146,36.6,0.254,51,1
10,125,70,26,115,31.1,0.205,41,1
7,147,76,0,0,39.4,0.257,43,1
1,97,66,15,140,23.2,0.487,22,0
13,145,82,19,110,22.2,0.245,57,0
5,117,92,0,0,34.1,0.337,38,0
5,109,75,26,0,36,0.546,60,0
3,158,76,36,245,31.6,0.851,28,1
3,88,58,11,54,24.8,0.267,22,0
6,92,92,0,0,19.9,0.188,28,0
10,122,78,31,0,27.6,0.512,45,0
4,103,60,33,192,24,0.966,33,0
11,138,76,0,0,33.2,0.42,35,0
9,102,76,37,0,32.9,0.665,46,1
2,90,68,42,0,38.2,0.503,27,1
4,111,72,47,207,37.1,1.39,56,1
3,180,64,25,70,34,0.271,26,0
7,133,84,0,0,40.2,0.696,37,0
7,106,92,18,0,22.7,0.235,48,0
9,171,110,24,240,45.4,0.721,54,1
7,159,64,0,0,27.4,0.294,40,0
0,180,66,39,0,42,1.893,25,1
1,146,56,0,0,29.7,0.564,29,0
2,71,70,27,0,28,0.586,22,0
7,103,66,32,0,39.1,0.344,31,1
7,105,0,0,0,0,0.305,24,0
1,103,80,11,82,19.4,0.491,22,0
1,101,50,15,36,24.2,0.526,26,0
5,88,66,21,23,24.4,0.342,30,0
8,176,90,34,300,33.7,0.467,58,1
7,150,66,42,342,34.7,0.718,42,0
1,73,50,10,0,23,0.248,21,0
7,187,68,39,304,37.7,0.254,41,1
0,100,88,60,110,46.8,0.962,31,0
0,146,82,0,0,40.5,1.781,44,0
0,105,64,41,142,41.5,0.173,22,0
2,84,0,0,0,0,0.304,21,0
8,133,72,0,0,32.9,0.27,39,1
5,44,62,0,0,25,0.587,36,0
2,141,58,34,128,25.4,0.699,24,0
7,114,66,0,0,32.8,0.258,42,1
5,99,74,27,0,29,0.203,32,0
0,109,88,30,0,32.5,0.855,38,1
2,109,92,0,0,42.7,0.845,54,0
1,95,66,13,38,19.6,0.334,25,0
4,146,85,27,100,28.9,0.189,27,0
2,100,66,20,90,32.9,0.867,28,1
5,139,64,35,140,28.6,0.411,26,0
13,126,90,0,0,43.4,0.583,42,1
4,129,86,20,270,35.1,0.231,23,0
1,79,75,30,0,32,0.396,22,0
1,0,48,20,0,24.7,0.14,22,0
7,62,78,0,0,32.6,0.391,41,0
5,95,72,33,0,37.7,0.37,27,0
0,131,0,0,0,43.2,0.27,26,1
2,112,66,22,0,25,0.307,24,0
3,113,44,13,0,22.4,0.14,22,0
2,74,0,0,0,0,0.102,22,0
7,83,78,26,71,29.3,0.767,36,0
0,101,65,28,0,24.6,0.237,22,0
5,137,108,0,0,48.8,0.227,37,1
2,110,74,29,125,32.4,0.698,27,0
13,106,72,54,0,36.6,0.178,45,0
2,100,68,25,71,38.5,0.324,26,0
15,136,70,32,110,37.1,0.153,43,1
1,107,68,19,0,26.5,0.165,24,0
1,80,55,0,0,19.1,0.258,21,0
4,123,80,15,176,32,0.443,34,0
7,81,78,40,48,46.7,0.261,42,0
4,134,72,0,0,23.8,0.277,60,1
2,142,82,18,64,24.7,0.761,21,0
6,144,72,27,228,33.9,0.255,40,0
2,92,62,28,0,31.6,0.13,24,0
1,71,48,18,76,20.4,0.323,22,0
6,93,50,30,64,28.7,0.356,23,0
1,122,90,51,220,49.7,0.325,31,1
1,163,72,0,0,39,1.222,33,1
1,151,60,0,0,26.1,0.179,22,0
0,125,96,0,0,22.5,0.262,21,0
1,81,72,18,40,26.6,0.283,24,0
2,85,65,0,0,39.6,0.93,27,0
1,126,56,29,152,28.7,0.801,21,0
1,96,122,0,0,22.4,0.207,27,0
4,144,58,28,140,29.5,0.287,37,0
3,83,58,31,18,34.3,0.336,25,0
0,95,85,25,36,37.4,0.247,24,1
3,171,72,33,135,33.3,0.199,24,1
8,155,62,26,495,34,0.543,46,1
1,89,76,34,37,31.2,0.192,23,0
4,76,62,0,0,34,0.391,25,0
7,160,54,32,175,30.5,0.588,39,1
4,146,92,0,0,31.2,0.539,61,1
5,124,74,0,0,34,0.22,38,1
5,78,48,0,0,33.7,0.654,25,0
4,97,60,23,0,28.2,0.443,22,0
4,99,76,15,51,23.2,0.223,21,0
0,162,76,56,100,53.2,0.759,25,1
6,111,64,39,0,34.2,0.26,24,0
2,107,74,30,100,33.6,0.404,23,0
5,132,80,0,0,26.8,0.186,69,0
0,113,76,0,0,33.3,0.278,23,1
1,88,30,42,99,55,0.496,26,1
3,120,70,30,135,42.9,0.452,30,0
1,118,58,36,94,33.3,0.261,23,0
1,117,88,24,145,34.5,0.403,40,1
0,105,84,0,0,27.9,0.741,62,1
4,173,70,14,168,29.7,0.361,33,1
9,122,56,0,0,33.3,1.114,33,1
3,170,64,37,225,34.5,0.356,30,1
8,84,74,31,0,38.3,0.457,39,0
2,96,68,13,49,21.1,0.647,26,0
2,125,60,20,140,33.8,0.088,31,0
0,100,70,26,50,30.8,0.597,21,0
0,93,60,25,92,28.7,0.532,22,0
0,129,80,0,0,31.2,0.703,29,0
5,105,72,29,325,36.9,0.159,28,0
3,128,78,0,0,21.1,0.268,55,0
5,106,82,30,0,39.5,0.286,38,0
2,108,52,26,63,32.5,0.318,22,0
10,108,66,0,0,32.4,0.272,42,1
4,154,62,31,284,32.8,0.237,23,0
0,102,75,23,0,0,0.572,21,0
9,57,80,37,0,32.8,0.096,41,0
2,106,64,35,119,30.5,1.4,34,0
5,147,78,0,0,33.7,0.218,65,0
2,90,70,17,0,27.3,0.085,22,0
1,136,74,50,204,37.4,0.399,24,0
4,114,65,0,0,21.9,0.432,37,0
9,156,86,28,155,34.3,1.189,42,1
1,153,82,42,485,40.6,0.687,23,0
8,188,78,0,0,47.9,0.137,43,1
7,152,88,44,0,50,0.337,36,1
2,99,52,15,94,24.6,0.637,21,0
1,109,56,21,135,25.2,0.833,23,0
2,88,74,19,53,29,0.229,22,0
17,163,72,41,114,40.9,0.817,47,1
4,151,90,38,0,29.7,0.294,36,0
7,102,74,40,105,37.2,0.204,45,0
0,114,80,34,285,44.2,0.167,27,0
2,100,64,23,0,29.7,0.368,21,0
0,131,88,0,0,31.6,0.743,32,1
6,104,74,18,156,29.9,0.722,41,1
3,148,66,25,0,32.5,0.256,22,0
4,120,68,0,0,29.6,0.709,34,0
4,110,66,0,0,31.9,0.471,29,0
3,111,90,12,78,28.4,0.495,29,0
6,102,82,0,0,30.8,0.18,36,1
6,134,70,23,130,35.4,0.542,29,1
2,87,0,23,0,28.9,0.773,25,0
1,79,60,42,48,43.5,0.678,23,0
2,75,64,24,55,29.7,0.37,33,0
8,179,72,42,130,32.7,0.719,36,1
6,85,78,0,0,31.2,0.382,42,0
0,129,110,46,130,67.1,0.319,26,1
5,143,78,0,0,45,0.19,47,0
5,130,82,0,0,39.1,0.956,37,1
6,87,80,0,0,23.2,0.084,32,0
0,119,64,18,92,34.9,0.725,23,0
1,0,74,20,23,27.7,0.299,21,0
5,73,60,0,0,26.8,0.268,27,0
4,141,74,0,0,27.6,0.244,40,0
7,194,68,28,0,35.9,0.745,41,1
8,181,68,36,495,30.1,0.615,60,1
1,128,98,41,58,32,1.321,33,1
8,109,76,39,114,27.9,0.64,31,1
5,139,80,35,160,31.6,0.361,25,1
3,111,62,0,0,22.6,0.142,21,0
9,123,70,44,94,33.1,0.374,40,0
7,159,66,0,0,30.4,0.383,36,1
11,135,0,0,0,52.3,0.578,40,1
8,85,55,20,0,24.4,0.136,42,0
5,158,84,41,210,39.4,0.395,29,1
1,105,58,0,0,24.3,0.187,21,0
3,107,62,13,48,22.9,0.678,23,1
4,109,64,44,99,34.8,0.905,26,1
4,148,60,27,318,30.9,0.15,29,1
0,113,80,16,0,31,0.874,21,0
1,138,82,0,0,40.1,0.236,28,0
0,108,68,20,0,27.3,0.787,32,0
2,99,70,16,44,20.4,0.235,27,0
6,103,72,32,190,37.7,0.324,55,0
5,111,72,28,0,23.9,0.407,27,0
8,196,76,29,280,37.5,0.605,57,1
5,162,104,0,0,37.7,0.151,52,1
1,96,64,27,87,33.2,0.289,21,0
7,184,84,33,0,35.5,0.355,41,1
2,81,60,22,0,27.7,0.29,25,0
0,147,85,54,0,42.8,0.375,24,0
7,179,95,31,0,34.2,0.164,60,0
0,140,65,26,130,42.6,0.431,24,1
9,112,82,32,175,34.2,0.26,36,1
12,151,70,40,271,41.8,0.742,38,1
5,109,62,41,129,35.8,0.514,25,1
6,125,68,30,120,30,0.464,32,0
5,85,74,22,0,29,1.224,32,1
5,112,66,0,0,37.8,0.261,41,1
0,177,60,29,478,34.6,1.072,21,1
2,158,90,0,0,31.6,0.805,66,1
7,119,0,0,0,25.2,0.209,37,0
7,142,60,33,190,28.8,0.687,61,0
1,100,66,15,56,23.6,0.666,26,0
1,87,78,27,32,34.6,0.101,22,0
0,101,76,0,0,35.7,0.198,26,0
3,162,52,38,0,37.2,0.652,24,1
4,197,70,39,744,36.7,2.329,31,0
0,117,80,31,53,45.2,0.089,24,0
4,142,86,0,0,44,0.645,22,1
6,134,80,37,370,46.2,0.238,46,1
1,79,80,25,37,25.4,0.583,22,0
4,122,68,0,0,35,0.394,29,0
3,74,68,28,45,29.7,0.293,23,0
4,171,72,0,0,43.6,0.479,26,1
7,181,84,21,192,35.9,0.586,51,1
0,179,90,27,0,44.1,0.686,23,1
9,164,84,21,0,30.8,0.831,32,1
0,104,76,0,0,18.4,0.582,27,0
1,91,64,24,0,29.2,0.192,21,0
4,91,70,32,88,33.1,0.446,22,0
3,139,54,0,0,25.6,0.402,22,1
6,119,50,22,176,27.1,1.318,33,1
2,146,76,35,194,38.2,0.329,29,0
9,184,85,15,0,30,1.213,49,1
10,122,68,0,0,31.2,0.258,41,0
0,165,90,33,680,52.3,0.427,23,0
9,124,70,33,402,35.4,0.282,34,0
1,111,86,19,0,30.1,0.143,23,0
9,106,52,0,0,31.2,0.38,42,0
2,129,84,0,0,28,0.284,27,0
2,90,80,14,55,24.4,0.249,24,0
0,86,68,32,0,35.8,0.238,25,0
12,92,62,7,258,27.6,0.926,44,1
1,113,64,35,0,33.6,0.543,21,1
3,111,56,39,0,30.1,0.557,30,0
2,114,68,22,0,28.7,0.092,25,0
1,193,50,16,375,25.9,0.655,24,0
11,155,76,28,150,33.3,1.353,51,1
3,191,68,15,130,30.9,0.299,34,0
3,141,0,0,0,30,0.761,27,1
4,95,70,32,0,32.1,0.612,24,0
3,142,80,15,0,32.4,0.2,63,0
4,123,62,0,0,32,0.226,35,1
5,96,74,18,67,33.6,0.997,43,0
0,138,0,0,0,36.3,0.933,25,1
2,128,64,42,0,40,1.101,24,0
0,102,52,0,0,25.1,0.078,21,0
2,146,0,0,0,27.5,0.24,28,1
10,101,86,37,0,45.6,1.136,38,1
2,108,62,32,56,25.2,0.128,21,0
3,122,78,0,0,23,0.254,40,0
1,71,78,50,45,33.2,0.422,21,0
13,106,70,0,0,34.2,0.251,52,0
2,100,70,52,57,40.5,0.677,25,0
7,106,60,24,0,26.5,0.296,29,1
0,104,64,23,116,27.8,0.454,23,0
5,114,74,0,0,24.9,0.744,57,0
2,108,62,10,278,25.3,0.881,22,0
0,146,70,0,0,37.9,0.334,28,1
10,129,76,28,122,35.9,0.28,39,0
7,133,88,15,155,32.4,0.262,37,0
7,161,86,0,0,30.4,0.165,47,1
2,108,80,0,0,27,0.259,52,1
7,136,74,26,135,26,0.647,51,0
5,155,84,44,545,38.7,0.619,34,0
1,119,86,39,220,45.6,0.808,29,1
4,96,56,17,49,20.8,0.34,26,0
5,108,72,43,75,36.1,0.263,33,0
0,78,88,29,40,36.9,0.434,21,0
0,107,62,30,74,36.6,0.757,25,1
2,128,78,37,182,43.3,1.224,31,1
1,128,48,45,194,40.5,0.613,24,1
0,161,50,0,0,21.9,0.254,65,0
6,151,62,31,120,35.5,0.692,28,0
2,146,70,38,360,28,0.337,29,1
0,126,84,29,215,30.7,0.52,24,0
14,100,78,25,184,36.6,0.412,46,1
8,112,72,0,0,23.6,0.84,58,0
0,167,0,0,0,32.3,0.839,30,1
2,144,58,33,135,31.6,0.422,25,1
5,77,82,41,42,35.8,0.156,35,0
5,115,98,0,0,52.9,0.209,28,1
3,150,76,0,0,21,0.207,37,0
2,120,76,37,105,39.7,0.215,29,0
10,161,68,23,132,25.5,0.326,47,1
0,137,68,14,148,24.8,0.143,21,0
0,128,68,19,180,30.5,1.391,25,1
2,124,68,28,205,32.9,0.875,30,1
6,80,66,30,0,26.2,0.313,41,0
0,106,70,37,148,39.4,0.605,22,0
2,155,74,17,96,26.6,0.433,27,1
3,113,50,10,85,29.5,0.626,25,0
7,109,80,31,0,35.9,1.127,43,1
2,112,68,22,94,34.1,0.315,26,0
3,99,80,11,64,19.3,0.284,30,0
3,182,74,0,0,30.5,0.345,29,1
3,115,66,39,140,38.1,0.15,28,0
6,194,78,0,0,23.5,0.129,59,1
4,129,60,12,231,27.5,0.527,31,0
3,112,74,30,0,31.6,0.197,25,1
0,124,70,20,0,27.4,0.254,36,1
13,152,90,33,29,26.8,0.731,43,1
2,112,75,32,0,35.7,0.148,21,0
1,157,72,21,168,25.6,0.123,24,0
1,122,64,32,156,35.1,0.692,30,1
10,179,70,0,0,35.1,0.2,37,0
2,102,86,36,120,45.5,0.127,23,1
6,105,70,32,68,30.8,0.122,37,0
8,118,72,19,0,23.1,1.476,46,0
2,87,58,16,52,32.7,0.166,25,0
1,180,0,0,0,43.3,0.282,41,1
12,106,80,0,0,23.6,0.137,44,0
1,95,60,18,58,23.9,0.26,22,0
0,165,76,43,255,47.9,0.259,26,0
0,117,0,0,0,33.8,0.932,44,0
5,115,76,0,0,31.2,0.343,44,1
9,152,78,34,171,34.2,0.893,33,1
7,178,84,0,0,39.9,0.331,41,1
1,130,70,13,105,25.9,0.472,22,0
1,95,74,21,73,25.9,0.673,36,0
1,0,68,35,0,32,0.389,22,0
5,122,86,0,0,34.7,0.29,33,0
8,95,72,0,0,36.8,0.485,57,0
8,126,88,36,108,38.5,0.349,49,0
1,139,46,19,83,28.7,0.654,22,0
3,116,0,0,0,23.5,0.187,23,0
3,99,62,19,74,21.8,0.279,26,0
5,0,80,32,0,41,0.346,37,1
4,92,80,0,0,42.2,0.237,29,0
4,137,84,0,0,31.2,0.252,30,0
3,61,82,28,0,34.4,0.243,46,0
1,90,62,12,43,27.2,0.58,24,0
3,90,78,0,0,42.7,0.559,21,0
9,165,88,0,0,30.4,0.302,49,1
1,125,50,40,167,33.3,0.962,28,1
13,129,0,30,0,39.9,0.569,44,1
12,88,74,40,54,35.3,0.378,48,0
1,196,76,36,249,36.5,0.875,29,1
5,189,64,33,325,31.2,0.583,29,1
5,158,70,0,0,29.8,0.207,63,0
5,103,108,37,0,39.2,0.305,65,0
4,146,78,0,0,38.5,0.52,67,1
4,147,74,25,293,34.9,0.385,30,0
5,99,54,28,83,34,0.499,30,0
6,124,72,0,0,27.6,0.368,29,1
0,101,64,17,0,21,0.252,21,0
3,81,86,16,66,27.5,0.306,22,0
1,133,102,28,140,32.8,0.234,45,1
3,173,82,48,465,38.4,2.137,25,1
0,118,64,23,89,0,1.731,21,0
0,84,64,22,66,35.8,0.545,21,0
2,105,58,40,94,34.9,0.225,25,0
2,122,52,43,158,36.2,0.816,28,0
12,140,82,43,325,39.2,0.528,58,1
0,98,82,15,84,25.2,0.299,22,0
1,87,60,37,75,37.2,0.509,22,0
4,156,75,0,0,48.3,0.238,32,1
0,93,100,39,72,43.4,1.021,35,0
1,107,72,30,82,30.8,0.821,24,0
0,105,68,22,0,20,0.236,22,0
1,109,60,8,182,25.4,0.947,21,0
1,90,62,18,59,25.1,1.268,25,0
1,125,70,24,110,24.3,0.221,25,0
1,119,54,13,50,22.3,0.205,24,0
5,116,74,29,0,32.3,0.66,35,1
8,105,100,36,0,43.3,0.239,45,1
5,144,82,26,285,32,0.452,58,1
3,100,68,23,81,31.6,0.949,28,0
1,100,66,29,196,32,0.444,42,0
5,166,76,0,0,45.7,0.34,27,1
1,131,64,14,415,23.7,0.389,21,0
4,116,72,12,87,22.1,0.463,37,0
4,158,78,0,0,32.9,0.803,31,1
2,127,58,24,275,27.7,1.6,25,0
3,96,56,34,115,24.7,0.944,39,0
0,131,66,40,0,34.3,0.196,22,1
3,82,70,0,0,21.1,0.389,25,0
3,193,70,31,0,34.9,0.241,25,1
4,95,64,0,0,32,0.161,31,1
6,137,61,0,0,24.2,0.151,55,0
5,136,84,41,88,35,0.286,35,1
9,72,78,25,0,31.6,0.28,38,0
5,168,64,0,0,32.9,0.135,41,1
2,123,48,32,165,42.1,0.52,26,0
4,115,72,0,0,28.9,0.376,46,1
0,101,62,0,0,21.9,0.336,25,0
8,197,74,0,0,25.9,1.191,39,1
1,172,68,49,579,42.4,0.702,28,1
6,102,90,39,0,35.7,0.674,28,0
1,112,72,30,176,34.4,0.528,25,0
1,143,84,23,310,42.4,1.076,22,0
1,143,74,22,61,26.2,0.256,21,0
0,138,60,35,167,34.6,0.534,21,1
3,173,84,33,474,35.7,0.258,22,1
1,97,68,21,0,27.2,1.095,22,0
4,144,82,32,0,38.5,0.554,37,1
1,83,68,0,0,18.2,0.624,27,0
3,129,64,29,115,26.4,0.219,28,1
1,119,88,41,170,45.3,0.507,26,0
2,94,68,18,76,26,0.561,21,0
0,102,64,46,78,40.6,0.496,21,0
2,115,64,22,0,30.8,0.421,21,0
8,151,78,32,210,42.9,0.516,36,1
4,184,78,39,277,37,0.264,31,1
0,94,0,0,0,0,0.256,25,0
1,181,64,30,180,34.1,0.328,38,1
0,135,94,46,145,40.6,0.284,26,0
1,95,82,25,180,35,0.233,43,1
2,99,0,0,0,22.2,0.108,23,0
3,89,74,16,85,30.4,0.551,38,0
1,80,74,11,60,30,0.527,22,0
2,139,75,0,0,25.6,0.167,29,0
1,90,68,8,0,24.5,1.138,36,0
0,141,0,0,0,42.4,0.205,29,1
12,140,85,33,0,37.4,0.244,41,0
5,147,75,0,0,29.9,0.434,28,0
1,97,70,15,0,18.2,0.147,21,0
6,107,88,0,0,36.8,0.727,31,0
0,189,104,25,0,34.3,0.435,41,1
2,83,66,23,50,32.2,0.497,22,0
4,117,64,27,120,33.2,0.23,24,0
8,108,70,0,0,30.5,0.955,33,1
4,117,62,12,0,29.7,0.38,30,1
0,180,78,63,14,59.4,2.42,25,1
1,100,72,12,70,25.3,0.658,28,0
0,95,80,45,92,36.5,0.33,26,0
0,104,64,37,64,33.6,0.51,22,1
0,120,74,18,63,30.5,0.285,26,0
1,82,64,13,95,21.2,0.415,23,0
2,134,70,0,0,28.9,0.542,23,1
0,91,68,32,210,39.9,0.381,25,0
2,119,0,0,0,19.6,0.832,72,0
2,100,54,28,105,37.8,0.498,24,0
14,175,62,30,0,33.6,0.212,38,1
1,135,54,0,0,26.7,0.687,62,0
5,86,68,28,71,30.2,0.364,24,0
10,148,84,48,237,37.6,1.001,51,1
9,134,74,33,60,25.9,0.46,81,0
9,120,72,22,56,20.8,0.733,48,0
1,71,62,0,0,21.8,0.416,26,0
8,74,70,40,49,35.3,0.705,39,0
5,88,78,30,0,27.6,0.258,37,0
10,115,98,0,0,24,1.022,34,0
0,124,56,13,105,21.8,0.452,21,0
0,74,52,10,36,27.8,0.269,22,0
0,97,64,36,100,36.8,0.6,25,0
8,120,0,0,0,30,0.183,38,1
6,154,78,41,140,46.1,0.571,27,0
1,144,82,40,0,41.3,0.607,28,0
0,137,70,38,0,33.2,0.17,22,0
0,119,66,27,0,38.8,0.259,22,0
7,136,90,0,0,29.9,0.21,50,0
4,114,64,0,0,28.9,0.126,24,0
0,137,84,27,0,27.3,0.231,59,0
2,105,80,45,191,33.7,0.711,29,1
7,114,76,17,110,23.8,0.466,31,0
8,126,74,38,75,25.9,0.162,39,0
4,132,86,31,0,28,0.419,63,0
3,158,70,30,328,35.5,0.344,35,1
0,123,88,37,0,35.2,0.197,29,0
4,85,58,22,49,27.8,0.306,28,0
0,84,82,31,125,38.2,0.233,23,0
0,145,0,0,0,44.2,0.63,31,1
0,135,68,42,250,42.3,0.365,24,1
1,139,62,41,480,40.7,0.536,21,0
0,173,78,32,265,46.5,1.159,58,0
4,99,72,17,0,25.6,0.294,28,0
8,194,80,0,0,26.1,0.551,67,0
2,83,65,28,66,36.8,0.629,24,0
2,89,90,30,0,33.5,0.292,42,0
4,99,68,38,0,32.8,0.145,33,0
4,125,70,18,122,28.9,1.144,45,1
3,80,0,0,0,0,0.174,22,0
6,166,74,0,0,26.6,0.304,66,0
5,110,68,0,0,26,0.292,30,0
2,81,72,15,76,30.1,0.547,25,0
7,195,70,33,145,25.1,0.163,55,1
6,154,74,32,193,29.3,0.839,39,0
2,117,90,19,71,25.2,0.313,21,0
3,84,72,32,0,37.2,0.267,28,0
6,0,68,41,0,39,0.727,41,1
7,94,64,25,79,33.3,0.738,41,0
3,96,78,39,0,37.3,0.238,40,0
10,75,82,0,0,33.3,0.263,38,0
0,180,90,26,90,36.5,0.314,35,1
1,130,60,23,170,28.6,0.692,21,0
2,84,50,23,76,30.4,0.968,21,0
8,120,78,0,0,25,0.409,64,0
12,84,72,31,0,29.7,0.297,46,1
0,139,62,17,210,22.1,0.207,21,0
9,91,68,0,0,24.2,0.2,58,0
2,91,62,0,0,27.3,0.525,22,0
3,99,54,19,86,25.6,0.154,24,0
3,163,70,18,105,31.6,0.268,28,1
9,145,88,34,165,30.3,0.771,53,1
7,125,86,0,0,37.6,0.304,51,0
13,76,60,0,0,32.8,0.18,41,0
6,129,90,7,326,19.6,0.582,60,0
2,68,70,32,66,25,0.187,25,0
3,124,80,33,130,33.2,0.305,26,0
6,114,0,0,0,0,0.189,26,0
9,130,70,0,0,34.2,0.652,45,1
3,125,58,0,0,31.6,0.151,24,0
3,87,60,18,0,21.8,0.444,21,0
1,97,64,19,82,18.2,0.299,21,0
3,116,74,15,105,26.3,0.107,24,0
0,117,66,31,188,30.8,0.493,22,0
0,111,65,0,0,24.6,0.66,31,0
2,122,60,18,106,29.8,0.717,22,0
0,107,76,0,0,45.3,0.686,24,0
1,86,66,52,65,41.3,0.917,29,0
6,91,0,0,0,29.8,0.501,31,0
1,77,56,30,56,33.3,1.251,24,0
4,132,0,0,0,32.9,0.302,23,1
0,105,90,0,0,29.6,0.197,46,0
0,57,60,0,0,21.7,0.735,67,0
0,127,80,37,210,36.3,0.804,23,0
3,129,92,49,155,36.4,0.968,32,1
8,100,74,40,215,39.4,0.661,43,1
3,128,72,25,190,32.4,0.549,27,1
10,90,85,32,0,34.9,0.825,56,1
4,84,90,23,56,39.5,0.159,25,0
1,88,78,29,76,32,0.365,29,0
8,186,90,35,225,34.5,0.423,37,1
5,187,76,27,207,43.6,1.034,53,1
4,131,68,21,166,33.1,0.16,28,0
1,164,82,43,67,32.8,0.341,50,0
4,189,110,31,0,28.5,0.68,37,0
1,116,70,28,0,27.4,0.204,21,0
3,84,68,30,106,31.9,0.591,25,0
6,114,88,0,0,27.8,0.247,66,0
1,88,62,24,44,29.9,0.422,23,0
1,84,64,23,115,36.9,0.471,28,0
7,124,70,33,215,25.5,0.161,37,0
1,97,70,40,0,38.1,0.218,30,0
8,110,76,0,0,27.8,0.237,58,0
11,103,68,40,0,46.2,0.126,42,0
11,85,74,0,0,30.1,0.3,35,0
6,125,76,0,0,33.8,0.121,54,1
0,198,66,32,274,41.3,0.502,28,1
1,87,68,34,77,37.6,0.401,24,0
6,99,60,19,54,26.9,0.497,32,0
0,91,80,0,0,32.4,0.601,27,0
2,95,54,14,88,26.1,0.748,22,0
1,99,72,30,18,38.6,0.412,21,0
6,92,62,32,126,32,0.085,46,0
4,154,72,29,126,31.3,0.338,37,0
0,121,66,30,165,34.3,0.203,33,1
3,78,70,0,0,32.5,0.27,39,0
2,130,96,0,0,22.6,0.268,21,0
3,111,58,31,44,29.5,0.43,22,0
2,98,60,17,120,34.7,0.198,22,0
1,143,86,30,330,30.1,0.892,23,0
1,119,44,47,63,35.5,0.28,25,0
6,108,44,20,130,24,0.813,35,0
2,118,80,0,0,42.9,0.693,21,1
10,133,68,0,0,27,0.245,36,0
2,197,70,99,0,34.7,0.575,62,1
0,151,90,46,0,42.1,0.371,21,1
6,109,60,27,0,25,0.206,27,0
12,121,78,17,0,26.5,0.259,62,0
8,100,76,0,0,38.7,0.19,42,0
8,124,76,24,600,28.7,0.687,52,1
1,93,56,11,0,22.5,0.417,22,0
8,143,66,0,0,34.9,0.129,41,1
6,103,66,0,0,24.3,0.249,29,0
3,176,86,27,156,33.3,1.154,52,1
0,73,0,0,0,21.1,0.342,25,0
11,111,84,40,0,46.8,0.925,45,1
2,112,78,50,140,39.4,0.175,24,0
3,132,80,0,0,34.4,0.402,44,1
2,82,52,22,115,28.5,1.699,25,0
6,123,72,45,230,33.6,0.733,34,0
0,188,82,14,185,32,0.682,22,1
0,67,76,0,0,45.3,0.194,46,0
1,89,24,19,25,27.8,0.559,21,0
1,173,74,0,0,36.8,0.088,38,1
1,109,38,18,120,23.1,0.407,26,0
1,108,88,19,0,27.1,0.4,24,0
6,96,0,0,0,23.7,0.19,28,0
1,124,74,36,0,27.8,0.1,30,0
7,150,78,29,126,35.2,0.692,54,1
4,183,0,0,0,28.4,0.212,36,1
1,124,60,32,0,35.8,0.514,21,0
1,181,78,42,293,40,1.258,22,1
1,92,62,25,41,19.5,0.482,25,0
0,152,82,39,272,41.5,0.27,27,0
1,111,62,13,182,24,0.138,23,0
3,106,54,21,158,30.9,0.292,24,0
3,174,58,22,194,32.9,0.593,36,1
7,168,88,42,321,38.2,0.787,40,1
6,105,80,28,0,32.5,0.878,26,0
11,138,74,26,144,36.1,0.557,50,1
3,106,72,0,0,25.8,0.207,27,0
6,117,96,0,0,28.7,0.157,30,0
2,68,62,13,15,20.1,0.257,23,0
9,112,82,24,0,28.2,1.282,50,1
0,119,0,0,0,32.4,0.141,24,1
2,112,86,42,160,38.4,0.246,28,0
2,92,76,20,0,24.2,1.698,28,0
6,183,94,0,0,40.8,1.461,45,0
0,94,70,27,115,43.5,0.347,21,0
2,108,64,0,0,30.8,0.158,21,0
4,90,88,47,54,37.7,0.362,29,0
0,125,68,0,0,24.7,0.206,21,0
0,132,78,0,0,32.4,0.393,21,0
5,128,80,0,0,34.6,0.144,45,0
4,94,65,22,0,24.7,0.148,21,0
7,114,64,0,0,27.4,0.732,34,1
0,102,78,40,90,34.5,0.238,24,0
2,111,60,0,0,26.2,0.343,23,0
1,128,82,17,183,27.5,0.115,22,0
10,92,62,0,0,25.9,0.167,31,0
13,104,72,0,0,31.2,0.465,38,1
5,104,74,0,0,28.8,0.153,48,0
2,94,76,18,66,31.6,0.649,23,0
7,97,76,32,91,40.9,0.871,32,1
1,100,74,12,46,19.5,0.149,28,0
0,102,86,17,105,29.3,0.695,27,0
4,128,70,0,0,34.3,0.303,24,0
6,147,80,0,0,29.5,0.178,50,1
4,90,0,0,0,28,0.61,31,0
3,103,72,30,152,27.6,0.73,27,0
2,157,74,35,440,39.4,0.134,30,0
1,167,74,17,144,23.4,0.447,33,1
0,179,50,36,159,37.8,0.455,22,1
11,136,84,35,130,28.3,0.26,42,1
0,107,60,25,0,26.4,0.133,23,0
1,91,54,25,100,25.2,0.234,23,0
1,117,60,23,106,33.8,0.466,27,0
5,123,74,40,77,34.1,0.269,28,0
2,120,54,0,0,26.8,0.455,27,0
1,106,70,28,135,34.2,0.142,22,0
2,155,52,27,540,38.7,0.24,25,1
2,101,58,35,90,21.8,0.155,22,0
1,120,80,48,200,38.9,1.162,41,0
11,127,106,0,0,39,0.19,51,0
3,80,82,31,70,34.2,1.292,27,1
10,162,84,0,0,27.7,0.182,54,0
1,199,76,43,0,42.9,1.394,22,1
8,167,106,46,231,37.6,0.165,43,1
9,145,80,46,130,37.9,0.637,40,1
6,115,60,39,0,33.7,0.245,40,1
1,112,80,45,132,34.8,0.217,24,0
4,145,82,18,0,32.5,0.235,70,1
10,111,70,27,0,27.5,0.141,40,1
6,98,58,33,190,34,0.43,43,0
9,154,78,30,100,30.9,0.164,45,0
6,165,68,26,168,33.6,0.631,49,0
1,99,58,10,0,25.4,0.551,21,0
10,68,106,23,49,35.5,0.285,47,0
3,123,100,35,240,57.3,0.88,22,0
8,91,82,0,0,35.6,0.587,68,0
6,195,70,0,0,30.9,0.328,31,1
9,156,86,0,0,24.8,0.23,53,1
0,93,60,0,0,35.3,0.263,25,0
3,121,52,0,0,36,0.127,25,1
2,101,58,17,265,24.2,0.614,23,0
2,56,56,28,45,24.2,0.332,22,0
0,162,76,36,0,49.6,0.364,26,1
0,95,64,39,105,44.6,0.366,22,0
4,125,80,0,0,32.3,0.536,27,1
5,136,82,0,0,0,0.64,69,0
2,129,74,26,205,33.2,0.591,25,0
3,130,64,0,0,23.1,0.314,22,0
1,107,50,19,0,28.3,0.181,29,0
1,140,74,26,180,24.1,0.828,23,0
1,144,82,46,180,46.1,0.335,46,1
8,107,80,0,0,24.6,0.856,34,0
13,158,114,0,0,42.3,0.257,44,1
2,121,70,32,95,39.1,0.886,23,0
7,129,68,49,125,38.5,0.439,43,1
2,90,60,0,0,23.5,0.191,25,0
7,142,90,24,480,30.4,0.128,43,1
3,169,74,19,125,29.9,0.268,31,1
0,99,0,0,0,25,0.253,22,0
4,127,88,11,155,34.5,0.598,28,0
4,118,70,0,0,44.5,0.904,26,0
2,122,76,27,200,35.9,0.483,26,0
6,125,78,31,0,27.6,0.565,49,1
1,168,88,29,0,35,0.905,52,1
2,129,0,0,0,38.5,0.304,41,0
4,110,76,20,100,28.4,0.118,27,0
6,80,80,36,0,39.8,0.177,28,0
10,115,0,0,0,0,0.261,30,1
2,127,46,21,335,34.4,0.176,22,0
9,164,78,0,0,32.8,0.148,45,1
2,93,64,32,160,38,0.674,23,1
3,158,64,13,387,31.2,0.295,24,0
5,126,78,27,22,29.6,0.439,40,0
10,129,62,36,0,41.2,0.441,38,1
0,134,58,20,291,26.4,0.352,21,0
3,102,74,0,0,29.5,0.121,32,0
7,187,50,33,392,33.9,0.826,34,1
3,173,78,39,185,33.8,0.97,31,1
10,94,72,18,0,23.1,0.595,56,0
1,108,60,46,178,35.5,0.415,24,0
5,97,76,27,0,35.6,0.378,52,1
4,83,86,19,0,29.3,0.317,34,0
1,114,66,36,200,38.1,0.289,21,0
1,149,68,29,127,29.3,0.349,42,1
5,117,86,30,105,39.1,0.251,42,0
1,111,94,0,0,32.8,0.265,45,0
4,112,78,40,0,39.4,0.236,38,0
1,116,78,29,180,36.1,0.496,25,0
0,141,84,26,0,32.4,0.433,22,0
2,175,88,0,0,22.9,0.326,22,0
2,92,52,0,0,30.1,0.141,22,0
3,130,78,23,79,28.4,0.323,34,1
8,120,86,0,0,28.4,0.259,22,1
2,174,88,37,120,44.5,0.646,24,1
2,106,56,27,165,29,0.426,22,0
2,105,75,0,0,23.3,0.56,53,0
4,95,60,32,0,35.4,0.284,28,0
0,126,86,27,120,27.4,0.515,21,0
8,65,72,23,0,32,0.6,42,0
2,99,60,17,160,36.6,0.453,21,0
1,102,74,0,0,39.5,0.293,42,1
11,120,80,37,150,42.3,0.785,48,1
3,102,44,20,94,30.8,0.4,26,0
1,109,58,18,116,28.5,0.219,22,0
9,140,94,0,0,32.7,0.734,45,1
13,153,88,37,140,40.6,1.174,39,0
12,100,84,33,105,30,0.488,46,0
1,147,94,41,0,49.3,0.358,27,1
1,81,74,41,57,46.3,1.096,32,0
3,187,70,22,200,36.4,0.408,36,1
6,162,62,0,0,24.3,0.178,50,1
4,136,70,0,0,31.2,1.182,22,1
1,121,78,39,74,39,0.261,28,0
3,108,62,24,0,26,0.223,25,0
0,181,88,44,510,43.3,0.222,26,1
8,154,78,32,0,32.4,0.443,45,1
1,128,88,39,110,36.5,1.057,37,1
7,137,90,41,0,32,0.391,39,0
0,123,72,0,0,36.3,0.258,52,1
1,106,76,0,0,37.5,0.197,26,0
6,190,92,0,0,35.5,0.278,66,1
2,88,58,26,16,28.4,0.766,22,0
9,170,74,31,0,44,0.403,43,1
9,89,62,0,0,22.5,0.142,33,0
10,101,76,48,180,32.9,0.171,63,0
2,122,70,27,0,36.8,0.34,27,0
5,121,72,23,112,26.2,0.245,30,0
1,126,60,0,0,30.1,0.349,47,1
1,93,70,31,0,30.4,0.315,23,0
1 Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
2 6 148 72 35 0 33.6 0.627 50 1
3 1 85 66 29 0 26.6 0.351 31 0
4 8 183 64 0 0 23.3 0.672 32 1
5 1 89 66 23 94 28.1 0.167 21 0
6 0 137 40 35 168 43.1 2.288 33 1
7 5 116 74 0 0 25.6 0.201 30 0
8 3 78 50 32 88 31 0.248 26 1
9 10 115 0 0 0 35.3 0.134 29 0
10 2 197 70 45 543 30.5 0.158 53 1
11 8 125 96 0 0 0 0.232 54 1
12 4 110 92 0 0 37.6 0.191 30 0
13 10 168 74 0 0 38 0.537 34 1
14 10 139 80 0 0 27.1 1.441 57 0
15 1 189 60 23 846 30.1 0.398 59 1
16 5 166 72 19 175 25.8 0.587 51 1
17 7 100 0 0 0 30 0.484 32 1
18 0 118 84 47 230 45.8 0.551 31 1
19 7 107 74 0 0 29.6 0.254 31 1
20 1 103 30 38 83 43.3 0.183 33 0
21 1 115 70 30 96 34.6 0.529 32 1
22 3 126 88 41 235 39.3 0.704 27 0
23 8 99 84 0 0 35.4 0.388 50 0
24 7 196 90 0 0 39.8 0.451 41 1
25 9 119 80 35 0 29 0.263 29 1
26 11 143 94 33 146 36.6 0.254 51 1
27 10 125 70 26 115 31.1 0.205 41 1
28 7 147 76 0 0 39.4 0.257 43 1
29 1 97 66 15 140 23.2 0.487 22 0
30 13 145 82 19 110 22.2 0.245 57 0
31 5 117 92 0 0 34.1 0.337 38 0
32 5 109 75 26 0 36 0.546 60 0
33 3 158 76 36 245 31.6 0.851 28 1
34 3 88 58 11 54 24.8 0.267 22 0
35 6 92 92 0 0 19.9 0.188 28 0
36 10 122 78 31 0 27.6 0.512 45 0
37 4 103 60 33 192 24 0.966 33 0
38 11 138 76 0 0 33.2 0.42 35 0
39 9 102 76 37 0 32.9 0.665 46 1
40 2 90 68 42 0 38.2 0.503 27 1
41 4 111 72 47 207 37.1 1.39 56 1
42 3 180 64 25 70 34 0.271 26 0
43 7 133 84 0 0 40.2 0.696 37 0
44 7 106 92 18 0 22.7 0.235 48 0
45 9 171 110 24 240 45.4 0.721 54 1
46 7 159 64 0 0 27.4 0.294 40 0
47 0 180 66 39 0 42 1.893 25 1
48 1 146 56 0 0 29.7 0.564 29 0
49 2 71 70 27 0 28 0.586 22 0
50 7 103 66 32 0 39.1 0.344 31 1
51 7 105 0 0 0 0 0.305 24 0
52 1 103 80 11 82 19.4 0.491 22 0
53 1 101 50 15 36 24.2 0.526 26 0
54 5 88 66 21 23 24.4 0.342 30 0
55 8 176 90 34 300 33.7 0.467 58 1
56 7 150 66 42 342 34.7 0.718 42 0
57 1 73 50 10 0 23 0.248 21 0
58 7 187 68 39 304 37.7 0.254 41 1
59 0 100 88 60 110 46.8 0.962 31 0
60 0 146 82 0 0 40.5 1.781 44 0
61 0 105 64 41 142 41.5 0.173 22 0
62 2 84 0 0 0 0 0.304 21 0
63 8 133 72 0 0 32.9 0.27 39 1
64 5 44 62 0 0 25 0.587 36 0
65 2 141 58 34 128 25.4 0.699 24 0
66 7 114 66 0 0 32.8 0.258 42 1
67 5 99 74 27 0 29 0.203 32 0
68 0 109 88 30 0 32.5 0.855 38 1
69 2 109 92 0 0 42.7 0.845 54 0
70 1 95 66 13 38 19.6 0.334 25 0
71 4 146 85 27 100 28.9 0.189 27 0
72 2 100 66 20 90 32.9 0.867 28 1
73 5 139 64 35 140 28.6 0.411 26 0
74 13 126 90 0 0 43.4 0.583 42 1
75 4 129 86 20 270 35.1 0.231 23 0
76 1 79 75 30 0 32 0.396 22 0
77 1 0 48 20 0 24.7 0.14 22 0
78 7 62 78 0 0 32.6 0.391 41 0
79 5 95 72 33 0 37.7 0.37 27 0
80 0 131 0 0 0 43.2 0.27 26 1
81 2 112 66 22 0 25 0.307 24 0
82 3 113 44 13 0 22.4 0.14 22 0
83 2 74 0 0 0 0 0.102 22 0
84 7 83 78 26 71 29.3 0.767 36 0
85 0 101 65 28 0 24.6 0.237 22 0
86 5 137 108 0 0 48.8 0.227 37 1
87 2 110 74 29 125 32.4 0.698 27 0
88 13 106 72 54 0 36.6 0.178 45 0
89 2 100 68 25 71 38.5 0.324 26 0
90 15 136 70 32 110 37.1 0.153 43 1
91 1 107 68 19 0 26.5 0.165 24 0
92 1 80 55 0 0 19.1 0.258 21 0
93 4 123 80 15 176 32 0.443 34 0
94 7 81 78 40 48 46.7 0.261 42 0
95 4 134 72 0 0 23.8 0.277 60 1
96 2 142 82 18 64 24.7 0.761 21 0
97 6 144 72 27 228 33.9 0.255 40 0
98 2 92 62 28 0 31.6 0.13 24 0
99 1 71 48 18 76 20.4 0.323 22 0
100 6 93 50 30 64 28.7 0.356 23 0
101 1 122 90 51 220 49.7 0.325 31 1
102 1 163 72 0 0 39 1.222 33 1
103 1 151 60 0 0 26.1 0.179 22 0
104 0 125 96 0 0 22.5 0.262 21 0
105 1 81 72 18 40 26.6 0.283 24 0
106 2 85 65 0 0 39.6 0.93 27 0
107 1 126 56 29 152 28.7 0.801 21 0
108 1 96 122 0 0 22.4 0.207 27 0
109 4 144 58 28 140 29.5 0.287 37 0
110 3 83 58 31 18 34.3 0.336 25 0
111 0 95 85 25 36 37.4 0.247 24 1
112 3 171 72 33 135 33.3 0.199 24 1
113 8 155 62 26 495 34 0.543 46 1
114 1 89 76 34 37 31.2 0.192 23 0
115 4 76 62 0 0 34 0.391 25 0
116 7 160 54 32 175 30.5 0.588 39 1
117 4 146 92 0 0 31.2 0.539 61 1
118 5 124 74 0 0 34 0.22 38 1
119 5 78 48 0 0 33.7 0.654 25 0
120 4 97 60 23 0 28.2 0.443 22 0
121 4 99 76 15 51 23.2 0.223 21 0
122 0 162 76 56 100 53.2 0.759 25 1
123 6 111 64 39 0 34.2 0.26 24 0
124 2 107 74 30 100 33.6 0.404 23 0
125 5 132 80 0 0 26.8 0.186 69 0
126 0 113 76 0 0 33.3 0.278 23 1
127 1 88 30 42 99 55 0.496 26 1
128 3 120 70 30 135 42.9 0.452 30 0
129 1 118 58 36 94 33.3 0.261 23 0
130 1 117 88 24 145 34.5 0.403 40 1
131 0 105 84 0 0 27.9 0.741 62 1
132 4 173 70 14 168 29.7 0.361 33 1
133 9 122 56 0 0 33.3 1.114 33 1
134 3 170 64 37 225 34.5 0.356 30 1
135 8 84 74 31 0 38.3 0.457 39 0
136 2 96 68 13 49 21.1 0.647 26 0
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650 11 136 84 35 130 28.3 0.26 42 1
651 0 107 60 25 0 26.4 0.133 23 0
652 1 91 54 25 100 25.2 0.234 23 0
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663 1 199 76 43 0 42.9 1.394 22 1
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671 9 154 78 30 100 30.9 0.164 45 0
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673 1 99 58 10 0 25.4 0.551 21 0
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age,sex,cp,trtbps,chol,fbs,restecg,thalachh,exng,oldpeak,slp,caa,thall,output
63,1,3,145,233,1,0,150,0,2.3,0,0,1,1
37,1,2,130,250,0,1,187,0,3.5,0,0,2,1
41,0,1,130,204,0,0,172,0,1.4,2,0,2,1
56,1,1,120,236,0,1,178,0,0.8,2,0,2,1
57,0,0,120,354,0,1,163,1,0.6,2,0,2,1
57,1,0,140,192,0,1,148,0,0.4,1,0,1,1
56,0,1,140,294,0,0,153,0,1.3,1,0,2,1
44,1,1,120,263,0,1,173,0,0,2,0,3,1
52,1,2,172,199,1,1,162,0,0.5,2,0,3,1
57,1,2,150,168,0,1,174,0,1.6,2,0,2,1
54,1,0,140,239,0,1,160,0,1.2,2,0,2,1
48,0,2,130,275,0,1,139,0,0.2,2,0,2,1
49,1,1,130,266,0,1,171,0,0.6,2,0,2,1
64,1,3,110,211,0,0,144,1,1.8,1,0,2,1
58,0,3,150,283,1,0,162,0,1,2,0,2,1
50,0,2,120,219,0,1,158,0,1.6,1,0,2,1
58,0,2,120,340,0,1,172,0,0,2,0,2,1
66,0,3,150,226,0,1,114,0,2.6,0,0,2,1
43,1,0,150,247,0,1,171,0,1.5,2,0,2,1
69,0,3,140,239,0,1,151,0,1.8,2,2,2,1
59,1,0,135,234,0,1,161,0,0.5,1,0,3,1
44,1,2,130,233,0,1,179,1,0.4,2,0,2,1
42,1,0,140,226,0,1,178,0,0,2,0,2,1
61,1,2,150,243,1,1,137,1,1,1,0,2,1
40,1,3,140,199,0,1,178,1,1.4,2,0,3,1
71,0,1,160,302,0,1,162,0,0.4,2,2,2,1
59,1,2,150,212,1,1,157,0,1.6,2,0,2,1
51,1,2,110,175,0,1,123,0,0.6,2,0,2,1
65,0,2,140,417,1,0,157,0,0.8,2,1,2,1
53,1,2,130,197,1,0,152,0,1.2,0,0,2,1
41,0,1,105,198,0,1,168,0,0,2,1,2,1
65,1,0,120,177,0,1,140,0,0.4,2,0,3,1
44,1,1,130,219,0,0,188,0,0,2,0,2,1
54,1,2,125,273,0,0,152,0,0.5,0,1,2,1
51,1,3,125,213,0,0,125,1,1.4,2,1,2,1
46,0,2,142,177,0,0,160,1,1.4,0,0,2,1
54,0,2,135,304,1,1,170,0,0,2,0,2,1
54,1,2,150,232,0,0,165,0,1.6,2,0,3,1
65,0,2,155,269,0,1,148,0,0.8,2,0,2,1
65,0,2,160,360,0,0,151,0,0.8,2,0,2,1
51,0,2,140,308,0,0,142,0,1.5,2,1,2,1
48,1,1,130,245,0,0,180,0,0.2,1,0,2,1
45,1,0,104,208,0,0,148,1,3,1,0,2,1
53,0,0,130,264,0,0,143,0,0.4,1,0,2,1
39,1,2,140,321,0,0,182,0,0,2,0,2,1
52,1,1,120,325,0,1,172,0,0.2,2,0,2,1
44,1,2,140,235,0,0,180,0,0,2,0,2,1
47,1,2,138,257,0,0,156,0,0,2,0,2,1
53,0,2,128,216,0,0,115,0,0,2,0,0,1
53,0,0,138,234,0,0,160,0,0,2,0,2,1
51,0,2,130,256,0,0,149,0,0.5,2,0,2,1
66,1,0,120,302,0,0,151,0,0.4,1,0,2,1
62,1,2,130,231,0,1,146,0,1.8,1,3,3,1
44,0,2,108,141,0,1,175,0,0.6,1,0,2,1
63,0,2,135,252,0,0,172,0,0,2,0,2,1
52,1,1,134,201,0,1,158,0,0.8,2,1,2,1
48,1,0,122,222,0,0,186,0,0,2,0,2,1
45,1,0,115,260,0,0,185,0,0,2,0,2,1
34,1,3,118,182,0,0,174,0,0,2,0,2,1
57,0,0,128,303,0,0,159,0,0,2,1,2,1
71,0,2,110,265,1,0,130,0,0,2,1,2,1
54,1,1,108,309,0,1,156,0,0,2,0,3,1
52,1,3,118,186,0,0,190,0,0,1,0,1,1
41,1,1,135,203,0,1,132,0,0,1,0,1,1
58,1,2,140,211,1,0,165,0,0,2,0,2,1
35,0,0,138,183,0,1,182,0,1.4,2,0,2,1
51,1,2,100,222,0,1,143,1,1.2,1,0,2,1
45,0,1,130,234,0,0,175,0,0.6,1,0,2,1
44,1,1,120,220,0,1,170,0,0,2,0,2,1
62,0,0,124,209,0,1,163,0,0,2,0,2,1
54,1,2,120,258,0,0,147,0,0.4,1,0,3,1
51,1,2,94,227,0,1,154,1,0,2,1,3,1
29,1,1,130,204,0,0,202,0,0,2,0,2,1
51,1,0,140,261,0,0,186,1,0,2,0,2,1
43,0,2,122,213,0,1,165,0,0.2,1,0,2,1
55,0,1,135,250,0,0,161,0,1.4,1,0,2,1
51,1,2,125,245,1,0,166,0,2.4,1,0,2,1
59,1,1,140,221,0,1,164,1,0,2,0,2,1
52,1,1,128,205,1,1,184,0,0,2,0,2,1
58,1,2,105,240,0,0,154,1,0.6,1,0,3,1
41,1,2,112,250,0,1,179,0,0,2,0,2,1
45,1,1,128,308,0,0,170,0,0,2,0,2,1
60,0,2,102,318,0,1,160,0,0,2,1,2,1
52,1,3,152,298,1,1,178,0,1.2,1,0,3,1
42,0,0,102,265,0,0,122,0,0.6,1,0,2,1
67,0,2,115,564,0,0,160,0,1.6,1,0,3,1
68,1,2,118,277,0,1,151,0,1,2,1,3,1
46,1,1,101,197,1,1,156,0,0,2,0,3,1
54,0,2,110,214,0,1,158,0,1.6,1,0,2,1
58,0,0,100,248,0,0,122,0,1,1,0,2,1
48,1,2,124,255,1,1,175,0,0,2,2,2,1
57,1,0,132,207,0,1,168,1,0,2,0,3,1
52,1,2,138,223,0,1,169,0,0,2,4,2,1
54,0,1,132,288,1,0,159,1,0,2,1,2,1
45,0,1,112,160,0,1,138,0,0,1,0,2,1
53,1,0,142,226,0,0,111,1,0,2,0,3,1
62,0,0,140,394,0,0,157,0,1.2,1,0,2,1
52,1,0,108,233,1,1,147,0,0.1,2,3,3,1
43,1,2,130,315,0,1,162,0,1.9,2,1,2,1
53,1,2,130,246,1,0,173,0,0,2,3,2,1
42,1,3,148,244,0,0,178,0,0.8,2,2,2,1
59,1,3,178,270,0,0,145,0,4.2,0,0,3,1
63,0,1,140,195,0,1,179,0,0,2,2,2,1
42,1,2,120,240,1,1,194,0,0.8,0,0,3,1
50,1,2,129,196,0,1,163,0,0,2,0,2,1
68,0,2,120,211,0,0,115,0,1.5,1,0,2,1
69,1,3,160,234,1,0,131,0,0.1,1,1,2,1
45,0,0,138,236,0,0,152,1,0.2,1,0,2,1
50,0,1,120,244,0,1,162,0,1.1,2,0,2,1
50,0,0,110,254,0,0,159,0,0,2,0,2,1
64,0,0,180,325,0,1,154,1,0,2,0,2,1
57,1,2,150,126,1,1,173,0,0.2,2,1,3,1
64,0,2,140,313,0,1,133,0,0.2,2,0,3,1
43,1,0,110,211,0,1,161,0,0,2,0,3,1
55,1,1,130,262,0,1,155,0,0,2,0,2,1
37,0,2,120,215,0,1,170,0,0,2,0,2,1
41,1,2,130,214,0,0,168,0,2,1,0,2,1
56,1,3,120,193,0,0,162,0,1.9,1,0,3,1
46,0,1,105,204,0,1,172,0,0,2,0,2,1
46,0,0,138,243,0,0,152,1,0,1,0,2,1
64,0,0,130,303,0,1,122,0,2,1,2,2,1
59,1,0,138,271,0,0,182,0,0,2,0,2,1
41,0,2,112,268,0,0,172,1,0,2,0,2,1
54,0,2,108,267,0,0,167,0,0,2,0,2,1
39,0,2,94,199,0,1,179,0,0,2,0,2,1
34,0,1,118,210,0,1,192,0,0.7,2,0,2,1
47,1,0,112,204,0,1,143,0,0.1,2,0,2,1
67,0,2,152,277,0,1,172,0,0,2,1,2,1
52,0,2,136,196,0,0,169,0,0.1,1,0,2,1
74,0,1,120,269,0,0,121,1,0.2,2,1,2,1
54,0,2,160,201,0,1,163,0,0,2,1,2,1
49,0,1,134,271,0,1,162,0,0,1,0,2,1
42,1,1,120,295,0,1,162,0,0,2,0,2,1
41,1,1,110,235,0,1,153,0,0,2,0,2,1
41,0,1,126,306,0,1,163,0,0,2,0,2,1
49,0,0,130,269,0,1,163,0,0,2,0,2,1
60,0,2,120,178,1,1,96,0,0,2,0,2,1
62,1,1,128,208,1,0,140,0,0,2,0,2,1
57,1,0,110,201,0,1,126,1,1.5,1,0,1,1
64,1,0,128,263,0,1,105,1,0.2,1,1,3,1
51,0,2,120,295,0,0,157,0,0.6,2,0,2,1
43,1,0,115,303,0,1,181,0,1.2,1,0,2,1
42,0,2,120,209,0,1,173,0,0,1,0,2,1
67,0,0,106,223,0,1,142,0,0.3,2,2,2,1
76,0,2,140,197,0,2,116,0,1.1,1,0,2,1
70,1,1,156,245,0,0,143,0,0,2,0,2,1
44,0,2,118,242,0,1,149,0,0.3,1,1,2,1
60,0,3,150,240,0,1,171,0,0.9,2,0,2,1
44,1,2,120,226,0,1,169,0,0,2,0,2,1
42,1,2,130,180,0,1,150,0,0,2,0,2,1
66,1,0,160,228,0,0,138,0,2.3,2,0,1,1
71,0,0,112,149,0,1,125,0,1.6,1,0,2,1
64,1,3,170,227,0,0,155,0,0.6,1,0,3,1
66,0,2,146,278,0,0,152,0,0,1,1,2,1
39,0,2,138,220,0,1,152,0,0,1,0,2,1
58,0,0,130,197,0,1,131,0,0.6,1,0,2,1
47,1,2,130,253,0,1,179,0,0,2,0,2,1
35,1,1,122,192,0,1,174,0,0,2,0,2,1
58,1,1,125,220,0,1,144,0,0.4,1,4,3,1
56,1,1,130,221,0,0,163,0,0,2,0,3,1
56,1,1,120,240,0,1,169,0,0,0,0,2,1
55,0,1,132,342,0,1,166,0,1.2,2,0,2,1
41,1,1,120,157,0,1,182,0,0,2,0,2,1
38,1,2,138,175,0,1,173,0,0,2,4,2,1
38,1,2,138,175,0,1,173,0,0,2,4,2,1
67,1,0,160,286,0,0,108,1,1.5,1,3,2,0
67,1,0,120,229,0,0,129,1,2.6,1,2,3,0
62,0,0,140,268,0,0,160,0,3.6,0,2,2,0
63,1,0,130,254,0,0,147,0,1.4,1,1,3,0
53,1,0,140,203,1,0,155,1,3.1,0,0,3,0
56,1,2,130,256,1,0,142,1,0.6,1,1,1,0
48,1,1,110,229,0,1,168,0,1,0,0,3,0
58,1,1,120,284,0,0,160,0,1.8,1,0,2,0
58,1,2,132,224,0,0,173,0,3.2,2,2,3,0
60,1,0,130,206,0,0,132,1,2.4,1,2,3,0
40,1,0,110,167,0,0,114,1,2,1,0,3,0
60,1,0,117,230,1,1,160,1,1.4,2,2,3,0
64,1,2,140,335,0,1,158,0,0,2,0,2,0
43,1,0,120,177,0,0,120,1,2.5,1,0,3,0
57,1,0,150,276,0,0,112,1,0.6,1,1,1,0
55,1,0,132,353,0,1,132,1,1.2,1,1,3,0
65,0,0,150,225,0,0,114,0,1,1,3,3,0
61,0,0,130,330,0,0,169,0,0,2,0,2,0
58,1,2,112,230,0,0,165,0,2.5,1,1,3,0
50,1,0,150,243,0,0,128,0,2.6,1,0,3,0
44,1,0,112,290,0,0,153,0,0,2,1,2,0
60,1,0,130,253,0,1,144,1,1.4,2,1,3,0
54,1,0,124,266,0,0,109,1,2.2,1,1,3,0
50,1,2,140,233,0,1,163,0,0.6,1,1,3,0
41,1,0,110,172,0,0,158,0,0,2,0,3,0
51,0,0,130,305,0,1,142,1,1.2,1,0,3,0
58,1,0,128,216,0,0,131,1,2.2,1,3,3,0
54,1,0,120,188,0,1,113,0,1.4,1,1,3,0
60,1,0,145,282,0,0,142,1,2.8,1,2,3,0
60,1,2,140,185,0,0,155,0,3,1,0,2,0
59,1,0,170,326,0,0,140,1,3.4,0,0,3,0
46,1,2,150,231,0,1,147,0,3.6,1,0,2,0
67,1,0,125,254,1,1,163,0,0.2,1,2,3,0
62,1,0,120,267,0,1,99,1,1.8,1,2,3,0
65,1,0,110,248,0,0,158,0,0.6,2,2,1,0
44,1,0,110,197,0,0,177,0,0,2,1,2,0
60,1,0,125,258,0,0,141,1,2.8,1,1,3,0
58,1,0,150,270,0,0,111,1,0.8,2,0,3,0
68,1,2,180,274,1,0,150,1,1.6,1,0,3,0
62,0,0,160,164,0,0,145,0,6.2,0,3,3,0
52,1,0,128,255,0,1,161,1,0,2,1,3,0
59,1,0,110,239,0,0,142,1,1.2,1,1,3,0
60,0,0,150,258,0,0,157,0,2.6,1,2,3,0
49,1,2,120,188,0,1,139,0,2,1,3,3,0
59,1,0,140,177,0,1,162,1,0,2,1,3,0
57,1,2,128,229,0,0,150,0,0.4,1,1,3,0
61,1,0,120,260,0,1,140,1,3.6,1,1,3,0
39,1,0,118,219,0,1,140,0,1.2,1,0,3,0
61,0,0,145,307,0,0,146,1,1,1,0,3,0
56,1,0,125,249,1,0,144,1,1.2,1,1,2,0
43,0,0,132,341,1,0,136,1,3,1,0,3,0
62,0,2,130,263,0,1,97,0,1.2,1,1,3,0
63,1,0,130,330,1,0,132,1,1.8,2,3,3,0
65,1,0,135,254,0,0,127,0,2.8,1,1,3,0
48,1,0,130,256,1,0,150,1,0,2,2,3,0
63,0,0,150,407,0,0,154,0,4,1,3,3,0
55,1,0,140,217,0,1,111,1,5.6,0,0,3,0
65,1,3,138,282,1,0,174,0,1.4,1,1,2,0
56,0,0,200,288,1,0,133,1,4,0,2,3,0
54,1,0,110,239,0,1,126,1,2.8,1,1,3,0
70,1,0,145,174,0,1,125,1,2.6,0,0,3,0
62,1,1,120,281,0,0,103,0,1.4,1,1,3,0
35,1,0,120,198,0,1,130,1,1.6,1,0,3,0
59,1,3,170,288,0,0,159,0,0.2,1,0,3,0
64,1,2,125,309,0,1,131,1,1.8,1,0,3,0
47,1,2,108,243,0,1,152,0,0,2,0,2,0
57,1,0,165,289,1,0,124,0,1,1,3,3,0
55,1,0,160,289,0,0,145,1,0.8,1,1,3,0
64,1,0,120,246,0,0,96,1,2.2,0,1,2,0
70,1,0,130,322,0,0,109,0,2.4,1,3,2,0
51,1,0,140,299,0,1,173,1,1.6,2,0,3,0
58,1,0,125,300,0,0,171,0,0,2,2,3,0
60,1,0,140,293,0,0,170,0,1.2,1,2,3,0
77,1,0,125,304,0,0,162,1,0,2,3,2,0
35,1,0,126,282,0,0,156,1,0,2,0,3,0
70,1,2,160,269,0,1,112,1,2.9,1,1,3,0
59,0,0,174,249,0,1,143,1,0,1,0,2,0
64,1,0,145,212,0,0,132,0,2,1,2,1,0
57,1,0,152,274,0,1,88,1,1.2,1,1,3,0
56,1,0,132,184,0,0,105,1,2.1,1,1,1,0
48,1,0,124,274,0,0,166,0,0.5,1,0,3,0
56,0,0,134,409,0,0,150,1,1.9,1,2,3,0
66,1,1,160,246,0,1,120,1,0,1,3,1,0
54,1,1,192,283,0,0,195,0,0,2,1,3,0
69,1,2,140,254,0,0,146,0,2,1,3,3,0
51,1,0,140,298,0,1,122,1,4.2,1,3,3,0
43,1,0,132,247,1,0,143,1,0.1,1,4,3,0
62,0,0,138,294,1,1,106,0,1.9,1,3,2,0
67,1,0,100,299,0,0,125,1,0.9,1,2,2,0
59,1,3,160,273,0,0,125,0,0,2,0,2,0
45,1,0,142,309,0,0,147,1,0,1,3,3,0
58,1,0,128,259,0,0,130,1,3,1,2,3,0
50,1,0,144,200,0,0,126,1,0.9,1,0,3,0
62,0,0,150,244,0,1,154,1,1.4,1,0,2,0
38,1,3,120,231,0,1,182,1,3.8,1,0,3,0
66,0,0,178,228,1,1,165,1,1,1,2,3,0
52,1,0,112,230,0,1,160,0,0,2,1,2,0
53,1,0,123,282,0,1,95,1,2,1,2,3,0
63,0,0,108,269,0,1,169,1,1.8,1,2,2,0
54,1,0,110,206,0,0,108,1,0,1,1,2,0
66,1,0,112,212,0,0,132,1,0.1,2,1,2,0
55,0,0,180,327,0,2,117,1,3.4,1,0,2,0
49,1,2,118,149,0,0,126,0,0.8,2,3,2,0
54,1,0,122,286,0,0,116,1,3.2,1,2,2,0
56,1,0,130,283,1,0,103,1,1.6,0,0,3,0
46,1,0,120,249,0,0,144,0,0.8,2,0,3,0
61,1,3,134,234,0,1,145,0,2.6,1,2,2,0
67,1,0,120,237,0,1,71,0,1,1,0,2,0
58,1,0,100,234,0,1,156,0,0.1,2,1,3,0
47,1,0,110,275,0,0,118,1,1,1,1,2,0
52,1,0,125,212,0,1,168,0,1,2,2,3,0
58,1,0,146,218,0,1,105,0,2,1,1,3,0
57,1,1,124,261,0,1,141,0,0.3,2,0,3,0
58,0,1,136,319,1,0,152,0,0,2,2,2,0
61,1,0,138,166,0,0,125,1,3.6,1,1,2,0
42,1,0,136,315,0,1,125,1,1.8,1,0,1,0
52,1,0,128,204,1,1,156,1,1,1,0,0,0
59,1,2,126,218,1,1,134,0,2.2,1,1,1,0
40,1,0,152,223,0,1,181,0,0,2,0,3,0
61,1,0,140,207,0,0,138,1,1.9,2,1,3,0
46,1,0,140,311,0,1,120,1,1.8,1,2,3,0
59,1,3,134,204,0,1,162,0,0.8,2,2,2,0
57,1,1,154,232,0,0,164,0,0,2,1,2,0
57,1,0,110,335,0,1,143,1,3,1,1,3,0
55,0,0,128,205,0,2,130,1,2,1,1,3,0
61,1,0,148,203,0,1,161,0,0,2,1,3,0
58,1,0,114,318,0,2,140,0,4.4,0,3,1,0
58,0,0,170,225,1,0,146,1,2.8,1,2,1,0
67,1,2,152,212,0,0,150,0,0.8,1,0,3,0
44,1,0,120,169,0,1,144,1,2.8,0,0,1,0
63,1,0,140,187,0,0,144,1,4,2,2,3,0
63,0,0,124,197,0,1,136,1,0,1,0,2,0
59,1,0,164,176,1,0,90,0,1,1,2,1,0
57,0,0,140,241,0,1,123,1,0.2,1,0,3,0
45,1,3,110,264,0,1,132,0,1.2,1,0,3,0
68,1,0,144,193,1,1,141,0,3.4,1,2,3,0
57,1,0,130,131,0,1,115,1,1.2,1,1,3,0
57,0,1,130,236,0,0,174,0,0,1,1,2,0
1 age sex cp trtbps chol fbs restecg thalachh exng oldpeak slp caa thall output
2 63 1 3 145 233 1 0 150 0 2.3 0 0 1 1
3 37 1 2 130 250 0 1 187 0 3.5 0 0 2 1
4 41 0 1 130 204 0 0 172 0 1.4 2 0 2 1
5 56 1 1 120 236 0 1 178 0 0.8 2 0 2 1
6 57 0 0 120 354 0 1 163 1 0.6 2 0 2 1
7 57 1 0 140 192 0 1 148 0 0.4 1 0 1 1
8 56 0 1 140 294 0 0 153 0 1.3 1 0 2 1
9 44 1 1 120 263 0 1 173 0 0 2 0 3 1
10 52 1 2 172 199 1 1 162 0 0.5 2 0 3 1
11 57 1 2 150 168 0 1 174 0 1.6 2 0 2 1
12 54 1 0 140 239 0 1 160 0 1.2 2 0 2 1
13 48 0 2 130 275 0 1 139 0 0.2 2 0 2 1
14 49 1 1 130 266 0 1 171 0 0.6 2 0 2 1
15 64 1 3 110 211 0 0 144 1 1.8 1 0 2 1
16 58 0 3 150 283 1 0 162 0 1 2 0 2 1
17 50 0 2 120 219 0 1 158 0 1.6 1 0 2 1
18 58 0 2 120 340 0 1 172 0 0 2 0 2 1
19 66 0 3 150 226 0 1 114 0 2.6 0 0 2 1
20 43 1 0 150 247 0 1 171 0 1.5 2 0 2 1
21 69 0 3 140 239 0 1 151 0 1.8 2 2 2 1
22 59 1 0 135 234 0 1 161 0 0.5 1 0 3 1
23 44 1 2 130 233 0 1 179 1 0.4 2 0 2 1
24 42 1 0 140 226 0 1 178 0 0 2 0 2 1
25 61 1 2 150 243 1 1 137 1 1 1 0 2 1
26 40 1 3 140 199 0 1 178 1 1.4 2 0 3 1
27 71 0 1 160 302 0 1 162 0 0.4 2 2 2 1
28 59 1 2 150 212 1 1 157 0 1.6 2 0 2 1
29 51 1 2 110 175 0 1 123 0 0.6 2 0 2 1
30 65 0 2 140 417 1 0 157 0 0.8 2 1 2 1
31 53 1 2 130 197 1 0 152 0 1.2 0 0 2 1
32 41 0 1 105 198 0 1 168 0 0 2 1 2 1
33 65 1 0 120 177 0 1 140 0 0.4 2 0 3 1
34 44 1 1 130 219 0 0 188 0 0 2 0 2 1
35 54 1 2 125 273 0 0 152 0 0.5 0 1 2 1
36 51 1 3 125 213 0 0 125 1 1.4 2 1 2 1
37 46 0 2 142 177 0 0 160 1 1.4 0 0 2 1
38 54 0 2 135 304 1 1 170 0 0 2 0 2 1
39 54 1 2 150 232 0 0 165 0 1.6 2 0 3 1
40 65 0 2 155 269 0 1 148 0 0.8 2 0 2 1
41 65 0 2 160 360 0 0 151 0 0.8 2 0 2 1
42 51 0 2 140 308 0 0 142 0 1.5 2 1 2 1
43 48 1 1 130 245 0 0 180 0 0.2 1 0 2 1
44 45 1 0 104 208 0 0 148 1 3 1 0 2 1
45 53 0 0 130 264 0 0 143 0 0.4 1 0 2 1
46 39 1 2 140 321 0 0 182 0 0 2 0 2 1
47 52 1 1 120 325 0 1 172 0 0.2 2 0 2 1
48 44 1 2 140 235 0 0 180 0 0 2 0 2 1
49 47 1 2 138 257 0 0 156 0 0 2 0 2 1
50 53 0 2 128 216 0 0 115 0 0 2 0 0 1
51 53 0 0 138 234 0 0 160 0 0 2 0 2 1
52 51 0 2 130 256 0 0 149 0 0.5 2 0 2 1
53 66 1 0 120 302 0 0 151 0 0.4 1 0 2 1
54 62 1 2 130 231 0 1 146 0 1.8 1 3 3 1
55 44 0 2 108 141 0 1 175 0 0.6 1 0 2 1
56 63 0 2 135 252 0 0 172 0 0 2 0 2 1
57 52 1 1 134 201 0 1 158 0 0.8 2 1 2 1
58 48 1 0 122 222 0 0 186 0 0 2 0 2 1
59 45 1 0 115 260 0 0 185 0 0 2 0 2 1
60 34 1 3 118 182 0 0 174 0 0 2 0 2 1
61 57 0 0 128 303 0 0 159 0 0 2 1 2 1
62 71 0 2 110 265 1 0 130 0 0 2 1 2 1
63 54 1 1 108 309 0 1 156 0 0 2 0 3 1
64 52 1 3 118 186 0 0 190 0 0 1 0 1 1
65 41 1 1 135 203 0 1 132 0 0 1 0 1 1
66 58 1 2 140 211 1 0 165 0 0 2 0 2 1
67 35 0 0 138 183 0 1 182 0 1.4 2 0 2 1
68 51 1 2 100 222 0 1 143 1 1.2 1 0 2 1
69 45 0 1 130 234 0 0 175 0 0.6 1 0 2 1
70 44 1 1 120 220 0 1 170 0 0 2 0 2 1
71 62 0 0 124 209 0 1 163 0 0 2 0 2 1
72 54 1 2 120 258 0 0 147 0 0.4 1 0 3 1
73 51 1 2 94 227 0 1 154 1 0 2 1 3 1
74 29 1 1 130 204 0 0 202 0 0 2 0 2 1
75 51 1 0 140 261 0 0 186 1 0 2 0 2 1
76 43 0 2 122 213 0 1 165 0 0.2 1 0 2 1
77 55 0 1 135 250 0 0 161 0 1.4 1 0 2 1
78 51 1 2 125 245 1 0 166 0 2.4 1 0 2 1
79 59 1 1 140 221 0 1 164 1 0 2 0 2 1
80 52 1 1 128 205 1 1 184 0 0 2 0 2 1
81 58 1 2 105 240 0 0 154 1 0.6 1 0 3 1
82 41 1 2 112 250 0 1 179 0 0 2 0 2 1
83 45 1 1 128 308 0 0 170 0 0 2 0 2 1
84 60 0 2 102 318 0 1 160 0 0 2 1 2 1
85 52 1 3 152 298 1 1 178 0 1.2 1 0 3 1
86 42 0 0 102 265 0 0 122 0 0.6 1 0 2 1
87 67 0 2 115 564 0 0 160 0 1.6 1 0 3 1
88 68 1 2 118 277 0 1 151 0 1 2 1 3 1
89 46 1 1 101 197 1 1 156 0 0 2 0 3 1
90 54 0 2 110 214 0 1 158 0 1.6 1 0 2 1
91 58 0 0 100 248 0 0 122 0 1 1 0 2 1
92 48 1 2 124 255 1 1 175 0 0 2 2 2 1
93 57 1 0 132 207 0 1 168 1 0 2 0 3 1
94 52 1 2 138 223 0 1 169 0 0 2 4 2 1
95 54 0 1 132 288 1 0 159 1 0 2 1 2 1
96 45 0 1 112 160 0 1 138 0 0 1 0 2 1
97 53 1 0 142 226 0 0 111 1 0 2 0 3 1
98 62 0 0 140 394 0 0 157 0 1.2 1 0 2 1
99 52 1 0 108 233 1 1 147 0 0.1 2 3 3 1
100 43 1 2 130 315 0 1 162 0 1.9 2 1 2 1
101 53 1 2 130 246 1 0 173 0 0 2 3 2 1
102 42 1 3 148 244 0 0 178 0 0.8 2 2 2 1
103 59 1 3 178 270 0 0 145 0 4.2 0 0 3 1
104 63 0 1 140 195 0 1 179 0 0 2 2 2 1
105 42 1 2 120 240 1 1 194 0 0.8 0 0 3 1
106 50 1 2 129 196 0 1 163 0 0 2 0 2 1
107 68 0 2 120 211 0 0 115 0 1.5 1 0 2 1
108 69 1 3 160 234 1 0 131 0 0.1 1 1 2 1
109 45 0 0 138 236 0 0 152 1 0.2 1 0 2 1
110 50 0 1 120 244 0 1 162 0 1.1 2 0 2 1
111 50 0 0 110 254 0 0 159 0 0 2 0 2 1
112 64 0 0 180 325 0 1 154 1 0 2 0 2 1
113 57 1 2 150 126 1 1 173 0 0.2 2 1 3 1
114 64 0 2 140 313 0 1 133 0 0.2 2 0 3 1
115 43 1 0 110 211 0 1 161 0 0 2 0 3 1
116 55 1 1 130 262 0 1 155 0 0 2 0 2 1
117 37 0 2 120 215 0 1 170 0 0 2 0 2 1
118 41 1 2 130 214 0 0 168 0 2 1 0 2 1
119 56 1 3 120 193 0 0 162 0 1.9 1 0 3 1
120 46 0 1 105 204 0 1 172 0 0 2 0 2 1
121 46 0 0 138 243 0 0 152 1 0 1 0 2 1
122 64 0 0 130 303 0 1 122 0 2 1 2 2 1
123 59 1 0 138 271 0 0 182 0 0 2 0 2 1
124 41 0 2 112 268 0 0 172 1 0 2 0 2 1
125 54 0 2 108 267 0 0 167 0 0 2 0 2 1
126 39 0 2 94 199 0 1 179 0 0 2 0 2 1
127 34 0 1 118 210 0 1 192 0 0.7 2 0 2 1
128 47 1 0 112 204 0 1 143 0 0.1 2 0 2 1
129 67 0 2 152 277 0 1 172 0 0 2 1 2 1
130 52 0 2 136 196 0 0 169 0 0.1 1 0 2 1
131 74 0 1 120 269 0 0 121 1 0.2 2 1 2 1
132 54 0 2 160 201 0 1 163 0 0 2 1 2 1
133 49 0 1 134 271 0 1 162 0 0 1 0 2 1
134 42 1 1 120 295 0 1 162 0 0 2 0 2 1
135 41 1 1 110 235 0 1 153 0 0 2 0 2 1
136 41 0 1 126 306 0 1 163 0 0 2 0 2 1
137 49 0 0 130 269 0 1 163 0 0 2 0 2 1
138 60 0 2 120 178 1 1 96 0 0 2 0 2 1
139 62 1 1 128 208 1 0 140 0 0 2 0 2 1
140 57 1 0 110 201 0 1 126 1 1.5 1 0 1 1
141 64 1 0 128 263 0 1 105 1 0.2 1 1 3 1
142 51 0 2 120 295 0 0 157 0 0.6 2 0 2 1
143 43 1 0 115 303 0 1 181 0 1.2 1 0 2 1
144 42 0 2 120 209 0 1 173 0 0 1 0 2 1
145 67 0 0 106 223 0 1 142 0 0.3 2 2 2 1
146 76 0 2 140 197 0 2 116 0 1.1 1 0 2 1
147 70 1 1 156 245 0 0 143 0 0 2 0 2 1
148 44 0 2 118 242 0 1 149 0 0.3 1 1 2 1
149 60 0 3 150 240 0 1 171 0 0.9 2 0 2 1
150 44 1 2 120 226 0 1 169 0 0 2 0 2 1
151 42 1 2 130 180 0 1 150 0 0 2 0 2 1
152 66 1 0 160 228 0 0 138 0 2.3 2 0 1 1
153 71 0 0 112 149 0 1 125 0 1.6 1 0 2 1
154 64 1 3 170 227 0 0 155 0 0.6 1 0 3 1
155 66 0 2 146 278 0 0 152 0 0 1 1 2 1
156 39 0 2 138 220 0 1 152 0 0 1 0 2 1
157 58 0 0 130 197 0 1 131 0 0.6 1 0 2 1
158 47 1 2 130 253 0 1 179 0 0 2 0 2 1
159 35 1 1 122 192 0 1 174 0 0 2 0 2 1
160 58 1 1 125 220 0 1 144 0 0.4 1 4 3 1
161 56 1 1 130 221 0 0 163 0 0 2 0 3 1
162 56 1 1 120 240 0 1 169 0 0 0 0 2 1
163 55 0 1 132 342 0 1 166 0 1.2 2 0 2 1
164 41 1 1 120 157 0 1 182 0 0 2 0 2 1
165 38 1 2 138 175 0 1 173 0 0 2 4 2 1
166 38 1 2 138 175 0 1 173 0 0 2 4 2 1
167 67 1 0 160 286 0 0 108 1 1.5 1 3 2 0
168 67 1 0 120 229 0 0 129 1 2.6 1 2 3 0
169 62 0 0 140 268 0 0 160 0 3.6 0 2 2 0
170 63 1 0 130 254 0 0 147 0 1.4 1 1 3 0
171 53 1 0 140 203 1 0 155 1 3.1 0 0 3 0
172 56 1 2 130 256 1 0 142 1 0.6 1 1 1 0
173 48 1 1 110 229 0 1 168 0 1 0 0 3 0
174 58 1 1 120 284 0 0 160 0 1.8 1 0 2 0
175 58 1 2 132 224 0 0 173 0 3.2 2 2 3 0
176 60 1 0 130 206 0 0 132 1 2.4 1 2 3 0
177 40 1 0 110 167 0 0 114 1 2 1 0 3 0
178 60 1 0 117 230 1 1 160 1 1.4 2 2 3 0
179 64 1 2 140 335 0 1 158 0 0 2 0 2 0
180 43 1 0 120 177 0 0 120 1 2.5 1 0 3 0
181 57 1 0 150 276 0 0 112 1 0.6 1 1 1 0
182 55 1 0 132 353 0 1 132 1 1.2 1 1 3 0
183 65 0 0 150 225 0 0 114 0 1 1 3 3 0
184 61 0 0 130 330 0 0 169 0 0 2 0 2 0
185 58 1 2 112 230 0 0 165 0 2.5 1 1 3 0
186 50 1 0 150 243 0 0 128 0 2.6 1 0 3 0
187 44 1 0 112 290 0 0 153 0 0 2 1 2 0
188 60 1 0 130 253 0 1 144 1 1.4 2 1 3 0
189 54 1 0 124 266 0 0 109 1 2.2 1 1 3 0
190 50 1 2 140 233 0 1 163 0 0.6 1 1 3 0
191 41 1 0 110 172 0 0 158 0 0 2 0 3 0
192 51 0 0 130 305 0 1 142 1 1.2 1 0 3 0
193 58 1 0 128 216 0 0 131 1 2.2 1 3 3 0
194 54 1 0 120 188 0 1 113 0 1.4 1 1 3 0
195 60 1 0 145 282 0 0 142 1 2.8 1 2 3 0
196 60 1 2 140 185 0 0 155 0 3 1 0 2 0
197 59 1 0 170 326 0 0 140 1 3.4 0 0 3 0
198 46 1 2 150 231 0 1 147 0 3.6 1 0 2 0
199 67 1 0 125 254 1 1 163 0 0.2 1 2 3 0
200 62 1 0 120 267 0 1 99 1 1.8 1 2 3 0
201 65 1 0 110 248 0 0 158 0 0.6 2 2 1 0
202 44 1 0 110 197 0 0 177 0 0 2 1 2 0
203 60 1 0 125 258 0 0 141 1 2.8 1 1 3 0
204 58 1 0 150 270 0 0 111 1 0.8 2 0 3 0
205 68 1 2 180 274 1 0 150 1 1.6 1 0 3 0
206 62 0 0 160 164 0 0 145 0 6.2 0 3 3 0
207 52 1 0 128 255 0 1 161 1 0 2 1 3 0
208 59 1 0 110 239 0 0 142 1 1.2 1 1 3 0
209 60 0 0 150 258 0 0 157 0 2.6 1 2 3 0
210 49 1 2 120 188 0 1 139 0 2 1 3 3 0
211 59 1 0 140 177 0 1 162 1 0 2 1 3 0
212 57 1 2 128 229 0 0 150 0 0.4 1 1 3 0
213 61 1 0 120 260 0 1 140 1 3.6 1 1 3 0
214 39 1 0 118 219 0 1 140 0 1.2 1 0 3 0
215 61 0 0 145 307 0 0 146 1 1 1 0 3 0
216 56 1 0 125 249 1 0 144 1 1.2 1 1 2 0
217 43 0 0 132 341 1 0 136 1 3 1 0 3 0
218 62 0 2 130 263 0 1 97 0 1.2 1 1 3 0
219 63 1 0 130 330 1 0 132 1 1.8 2 3 3 0
220 65 1 0 135 254 0 0 127 0 2.8 1 1 3 0
221 48 1 0 130 256 1 0 150 1 0 2 2 3 0
222 63 0 0 150 407 0 0 154 0 4 1 3 3 0
223 55 1 0 140 217 0 1 111 1 5.6 0 0 3 0
224 65 1 3 138 282 1 0 174 0 1.4 1 1 2 0
225 56 0 0 200 288 1 0 133 1 4 0 2 3 0
226 54 1 0 110 239 0 1 126 1 2.8 1 1 3 0
227 70 1 0 145 174 0 1 125 1 2.6 0 0 3 0
228 62 1 1 120 281 0 0 103 0 1.4 1 1 3 0
229 35 1 0 120 198 0 1 130 1 1.6 1 0 3 0
230 59 1 3 170 288 0 0 159 0 0.2 1 0 3 0
231 64 1 2 125 309 0 1 131 1 1.8 1 0 3 0
232 47 1 2 108 243 0 1 152 0 0 2 0 2 0
233 57 1 0 165 289 1 0 124 0 1 1 3 3 0
234 55 1 0 160 289 0 0 145 1 0.8 1 1 3 0
235 64 1 0 120 246 0 0 96 1 2.2 0 1 2 0
236 70 1 0 130 322 0 0 109 0 2.4 1 3 2 0
237 51 1 0 140 299 0 1 173 1 1.6 2 0 3 0
238 58 1 0 125 300 0 0 171 0 0 2 2 3 0
239 60 1 0 140 293 0 0 170 0 1.2 1 2 3 0
240 77 1 0 125 304 0 0 162 1 0 2 3 2 0
241 35 1 0 126 282 0 0 156 1 0 2 0 3 0
242 70 1 2 160 269 0 1 112 1 2.9 1 1 3 0
243 59 0 0 174 249 0 1 143 1 0 1 0 2 0
244 64 1 0 145 212 0 0 132 0 2 1 2 1 0
245 57 1 0 152 274 0 1 88 1 1.2 1 1 3 0
246 56 1 0 132 184 0 0 105 1 2.1 1 1 1 0
247 48 1 0 124 274 0 0 166 0 0.5 1 0 3 0
248 56 0 0 134 409 0 0 150 1 1.9 1 2 3 0
249 66 1 1 160 246 0 1 120 1 0 1 3 1 0
250 54 1 1 192 283 0 0 195 0 0 2 1 3 0
251 69 1 2 140 254 0 0 146 0 2 1 3 3 0
252 51 1 0 140 298 0 1 122 1 4.2 1 3 3 0
253 43 1 0 132 247 1 0 143 1 0.1 1 4 3 0
254 62 0 0 138 294 1 1 106 0 1.9 1 3 2 0
255 67 1 0 100 299 0 0 125 1 0.9 1 2 2 0
256 59 1 3 160 273 0 0 125 0 0 2 0 2 0
257 45 1 0 142 309 0 0 147 1 0 1 3 3 0
258 58 1 0 128 259 0 0 130 1 3 1 2 3 0
259 50 1 0 144 200 0 0 126 1 0.9 1 0 3 0
260 62 0 0 150 244 0 1 154 1 1.4 1 0 2 0
261 38 1 3 120 231 0 1 182 1 3.8 1 0 3 0
262 66 0 0 178 228 1 1 165 1 1 1 2 3 0
263 52 1 0 112 230 0 1 160 0 0 2 1 2 0
264 53 1 0 123 282 0 1 95 1 2 1 2 3 0
265 63 0 0 108 269 0 1 169 1 1.8 1 2 2 0
266 54 1 0 110 206 0 0 108 1 0 1 1 2 0
267 66 1 0 112 212 0 0 132 1 0.1 2 1 2 0
268 55 0 0 180 327 0 2 117 1 3.4 1 0 2 0
269 49 1 2 118 149 0 0 126 0 0.8 2 3 2 0
270 54 1 0 122 286 0 0 116 1 3.2 1 2 2 0
271 56 1 0 130 283 1 0 103 1 1.6 0 0 3 0
272 46 1 0 120 249 0 0 144 0 0.8 2 0 3 0
273 61 1 3 134 234 0 1 145 0 2.6 1 2 2 0
274 67 1 0 120 237 0 1 71 0 1 1 0 2 0
275 58 1 0 100 234 0 1 156 0 0.1 2 1 3 0
276 47 1 0 110 275 0 0 118 1 1 1 1 2 0
277 52 1 0 125 212 0 1 168 0 1 2 2 3 0
278 58 1 0 146 218 0 1 105 0 2 1 1 3 0
279 57 1 1 124 261 0 1 141 0 0.3 2 0 3 0
280 58 0 1 136 319 1 0 152 0 0 2 2 2 0
281 61 1 0 138 166 0 0 125 1 3.6 1 1 2 0
282 42 1 0 136 315 0 1 125 1 1.8 1 0 1 0
283 52 1 0 128 204 1 1 156 1 1 1 0 0 0
284 59 1 2 126 218 1 1 134 0 2.2 1 1 1 0
285 40 1 0 152 223 0 1 181 0 0 2 0 3 0
286 61 1 0 140 207 0 0 138 1 1.9 2 1 3 0
287 46 1 0 140 311 0 1 120 1 1.8 1 2 3 0
288 59 1 3 134 204 0 1 162 0 0.8 2 2 2 0
289 57 1 1 154 232 0 0 164 0 0 2 1 2 0
290 57 1 0 110 335 0 1 143 1 3 1 1 3 0
291 55 0 0 128 205 0 2 130 1 2 1 1 3 0
292 61 1 0 148 203 0 1 161 0 0 2 1 3 0
293 58 1 0 114 318 0 2 140 0 4.4 0 3 1 0
294 58 0 0 170 225 1 0 146 1 2.8 1 2 1 0
295 67 1 2 152 212 0 0 150 0 0.8 1 0 3 0
296 44 1 0 120 169 0 1 144 1 2.8 0 0 1 0
297 63 1 0 140 187 0 0 144 1 4 2 2 3 0
298 63 0 0 124 197 0 1 136 1 0 1 0 2 0
299 59 1 0 164 176 1 0 90 0 1 1 2 1 0
300 57 0 0 140 241 0 1 123 1 0.2 1 0 3 0
301 45 1 3 110 264 0 1 132 0 1.2 1 0 3 0
302 68 1 0 144 193 1 1 141 0 3.4 1 2 3 0
303 57 1 0 130 131 0 1 115 1 1.2 1 1 3 0
304 57 0 1 130 236 0 0 174 0 0 1 1 2 0

48
data/housing-data.csv Normal file
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sqft,bdrms,age,price
2104,3,70,399900
1600,3,28,329900
2400,3,44,369000
1416,2,49,232000
3000,4,75,539900
1985,4,61,299900
1534,3,12,314900
1427,3,57,198999
1380,3,14,212000
1494,3,15,242500
1940,4,7,239999
2000,3,27,347000
1890,3,45,329999
4478,5,49,699900
1268,3,58,259900
2300,4,77,449900
1320,2,62,299900
1236,3,78,199900
2609,4,5,499998
3031,4,21,599000
1767,3,44,252900
1888,2,79,255000
1604,3,13,242900
1962,4,53,259900
3890,3,36,573900
1100,3,60,249900
1458,3,29,464500
2526,3,13,469000
2200,3,28,475000
2637,3,25,299900
1839,2,40,349900
1000,1,5,169900
2040,4,75,314900
3137,3,67,579900
1811,4,24,285900
1437,3,50,249900
1239,3,22,229900
2132,4,28,345000
4215,4,66,549000
2162,4,43,287000
1664,2,40,368500
2238,3,37,329900
2567,4,57,314000
1200,3,76,299000
852,2,70,179900
1852,4,64,299900
1203,3,11,239500
1 sqft bdrms age price
2 2104 3 70 399900
3 1600 3 28 329900
4 2400 3 44 369000
5 1416 2 49 232000
6 3000 4 75 539900
7 1985 4 61 299900
8 1534 3 12 314900
9 1427 3 57 198999
10 1380 3 14 212000
11 1494 3 15 242500
12 1940 4 7 239999
13 2000 3 27 347000
14 1890 3 45 329999
15 4478 5 49 699900
16 1268 3 58 259900
17 2300 4 77 449900
18 1320 2 62 299900
19 1236 3 78 199900
20 2609 4 5 499998
21 3031 4 21 599000
22 1767 3 44 252900
23 1888 2 79 255000
24 1604 3 13 242900
25 1962 4 53 259900
26 3890 3 36 573900
27 1100 3 60 249900
28 1458 3 29 464500
29 2526 3 13 469000
30 2200 3 28 475000
31 2637 3 25 299900
32 1839 2 40 349900
33 1000 1 5 169900
34 2040 4 75 314900
35 3137 3 67 579900
36 1811 4 24 285900
37 1437 3 50 249900
38 1239 3 22 229900
39 2132 4 28 345000
40 4215 4 66 549000
41 2162 4 43 287000
42 1664 2 40 368500
43 2238 3 37 329900
44 2567 4 57 314000
45 1200 3 76 299000
46 852 2 70 179900
47 1852 4 64 299900
48 1203 3 11 239500

145
data/international-airline-passengers.csv Normal file
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"Month","Thousand Passengers"
"1949-01",112
"1949-02",118
"1949-03",132
"1949-04",129
"1949-05",121
"1949-06",135
"1949-07",148
"1949-08",148
"1949-09",136
"1949-10",119
"1949-11",104
"1949-12",118
"1950-01",115
"1950-02",126
"1950-03",141
"1950-04",135
"1950-05",125
"1950-06",149
"1950-07",170
"1950-08",170
"1950-09",158
"1950-10",133
"1950-11",114
"1950-12",140
"1951-01",145
"1951-02",150
"1951-03",178
"1951-04",163
"1951-05",172
"1951-06",178
"1951-07",199
"1951-08",199
"1951-09",184
"1951-10",162
"1951-11",146
"1951-12",166
"1952-01",171
"1952-02",180
"1952-03",193
"1952-04",181
"1952-05",183
"1952-06",218
"1952-07",230
"1952-08",242
"1952-09",209
"1952-10",191
"1952-11",172
"1952-12",194
"1953-01",196
"1953-02",196
"1953-03",236
"1953-04",235
"1953-05",229
"1953-06",243
"1953-07",264
"1953-08",272
"1953-09",237
"1953-10",211
"1953-11",180
"1953-12",201
"1954-01",204
"1954-02",188
"1954-03",235
"1954-04",227
"1954-05",234
"1954-06",264
"1954-07",302
"1954-08",293
"1954-09",259
"1954-10",229
"1954-11",203
"1954-12",229
"1955-01",242
"1955-02",233
"1955-03",267
"1955-04",269
"1955-05",270
"1955-06",315
"1955-07",364
"1955-08",347
"1955-09",312
"1955-10",274
"1955-11",237
"1955-12",278
"1956-01",284
"1956-02",277
"1956-03",317
"1956-04",313
"1956-05",318
"1956-06",374
"1956-07",413
"1956-08",405
"1956-09",355
"1956-10",306
"1956-11",271
"1956-12",306
"1957-01",315
"1957-02",301
"1957-03",356
"1957-04",348
"1957-05",355
"1957-06",422
"1957-07",465
"1957-08",467
"1957-09",404
"1957-10",347
"1957-11",305
"1957-12",336
"1958-01",340
"1958-02",318
"1958-03",362
"1958-04",348
"1958-05",363
"1958-06",435
"1958-07",491
"1958-08",505
"1958-09",404
"1958-10",359
"1958-11",310
"1958-12",337
"1959-01",360
"1959-02",342
"1959-03",406
"1959-04",396
"1959-05",420
"1959-06",472
"1959-07",548
"1959-08",559
"1959-09",463
"1959-10",407
"1959-11",362
"1959-12",405
"1960-01",417
"1960-02",391
"1960-03",419
"1960-04",461
"1960-05",472
"1960-06",535
"1960-07",622
"1960-08",606
"1960-09",508
"1960-10",461
"1960-11",390
"1960-12",432
1 Month Thousand Passengers
2 1949-01 112
3 1949-02 118
4 1949-03 132
5 1949-04 129
6 1949-05 121
7 1949-06 135
8 1949-07 148
9 1949-08 148
10 1949-09 136
11 1949-10 119
12 1949-11 104
13 1949-12 118
14 1950-01 115
15 1950-02 126
16 1950-03 141
17 1950-04 135
18 1950-05 125
19 1950-06 149
20 1950-07 170
21 1950-08 170
22 1950-09 158
23 1950-10 133
24 1950-11 114
25 1950-12 140
26 1951-01 145
27 1951-02 150
28 1951-03 178
29 1951-04 163
30 1951-05 172
31 1951-06 178
32 1951-07 199
33 1951-08 199
34 1951-09 184
35 1951-10 162
36 1951-11 146
37 1951-12 166
38 1952-01 171
39 1952-02 180
40 1952-03 193
41 1952-04 181
42 1952-05 183
43 1952-06 218
44 1952-07 230
45 1952-08 242
46 1952-09 209
47 1952-10 191
48 1952-11 172
49 1952-12 194
50 1953-01 196
51 1953-02 196
52 1953-03 236
53 1953-04 235
54 1953-05 229
55 1953-06 243
56 1953-07 264
57 1953-08 272
58 1953-09 237
59 1953-10 211
60 1953-11 180
61 1953-12 201
62 1954-01 204
63 1954-02 188
64 1954-03 235
65 1954-04 227
66 1954-05 234
67 1954-06 264
68 1954-07 302
69 1954-08 293
70 1954-09 259
71 1954-10 229
72 1954-11 203
73 1954-12 229
74 1955-01 242
75 1955-02 233
76 1955-03 267
77 1955-04 269
78 1955-05 270
79 1955-06 315
80 1955-07 364
81 1955-08 347
82 1955-09 312
83 1955-10 274
84 1955-11 237
85 1955-12 278
86 1956-01 284
87 1956-02 277
88 1956-03 317
89 1956-04 313
90 1956-05 318
91 1956-06 374
92 1956-07 413
93 1956-08 405
94 1956-09 355
95 1956-10 306
96 1956-11 271
97 1956-12 306
98 1957-01 315
99 1957-02 301
100 1957-03 356
101 1957-04 348
102 1957-05 355
103 1957-06 422
104 1957-07 465
105 1957-08 467
106 1957-09 404
107 1957-10 347
108 1957-11 305
109 1957-12 336
110 1958-01 340
111 1958-02 318
112 1958-03 362
113 1958-04 348
114 1958-05 363
115 1958-06 435
116 1958-07 491
117 1958-08 505
118 1958-09 404
119 1958-10 359
120 1958-11 310
121 1958-12 337
122 1959-01 360
123 1959-02 342
124 1959-03 406
125 1959-04 396
126 1959-05 420
127 1959-06 472
128 1959-07 548
129 1959-08 559
130 1959-09 463
131 1959-10 407
132 1959-11 362
133 1959-12 405
134 1960-01 417
135 1960-02 391
136 1960-03 419
137 1960-04 461
138 1960-05 472
139 1960-06 535
140 1960-07 622
141 1960-08 606
142 1960-09 508
143 1960-10 461
144 1960-11 390
145 1960-12 432

151
data/iris.csv Normal file
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sepal_length,sepal_width,petal_length,petal_width,species
5.1,3.5,1.4,0.2,setosa
4.9,3.0,1.4,0.2,setosa
4.7,3.2,1.3,0.2,setosa
4.6,3.1,1.5,0.2,setosa
5.0,3.6,1.4,0.2,setosa
5.4,3.9,1.7,0.4,setosa
4.6,3.4,1.4,0.3,setosa
5.0,3.4,1.5,0.2,setosa
4.4,2.9,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5.4,3.7,1.5,0.2,setosa
4.8,3.4,1.6,0.2,setosa
4.8,3.0,1.4,0.1,setosa
4.3,3.0,1.1,0.1,setosa
5.8,4.0,1.2,0.2,setosa
5.7,4.4,1.5,0.4,setosa
5.4,3.9,1.3,0.4,setosa
5.1,3.5,1.4,0.3,setosa
5.7,3.8,1.7,0.3,setosa
5.1,3.8,1.5,0.3,setosa
5.4,3.4,1.7,0.2,setosa
5.1,3.7,1.5,0.4,setosa
4.6,3.6,1.0,0.2,setosa
5.1,3.3,1.7,0.5,setosa
4.8,3.4,1.9,0.2,setosa
5.0,3.0,1.6,0.2,setosa
5.0,3.4,1.6,0.4,setosa
5.2,3.5,1.5,0.2,setosa
5.2,3.4,1.4,0.2,setosa
4.7,3.2,1.6,0.2,setosa
4.8,3.1,1.6,0.2,setosa
5.4,3.4,1.5,0.4,setosa
5.2,4.1,1.5,0.1,setosa
5.5,4.2,1.4,0.2,setosa
4.9,3.1,1.5,0.2,setosa
5.0,3.2,1.2,0.2,setosa
5.5,3.5,1.3,0.2,setosa
4.9,3.6,1.4,0.1,setosa
4.4,3.0,1.3,0.2,setosa
5.1,3.4,1.5,0.2,setosa
5.0,3.5,1.3,0.3,setosa
4.5,2.3,1.3,0.3,setosa
4.4,3.2,1.3,0.2,setosa
5.0,3.5,1.6,0.6,setosa
5.1,3.8,1.9,0.4,setosa
4.8,3.0,1.4,0.3,setosa
5.1,3.8,1.6,0.2,setosa
4.6,3.2,1.4,0.2,setosa
5.3,3.7,1.5,0.2,setosa
5.0,3.3,1.4,0.2,setosa
7.0,3.2,4.7,1.4,versicolor
6.4,3.2,4.5,1.5,versicolor
6.9,3.1,4.9,1.5,versicolor
5.5,2.3,4.0,1.3,versicolor
6.5,2.8,4.6,1.5,versicolor
5.7,2.8,4.5,1.3,versicolor
6.3,3.3,4.7,1.6,versicolor
4.9,2.4,3.3,1.0,versicolor
6.6,2.9,4.6,1.3,versicolor
5.2,2.7,3.9,1.4,versicolor
5.0,2.0,3.5,1.0,versicolor
5.9,3.0,4.2,1.5,versicolor
6.0,2.2,4.0,1.0,versicolor
6.1,2.9,4.7,1.4,versicolor
5.6,2.9,3.6,1.3,versicolor
6.7,3.1,4.4,1.4,versicolor
5.6,3.0,4.5,1.5,versicolor
5.8,2.7,4.1,1.0,versicolor
6.2,2.2,4.5,1.5,versicolor
5.6,2.5,3.9,1.1,versicolor
5.9,3.2,4.8,1.8,versicolor
6.1,2.8,4.0,1.3,versicolor
6.3,2.5,4.9,1.5,versicolor
6.1,2.8,4.7,1.2,versicolor
6.4,2.9,4.3,1.3,versicolor
6.6,3.0,4.4,1.4,versicolor
6.8,2.8,4.8,1.4,versicolor
6.7,3.0,5.0,1.7,versicolor
6.0,2.9,4.5,1.5,versicolor
5.7,2.6,3.5,1.0,versicolor
5.5,2.4,3.8,1.1,versicolor
5.5,2.4,3.7,1.0,versicolor
5.8,2.7,3.9,1.2,versicolor
6.0,2.7,5.1,1.6,versicolor
5.4,3.0,4.5,1.5,versicolor
6.0,3.4,4.5,1.6,versicolor
6.7,3.1,4.7,1.5,versicolor
6.3,2.3,4.4,1.3,versicolor
5.6,3.0,4.1,1.3,versicolor
5.5,2.5,4.0,1.3,versicolor
5.5,2.6,4.4,1.2,versicolor
6.1,3.0,4.6,1.4,versicolor
5.8,2.6,4.0,1.2,versicolor
5.0,2.3,3.3,1.0,versicolor
5.6,2.7,4.2,1.3,versicolor
5.7,3.0,4.2,1.2,versicolor
5.7,2.9,4.2,1.3,versicolor
6.2,2.9,4.3,1.3,versicolor
5.1,2.5,3.0,1.1,versicolor
5.7,2.8,4.1,1.3,versicolor
6.3,3.3,6.0,2.5,virginica
5.8,2.7,5.1,1.9,virginica
7.1,3.0,5.9,2.1,virginica
6.3,2.9,5.6,1.8,virginica
6.5,3.0,5.8,2.2,virginica
7.6,3.0,6.6,2.1,virginica
4.9,2.5,4.5,1.7,virginica
7.3,2.9,6.3,1.8,virginica
6.7,2.5,5.8,1.8,virginica
7.2,3.6,6.1,2.5,virginica
6.5,3.2,5.1,2.0,virginica
6.4,2.7,5.3,1.9,virginica
6.8,3.0,5.5,2.1,virginica
5.7,2.5,5.0,2.0,virginica
5.8,2.8,5.1,2.4,virginica
6.4,3.2,5.3,2.3,virginica
6.5,3.0,5.5,1.8,virginica
7.7,3.8,6.7,2.2,virginica
7.7,2.6,6.9,2.3,virginica
6.0,2.2,5.0,1.5,virginica
6.9,3.2,5.7,2.3,virginica
5.6,2.8,4.9,2.0,virginica
7.7,2.8,6.7,2.0,virginica
6.3,2.7,4.9,1.8,virginica
6.7,3.3,5.7,2.1,virginica
7.2,3.2,6.0,1.8,virginica
6.2,2.8,4.8,1.8,virginica
6.1,3.0,4.9,1.8,virginica
6.4,2.8,5.6,2.1,virginica
7.2,3.0,5.8,1.6,virginica
7.4,2.8,6.1,1.9,virginica
7.9,3.8,6.4,2.0,virginica
6.4,2.8,5.6,2.2,virginica
6.3,2.8,5.1,1.5,virginica
6.1,2.6,5.6,1.4,virginica
7.7,3.0,6.1,2.3,virginica
6.3,3.4,5.6,2.4,virginica
6.4,3.1,5.5,1.8,virginica
6.0,3.0,4.8,1.8,virginica
6.9,3.1,5.4,2.1,virginica
6.7,3.1,5.6,2.4,virginica
6.9,3.1,5.1,2.3,virginica
5.8,2.7,5.1,1.9,virginica
6.8,3.2,5.9,2.3,virginica
6.7,3.3,5.7,2.5,virginica
6.7,3.0,5.2,2.3,virginica
6.3,2.5,5.0,1.9,virginica
6.5,3.0,5.2,2.0,virginica
6.2,3.4,5.4,2.3,virginica
5.9,3.0,5.1,1.8,virginica
1 sepal_length sepal_width petal_length petal_width species
2 5.1 3.5 1.4 0.2 setosa
3 4.9 3.0 1.4 0.2 setosa
4 4.7 3.2 1.3 0.2 setosa
5 4.6 3.1 1.5 0.2 setosa
6 5.0 3.6 1.4 0.2 setosa
7 5.4 3.9 1.7 0.4 setosa
8 4.6 3.4 1.4 0.3 setosa
9 5.0 3.4 1.5 0.2 setosa
10 4.4 2.9 1.4 0.2 setosa
11 4.9 3.1 1.5 0.1 setosa
12 5.4 3.7 1.5 0.2 setosa
13 4.8 3.4 1.6 0.2 setosa
14 4.8 3.0 1.4 0.1 setosa
15 4.3 3.0 1.1 0.1 setosa
16 5.8 4.0 1.2 0.2 setosa
17 5.7 4.4 1.5 0.4 setosa
18 5.4 3.9 1.3 0.4 setosa
19 5.1 3.5 1.4 0.3 setosa
20 5.7 3.8 1.7 0.3 setosa
21 5.1 3.8 1.5 0.3 setosa
22 5.4 3.4 1.7 0.2 setosa
23 5.1 3.7 1.5 0.4 setosa
24 4.6 3.6 1.0 0.2 setosa
25 5.1 3.3 1.7 0.5 setosa
26 4.8 3.4 1.9 0.2 setosa
27 5.0 3.0 1.6 0.2 setosa
28 5.0 3.4 1.6 0.4 setosa
29 5.2 3.5 1.5 0.2 setosa
30 5.2 3.4 1.4 0.2 setosa
31 4.7 3.2 1.6 0.2 setosa
32 4.8 3.1 1.6 0.2 setosa
33 5.4 3.4 1.5 0.4 setosa
34 5.2 4.1 1.5 0.1 setosa
35 5.5 4.2 1.4 0.2 setosa
36 4.9 3.1 1.5 0.2 setosa
37 5.0 3.2 1.2 0.2 setosa
38 5.5 3.5 1.3 0.2 setosa
39 4.9 3.6 1.4 0.1 setosa
40 4.4 3.0 1.3 0.2 setosa
41 5.1 3.4 1.5 0.2 setosa
42 5.0 3.5 1.3 0.3 setosa
43 4.5 2.3 1.3 0.3 setosa
44 4.4 3.2 1.3 0.2 setosa
45 5.0 3.5 1.6 0.6 setosa
46 5.1 3.8 1.9 0.4 setosa
47 4.8 3.0 1.4 0.3 setosa
48 5.1 3.8 1.6 0.2 setosa
49 4.6 3.2 1.4 0.2 setosa
50 5.3 3.7 1.5 0.2 setosa
51 5.0 3.3 1.4 0.2 setosa
52 7.0 3.2 4.7 1.4 versicolor
53 6.4 3.2 4.5 1.5 versicolor
54 6.9 3.1 4.9 1.5 versicolor
55 5.5 2.3 4.0 1.3 versicolor
56 6.5 2.8 4.6 1.5 versicolor
57 5.7 2.8 4.5 1.3 versicolor
58 6.3 3.3 4.7 1.6 versicolor
59 4.9 2.4 3.3 1.0 versicolor
60 6.6 2.9 4.6 1.3 versicolor
61 5.2 2.7 3.9 1.4 versicolor
62 5.0 2.0 3.5 1.0 versicolor
63 5.9 3.0 4.2 1.5 versicolor
64 6.0 2.2 4.0 1.0 versicolor
65 6.1 2.9 4.7 1.4 versicolor
66 5.6 2.9 3.6 1.3 versicolor
67 6.7 3.1 4.4 1.4 versicolor
68 5.6 3.0 4.5 1.5 versicolor
69 5.8 2.7 4.1 1.0 versicolor
70 6.2 2.2 4.5 1.5 versicolor
71 5.6 2.5 3.9 1.1 versicolor
72 5.9 3.2 4.8 1.8 versicolor
73 6.1 2.8 4.0 1.3 versicolor
74 6.3 2.5 4.9 1.5 versicolor
75 6.1 2.8 4.7 1.2 versicolor
76 6.4 2.9 4.3 1.3 versicolor
77 6.6 3.0 4.4 1.4 versicolor
78 6.8 2.8 4.8 1.4 versicolor
79 6.7 3.0 5.0 1.7 versicolor
80 6.0 2.9 4.5 1.5 versicolor
81 5.7 2.6 3.5 1.0 versicolor
82 5.5 2.4 3.8 1.1 versicolor
83 5.5 2.4 3.7 1.0 versicolor
84 5.8 2.7 3.9 1.2 versicolor
85 6.0 2.7 5.1 1.6 versicolor
86 5.4 3.0 4.5 1.5 versicolor
87 6.0 3.4 4.5 1.6 versicolor
88 6.7 3.1 4.7 1.5 versicolor
89 6.3 2.3 4.4 1.3 versicolor
90 5.6 3.0 4.1 1.3 versicolor
91 5.5 2.5 4.0 1.3 versicolor
92 5.5 2.6 4.4 1.2 versicolor
93 6.1 3.0 4.6 1.4 versicolor
94 5.8 2.6 4.0 1.2 versicolor
95 5.0 2.3 3.3 1.0 versicolor
96 5.6 2.7 4.2 1.3 versicolor
97 5.7 3.0 4.2 1.2 versicolor
98 5.7 2.9 4.2 1.3 versicolor
99 6.2 2.9 4.3 1.3 versicolor
100 5.1 2.5 3.0 1.1 versicolor
101 5.7 2.8 4.1 1.3 versicolor
102 6.3 3.3 6.0 2.5 virginica
103 5.8 2.7 5.1 1.9 virginica
104 7.1 3.0 5.9 2.1 virginica
105 6.3 2.9 5.6 1.8 virginica
106 6.5 3.0 5.8 2.2 virginica
107 7.6 3.0 6.6 2.1 virginica
108 4.9 2.5 4.5 1.7 virginica
109 7.3 2.9 6.3 1.8 virginica
110 6.7 2.5 5.8 1.8 virginica
111 7.2 3.6 6.1 2.5 virginica
112 6.5 3.2 5.1 2.0 virginica
113 6.4 2.7 5.3 1.9 virginica
114 6.8 3.0 5.5 2.1 virginica
115 5.7 2.5 5.0 2.0 virginica
116 5.8 2.8 5.1 2.4 virginica
117 6.4 3.2 5.3 2.3 virginica
118 6.5 3.0 5.5 1.8 virginica
119 7.7 3.8 6.7 2.2 virginica
120 7.7 2.6 6.9 2.3 virginica
121 6.0 2.2 5.0 1.5 virginica
122 6.9 3.2 5.7 2.3 virginica
123 5.6 2.8 4.9 2.0 virginica
124 7.7 2.8 6.7 2.0 virginica
125 6.3 2.7 4.9 1.8 virginica
126 6.7 3.3 5.7 2.1 virginica
127 7.2 3.2 6.0 1.8 virginica
128 6.2 2.8 4.8 1.8 virginica
129 6.1 3.0 4.9 1.8 virginica
130 6.4 2.8 5.6 2.1 virginica
131 7.2 3.0 5.8 1.6 virginica
132 7.4 2.8 6.1 1.9 virginica
133 7.9 3.8 6.4 2.0 virginica
134 6.4 2.8 5.6 2.2 virginica
135 6.3 2.8 5.1 1.5 virginica
136 6.1 2.6 5.6 1.4 virginica
137 7.7 3.0 6.1 2.3 virginica
138 6.3 3.4 5.6 2.4 virginica
139 6.4 3.1 5.5 1.8 virginica
140 6.0 3.0 4.8 1.8 virginica
141 6.9 3.1 5.4 2.1 virginica
142 6.7 3.1 5.6 2.4 virginica
143 6.9 3.1 5.1 2.3 virginica
144 5.8 2.7 5.1 1.9 virginica
145 6.8 3.2 5.9 2.3 virginica
146 6.7 3.3 5.7 2.5 virginica
147 6.7 3.0 5.2 2.3 virginica
148 6.3 2.5 5.0 1.9 virginica
149 6.5 3.0 5.2 2.0 virginica
150 6.2 3.4 5.4 2.3 virginica
151 5.9 3.0 5.1 1.8 virginica

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892
data/titanic-train.csv Normal file
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@@ -0,0 +1,892 @@
PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
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76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
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106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
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110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
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114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
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128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
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143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
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148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S
149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S
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156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
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160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S
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164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S
165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S
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177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S
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180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S
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188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S
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191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S
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214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S
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216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C
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245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C
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255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S
256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C
257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C
258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S
259,1,1,"Ward, Miss. Anna",female,35,0,0,PC 17755,512.3292,,C
260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S
261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q
262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S
263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S
264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S
265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q
266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S
267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S
268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S
269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S
270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S
271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S
272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S
273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S
274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C
275,1,3,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q
276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S
277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S
278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S
279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q
280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S
281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q
282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S
283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S
284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S
285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S
286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C
287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S
288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S
289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S
290,1,3,"Connolly, Miss. Kate",female,22,0,0,370373,7.75,,Q
291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S
292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C
293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C
294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S
295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S
296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C
297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C
298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S
299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S
300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C
301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q
302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q
303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S
304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q
305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S
306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S
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308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17,1,0,PC 17758,108.9,C65,C
309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C
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311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C
312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C
313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S
314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S
315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S
316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S
317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S
318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S
319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S
320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C
321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S
322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S
323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q
324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S
325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S
326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C
327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S
328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S
329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S
330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C
331,1,3,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q
332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S
333,0,1,"Graham, Mr. George Edward",male,38,0,1,PC 17582,153.4625,C91,S
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335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S
336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S
337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S
338,1,1,"Burns, Miss. Elizabeth Margaret",female,41,0,0,16966,134.5,E40,C
339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S
340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S
341,1,2,"Navratil, Master. Edmond Roger",male,2,1,1,230080,26,F2,S
342,1,1,"Fortune, Miss. Alice Elizabeth",female,24,3,2,19950,263,C23 C25 C27,S
343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S
344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S
345,0,2,"Fox, Mr. Stanley Hubert",male,36,0,0,229236,13,,S
346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S
347,1,2,"Smith, Miss. Marion Elsie",female,40,0,0,31418,13,,S
348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S
349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S
350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S
351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S
352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S
353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C
354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S
355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C
356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S
357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S
358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S
359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q
360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q
361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S
362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C
363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C
364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S
365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q
366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S
367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60,1,0,110813,75.25,D37,C
368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C
369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q
370,1,1,"Aubart, Mme. Leontine Pauline",female,24,0,0,PC 17477,69.3,B35,C
371,1,1,"Harder, Mr. George Achilles",male,25,1,0,11765,55.4417,E50,C
372,0,3,"Wiklund, Mr. Jakob Alfred",male,18,1,0,3101267,6.4958,,S
373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S
374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C
375,0,3,"Palsson, Miss. Stina Viola",female,3,3,1,349909,21.075,,S
376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C
377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S
378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C
379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C
380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S
381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C
382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C
383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S
384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35,1,0,113789,52,,S
385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S
386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S
387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S
388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S
389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q
390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C
391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S
392,1,3,"Jansson, Mr. Carl Olof",male,21,0,0,350034,7.7958,,S
393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S
394,1,1,"Newell, Miss. Marjorie",female,23,1,0,35273,113.275,D36,C
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396,0,3,"Johansson, Mr. Erik",male,22,0,0,350052,7.7958,,S
397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S
398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S
399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S
400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S
401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S
402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S
403,0,3,"Jussila, Miss. Mari Aina",female,21,1,0,4137,9.825,,S
404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S
405,0,3,"Oreskovic, Miss. Marija",female,20,0,0,315096,8.6625,,S
406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S
407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.75,,S
408,1,2,"Richards, Master. William Rowe",male,3,1,1,29106,18.75,,S
409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S
410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S
411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S
412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q
413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q
414,0,2,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0,,S
415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S
416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S
417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S
418,1,2,"Silven, Miss. Lyyli Karoliina",female,18,0,2,250652,13,,S
419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S
420,0,3,"Van Impe, Miss. Catharina",female,10,0,2,345773,24.15,,S
421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C
422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q
423,0,3,"Zimmerman, Mr. Leo",male,29,0,0,315082,7.875,,S
424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S
425,0,3,"Rosblom, Mr. Viktor Richard",male,18,1,1,370129,20.2125,,S
426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S
427,1,2,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28,1,0,2003,26,,S
428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S
429,0,3,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q
430,1,3,"Pickard, Mr. Berk (Berk Trembisky)",male,32,0,0,SOTON/O.Q. 392078,8.05,E10,S
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434,0,3,"Kallio, Mr. Nikolai Erland",male,17,0,0,STON/O 2. 3101274,7.125,,S
435,0,1,"Silvey, Mr. William Baird",male,50,1,0,13507,55.9,E44,S
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437,0,3,"Ford, Miss. Doolina Margaret ""Daisy""",female,21,2,2,W./C. 6608,34.375,,S
438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S
439,0,1,"Fortune, Mr. Mark",male,64,1,4,19950,263,C23 C25 C27,S
440,0,2,"Kvillner, Mr. Johan Henrik Johannesson",male,31,0,0,C.A. 18723,10.5,,S
441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45,1,1,F.C.C. 13529,26.25,,S
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446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S
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474,1,2,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23,0,0,SC/AH Basle 541,13.7917,D,C
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479,0,3,"Karlsson, Mr. Nils August",male,22,0,0,350060,7.5208,,S
480,1,3,"Hirvonen, Miss. Hildur E",female,2,0,1,3101298,12.2875,,S
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485,1,1,"Bishop, Mr. Dickinson H",male,25,1,0,11967,91.0792,B49,C
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487,1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35,1,0,19943,90,C93,S
488,0,1,"Kent, Mr. Edward Austin",male,58,0,0,11771,29.7,B37,C
489,0,3,"Somerton, Mr. Francis William",male,30,0,0,A.5. 18509,8.05,,S
490,1,3,"Coutts, Master. Eden Leslie ""Neville""",male,9,1,1,C.A. 37671,15.9,,S
491,0,3,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S
492,0,3,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.25,,S
493,0,1,"Molson, Mr. Harry Markland",male,55,0,0,113787,30.5,C30,S
494,0,1,"Artagaveytia, Mr. Ramon",male,71,0,0,PC 17609,49.5042,,C
495,0,3,"Stanley, Mr. Edward Roland",male,21,0,0,A/4 45380,8.05,,S
496,0,3,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C
497,1,1,"Eustis, Miss. Elizabeth Mussey",female,54,1,0,36947,78.2667,D20,C
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499,0,1,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25,1,2,113781,151.55,C22 C26,S
500,0,3,"Svensson, Mr. Olof",male,24,0,0,350035,7.7958,,S
501,0,3,"Calic, Mr. Petar",male,17,0,0,315086,8.6625,,S
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503,0,3,"O'Sullivan, Miss. Bridget Mary",female,,0,0,330909,7.6292,,Q
504,0,3,"Laitinen, Miss. Kristina Sofia",female,37,0,0,4135,9.5875,,S
505,1,1,"Maioni, Miss. Roberta",female,16,0,0,110152,86.5,B79,S
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510,1,3,"Lang, Mr. Fang",male,26,0,0,1601,56.4958,,S
511,1,3,"Daly, Mr. Eugene Patrick",male,29,0,0,382651,7.75,,Q
512,0,3,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.05,,S
513,1,1,"McGough, Mr. James Robert",male,36,0,0,PC 17473,26.2875,E25,S
514,1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54,1,0,PC 17603,59.4,,C
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516,0,1,"Walker, Mr. William Anderson",male,47,0,0,36967,34.0208,D46,S
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519,1,2,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36,1,0,226875,26,,S
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521,1,1,"Perreault, Miss. Anne",female,30,0,0,12749,93.5,B73,S
522,0,3,"Vovk, Mr. Janko",male,22,0,0,349252,7.8958,,S
523,0,3,"Lahoud, Mr. Sarkis",male,,0,0,2624,7.225,,C
524,1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44,0,1,111361,57.9792,B18,C
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527,1,2,"Ridsdale, Miss. Lucy",female,50,0,0,W./C. 14258,10.5,,S
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529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S
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531,1,2,"Quick, Miss. Phyllis May",female,2,1,1,26360,26,,S
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533,0,3,"Elias, Mr. Joseph Jr",male,17,1,1,2690,7.2292,,C
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535,0,3,"Cacic, Miss. Marija",female,30,0,0,315084,8.6625,,S
536,1,2,"Hart, Miss. Eva Miriam",female,7,0,2,F.C.C. 13529,26.25,,S
537,0,1,"Butt, Major. Archibald Willingham",male,45,0,0,113050,26.55,B38,S
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539,0,3,"Risien, Mr. Samuel Beard",male,,0,0,364498,14.5,,S
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543,0,3,"Andersson, Miss. Sigrid Elisabeth",female,11,4,2,347082,31.275,,S
544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S
545,0,1,"Douglas, Mr. Walter Donald",male,50,1,0,PC 17761,106.425,C86,C
546,0,1,"Nicholson, Mr. Arthur Ernest",male,64,0,0,693,26,,S
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548,1,2,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C
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550,1,2,"Davies, Master. John Morgan Jr",male,8,1,1,C.A. 33112,36.75,,S
551,1,1,"Thayer, Mr. John Borland Jr",male,17,0,2,17421,110.8833,C70,C
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553,0,3,"O'Brien, Mr. Timothy",male,,0,0,330979,7.8292,,Q
554,1,3,"Leeni, Mr. Fahim (""Philip Zenni"")",male,22,0,0,2620,7.225,,C
555,1,3,"Ohman, Miss. Velin",female,22,0,0,347085,7.775,,S
556,0,1,"Wright, Mr. George",male,62,0,0,113807,26.55,,S
557,1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48,1,0,11755,39.6,A16,C
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561,0,3,"Morrow, Mr. Thomas Rowan",male,,0,0,372622,7.75,,Q
562,0,3,"Sivic, Mr. Husein",male,40,0,0,349251,7.8958,,S
563,0,2,"Norman, Mr. Robert Douglas",male,28,0,0,218629,13.5,,S
564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S
565,0,3,"Meanwell, Miss. (Marion Ogden)",female,,0,0,SOTON/O.Q. 392087,8.05,,S
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568,0,3,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",female,29,0,4,349909,21.075,,S
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570,1,3,"Jonsson, Mr. Carl",male,32,0,0,350417,7.8542,,S
571,1,2,"Harris, Mr. George",male,62,0,0,S.W./PP 752,10.5,,S
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575,0,3,"Rush, Mr. Alfred George John",male,16,0,0,A/4. 20589,8.05,,S
576,0,3,"Patchett, Mr. George",male,19,0,0,358585,14.5,,S
577,1,2,"Garside, Miss. Ethel",female,34,0,0,243880,13,,S
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590,0,3,"Murdlin, Mr. Joseph",male,,0,0,A./5. 3235,8.05,,S
591,0,3,"Rintamaki, Mr. Matti",male,35,0,0,STON/O 2. 3101273,7.125,,S
592,1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52,1,0,36947,78.2667,D20,C
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615,0,3,"Brocklebank, Mr. William Alfred",male,35,0,0,364512,8.05,,S
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617,0,3,"Danbom, Mr. Ernst Gilbert",male,34,1,1,347080,14.4,,S
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624,0,3,"Hansen, Mr. Henry Damsgaard",male,21,0,0,350029,7.8542,,S
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626,0,1,"Sutton, Mr. Frederick",male,61,0,0,36963,32.3208,D50,S
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636,1,2,"Davis, Miss. Mary",female,28,0,0,237668,13,,S
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655,0,3,"Hegarty, Miss. Hanora ""Nora""",female,18,0,0,365226,6.75,,Q
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667,0,2,"Butler, Mr. Reginald Fenton",male,25,0,0,234686,13,,S
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682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C
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685,0,2,"Brown, Mr. Thomas William Solomon",male,60,1,1,29750,39,,S
686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25,1,2,SC/Paris 2123,41.5792,,C
687,0,3,"Panula, Mr. Jaako Arnold",male,14,4,1,3101295,39.6875,,S
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689,0,3,"Fischer, Mr. Eberhard Thelander",male,18,0,0,350036,7.7958,,S
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709,1,1,"Cleaver, Miss. Alice",female,22,0,0,113781,151.55,,S
710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C
711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C
712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S
713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S
714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S
715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S
716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S
717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C
718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S
719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q
720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S
721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S
722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S
723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S
724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S
725,1,1,"Chambers, Mr. Norman Campbell",male,27,1,0,113806,53.1,E8,S
726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S
727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S
728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q
729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S
730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S
731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S
732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C
733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S
734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S
735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S
736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S
737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S
738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C
739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S
740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S
741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S
742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S
743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C
744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S
745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S
746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S
747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S
748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S
749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S
750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q
751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S
752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S
753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S
754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S
755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S
756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S
757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S
758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S
759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S
760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S
761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S
762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S
763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C
764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36,1,2,113760,120,B96 B98,S
765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S
766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S
767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C
768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q
769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q
770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S
771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S
772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S
773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S
774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C
775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S
776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S
777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q
778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S
779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q
780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S
781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C
782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S
783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S
784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S
785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S
786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S
787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S
788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q
789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S
790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C
791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q
792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S
793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S
794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C
795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S
796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S
797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S
798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S
799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C
800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S
801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S
802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S
803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S
804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C
805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S
806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S
807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S
808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S
809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S
810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S
811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S
812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S
813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S
814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S
815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S
816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S
817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S
818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C
819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S
820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S
821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S
824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S
825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C
829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C
832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S
835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S
836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q
1 PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
2 1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.25 S
3 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0 PC 17599 71.2833 C85 C
4 3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.925 S
5 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1 C123 S
6 5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.05 S
7 6 0 3 Moran, Mr. James male 0 0 330877 8.4583 Q
8 7 0 1 McCarthy, Mr. Timothy J male 54 0 0 17463 51.8625 E46 S
9 8 0 3 Palsson, Master. Gosta Leonard male 2 3 1 349909 21.075 S
10 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0 2 347742 11.1333 S
11 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14 1 0 237736 30.0708 C
12 11 1 3 Sandstrom, Miss. Marguerite Rut female 4 1 1 PP 9549 16.7 G6 S
13 12 1 1 Bonnell, Miss. Elizabeth female 58 0 0 113783 26.55 C103 S
14 13 0 3 Saundercock, Mr. William Henry male 20 0 0 A/5. 2151 8.05 S
15 14 0 3 Andersson, Mr. Anders Johan male 39 1 5 347082 31.275 S
16 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female 14 0 0 350406 7.8542 S
17 16 1 2 Hewlett, Mrs. (Mary D Kingcome) female 55 0 0 248706 16 S
18 17 0 3 Rice, Master. Eugene male 2 4 1 382652 29.125 Q
19 18 1 2 Williams, Mr. Charles Eugene male 0 0 244373 13 S
20 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) female 31 1 0 345763 18 S
21 20 1 3 Masselmani, Mrs. Fatima female 0 0 2649 7.225 C
22 21 0 2 Fynney, Mr. Joseph J male 35 0 0 239865 26 S
23 22 1 2 Beesley, Mr. Lawrence male 34 0 0 248698 13 D56 S
24 23 1 3 McGowan, Miss. Anna "Annie" female 15 0 0 330923 8.0292 Q
25 24 1 1 Sloper, Mr. William Thompson male 28 0 0 113788 35.5 A6 S
26 25 0 3 Palsson, Miss. Torborg Danira female 8 3 1 349909 21.075 S
27 26 1 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson) female 38 1 5 347077 31.3875 S
28 27 0 3 Emir, Mr. Farred Chehab male 0 0 2631 7.225 C
29 28 0 1 Fortune, Mr. Charles Alexander male 19 3 2 19950 263 C23 C25 C27 S
30 29 1 3 O'Dwyer, Miss. Ellen "Nellie" female 0 0 330959 7.8792 Q
31 30 0 3 Todoroff, Mr. Lalio male 0 0 349216 7.8958 S
32 31 0 1 Uruchurtu, Don. Manuel E male 40 0 0 PC 17601 27.7208 C
33 32 1 1 Spencer, Mrs. William Augustus (Marie Eugenie) female 1 0 PC 17569 146.5208 B78 C
34 33 1 3 Glynn, Miss. Mary Agatha female 0 0 335677 7.75 Q
35 34 0 2 Wheadon, Mr. Edward H male 66 0 0 C.A. 24579 10.5 S
36 35 0 1 Meyer, Mr. Edgar Joseph male 28 1 0 PC 17604 82.1708 C
37 36 0 1 Holverson, Mr. Alexander Oskar male 42 1 0 113789 52 S
38 37 1 3 Mamee, Mr. Hanna male 0 0 2677 7.2292 C
39 38 0 3 Cann, Mr. Ernest Charles male 21 0 0 A./5. 2152 8.05 S
40 39 0 3 Vander Planke, Miss. Augusta Maria female 18 2 0 345764 18 S
41 40 1 3 Nicola-Yarred, Miss. Jamila female 14 1 0 2651 11.2417 C
42 41 0 3 Ahlin, Mrs. Johan (Johanna Persdotter Larsson) female 40 1 0 7546 9.475 S
43 42 0 2 Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott) female 27 1 0 11668 21 S
44 43 0 3 Kraeff, Mr. Theodor male 0 0 349253 7.8958 C
45 44 1 2 Laroche, Miss. Simonne Marie Anne Andree female 3 1 2 SC/Paris 2123 41.5792 C
46 45 1 3 Devaney, Miss. Margaret Delia female 19 0 0 330958 7.8792 Q
47 46 0 3 Rogers, Mr. William John male 0 0 S.C./A.4. 23567 8.05 S
48 47 0 3 Lennon, Mr. Denis male 1 0 370371 15.5 Q
49 48 1 3 O'Driscoll, Miss. Bridget female 0 0 14311 7.75 Q
50 49 0 3 Samaan, Mr. Youssef male 2 0 2662 21.6792 C
51 50 0 3 Arnold-Franchi, Mrs. Josef (Josefine Franchi) female 18 1 0 349237 17.8 S
52 51 0 3 Panula, Master. Juha Niilo male 7 4 1 3101295 39.6875 S
53 52 0 3 Nosworthy, Mr. Richard Cater male 21 0 0 A/4. 39886 7.8 S
54 53 1 1 Harper, Mrs. Henry Sleeper (Myna Haxtun) female 49 1 0 PC 17572 76.7292 D33 C
55 54 1 2 Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson) female 29 1 0 2926 26 S
56 55 0 1 Ostby, Mr. Engelhart Cornelius male 65 0 1 113509 61.9792 B30 C
57 56 1 1 Woolner, Mr. Hugh male 0 0 19947 35.5 C52 S
58 57 1 2 Rugg, Miss. Emily female 21 0 0 C.A. 31026 10.5 S
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673 672 0 1 Davidson, Mr. Thornton male 31 1 0 F.C. 12750 52 B71 S
674 673 0 2 Mitchell, Mr. Henry Michael male 70 0 0 C.A. 24580 10.5 S
675 674 1 2 Wilhelms, Mr. Charles male 31 0 0 244270 13 S
676 675 0 2 Watson, Mr. Ennis Hastings male 0 0 239856 0 S
677 676 0 3 Edvardsson, Mr. Gustaf Hjalmar male 18 0 0 349912 7.775 S
678 677 0 3 Sawyer, Mr. Frederick Charles male 24.5 0 0 342826 8.05 S
679 678 1 3 Turja, Miss. Anna Sofia female 18 0 0 4138 9.8417 S
680 679 0 3 Goodwin, Mrs. Frederick (Augusta Tyler) female 43 1 6 CA 2144 46.9 S
681 680 1 1 Cardeza, Mr. Thomas Drake Martinez male 36 0 1 PC 17755 512.3292 B51 B53 B55 C
682 681 0 3 Peters, Miss. Katie female 0 0 330935 8.1375 Q
683 682 1 1 Hassab, Mr. Hammad male 27 0 0 PC 17572 76.7292 D49 C
684 683 0 3 Olsvigen, Mr. Thor Anderson male 20 0 0 6563 9.225 S
685 684 0 3 Goodwin, Mr. Charles Edward male 14 5 2 CA 2144 46.9 S
686 685 0 2 Brown, Mr. Thomas William Solomon male 60 1 1 29750 39 S
687 686 0 2 Laroche, Mr. Joseph Philippe Lemercier male 25 1 2 SC/Paris 2123 41.5792 C
688 687 0 3 Panula, Mr. Jaako Arnold male 14 4 1 3101295 39.6875 S
689 688 0 3 Dakic, Mr. Branko male 19 0 0 349228 10.1708 S
690 689 0 3 Fischer, Mr. Eberhard Thelander male 18 0 0 350036 7.7958 S
691 690 1 1 Madill, Miss. Georgette Alexandra female 15 0 1 24160 211.3375 B5 S
692 691 1 1 Dick, Mr. Albert Adrian male 31 1 0 17474 57 B20 S
693 692 1 3 Karun, Miss. Manca female 4 0 1 349256 13.4167 C
694 693 1 3 Lam, Mr. Ali male 0 0 1601 56.4958 S
695 694 0 3 Saad, Mr. Khalil male 25 0 0 2672 7.225 C
696 695 0 1 Weir, Col. John male 60 0 0 113800 26.55 S
697 696 0 2 Chapman, Mr. Charles Henry male 52 0 0 248731 13.5 S
698 697 0 3 Kelly, Mr. James male 44 0 0 363592 8.05 S
699 698 1 3 Mullens, Miss. Katherine "Katie" female 0 0 35852 7.7333 Q
700 699 0 1 Thayer, Mr. John Borland male 49 1 1 17421 110.8833 C68 C
701 700 0 3 Humblen, Mr. Adolf Mathias Nicolai Olsen male 42 0 0 348121 7.65 F G63 S
702 701 1 1 Astor, Mrs. John Jacob (Madeleine Talmadge Force) female 18 1 0 PC 17757 227.525 C62 C64 C
703 702 1 1 Silverthorne, Mr. Spencer Victor male 35 0 0 PC 17475 26.2875 E24 S
704 703 0 3 Barbara, Miss. Saiide female 18 0 1 2691 14.4542 C
705 704 0 3 Gallagher, Mr. Martin male 25 0 0 36864 7.7417 Q
706 705 0 3 Hansen, Mr. Henrik Juul male 26 1 0 350025 7.8542 S
707 706 0 2 Morley, Mr. Henry Samuel ("Mr Henry Marshall") male 39 0 0 250655 26 S
708 707 1 2 Kelly, Mrs. Florence "Fannie" female 45 0 0 223596 13.5 S
709 708 1 1 Calderhead, Mr. Edward Pennington male 42 0 0 PC 17476 26.2875 E24 S
710 709 1 1 Cleaver, Miss. Alice female 22 0 0 113781 151.55 S
711 710 1 3 Moubarek, Master. Halim Gonios ("William George") male 1 1 2661 15.2458 C
712 711 1 1 Mayne, Mlle. Berthe Antonine ("Mrs de Villiers") female 24 0 0 PC 17482 49.5042 C90 C
713 712 0 1 Klaber, Mr. Herman male 0 0 113028 26.55 C124 S
714 713 1 1 Taylor, Mr. Elmer Zebley male 48 1 0 19996 52 C126 S
715 714 0 3 Larsson, Mr. August Viktor male 29 0 0 7545 9.4833 S
716 715 0 2 Greenberg, Mr. Samuel male 52 0 0 250647 13 S
717 716 0 3 Soholt, Mr. Peter Andreas Lauritz Andersen male 19 0 0 348124 7.65 F G73 S
718 717 1 1 Endres, Miss. Caroline Louise female 38 0 0 PC 17757 227.525 C45 C
719 718 1 2 Troutt, Miss. Edwina Celia "Winnie" female 27 0 0 34218 10.5 E101 S
720 719 0 3 McEvoy, Mr. Michael male 0 0 36568 15.5 Q
721 720 0 3 Johnson, Mr. Malkolm Joackim male 33 0 0 347062 7.775 S
722 721 1 2 Harper, Miss. Annie Jessie "Nina" female 6 0 1 248727 33 S
723 722 0 3 Jensen, Mr. Svend Lauritz male 17 1 0 350048 7.0542 S
724 723 0 2 Gillespie, Mr. William Henry male 34 0 0 12233 13 S
725 724 0 2 Hodges, Mr. Henry Price male 50 0 0 250643 13 S
726 725 1 1 Chambers, Mr. Norman Campbell male 27 1 0 113806 53.1 E8 S
727 726 0 3 Oreskovic, Mr. Luka male 20 0 0 315094 8.6625 S
728 727 1 2 Renouf, Mrs. Peter Henry (Lillian Jefferys) female 30 3 0 31027 21 S
729 728 1 3 Mannion, Miss. Margareth female 0 0 36866 7.7375 Q
730 729 0 2 Bryhl, Mr. Kurt Arnold Gottfrid male 25 1 0 236853 26 S
731 730 0 3 Ilmakangas, Miss. Pieta Sofia female 25 1 0 STON/O2. 3101271 7.925 S
732 731 1 1 Allen, Miss. Elisabeth Walton female 29 0 0 24160 211.3375 B5 S
733 732 0 3 Hassan, Mr. Houssein G N male 11 0 0 2699 18.7875 C
734 733 0 2 Knight, Mr. Robert J male 0 0 239855 0 S
735 734 0 2 Berriman, Mr. William John male 23 0 0 28425 13 S
736 735 0 2 Troupiansky, Mr. Moses Aaron male 23 0 0 233639 13 S
737 736 0 3 Williams, Mr. Leslie male 28.5 0 0 54636 16.1 S
738 737 0 3 Ford, Mrs. Edward (Margaret Ann Watson) female 48 1 3 W./C. 6608 34.375 S
739 738 1 1 Lesurer, Mr. Gustave J male 35 0 0 PC 17755 512.3292 B101 C
740 739 0 3 Ivanoff, Mr. Kanio male 0 0 349201 7.8958 S
741 740 0 3 Nankoff, Mr. Minko male 0 0 349218 7.8958 S
742 741 1 1 Hawksford, Mr. Walter James male 0 0 16988 30 D45 S
743 742 0 1 Cavendish, Mr. Tyrell William male 36 1 0 19877 78.85 C46 S
744 743 1 1 Ryerson, Miss. Susan Parker "Suzette" female 21 2 2 PC 17608 262.375 B57 B59 B63 B66 C
745 744 0 3 McNamee, Mr. Neal male 24 1 0 376566 16.1 S
746 745 1 3 Stranden, Mr. Juho male 31 0 0 STON/O 2. 3101288 7.925 S
747 746 0 1 Crosby, Capt. Edward Gifford male 70 1 1 WE/P 5735 71 B22 S
748 747 0 3 Abbott, Mr. Rossmore Edward male 16 1 1 C.A. 2673 20.25 S
749 748 1 2 Sinkkonen, Miss. Anna female 30 0 0 250648 13 S
750 749 0 1 Marvin, Mr. Daniel Warner male 19 1 0 113773 53.1 D30 S
751 750 0 3 Connaghton, Mr. Michael male 31 0 0 335097 7.75 Q
752 751 1 2 Wells, Miss. Joan female 4 1 1 29103 23 S
753 752 1 3 Moor, Master. Meier male 6 0 1 392096 12.475 E121 S
754 753 0 3 Vande Velde, Mr. Johannes Joseph male 33 0 0 345780 9.5 S
755 754 0 3 Jonkoff, Mr. Lalio male 23 0 0 349204 7.8958 S
756 755 1 2 Herman, Mrs. Samuel (Jane Laver) female 48 1 2 220845 65 S
757 756 1 2 Hamalainen, Master. Viljo male 0.67 1 1 250649 14.5 S
758 757 0 3 Carlsson, Mr. August Sigfrid male 28 0 0 350042 7.7958 S
759 758 0 2 Bailey, Mr. Percy Andrew male 18 0 0 29108 11.5 S
760 759 0 3 Theobald, Mr. Thomas Leonard male 34 0 0 363294 8.05 S
761 760 1 1 Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards) female 33 0 0 110152 86.5 B77 S
762 761 0 3 Garfirth, Mr. John male 0 0 358585 14.5 S
763 762 0 3 Nirva, Mr. Iisakki Antino Aijo male 41 0 0 SOTON/O2 3101272 7.125 S
764 763 1 3 Barah, Mr. Hanna Assi male 20 0 0 2663 7.2292 C
765 764 1 1 Carter, Mrs. William Ernest (Lucile Polk) female 36 1 2 113760 120 B96 B98 S
766 765 0 3 Eklund, Mr. Hans Linus male 16 0 0 347074 7.775 S
767 766 1 1 Hogeboom, Mrs. John C (Anna Andrews) female 51 1 0 13502 77.9583 D11 S
768 767 0 1 Brewe, Dr. Arthur Jackson male 0 0 112379 39.6 C
769 768 0 3 Mangan, Miss. Mary female 30.5 0 0 364850 7.75 Q
770 769 0 3 Moran, Mr. Daniel J male 1 0 371110 24.15 Q
771 770 0 3 Gronnestad, Mr. Daniel Danielsen male 32 0 0 8471 8.3625 S
772 771 0 3 Lievens, Mr. Rene Aime male 24 0 0 345781 9.5 S
773 772 0 3 Jensen, Mr. Niels Peder male 48 0 0 350047 7.8542 S
774 773 0 2 Mack, Mrs. (Mary) female 57 0 0 S.O./P.P. 3 10.5 E77 S
775 774 0 3 Elias, Mr. Dibo male 0 0 2674 7.225 C
776 775 1 2 Hocking, Mrs. Elizabeth (Eliza Needs) female 54 1 3 29105 23 S
777 776 0 3 Myhrman, Mr. Pehr Fabian Oliver Malkolm male 18 0 0 347078 7.75 S
778 777 0 3 Tobin, Mr. Roger male 0 0 383121 7.75 F38 Q
779 778 1 3 Emanuel, Miss. Virginia Ethel female 5 0 0 364516 12.475 S
780 779 0 3 Kilgannon, Mr. Thomas J male 0 0 36865 7.7375 Q
781 780 1 1 Robert, Mrs. Edward Scott (Elisabeth Walton McMillan) female 43 0 1 24160 211.3375 B3 S
782 781 1 3 Ayoub, Miss. Banoura female 13 0 0 2687 7.2292 C
783 782 1 1 Dick, Mrs. Albert Adrian (Vera Gillespie) female 17 1 0 17474 57 B20 S
784 783 0 1 Long, Mr. Milton Clyde male 29 0 0 113501 30 D6 S
785 784 0 3 Johnston, Mr. Andrew G male 1 2 W./C. 6607 23.45 S
786 785 0 3 Ali, Mr. William male 25 0 0 SOTON/O.Q. 3101312 7.05 S
787 786 0 3 Harmer, Mr. Abraham (David Lishin) male 25 0 0 374887 7.25 S
788 787 1 3 Sjoblom, Miss. Anna Sofia female 18 0 0 3101265 7.4958 S
789 788 0 3 Rice, Master. George Hugh male 8 4 1 382652 29.125 Q
790 789 1 3 Dean, Master. Bertram Vere male 1 1 2 C.A. 2315 20.575 S
791 790 0 1 Guggenheim, Mr. Benjamin male 46 0 0 PC 17593 79.2 B82 B84 C
792 791 0 3 Keane, Mr. Andrew "Andy" male 0 0 12460 7.75 Q
793 792 0 2 Gaskell, Mr. Alfred male 16 0 0 239865 26 S
794 793 0 3 Sage, Miss. Stella Anna female 8 2 CA. 2343 69.55 S
795 794 0 1 Hoyt, Mr. William Fisher male 0 0 PC 17600 30.6958 C
796 795 0 3 Dantcheff, Mr. Ristiu male 25 0 0 349203 7.8958 S
797 796 0 2 Otter, Mr. Richard male 39 0 0 28213 13 S
798 797 1 1 Leader, Dr. Alice (Farnham) female 49 0 0 17465 25.9292 D17 S
799 798 1 3 Osman, Mrs. Mara female 31 0 0 349244 8.6833 S
800 799 0 3 Ibrahim Shawah, Mr. Yousseff male 30 0 0 2685 7.2292 C
801 800 0 3 Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert) female 30 1 1 345773 24.15 S
802 801 0 2 Ponesell, Mr. Martin male 34 0 0 250647 13 S
803 802 1 2 Collyer, Mrs. Harvey (Charlotte Annie Tate) female 31 1 1 C.A. 31921 26.25 S
804 803 1 1 Carter, Master. William Thornton II male 11 1 2 113760 120 B96 B98 S
805 804 1 3 Thomas, Master. Assad Alexander male 0.42 0 1 2625 8.5167 C
806 805 1 3 Hedman, Mr. Oskar Arvid male 27 0 0 347089 6.975 S
807 806 0 3 Johansson, Mr. Karl Johan male 31 0 0 347063 7.775 S
808 807 0 1 Andrews, Mr. Thomas Jr male 39 0 0 112050 0 A36 S
809 808 0 3 Pettersson, Miss. Ellen Natalia female 18 0 0 347087 7.775 S
810 809 0 2 Meyer, Mr. August male 39 0 0 248723 13 S
811 810 1 1 Chambers, Mrs. Norman Campbell (Bertha Griggs) female 33 1 0 113806 53.1 E8 S
812 811 0 3 Alexander, Mr. William male 26 0 0 3474 7.8875 S
813 812 0 3 Lester, Mr. James male 39 0 0 A/4 48871 24.15 S
814 813 0 2 Slemen, Mr. Richard James male 35 0 0 28206 10.5 S
815 814 0 3 Andersson, Miss. Ebba Iris Alfrida female 6 4 2 347082 31.275 S
816 815 0 3 Tomlin, Mr. Ernest Portage male 30.5 0 0 364499 8.05 S
817 816 0 1 Fry, Mr. Richard male 0 0 112058 0 B102 S
818 817 0 3 Heininen, Miss. Wendla Maria female 23 0 0 STON/O2. 3101290 7.925 S
819 818 0 2 Mallet, Mr. Albert male 31 1 1 S.C./PARIS 2079 37.0042 C
820 819 0 3 Holm, Mr. John Fredrik Alexander male 43 0 0 C 7075 6.45 S
821 820 0 3 Skoog, Master. Karl Thorsten male 10 3 2 347088 27.9 S
822 821 1 1 Hays, Mrs. Charles Melville (Clara Jennings Gregg) female 52 1 1 12749 93.5 B69 S
823 822 1 3 Lulic, Mr. Nikola male 27 0 0 315098 8.6625 S
824 823 0 1 Reuchlin, Jonkheer. John George male 38 0 0 19972 0 S
825 824 1 3 Moor, Mrs. (Beila) female 27 0 1 392096 12.475 E121 S
826 825 0 3 Panula, Master. Urho Abraham male 2 4 1 3101295 39.6875 S
827 826 0 3 Flynn, Mr. John male 0 0 368323 6.95 Q
828 827 0 3 Lam, Mr. Len male 0 0 1601 56.4958 S
829 828 1 2 Mallet, Master. Andre male 1 0 2 S.C./PARIS 2079 37.0042 C
830 829 1 3 McCormack, Mr. Thomas Joseph male 0 0 367228 7.75 Q
831 830 1 1 Stone, Mrs. George Nelson (Martha Evelyn) female 62 0 0 113572 80 B28
832 831 1 3 Yasbeck, Mrs. Antoni (Selini Alexander) female 15 1 0 2659 14.4542 C
833 832 1 2 Richards, Master. George Sibley male 0.83 1 1 29106 18.75 S
834 833 0 3 Saad, Mr. Amin male 0 0 2671 7.2292 C
835 834 0 3 Augustsson, Mr. Albert male 23 0 0 347468 7.8542 S
836 835 0 3 Allum, Mr. Owen George male 18 0 0 2223 8.3 S
837 836 1 1 Compton, Miss. Sara Rebecca female 39 1 1 PC 17756 83.1583 E49 C
838 837 0 3 Pasic, Mr. Jakob male 21 0 0 315097 8.6625 S
839 838 0 3 Sirota, Mr. Maurice male 0 0 392092 8.05 S
840 839 1 3 Chip, Mr. Chang male 32 0 0 1601 56.4958 S
841 840 1 1 Marechal, Mr. Pierre male 0 0 11774 29.7 C47 C
842 841 0 3 Alhomaki, Mr. Ilmari Rudolf male 20 0 0 SOTON/O2 3101287 7.925 S
843 842 0 2 Mudd, Mr. Thomas Charles male 16 0 0 S.O./P.P. 3 10.5 S
844 843 1 1 Serepeca, Miss. Augusta female 30 0 0 113798 31 C
845 844 0 3 Lemberopolous, Mr. Peter L male 34.5 0 0 2683 6.4375 C
846 845 0 3 Culumovic, Mr. Jeso male 17 0 0 315090 8.6625 S
847 846 0 3 Abbing, Mr. Anthony male 42 0 0 C.A. 5547 7.55 S
848 847 0 3 Sage, Mr. Douglas Bullen male 8 2 CA. 2343 69.55 S
849 848 0 3 Markoff, Mr. Marin male 35 0 0 349213 7.8958 C
850 849 0 2 Harper, Rev. John male 28 0 1 248727 33 S
851 850 1 1 Goldenberg, Mrs. Samuel L (Edwiga Grabowska) female 1 0 17453 89.1042 C92 C
852 851 0 3 Andersson, Master. Sigvard Harald Elias male 4 4 2 347082 31.275 S
853 852 0 3 Svensson, Mr. Johan male 74 0 0 347060 7.775 S
854 853 0 3 Boulos, Miss. Nourelain female 9 1 1 2678 15.2458 C
855 854 1 1 Lines, Miss. Mary Conover female 16 0 1 PC 17592 39.4 D28 S
856 855 0 2 Carter, Mrs. Ernest Courtenay (Lilian Hughes) female 44 1 0 244252 26 S
857 856 1 3 Aks, Mrs. Sam (Leah Rosen) female 18 0 1 392091 9.35 S
858 857 1 1 Wick, Mrs. George Dennick (Mary Hitchcock) female 45 1 1 36928 164.8667 S
859 858 1 1 Daly, Mr. Peter Denis male 51 0 0 113055 26.55 E17 S
860 859 1 3 Baclini, Mrs. Solomon (Latifa Qurban) female 24 0 3 2666 19.2583 C
861 860 0 3 Razi, Mr. Raihed male 0 0 2629 7.2292 C
862 861 0 3 Hansen, Mr. Claus Peter male 41 2 0 350026 14.1083 S
863 862 0 2 Giles, Mr. Frederick Edward male 21 1 0 28134 11.5 S
864 863 1 1 Swift, Mrs. Frederick Joel (Margaret Welles Barron) female 48 0 0 17466 25.9292 D17 S
865 864 0 3 Sage, Miss. Dorothy Edith "Dolly" female 8 2 CA. 2343 69.55 S
866 865 0 2 Gill, Mr. John William male 24 0 0 233866 13 S
867 866 1 2 Bystrom, Mrs. (Karolina) female 42 0 0 236852 13 S
868 867 1 2 Duran y More, Miss. Asuncion female 27 1 0 SC/PARIS 2149 13.8583 C
869 868 0 1 Roebling, Mr. Washington Augustus II male 31 0 0 PC 17590 50.4958 A24 S
870 869 0 3 van Melkebeke, Mr. Philemon male 0 0 345777 9.5 S
871 870 1 3 Johnson, Master. Harold Theodor male 4 1 1 347742 11.1333 S
872 871 0 3 Balkic, Mr. Cerin male 26 0 0 349248 7.8958 S
873 872 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47 1 1 11751 52.5542 D35 S
874 873 0 1 Carlsson, Mr. Frans Olof male 33 0 0 695 5 B51 B53 B55 S
875 874 0 3 Vander Cruyssen, Mr. Victor male 47 0 0 345765 9 S
876 875 1 2 Abelson, Mrs. Samuel (Hannah Wizosky) female 28 1 0 P/PP 3381 24 C
877 876 1 3 Najib, Miss. Adele Kiamie "Jane" female 15 0 0 2667 7.225 C
878 877 0 3 Gustafsson, Mr. Alfred Ossian male 20 0 0 7534 9.8458 S
879 878 0 3 Petroff, Mr. Nedelio male 19 0 0 349212 7.8958 S
880 879 0 3 Laleff, Mr. Kristo male 0 0 349217 7.8958 S
881 880 1 1 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56 0 1 11767 83.1583 C50 C
882 881 1 2 Shelley, Mrs. William (Imanita Parrish Hall) female 25 0 1 230433 26 S
883 882 0 3 Markun, Mr. Johann male 33 0 0 349257 7.8958 S
884 883 0 3 Dahlberg, Miss. Gerda Ulrika female 22 0 0 7552 10.5167 S
885 884 0 2 Banfield, Mr. Frederick James male 28 0 0 C.A./SOTON 34068 10.5 S
886 885 0 3 Sutehall, Mr. Henry Jr male 25 0 0 SOTON/OQ 392076 7.05 S
887 886 0 3 Rice, Mrs. William (Margaret Norton) female 39 0 5 382652 29.125 Q
888 887 0 2 Montvila, Rev. Juozas male 27 0 0 211536 13 S
889 888 1 1 Graham, Miss. Margaret Edith female 19 0 0 112053 30 B42 S
890 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female 1 2 W./C. 6607 23.45 S
891 890 1 1 Behr, Mr. Karl Howell male 26 0 0 111369 30 C148 C
892 891 0 3 Dooley, Mr. Patrick male 32 0 0 370376 7.75 Q

1
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YEAR,JAN,FEB,MAR,APR,MAY,JUN,JUL,AUG,SEP,OCT,NOV,DEC
1 YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1992 164083 164260 163747 164759 165617 166098 167305 167797 169407 170681 171025 172995 1993 175078 173770 172328 176766 178445 178201 180759 180692 181800 182910 184746 186339 1994 185128 188077 191588 191632 190940 193196 193763 196157 197754 199579 199723 200670 1995 201583 198383 200230 201048 202993 205507 204959 206529 206978 206157 208661 210434 1996 208731 212011 213855 214644 216304 216059 216374 216355 219240 221039 220989 221898 1997 223524 225409 226136 224588 222906 226048 228738 229317 230284 229822 230486 231197 1998 231605 231664 233043 235976 237055 238958 237423 236412 238542 242531 244307 246577 1999 246891 249510 250657 252418 254738 255472 257441 260253 261352 261825 264883 269876 2000 268091 272020 275214 271004 271418 273440 272638 272943 277523 276973 275923 275736 2001 278916 278799 276468 280804 281540 280399 279522 281423 276095 294613 286960 283708 2002 283577 285061 284263 288820 284994 287401 290427 292582 288434 289634 291475 293819 2003 295294 291178 296347 295643 296395 299662 302788 307745 305916 304824 308551 307362 2004 309225 311427 316935 313531 318962 314971 318532 318945 324607 326680 327837 331877 2005 329187 332847 332517 337608 334551 344034 346715 342950 343711 344390 347047 347441 2006 357626 354614 356357 357320 356423 357763 359217 360497 358468 358435 359256 364300 2007 363462 364090 367714 366065 370887 367933 369472 370982 373138 375048 378371 375349 2008 375303 371661 371961 373877 375790 376584 374774 372091 366167 352900 339942 332065 2009 336977 335765 330219 331314 334290 339532 340286 346705 338356 341590 344911 346258 2010 346349 346972 354625 357086 354430 353830 354536 356560 359440 363855 367352 369299 2011 372041 375003 378472 380284 380398 383081 382966 383541 387313 390302 391478 391695 2012 395280 400181 401830 400095 399463 395723 397516 401937 405707 405598 407327 409468 2013 412207 416783 412514 413391 414207 415989 417962 417101 417682 419686 421056 422891 2014 419195 424657 429707 433082 434161 435587 435429 438598 437932 439829 441445 437996 2015 435881 433579 440463 440355 444400 444747 447112 447738 447641 446489 448724 450831 2016 446757 448895 447757 453397 454135 457409 457849 457722 462284 465321 466028 470616 2017 473104 471865 470844

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Time (min),Buy
2.0,0
0.6833333333333333,0
3.216666666666667,1
0.9,0
1.5333333333333334,1
2.8833333333333333,1
0.8,0
1.4666666666666666,0
1.1166666666666667,0
0.6,0
1.35,1
3.183333333333333,1
2.7666666666666666,0
2.183333333333333,1
1.5,1
1.3333333333333333,1
1.5333333333333334,0
0.7833333333333333,0
2.9833333333333334,1
4.15,1
0.85,0
2.033333333333333,1
1.6,0
2.6166666666666667,1
2.683333333333333,1
1.95,0
0.4666666666666667,1
2.716666666666667,1
2.333333333333333,1
3.4166666666666665,1
0.26666666666666666,0
1.3833333333333333,1
0.5166666666666667,0
2.7,1
2.05,0
2.95,1
1.2333333333333334,0
3.6166666666666663,1
1.4333333333333331,1
2.066666666666667,0
2.066666666666667,1
1.5,0
2.433333333333333,0
2.95,1
2.216666666666667,1
0.9166666666666666,0
2.1,1
3.75,1
1.0,0
0.0,0
2.65,1
1.55,0
1.0666666666666669,0
2.0166666666666666,1
0.0,0
0.0,0
0.6666666666666666,0
2.5166666666666666,1
1.0666666666666669,0
1.25,0
2.95,1
0.0,0
1.9666666666666663,0
2.2,1
2.9,1
3.85,1
2.3833333333333333,1
2.083333333333333,1
3.183333333333333,1
3.8666666666666663,1
2.183333333333333,0
2.833333333333333,1
2.7333333333333334,1
1.3833333333333333,0
1.1666666666666667,0
0.38333333333333336,0
1.1666666666666667,0
1.5166666666666666,0
3.216666666666667,1
1.1333333333333333,0
0.7,0
0.8166666666666667,0
3.883333333333333,1
2.216666666666667,1
0.75,0
2.566666666666667,0
0.0,0
0.0,0
1.7666666666666666,1
1.6833333333333331,1
0.21666666666666667,0
0.0,0
2.8833333333333333,1
2.466666666666667,1
1.2666666666666666,0
3.75,1
3.883333333333333,1
1.5666666666666669,0
1.6666666666666667,0
2.15,1
1 Time (min) Buy
2 2.0 0
3 0.6833333333333333 0
4 3.216666666666667 1
5 0.9 0
6 1.5333333333333334 1
7 2.8833333333333333 1
8 0.8 0
9 1.4666666666666666 0
10 1.1166666666666667 0
11 0.6 0
12 1.35 1
13 3.183333333333333 1
14 2.7666666666666666 0
15 2.183333333333333 1
16 1.5 1
17 1.3333333333333333 1
18 1.5333333333333334 0
19 0.7833333333333333 0
20 2.9833333333333334 1
21 4.15 1
22 0.85 0
23 2.033333333333333 1
24 1.6 0
25 2.6166666666666667 1
26 2.683333333333333 1
27 1.95 0
28 0.4666666666666667 1
29 2.716666666666667 1
30 2.333333333333333 1
31 3.4166666666666665 1
32 0.26666666666666666 0
33 1.3833333333333333 1
34 0.5166666666666667 0
35 2.7 1
36 2.05 0
37 2.95 1
38 1.2333333333333334 0
39 3.6166666666666663 1
40 1.4333333333333331 1
41 2.066666666666667 0
42 2.066666666666667 1
43 1.5 0
44 2.433333333333333 0
45 2.95 1
46 2.216666666666667 1
47 0.9166666666666666 0
48 2.1 1
49 3.75 1
50 1.0 0
51 0.0 0
52 2.65 1
53 1.55 0
54 1.0666666666666669 0
55 2.0166666666666666 1
56 0.0 0
57 0.0 0
58 0.6666666666666666 0
59 2.5166666666666666 1
60 1.0666666666666669 0
61 1.25 0
62 2.95 1
63 0.0 0
64 1.9666666666666663 0
65 2.2 1
66 2.9 1
67 3.85 1
68 2.3833333333333333 1
69 2.083333333333333 1
70 3.183333333333333 1
71 3.8666666666666663 1
72 2.183333333333333 0
73 2.833333333333333 1
74 2.7333333333333334 1
75 1.3833333333333333 0
76 1.1666666666666667 0
77 0.38333333333333336 0
78 1.1666666666666667 0
79 1.5166666666666666 0
80 3.216666666666667 1
81 1.1333333333333333 0
82 0.7 0
83 0.8166666666666667 0
84 3.883333333333333 1
85 2.216666666666667 1
86 0.75 0
87 2.566666666666667 0
88 0.0 0
89 0.0 0
90 1.7666666666666666 1
91 1.6833333333333331 1
92 0.21666666666666667 0
93 0.0 0
94 2.8833333333333333 1
95 2.466666666666667 1
96 1.2666666666666666 0
97 3.75 1
98 3.883333333333333 1
99 1.5666666666666669 0
100 1.6666666666666667 0
101 2.15 1

10001
data/weight-height.csv Normal file
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data/wines.csv Normal file
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Class,Alcohol,Malic_acid,Ash,Alcalinity_of_ash,Magnesium,Total_phenols,Flavanoids,Nonflavanoid_phenols,Proanthocyanins,Color_intensity,Hue,OD280-OD315_of_diluted_wines,Proline
1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065
1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050
1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185
1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480
1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735
1,14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450
1,14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290
1,14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295
1,14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045
1,13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045
1,14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510
1,14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280
1,13.75,1.73,2.41,16,89,2.6,2.76,.29,1.81,5.6,1.15,2.9,1320
1,14.75,1.73,2.39,11.4,91,3.1,3.69,.43,2.81,5.4,1.25,2.73,1150
1,14.38,1.87,2.38,12,102,3.3,3.64,.29,2.96,7.5,1.2,3,1547
1,13.63,1.81,2.7,17.2,112,2.85,2.91,.3,1.46,7.3,1.28,2.88,1310
1,14.3,1.92,2.72,20,120,2.8,3.14,.33,1.97,6.2,1.07,2.65,1280
1,13.83,1.57,2.62,20,115,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130
1,14.19,1.59,2.48,16.5,108,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680
1,13.64,3.1,2.56,15.2,116,2.7,3.03,.17,1.66,5.1,.96,3.36,845
1,14.06,1.63,2.28,16,126,3,3.17,.24,2.1,5.65,1.09,3.71,780
1,12.93,3.8,2.65,18.6,102,2.41,2.41,.25,1.98,4.5,1.03,3.52,770
1,13.71,1.86,2.36,16.6,101,2.61,2.88,.27,1.69,3.8,1.11,4,1035
1,12.85,1.6,2.52,17.8,95,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015
1,13.5,1.81,2.61,20,96,2.53,2.61,.28,1.66,3.52,1.12,3.82,845
1,13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830
1,13.39,1.77,2.62,16.1,93,2.85,2.94,.34,1.45,4.8,.92,3.22,1195
1,13.3,1.72,2.14,17,94,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285
1,13.87,1.9,2.8,19.4,107,2.95,2.97,.37,1.76,4.5,1.25,3.4,915
1,14.02,1.68,2.21,16,96,2.65,2.33,.26,1.98,4.7,1.04,3.59,1035
1,13.73,1.5,2.7,22.5,101,3,3.25,.29,2.38,5.7,1.19,2.71,1285
1,13.58,1.66,2.36,19.1,106,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515
1,13.68,1.83,2.36,17.2,104,2.42,2.69,.42,1.97,3.84,1.23,2.87,990
1,13.76,1.53,2.7,19.5,132,2.95,2.74,.5,1.35,5.4,1.25,3,1235
1,13.51,1.8,2.65,19,110,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095
1,13.48,1.81,2.41,20.5,100,2.7,2.98,.26,1.86,5.1,1.04,3.47,920
1,13.28,1.64,2.84,15.5,110,2.6,2.68,.34,1.36,4.6,1.09,2.78,880
1,13.05,1.65,2.55,18,98,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105
1,13.07,1.5,2.1,15.5,98,2.4,2.64,.28,1.37,3.7,1.18,2.69,1020
1,14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760
1,13.56,1.71,2.31,16.2,117,3.15,3.29,.34,2.34,6.13,.95,3.38,795
1,13.41,3.84,2.12,18.8,90,2.45,2.68,.27,1.48,4.28,.91,3,1035
1,13.88,1.89,2.59,15,101,3.25,3.56,.17,1.7,5.43,.88,3.56,1095
1,13.24,3.98,2.29,17.5,103,2.64,2.63,.32,1.66,4.36,.82,3,680
1,13.05,1.77,2.1,17,107,3,3,.28,2.03,5.04,.88,3.35,885
1,14.21,4.04,2.44,18.9,111,2.85,2.65,.3,1.25,5.24,.87,3.33,1080
1,14.38,3.59,2.28,16,102,3.25,3.17,.27,2.19,4.9,1.04,3.44,1065
1,13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985
1,14.1,2.02,2.4,18.8,103,2.75,2.92,.32,2.38,6.2,1.07,2.75,1060
1,13.94,1.73,2.27,17.4,108,2.88,3.54,.32,2.08,8.90,1.12,3.1,1260
1,13.05,1.73,2.04,12.4,92,2.72,3.27,.17,2.91,7.2,1.12,2.91,1150
1,13.83,1.65,2.6,17.2,94,2.45,2.99,.22,2.29,5.6,1.24,3.37,1265
1,13.82,1.75,2.42,14,111,3.88,3.74,.32,1.87,7.05,1.01,3.26,1190
1,13.77,1.9,2.68,17.1,115,3,2.79,.39,1.68,6.3,1.13,2.93,1375
1,13.74,1.67,2.25,16.4,118,2.6,2.9,.21,1.62,5.85,.92,3.2,1060
1,13.56,1.73,2.46,20.5,116,2.96,2.78,.2,2.45,6.25,.98,3.03,1120
1,14.22,1.7,2.3,16.3,118,3.2,3,.26,2.03,6.38,.94,3.31,970
1,13.29,1.97,2.68,16.8,102,3,3.23,.31,1.66,6,1.07,2.84,1270
1,13.72,1.43,2.5,16.7,108,3.4,3.67,.19,2.04,6.8,.89,2.87,1285
2,12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520
2,12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680
2,12.64,1.36,2.02,16.8,100,2.02,1.41,.53,.62,5.75,.98,1.59,450
2,13.67,1.25,1.92,18,94,2.1,1.79,.32,.73,3.8,1.23,2.46,630
2,12.37,1.13,2.16,19,87,3.5,3.1,.19,1.87,4.45,1.22,2.87,420
2,12.17,1.45,2.53,19,104,1.89,1.75,.45,1.03,2.95,1.45,2.23,355
2,12.37,1.21,2.56,18.1,98,2.42,2.65,.37,2.08,4.6,1.19,2.3,678
2,13.11,1.01,1.7,15,78,2.98,3.18,.26,2.28,5.3,1.12,3.18,502
2,12.37,1.17,1.92,19.6,78,2.11,2,.27,1.04,4.68,1.12,3.48,510
2,13.34,.94,2.36,17,110,2.53,1.3,.55,.42,3.17,1.02,1.93,750
2,12.21,1.19,1.75,16.8,151,1.85,1.28,.14,2.5,2.85,1.28,3.07,718
2,12.29,1.61,2.21,20.4,103,1.1,1.02,.37,1.46,3.05,.906,1.82,870
2,13.86,1.51,2.67,25,86,2.95,2.86,.21,1.87,3.38,1.36,3.16,410
2,13.49,1.66,2.24,24,87,1.88,1.84,.27,1.03,3.74,.98,2.78,472
2,12.99,1.67,2.6,30,139,3.3,2.89,.21,1.96,3.35,1.31,3.5,985
2,11.96,1.09,2.3,21,101,3.38,2.14,.13,1.65,3.21,.99,3.13,886
2,11.66,1.88,1.92,16,97,1.61,1.57,.34,1.15,3.8,1.23,2.14,428
2,13.03,.9,1.71,16,86,1.95,2.03,.24,1.46,4.6,1.19,2.48,392
2,11.84,2.89,2.23,18,112,1.72,1.32,.43,.95,2.65,.96,2.52,500
2,12.33,.99,1.95,14.8,136,1.9,1.85,.35,2.76,3.4,1.06,2.31,750
2,12.7,3.87,2.4,23,101,2.83,2.55,.43,1.95,2.57,1.19,3.13,463
2,12,.92,2,19,86,2.42,2.26,.3,1.43,2.5,1.38,3.12,278
2,12.72,1.81,2.2,18.8,86,2.2,2.53,.26,1.77,3.9,1.16,3.14,714
2,12.08,1.13,2.51,24,78,2,1.58,.4,1.4,2.2,1.31,2.72,630
2,13.05,3.86,2.32,22.5,85,1.65,1.59,.61,1.62,4.8,.84,2.01,515
2,11.84,.89,2.58,18,94,2.2,2.21,.22,2.35,3.05,.79,3.08,520
2,12.67,.98,2.24,18,99,2.2,1.94,.3,1.46,2.62,1.23,3.16,450
2,12.16,1.61,2.31,22.8,90,1.78,1.69,.43,1.56,2.45,1.33,2.26,495
2,11.65,1.67,2.62,26,88,1.92,1.61,.4,1.34,2.6,1.36,3.21,562
2,11.64,2.06,2.46,21.6,84,1.95,1.69,.48,1.35,2.8,1,2.75,680
2,12.08,1.33,2.3,23.6,70,2.2,1.59,.42,1.38,1.74,1.07,3.21,625
2,12.08,1.83,2.32,18.5,81,1.6,1.5,.52,1.64,2.4,1.08,2.27,480
2,12,1.51,2.42,22,86,1.45,1.25,.5,1.63,3.6,1.05,2.65,450
2,12.69,1.53,2.26,20.7,80,1.38,1.46,.58,1.62,3.05,.96,2.06,495
2,12.29,2.83,2.22,18,88,2.45,2.25,.25,1.99,2.15,1.15,3.3,290
2,11.62,1.99,2.28,18,98,3.02,2.26,.17,1.35,3.25,1.16,2.96,345
2,12.47,1.52,2.2,19,162,2.5,2.27,.32,3.28,2.6,1.16,2.63,937
2,11.81,2.12,2.74,21.5,134,1.6,.99,.14,1.56,2.5,.95,2.26,625
2,12.29,1.41,1.98,16,85,2.55,2.5,.29,1.77,2.9,1.23,2.74,428
2,12.37,1.07,2.1,18.5,88,3.52,3.75,.24,1.95,4.5,1.04,2.77,660
2,12.29,3.17,2.21,18,88,2.85,2.99,.45,2.81,2.3,1.42,2.83,406
2,12.08,2.08,1.7,17.5,97,2.23,2.17,.26,1.4,3.3,1.27,2.96,710
2,12.6,1.34,1.9,18.5,88,1.45,1.36,.29,1.35,2.45,1.04,2.77,562
2,12.34,2.45,2.46,21,98,2.56,2.11,.34,1.31,2.8,.8,3.38,438
2,11.82,1.72,1.88,19.5,86,2.5,1.64,.37,1.42,2.06,.94,2.44,415
2,12.51,1.73,1.98,20.5,85,2.2,1.92,.32,1.48,2.94,1.04,3.57,672
2,12.42,2.55,2.27,22,90,1.68,1.84,.66,1.42,2.7,.86,3.3,315
2,12.25,1.73,2.12,19,80,1.65,2.03,.37,1.63,3.4,1,3.17,510
2,12.72,1.75,2.28,22.5,84,1.38,1.76,.48,1.63,3.3,.88,2.42,488
2,12.22,1.29,1.94,19,92,2.36,2.04,.39,2.08,2.7,.86,3.02,312
2,11.61,1.35,2.7,20,94,2.74,2.92,.29,2.49,2.65,.96,3.26,680
2,11.46,3.74,1.82,19.5,107,3.18,2.58,.24,3.58,2.9,.75,2.81,562
2,12.52,2.43,2.17,21,88,2.55,2.27,.26,1.22,2,.9,2.78,325
2,11.76,2.68,2.92,20,103,1.75,2.03,.6,1.05,3.8,1.23,2.5,607
2,11.41,.74,2.5,21,88,2.48,2.01,.42,1.44,3.08,1.1,2.31,434
2,12.08,1.39,2.5,22.5,84,2.56,2.29,.43,1.04,2.9,.93,3.19,385
2,11.03,1.51,2.2,21.5,85,2.46,2.17,.52,2.01,1.9,1.71,2.87,407
2,11.82,1.47,1.99,20.8,86,1.98,1.6,.3,1.53,1.95,.95,3.33,495
2,12.42,1.61,2.19,22.5,108,2,2.09,.34,1.61,2.06,1.06,2.96,345
2,12.77,3.43,1.98,16,80,1.63,1.25,.43,.83,3.4,.7,2.12,372
2,12,3.43,2,19,87,2,1.64,.37,1.87,1.28,.93,3.05,564
2,11.45,2.4,2.42,20,96,2.9,2.79,.32,1.83,3.25,.8,3.39,625
2,11.56,2.05,3.23,28.5,119,3.18,5.08,.47,1.87,6,.93,3.69,465
2,12.42,4.43,2.73,26.5,102,2.2,2.13,.43,1.71,2.08,.92,3.12,365
2,13.05,5.8,2.13,21.5,86,2.62,2.65,.3,2.01,2.6,.73,3.1,380
2,11.87,4.31,2.39,21,82,2.86,3.03,.21,2.91,2.8,.75,3.64,380
2,12.07,2.16,2.17,21,85,2.6,2.65,.37,1.35,2.76,.86,3.28,378
2,12.43,1.53,2.29,21.5,86,2.74,3.15,.39,1.77,3.94,.69,2.84,352
2,11.79,2.13,2.78,28.5,92,2.13,2.24,.58,1.76,3,.97,2.44,466
2,12.37,1.63,2.3,24.5,88,2.22,2.45,.4,1.9,2.12,.89,2.78,342
2,12.04,4.3,2.38,22,80,2.1,1.75,.42,1.35,2.6,.79,2.57,580
3,12.86,1.35,2.32,18,122,1.51,1.25,.21,.94,4.1,.76,1.29,630
3,12.88,2.99,2.4,20,104,1.3,1.22,.24,.83,5.4,.74,1.42,530
3,12.81,2.31,2.4,24,98,1.15,1.09,.27,.83,5.7,.66,1.36,560
3,12.7,3.55,2.36,21.5,106,1.7,1.2,.17,.84,5,.78,1.29,600
3,12.51,1.24,2.25,17.5,85,2,.58,.6,1.25,5.45,.75,1.51,650
3,12.6,2.46,2.2,18.5,94,1.62,.66,.63,.94,7.1,.73,1.58,695
3,12.25,4.72,2.54,21,89,1.38,.47,.53,.8,3.85,.75,1.27,720
3,12.53,5.51,2.64,25,96,1.79,.6,.63,1.1,5,.82,1.69,515
3,13.49,3.59,2.19,19.5,88,1.62,.48,.58,.88,5.7,.81,1.82,580
3,12.84,2.96,2.61,24,101,2.32,.6,.53,.81,4.92,.89,2.15,590
3,12.93,2.81,2.7,21,96,1.54,.5,.53,.75,4.6,.77,2.31,600
3,13.36,2.56,2.35,20,89,1.4,.5,.37,.64,5.6,.7,2.47,780
3,13.52,3.17,2.72,23.5,97,1.55,.52,.5,.55,4.35,.89,2.06,520
3,13.62,4.95,2.35,20,92,2,.8,.47,1.02,4.4,.91,2.05,550
3,12.25,3.88,2.2,18.5,112,1.38,.78,.29,1.14,8.21,.65,2,855
3,13.16,3.57,2.15,21,102,1.5,.55,.43,1.3,4,.6,1.68,830
3,13.88,5.04,2.23,20,80,.98,.34,.4,.68,4.9,.58,1.33,415
3,12.87,4.61,2.48,21.5,86,1.7,.65,.47,.86,7.65,.54,1.86,625
3,13.32,3.24,2.38,21.5,92,1.93,.76,.45,1.25,8.42,.55,1.62,650
3,13.08,3.9,2.36,21.5,113,1.41,1.39,.34,1.14,9.40,.57,1.33,550
3,13.5,3.12,2.62,24,123,1.4,1.57,.22,1.25,8.60,.59,1.3,500
3,12.79,2.67,2.48,22,112,1.48,1.36,.24,1.26,10.8,.48,1.47,480
3,13.11,1.9,2.75,25.5,116,2.2,1.28,.26,1.56,7.1,.61,1.33,425
3,13.23,3.3,2.28,18.5,98,1.8,.83,.61,1.87,10.52,.56,1.51,675
3,12.58,1.29,2.1,20,103,1.48,.58,.53,1.4,7.6,.58,1.55,640
3,13.17,5.19,2.32,22,93,1.74,.63,.61,1.55,7.9,.6,1.48,725
3,13.84,4.12,2.38,19.5,89,1.8,.83,.48,1.56,9.01,.57,1.64,480
3,12.45,3.03,2.64,27,97,1.9,.58,.63,1.14,7.5,.67,1.73,880
3,14.34,1.68,2.7,25,98,2.8,1.31,.53,2.7,13,.57,1.96,660
3,13.48,1.67,2.64,22.5,89,2.6,1.1,.52,2.29,11.75,.57,1.78,620
3,12.36,3.83,2.38,21,88,2.3,.92,.5,1.04,7.65,.56,1.58,520
3,13.69,3.26,2.54,20,107,1.83,.56,.5,.8,5.88,.96,1.82,680
3,12.85,3.27,2.58,22,106,1.65,.6,.6,.96,5.58,.87,2.11,570
3,12.96,3.45,2.35,18.5,106,1.39,.7,.4,.94,5.28,.68,1.75,675
3,13.78,2.76,2.3,22,90,1.35,.68,.41,1.03,9.58,.7,1.68,615
3,13.73,4.36,2.26,22.5,88,1.28,.47,.52,1.15,6.62,.78,1.75,520
3,13.45,3.7,2.6,23,111,1.7,.92,.43,1.46,10.68,.85,1.56,695
3,12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,685
3,13.58,2.58,2.69,24.5,105,1.55,.84,.39,1.54,8.66,.74,1.8,750
3,13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630
3,12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510
3,12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470
3,14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660
3,13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740
3,13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750
3,13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835
3,13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840
3,14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560
1 Class Alcohol Malic_acid Ash Alcalinity_of_ash Magnesium Total_phenols Flavanoids Nonflavanoid_phenols Proanthocyanins Color_intensity Hue OD280-OD315_of_diluted_wines Proline
2 1 14.23 1.71 2.43 15.6 127 2.8 3.06 .28 2.29 5.64 1.04 3.92 1065
3 1 13.2 1.78 2.14 11.2 100 2.65 2.76 .26 1.28 4.38 1.05 3.4 1050
4 1 13.16 2.36 2.67 18.6 101 2.8 3.24 .3 2.81 5.68 1.03 3.17 1185
5 1 14.37 1.95 2.5 16.8 113 3.85 3.49 .24 2.18 7.8 .86 3.45 1480
6 1 13.24 2.59 2.87 21 118 2.8 2.69 .39 1.82 4.32 1.04 2.93 735
7 1 14.2 1.76 2.45 15.2 112 3.27 3.39 .34 1.97 6.75 1.05 2.85 1450
8 1 14.39 1.87 2.45 14.6 96 2.5 2.52 .3 1.98 5.25 1.02 3.58 1290
9 1 14.06 2.15 2.61 17.6 121 2.6 2.51 .31 1.25 5.05 1.06 3.58 1295
10 1 14.83 1.64 2.17 14 97 2.8 2.98 .29 1.98 5.2 1.08 2.85 1045
11 1 13.86 1.35 2.27 16 98 2.98 3.15 .22 1.85 7.22 1.01 3.55 1045
12 1 14.1 2.16 2.3 18 105 2.95 3.32 .22 2.38 5.75 1.25 3.17 1510
13 1 14.12 1.48 2.32 16.8 95 2.2 2.43 .26 1.57 5 1.17 2.82 1280
14 1 13.75 1.73 2.41 16 89 2.6 2.76 .29 1.81 5.6 1.15 2.9 1320
15 1 14.75 1.73 2.39 11.4 91 3.1 3.69 .43 2.81 5.4 1.25 2.73 1150
16 1 14.38 1.87 2.38 12 102 3.3 3.64 .29 2.96 7.5 1.2 3 1547
17 1 13.63 1.81 2.7 17.2 112 2.85 2.91 .3 1.46 7.3 1.28 2.88 1310
18 1 14.3 1.92 2.72 20 120 2.8 3.14 .33 1.97 6.2 1.07 2.65 1280
19 1 13.83 1.57 2.62 20 115 2.95 3.4 .4 1.72 6.6 1.13 2.57 1130
20 1 14.19 1.59 2.48 16.5 108 3.3 3.93 .32 1.86 8.7 1.23 2.82 1680
21 1 13.64 3.1 2.56 15.2 116 2.7 3.03 .17 1.66 5.1 .96 3.36 845
22 1 14.06 1.63 2.28 16 126 3 3.17 .24 2.1 5.65 1.09 3.71 780
23 1 12.93 3.8 2.65 18.6 102 2.41 2.41 .25 1.98 4.5 1.03 3.52 770
24 1 13.71 1.86 2.36 16.6 101 2.61 2.88 .27 1.69 3.8 1.11 4 1035
25 1 12.85 1.6 2.52 17.8 95 2.48 2.37 .26 1.46 3.93 1.09 3.63 1015
26 1 13.5 1.81 2.61 20 96 2.53 2.61 .28 1.66 3.52 1.12 3.82 845
27 1 13.05 2.05 3.22 25 124 2.63 2.68 .47 1.92 3.58 1.13 3.2 830
28 1 13.39 1.77 2.62 16.1 93 2.85 2.94 .34 1.45 4.8 .92 3.22 1195
29 1 13.3 1.72 2.14 17 94 2.4 2.19 .27 1.35 3.95 1.02 2.77 1285
30 1 13.87 1.9 2.8 19.4 107 2.95 2.97 .37 1.76 4.5 1.25 3.4 915
31 1 14.02 1.68 2.21 16 96 2.65 2.33 .26 1.98 4.7 1.04 3.59 1035
32 1 13.73 1.5 2.7 22.5 101 3 3.25 .29 2.38 5.7 1.19 2.71 1285
33 1 13.58 1.66 2.36 19.1 106 2.86 3.19 .22 1.95 6.9 1.09 2.88 1515
34 1 13.68 1.83 2.36 17.2 104 2.42 2.69 .42 1.97 3.84 1.23 2.87 990
35 1 13.76 1.53 2.7 19.5 132 2.95 2.74 .5 1.35 5.4 1.25 3 1235
36 1 13.51 1.8 2.65 19 110 2.35 2.53 .29 1.54 4.2 1.1 2.87 1095
37 1 13.48 1.81 2.41 20.5 100 2.7 2.98 .26 1.86 5.1 1.04 3.47 920
38 1 13.28 1.64 2.84 15.5 110 2.6 2.68 .34 1.36 4.6 1.09 2.78 880
39 1 13.05 1.65 2.55 18 98 2.45 2.43 .29 1.44 4.25 1.12 2.51 1105
40 1 13.07 1.5 2.1 15.5 98 2.4 2.64 .28 1.37 3.7 1.18 2.69 1020
41 1 14.22 3.99 2.51 13.2 128 3 3.04 .2 2.08 5.1 .89 3.53 760
42 1 13.56 1.71 2.31 16.2 117 3.15 3.29 .34 2.34 6.13 .95 3.38 795
43 1 13.41 3.84 2.12 18.8 90 2.45 2.68 .27 1.48 4.28 .91 3 1035
44 1 13.88 1.89 2.59 15 101 3.25 3.56 .17 1.7 5.43 .88 3.56 1095
45 1 13.24 3.98 2.29 17.5 103 2.64 2.63 .32 1.66 4.36 .82 3 680
46 1 13.05 1.77 2.1 17 107 3 3 .28 2.03 5.04 .88 3.35 885
47 1 14.21 4.04 2.44 18.9 111 2.85 2.65 .3 1.25 5.24 .87 3.33 1080
48 1 14.38 3.59 2.28 16 102 3.25 3.17 .27 2.19 4.9 1.04 3.44 1065
49 1 13.9 1.68 2.12 16 101 3.1 3.39 .21 2.14 6.1 .91 3.33 985
50 1 14.1 2.02 2.4 18.8 103 2.75 2.92 .32 2.38 6.2 1.07 2.75 1060
51 1 13.94 1.73 2.27 17.4 108 2.88 3.54 .32 2.08 8.90 1.12 3.1 1260
52 1 13.05 1.73 2.04 12.4 92 2.72 3.27 .17 2.91 7.2 1.12 2.91 1150
53 1 13.83 1.65 2.6 17.2 94 2.45 2.99 .22 2.29 5.6 1.24 3.37 1265
54 1 13.82 1.75 2.42 14 111 3.88 3.74 .32 1.87 7.05 1.01 3.26 1190
55 1 13.77 1.9 2.68 17.1 115 3 2.79 .39 1.68 6.3 1.13 2.93 1375
56 1 13.74 1.67 2.25 16.4 118 2.6 2.9 .21 1.62 5.85 .92 3.2 1060
57 1 13.56 1.73 2.46 20.5 116 2.96 2.78 .2 2.45 6.25 .98 3.03 1120
58 1 14.22 1.7 2.3 16.3 118 3.2 3 .26 2.03 6.38 .94 3.31 970
59 1 13.29 1.97 2.68 16.8 102 3 3.23 .31 1.66 6 1.07 2.84 1270
60 1 13.72 1.43 2.5 16.7 108 3.4 3.67 .19 2.04 6.8 .89 2.87 1285
61 2 12.37 .94 1.36 10.6 88 1.98 .57 .28 .42 1.95 1.05 1.82 520
62 2 12.33 1.1 2.28 16 101 2.05 1.09 .63 .41 3.27 1.25 1.67 680
63 2 12.64 1.36 2.02 16.8 100 2.02 1.41 .53 .62 5.75 .98 1.59 450
64 2 13.67 1.25 1.92 18 94 2.1 1.79 .32 .73 3.8 1.23 2.46 630
65 2 12.37 1.13 2.16 19 87 3.5 3.1 .19 1.87 4.45 1.22 2.87 420
66 2 12.17 1.45 2.53 19 104 1.89 1.75 .45 1.03 2.95 1.45 2.23 355
67 2 12.37 1.21 2.56 18.1 98 2.42 2.65 .37 2.08 4.6 1.19 2.3 678
68 2 13.11 1.01 1.7 15 78 2.98 3.18 .26 2.28 5.3 1.12 3.18 502
69 2 12.37 1.17 1.92 19.6 78 2.11 2 .27 1.04 4.68 1.12 3.48 510
70 2 13.34 .94 2.36 17 110 2.53 1.3 .55 .42 3.17 1.02 1.93 750
71 2 12.21 1.19 1.75 16.8 151 1.85 1.28 .14 2.5 2.85 1.28 3.07 718
72 2 12.29 1.61 2.21 20.4 103 1.1 1.02 .37 1.46 3.05 .906 1.82 870
73 2 13.86 1.51 2.67 25 86 2.95 2.86 .21 1.87 3.38 1.36 3.16 410
74 2 13.49 1.66 2.24 24 87 1.88 1.84 .27 1.03 3.74 .98 2.78 472
75 2 12.99 1.67 2.6 30 139 3.3 2.89 .21 1.96 3.35 1.31 3.5 985
76 2 11.96 1.09 2.3 21 101 3.38 2.14 .13 1.65 3.21 .99 3.13 886
77 2 11.66 1.88 1.92 16 97 1.61 1.57 .34 1.15 3.8 1.23 2.14 428
78 2 13.03 .9 1.71 16 86 1.95 2.03 .24 1.46 4.6 1.19 2.48 392
79 2 11.84 2.89 2.23 18 112 1.72 1.32 .43 .95 2.65 .96 2.52 500
80 2 12.33 .99 1.95 14.8 136 1.9 1.85 .35 2.76 3.4 1.06 2.31 750
81 2 12.7 3.87 2.4 23 101 2.83 2.55 .43 1.95 2.57 1.19 3.13 463
82 2 12 .92 2 19 86 2.42 2.26 .3 1.43 2.5 1.38 3.12 278
83 2 12.72 1.81 2.2 18.8 86 2.2 2.53 .26 1.77 3.9 1.16 3.14 714
84 2 12.08 1.13 2.51 24 78 2 1.58 .4 1.4 2.2 1.31 2.72 630
85 2 13.05 3.86 2.32 22.5 85 1.65 1.59 .61 1.62 4.8 .84 2.01 515
86 2 11.84 .89 2.58 18 94 2.2 2.21 .22 2.35 3.05 .79 3.08 520
87 2 12.67 .98 2.24 18 99 2.2 1.94 .3 1.46 2.62 1.23 3.16 450
88 2 12.16 1.61 2.31 22.8 90 1.78 1.69 .43 1.56 2.45 1.33 2.26 495
89 2 11.65 1.67 2.62 26 88 1.92 1.61 .4 1.34 2.6 1.36 3.21 562
90 2 11.64 2.06 2.46 21.6 84 1.95 1.69 .48 1.35 2.8 1 2.75 680
91 2 12.08 1.33 2.3 23.6 70 2.2 1.59 .42 1.38 1.74 1.07 3.21 625
92 2 12.08 1.83 2.32 18.5 81 1.6 1.5 .52 1.64 2.4 1.08 2.27 480
93 2 12 1.51 2.42 22 86 1.45 1.25 .5 1.63 3.6 1.05 2.65 450
94 2 12.69 1.53 2.26 20.7 80 1.38 1.46 .58 1.62 3.05 .96 2.06 495
95 2 12.29 2.83 2.22 18 88 2.45 2.25 .25 1.99 2.15 1.15 3.3 290
96 2 11.62 1.99 2.28 18 98 3.02 2.26 .17 1.35 3.25 1.16 2.96 345
97 2 12.47 1.52 2.2 19 162 2.5 2.27 .32 3.28 2.6 1.16 2.63 937
98 2 11.81 2.12 2.74 21.5 134 1.6 .99 .14 1.56 2.5 .95 2.26 625
99 2 12.29 1.41 1.98 16 85 2.55 2.5 .29 1.77 2.9 1.23 2.74 428
100 2 12.37 1.07 2.1 18.5 88 3.52 3.75 .24 1.95 4.5 1.04 2.77 660
101 2 12.29 3.17 2.21 18 88 2.85 2.99 .45 2.81 2.3 1.42 2.83 406
102 2 12.08 2.08 1.7 17.5 97 2.23 2.17 .26 1.4 3.3 1.27 2.96 710
103 2 12.6 1.34 1.9 18.5 88 1.45 1.36 .29 1.35 2.45 1.04 2.77 562
104 2 12.34 2.45 2.46 21 98 2.56 2.11 .34 1.31 2.8 .8 3.38 438
105 2 11.82 1.72 1.88 19.5 86 2.5 1.64 .37 1.42 2.06 .94 2.44 415
106 2 12.51 1.73 1.98 20.5 85 2.2 1.92 .32 1.48 2.94 1.04 3.57 672
107 2 12.42 2.55 2.27 22 90 1.68 1.84 .66 1.42 2.7 .86 3.3 315
108 2 12.25 1.73 2.12 19 80 1.65 2.03 .37 1.63 3.4 1 3.17 510
109 2 12.72 1.75 2.28 22.5 84 1.38 1.76 .48 1.63 3.3 .88 2.42 488
110 2 12.22 1.29 1.94 19 92 2.36 2.04 .39 2.08 2.7 .86 3.02 312
111 2 11.61 1.35 2.7 20 94 2.74 2.92 .29 2.49 2.65 .96 3.26 680
112 2 11.46 3.74 1.82 19.5 107 3.18 2.58 .24 3.58 2.9 .75 2.81 562
113 2 12.52 2.43 2.17 21 88 2.55 2.27 .26 1.22 2 .9 2.78 325
114 2 11.76 2.68 2.92 20 103 1.75 2.03 .6 1.05 3.8 1.23 2.5 607
115 2 11.41 .74 2.5 21 88 2.48 2.01 .42 1.44 3.08 1.1 2.31 434
116 2 12.08 1.39 2.5 22.5 84 2.56 2.29 .43 1.04 2.9 .93 3.19 385
117 2 11.03 1.51 2.2 21.5 85 2.46 2.17 .52 2.01 1.9 1.71 2.87 407
118 2 11.82 1.47 1.99 20.8 86 1.98 1.6 .3 1.53 1.95 .95 3.33 495
119 2 12.42 1.61 2.19 22.5 108 2 2.09 .34 1.61 2.06 1.06 2.96 345
120 2 12.77 3.43 1.98 16 80 1.63 1.25 .43 .83 3.4 .7 2.12 372
121 2 12 3.43 2 19 87 2 1.64 .37 1.87 1.28 .93 3.05 564
122 2 11.45 2.4 2.42 20 96 2.9 2.79 .32 1.83 3.25 .8 3.39 625
123 2 11.56 2.05 3.23 28.5 119 3.18 5.08 .47 1.87 6 .93 3.69 465
124 2 12.42 4.43 2.73 26.5 102 2.2 2.13 .43 1.71 2.08 .92 3.12 365
125 2 13.05 5.8 2.13 21.5 86 2.62 2.65 .3 2.01 2.6 .73 3.1 380
126 2 11.87 4.31 2.39 21 82 2.86 3.03 .21 2.91 2.8 .75 3.64 380
127 2 12.07 2.16 2.17 21 85 2.6 2.65 .37 1.35 2.76 .86 3.28 378
128 2 12.43 1.53 2.29 21.5 86 2.74 3.15 .39 1.77 3.94 .69 2.84 352
129 2 11.79 2.13 2.78 28.5 92 2.13 2.24 .58 1.76 3 .97 2.44 466
130 2 12.37 1.63 2.3 24.5 88 2.22 2.45 .4 1.9 2.12 .89 2.78 342
131 2 12.04 4.3 2.38 22 80 2.1 1.75 .42 1.35 2.6 .79 2.57 580
132 3 12.86 1.35 2.32 18 122 1.51 1.25 .21 .94 4.1 .76 1.29 630
133 3 12.88 2.99 2.4 20 104 1.3 1.22 .24 .83 5.4 .74 1.42 530
134 3 12.81 2.31 2.4 24 98 1.15 1.09 .27 .83 5.7 .66 1.36 560
135 3 12.7 3.55 2.36 21.5 106 1.7 1.2 .17 .84 5 .78 1.29 600
136 3 12.51 1.24 2.25 17.5 85 2 .58 .6 1.25 5.45 .75 1.51 650
137 3 12.6 2.46 2.2 18.5 94 1.62 .66 .63 .94 7.1 .73 1.58 695
138 3 12.25 4.72 2.54 21 89 1.38 .47 .53 .8 3.85 .75 1.27 720
139 3 12.53 5.51 2.64 25 96 1.79 .6 .63 1.1 5 .82 1.69 515
140 3 13.49 3.59 2.19 19.5 88 1.62 .48 .58 .88 5.7 .81 1.82 580
141 3 12.84 2.96 2.61 24 101 2.32 .6 .53 .81 4.92 .89 2.15 590
142 3 12.93 2.81 2.7 21 96 1.54 .5 .53 .75 4.6 .77 2.31 600
143 3 13.36 2.56 2.35 20 89 1.4 .5 .37 .64 5.6 .7 2.47 780
144 3 13.52 3.17 2.72 23.5 97 1.55 .52 .5 .55 4.35 .89 2.06 520
145 3 13.62 4.95 2.35 20 92 2 .8 .47 1.02 4.4 .91 2.05 550
146 3 12.25 3.88 2.2 18.5 112 1.38 .78 .29 1.14 8.21 .65 2 855
147 3 13.16 3.57 2.15 21 102 1.5 .55 .43 1.3 4 .6 1.68 830
148 3 13.88 5.04 2.23 20 80 .98 .34 .4 .68 4.9 .58 1.33 415
149 3 12.87 4.61 2.48 21.5 86 1.7 .65 .47 .86 7.65 .54 1.86 625
150 3 13.32 3.24 2.38 21.5 92 1.93 .76 .45 1.25 8.42 .55 1.62 650
151 3 13.08 3.9 2.36 21.5 113 1.41 1.39 .34 1.14 9.40 .57 1.33 550
152 3 13.5 3.12 2.62 24 123 1.4 1.57 .22 1.25 8.60 .59 1.3 500
153 3 12.79 2.67 2.48 22 112 1.48 1.36 .24 1.26 10.8 .48 1.47 480
154 3 13.11 1.9 2.75 25.5 116 2.2 1.28 .26 1.56 7.1 .61 1.33 425
155 3 13.23 3.3 2.28 18.5 98 1.8 .83 .61 1.87 10.52 .56 1.51 675
156 3 12.58 1.29 2.1 20 103 1.48 .58 .53 1.4 7.6 .58 1.55 640
157 3 13.17 5.19 2.32 22 93 1.74 .63 .61 1.55 7.9 .6 1.48 725
158 3 13.84 4.12 2.38 19.5 89 1.8 .83 .48 1.56 9.01 .57 1.64 480
159 3 12.45 3.03 2.64 27 97 1.9 .58 .63 1.14 7.5 .67 1.73 880
160 3 14.34 1.68 2.7 25 98 2.8 1.31 .53 2.7 13 .57 1.96 660
161 3 13.48 1.67 2.64 22.5 89 2.6 1.1 .52 2.29 11.75 .57 1.78 620
162 3 12.36 3.83 2.38 21 88 2.3 .92 .5 1.04 7.65 .56 1.58 520
163 3 13.69 3.26 2.54 20 107 1.83 .56 .5 .8 5.88 .96 1.82 680
164 3 12.85 3.27 2.58 22 106 1.65 .6 .6 .96 5.58 .87 2.11 570
165 3 12.96 3.45 2.35 18.5 106 1.39 .7 .4 .94 5.28 .68 1.75 675
166 3 13.78 2.76 2.3 22 90 1.35 .68 .41 1.03 9.58 .7 1.68 615
167 3 13.73 4.36 2.26 22.5 88 1.28 .47 .52 1.15 6.62 .78 1.75 520
168 3 13.45 3.7 2.6 23 111 1.7 .92 .43 1.46 10.68 .85 1.56 695
169 3 12.82 3.37 2.3 19.5 88 1.48 .66 .4 .97 10.26 .72 1.75 685
170 3 13.58 2.58 2.69 24.5 105 1.55 .84 .39 1.54 8.66 .74 1.8 750
171 3 13.4 4.6 2.86 25 112 1.98 .96 .27 1.11 8.5 .67 1.92 630
172 3 12.2 3.03 2.32 19 96 1.25 .49 .4 .73 5.5 .66 1.83 510
173 3 12.77 2.39 2.28 19.5 86 1.39 .51 .48 .64 9.899999 .57 1.63 470
174 3 14.16 2.51 2.48 20 91 1.68 .7 .44 1.24 9.7 .62 1.71 660
175 3 13.71 5.65 2.45 20.5 95 1.68 .61 .52 1.06 7.7 .64 1.74 740
176 3 13.4 3.91 2.48 23 102 1.8 .75 .43 1.41 7.3 .7 1.56 750
177 3 13.27 4.28 2.26 20 120 1.59 .69 .43 1.35 10.2 .59 1.56 835
178 3 13.17 2.59 2.37 20 120 1.65 .68 .53 1.46 9.3 .6 1.62 840
179 3 14.13 4.1 2.74 24.5 96 2.05 .76 .56 1.35 9.2 .61 1.6 560

21
environment.yml Normal file
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@@ -0,0 +1,21 @@
name: ztdl
channels:
- defaults
dependencies:
- python=3.7.*
- bz2file==0.98
- cython==0.29.*
- pip==21.0.*
- numpy==1.19.*
- jupyter==1.0.*
- matplotlib==3.3.*
- setuptools==52.0.*
- scikit-learn==0.24.*
- scipy==1.6.*
- pandas==1.2.*
- pillow==8.2.*
- seaborn==0.11.*
- pytest==6.2.*
- twisted==21.2.*
- pip:
- tensorflow==2.5.*

197
exercises/First Deep Learning Model commented.ipynb Normal file
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@@ -0,0 +1,197 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# First Deep Learning Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np # import the numpy library and assign the name np to it\n",
"%matplotlib inline # magic function that sets the backend of matplotlib to the inline backend\n",
"import matplotlib.pyplot as plt # import the matplotlib.pyplot and assign the name plt to it"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import make_circles # import the make_circles module from the sklearn.datasets module"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X, y = make_circles(n_samples=1000,\n",
" noise=0.1,\n",
" factor=0.2,\n",
" random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(5, 5))\n",
"plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
"plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
"plt.xlim(-1.5, 1.5)\n",
"plt.ylim(-1.5, 1.5)\n",
"plt.legend(['0', '1'])\n",
"plt.title(\"Blue circles and Red crosses\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense\n",
"from tensorflow.keras.optimizers import SGD"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.add(Dense(4, input_shape=(2,), activation='tanh'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.add(Dense(1, activation='sigmoid'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.compile(SGD(learning_rate=0.5), 'binary_crossentropy', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X, y, epochs=20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"hticks = np.linspace(-1.5, 1.5, 101)\n",
"vticks = np.linspace(-1.5, 1.5, 101)\n",
"aa, bb = np.meshgrid(hticks, vticks)\n",
"ab = np.c_[aa.ravel(), bb.ravel()]\n",
"c = model.predict(ab)\n",
"cc = c.reshape(aa.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(5, 5))\n",
"plt.contourf(aa, bb, cc, cmap='bwr', alpha=0.2)\n",
"plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
"plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
"plt.xlim(-1.5, 1.5)\n",
"plt.ylim(-1.5, 1.5)\n",
"plt.legend(['0', '1'])\n",
"plt.title(\"Blue circles and Red crosses\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 1\n",
"- load the dataset: `../data/international-airline-passengers.csv`\n",
"- inspect it using the `.info()` and `.head()` commands\n",
"- use the function `pd.to_datetime()` to change the column type of 'Month' to a datatime type\n",
"- set the index of df to be a datetime index using the column 'Month' and the `df.set_index()` method\n",
"- choose the appropriate plot and display the data\n",
"- choose appropriate scale\n",
"- label the axes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# - load the dataset: ../data/international-airline-passengers.csv\n",
"df = pd.read_csv('../data/international-airline-passengers.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# - inspect it using the .info() and .head() commands\n",
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# - use the function to_datetime() to change the column type of 'Month' to a datatime type\n",
"# - set the index of df to be a datetime index using the column 'Month' and tthe set_index() method\n",
"\n",
"df['Month'] = pd.to_datetime(df['Month'])\n",
"df = df.set_index('Month')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# - choose the appropriate plot and display the data\n",
"# - choose appropriate scale\n",
"# - label the axes\n",
"\n",
"df.plot();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 2\n",
"- load the dataset: `../data/weight-height.csv`\n",
"- inspect it\n",
"- plot it using a scatter plot with Weight as a function of Height\n",
"- plot the male and female populations with 2 different colors on a new scatter plot\n",
"- remember to label the axes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# - load the dataset: ../data/weight-height.csv\n",
"# - inspect it\n",
"df = pd.read_csv('../data/weight-height.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"df['Gender'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# - plot it using a scatter plot with Weight as a function of Height\n",
"_ = df.plot(kind='scatter', x='Height', y='Weight');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# - plot the male and female populations with 2 different colors on a new scatter plot\n",
"# - remember to label the axes\n",
"\n",
"# this can be done in several ways, showing 2 here:\n",
"males = df[df['Gender'] == 'Male']\n",
"females = df.query('Gender == \"Female\"')\n",
"fig, ax = plt.subplots()\n",
"\n",
"males.plot(kind='scatter', x='Height', y='Weight',\n",
" ax=ax, color='blue', alpha=0.3,\n",
" title='Male & Female Populations')\n",
"\n",
"females.plot(kind='scatter', x='Height', y='Weight',\n",
" ax=ax, color='red', alpha=0.3);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df['Gendercolor'] = df['Gender'].map({'Male': 'blue', 'Female': 'red'})\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.plot(kind='scatter', \n",
" x='Height',\n",
" y='Weight',\n",
" c=df['Gendercolor'],\n",
" alpha=0.3,\n",
" title='Male & Female Populations');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, ax = plt.subplots()\n",
"ax.plot(males['Height'], males['Weight'], 'ob', \n",
" females['Height'], females['Weight'], 'or', alpha=0.3)\n",
"plt.xlabel('Height')\n",
"plt.ylabel('Weight')\n",
"plt.title('Male & Female Populations');"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Exercise 3\n",
"- plot the histogram of the heights for males and for females on the same plot\n",
"- use alpha to control transparency in the plot comand\n",
"- plot a vertical line at the mean of each population using `plt.axvline()`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"males['Height'].plot(kind='hist',\n",
" bins=50,\n",
" range=(50, 80),\n",
" alpha=0.3,\n",
" color='blue')\n",
"\n",
"females['Height'].plot(kind='hist',\n",
" bins=50,\n",
" range=(50, 80),\n",
" alpha=0.3,\n",
" color='red')\n",
"\n",
"plt.title('Height distribution')\n",
"plt.legend([\"Males\", \"Females\"])\n",
"plt.xlabel(\"Heigth (in)\")\n",
"\n",
"\n",
"plt.axvline(males['Height'].mean(), color='blue', linewidth=2)\n",
"plt.axvline(females['Height'].mean(), color='red', linewidth=2);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"males['Height'].plot(kind='hist',\n",
" bins=200,\n",
" range=(50, 80),\n",
" alpha=0.3,\n",
" color='blue',\n",
" cumulative=True,\n",
" density=True)\n",
"\n",
"females['Height'].plot(kind='hist',\n",
" bins=200,\n",
" range=(50, 80),\n",
" alpha=0.3,\n",
" color='red',\n",
" cumulative=True,\n",
" density=True)\n",
"\n",
"plt.title('Height distribution')\n",
"plt.legend([\"Males\", \"Females\"])\n",
"plt.xlabel(\"Heigth (in)\")\n",
"\n",
"plt.axhline(0.8)\n",
"plt.axhline(0.5)\n",
"plt.axhline(0.2);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 4\n",
"- plot the weights of the males and females using a box plot\n",
"- which one is easier to read?\n",
"- (remember to put in titles, axes and legends)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dfpvt = df.pivot(columns = 'Gender', values = 'Weight')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dfpvt.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dfpvt.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dfpvt.plot(kind='box')\n",
"plt.title('Weight Box Plot')\n",
"plt.ylabel(\"Weight (lbs)\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 5\n",
"- load the dataset: `../data/titanic-train.csv`\n",
"- learn about scattermatrix here: http://pandas.pydata.org/pandas-docs/stable/visualization.html\n",
"- display the data using a scattermatrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('../data/titanic-train.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pandas.plotting import scatter_matrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_ = scatter_matrix(df.drop('PassengerId', axis=1), figsize=(10, 10))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 1
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Machine Learning Exercises Solution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 1\n",
"\n",
"You've just been hired at a real estate investment firm and they would like you to build a model for pricing houses. You are given a dataset that contains data for house prices and a few features like number of bedrooms, size in square feet and age of the house. Let's see if you can build a model that is able to predict the price. In this exercise we extend what we have learned about linear regression to a dataset with more than one feature. Here are the steps to complete it:\n",
"\n",
"1. Load the dataset ../data/housing-data.csv\n",
"- plot the histograms for each feature\n",
"- create 2 variables called X and y: X shall be a matrix with 3 columns (sqft,bdrms,age) and y shall be a vector with 1 column (price)\n",
"- create a linear regression model in Keras with the appropriate number of inputs and output\n",
"- split the data into train and test with a 20% test size\n",
"- train the model on the training set and check its accuracy on training and test set\n",
"- how's your model doing? Is the loss growing smaller?\n",
"- try to improve your model with these experiments:\n",
" - normalize the input features with one of the rescaling techniques mentioned above\n",
" - use a different value for the learning rate of your model\n",
" - use a different optimizer\n",
"- once you're satisfied with training, check the R2score on the test set"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load the dataset ../data/housing-data.csv\n",
"df = pd.read_csv('../data/housing-data.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plot the histograms for each feature\n",
"plt.figure(figsize=(15, 5))\n",
"for i, feature in enumerate(df.columns):\n",
" plt.subplot(1, 4, i+1)\n",
" df[feature].plot(kind='hist', title=feature)\n",
" plt.xlabel(feature)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# create 2 variables called X and y:\n",
"# X shall be a matrix with 3 columns (sqft,bdrms,age)\n",
"# and y shall be a vector with 1 column (price)\n",
"X = df[['sqft', 'bdrms', 'age']].values\n",
"y = df['price'].values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense\n",
"from tensorflow.keras.optimizers import Adam"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# create a linear regression model in Keras\n",
"# with the appropriate number of inputs and output\n",
"model = Sequential()\n",
"model.add(Dense(1, input_shape=(3,)))\n",
"model.compile(Adam(learning_rate=0.8), 'mean_squared_error')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# split the data into train and test with a 20% test size\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(X_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(X)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# train the model on the training set and check its accuracy on training and test set\n",
"# how's your model doing? Is the loss growing smaller?\n",
"model.fit(X_train, y_train, epochs=10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import r2_score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# check the R2score on training and test set (probably very bad)\n",
"\n",
"y_train_pred = model.predict(X_train)\n",
"y_test_pred = model.predict(X_test)\n",
"\n",
"print(\"The R2 score on the Train set is:\\t{:0.3f}\".format(r2_score(y_train, y_train_pred)))\n",
"print(\"The R2 score on the Test set is:\\t{:0.3f}\".format(r2_score(y_test, y_test_pred)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# try to improve your model with these experiments:\n",
"# - normalize the input features with one of the rescaling techniques mentioned above\n",
"# - use a different value for the learning rate of your model\n",
"# - use a different optimizer\n",
"df['sqft1000'] = df['sqft']/1000.0\n",
"df['age10'] = df['age']/10.0\n",
"df['price100k'] = df['price']/1e5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X = df[['sqft1000', 'bdrms', 'age10']].values\n",
"y = df['price100k'].values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Dense(1, input_dim=3))\n",
"model.compile(Adam(learning_rate=0.1), 'mean_squared_error')\n",
"model.fit(X_train, y_train, epochs=20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# once you're satisfied with training, check the R2score on the test set\n",
"\n",
"y_train_pred = model.predict(X_train)\n",
"y_test_pred = model.predict(X_test)\n",
"\n",
"print(\"The R2 score on the Train set is:\\t{:0.3f}\".format(r2_score(y_train, y_train_pred)))\n",
"print(\"The R2 score on the Test set is:\\t{:0.3f}\".format(r2_score(y_test, y_test_pred)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train, y_train, epochs=40, verbose=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# once you're satisfied with training, check the R2score on the test set\n",
"\n",
"y_train_pred = model.predict(X_train)\n",
"y_test_pred = model.predict(X_test)\n",
"\n",
"print(\"The R2 score on the Train set is:\\t{:0.3f}\".format(r2_score(y_train, y_train_pred)))\n",
"print(\"The R2 score on the Test set is:\\t{:0.3f}\".format(r2_score(y_test, y_test_pred)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 2\n",
"\n",
"Your boss was extremely happy with your work on the housing price prediction model and decided to entrust you with a more challenging task. They've seen a lot of people leave the company recently and they would like to understand why that's happening. They have collected historical data on employees and they would like you to build a model that is able to predict which employee will leave next. The would like a model that is better than random guessing. They also prefer false negatives than false positives, in this first phase. Fields in the dataset include:\n",
"\n",
"- Employee satisfaction level\n",
"- Last evaluation\n",
"- Number of projects\n",
"- Average monthly hours\n",
"- Time spent at the company\n",
"- Whether they have had a work accident\n",
"- Whether they have had a promotion in the last 5 years\n",
"- Department\n",
"- Salary\n",
"- Whether the employee has left\n",
"\n",
"Your goal is to predict the binary outcome variable `left` using the rest of the data. Since the outcome is binary, this is a classification problem. Here are some things you may want to try out:\n",
"\n",
"1. load the dataset at ../data/HR_comma_sep.csv, inspect it with `.head()`, `.info()` and `.describe()`.\n",
"- Establish a benchmark: what would be your accuracy score if you predicted everyone stay?\n",
"- Check if any feature needs rescaling. You may plot a histogram of the feature to decide which rescaling method is more appropriate.\n",
"- convert the categorical features into binary dummy columns. You will then have to combine them with the numerical features using `pd.concat`.\n",
"- do the usual train/test split with a 20% test size\n",
"- play around with learning rate and optimizer\n",
"- check the confusion matrix, precision and recall\n",
"- check if you still get the same results if you use a 5-Fold cross validation on all the data\n",
"- Is the model good enough for your boss?\n",
"\n",
"As you will see in this exercise, the a logistic regression model is not good enough to help your boss. In the next chapter we will learn how to go beyond linear models.\n",
"\n",
"This dataset comes from https://www.kaggle.com/ludobenistant/hr-analytics/ and is released under [CC BY-SA 4.0 License](https://creativecommons.org/licenses/by-sa/4.0/)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# load the dataset at ../data/HR_comma_sep.csv, inspect it with `.head()`, `.info()` and `.describe()`.\n",
"\n",
"df = pd.read_csv('../data/HR_comma_sep.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Establish a benchmark: what would be your accuracy score if you predicted everyone stay?\n",
"\n",
"df.left.value_counts() / len(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Predicting 0 all the time would yield an accuracy of 76%"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check if any feature needs rescaling.\n",
"# You may plot a histogram of the feature to decide which rescaling method is more appropriate.\n",
"df['average_montly_hours'].plot(kind='hist');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df['average_montly_hours_100'] = df['average_montly_hours']/100.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df['average_montly_hours_100'].plot(kind='hist');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df['time_spend_company'].plot(kind='hist');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# convert the categorical features into binary dummy columns.\n",
"# You will then have to combine them with\n",
"# the numerical features using `pd.concat`.\n",
"df_dummies = pd.get_dummies(df[['sales', 'salary']])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_dummies.head()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X = pd.concat([df[['satisfaction_level', 'last_evaluation', 'number_project',\n",
" 'time_spend_company', 'Work_accident',\n",
" 'promotion_last_5years', 'average_montly_hours_100']],\n",
" df_dummies], axis=1).values\n",
"y = df['left'].values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# do the usual train/test split with a 20% test size\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# play around with learning rate and optimizer\n",
"\n",
"model = Sequential()\n",
"model.add(Dense(1, input_dim=20, activation='sigmoid'))\n",
"model.compile(Adam(learning_rate=0.5), 'binary_crossentropy', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train, y_train, epochs=10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_test_pred = model.predict_classes(X_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix, classification_report"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def pretty_confusion_matrix(y_true, y_pred, labels=[\"False\", \"True\"]):\n",
" cm = confusion_matrix(y_true, y_pred)\n",
" pred_labels = ['Predicted '+ l for l in labels]\n",
" df = pd.DataFrame(cm, index=labels, columns=pred_labels)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# check the confusion matrix, precision and recall\n",
"\n",
"pretty_confusion_matrix(y_test, y_test_pred, labels=['Stay', 'Leave'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(classification_report(y_test, y_test_pred))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.wrappers.scikit_learn import KerasClassifier"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# check if you still get the same results if you use a 5-Fold cross validation on all the data\n",
"\n",
"def build_logistic_regression_model():\n",
" model = Sequential()\n",
" model.add(Dense(1, input_dim=20, activation='sigmoid'))\n",
" model.compile(Adam(learning_rate=0.5), 'binary_crossentropy', metrics=['accuracy'])\n",
" return model\n",
"\n",
"model = KerasClassifier(build_fn=build_logistic_regression_model,\n",
" epochs=10, verbose=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import KFold, cross_val_score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cv = KFold(5, shuffle=True)\n",
"scores = cross_val_score(model, X, y, cv=cv)\n",
"\n",
"print(\"The cross validation accuracy is {:0.4f} ± {:0.4f}\".format(scores.mean(), scores.std()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"scores"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Is the model good enough for your boss?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"No, the model is not good enough for my boss, since it performs no better than the benchmark."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

413
solutions/4 Deep Learning Intro Exercises Solution.ipynb Normal file
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@@ -0,0 +1,413 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deep Learning Intro"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"The [Pima Indians dataset](https://archive.ics.uci.edu/ml/datasets/diabetes) is a very famous dataset distributed by UCI and originally collected from the National Institute of Diabetes and Digestive and Kidney Diseases. It contains data from clinical exams for women age 21 and above of Pima indian origins. The objective is to predict based on diagnostic measurements whether a patient has diabetes.\n",
"\n",
"It has the following features:\n",
"\n",
"- Pregnancies: Number of times pregnant\n",
"- Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n",
"- BloodPressure: Diastolic blood pressure (mm Hg)\n",
"- SkinThickness: Triceps skin fold thickness (mm)\n",
"- Insulin: 2-Hour serum insulin (mu U/ml)\n",
"- BMI: Body mass index (weight in kg/(height in m)^2)\n",
"- DiabetesPedigreeFunction: Diabetes pedigree function\n",
"- Age: Age (years)\n",
"\n",
"The last colum is the outcome, and it is a binary variable.\n",
"\n",
"In this first exercise we will explore it through the following steps:\n",
"\n",
"1. Load the ..data/diabetes.csv dataset, use pandas to explore the range of each feature\n",
"- For each feature draw a histogram. Bonus points if you draw all the histograms in the same figure.\n",
"- Explore correlations of features with the outcome column. You can do this in several ways, for example using the `sns.pairplot` we used above or drawing a heatmap of the correlations.\n",
"- Do features need standardization? If so what stardardization technique will you use? MinMax? Standard?\n",
"- Prepare your final `X` and `y` variables to be used by a ML model. Make sure you define your target variable well. Will you need dummy columns?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('../data/diabetes.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_ = df.hist(figsize=(12, 10))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.pairplot(df, hue='Outcome');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.heatmap(df.corr(), annot = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.utils import to_categorical"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sc = StandardScaler()\n",
"X = sc.fit_transform(df.drop('Outcome', axis=1))\n",
"y = df['Outcome'].values\n",
"y_cat = to_categorical(y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_cat.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 2"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Build a fully connected NN model that predicts diabetes. Follow these steps:\n",
"\n",
"1. Split your data in a train/test with a test size of 20% and a `random_state = 22`\n",
"- define a sequential model with at least one inner layer. You will have to make choices for the following things:\n",
" - what is the size of the input?\n",
" - how many nodes will you use in each layer?\n",
" - what is the size of the output?\n",
" - what activation functions will you use in the inner layers?\n",
" - what activation function will you use at output?\n",
" - what loss function will you use?\n",
" - what optimizer will you use?\n",
"- fit your model on the training set, using a validation_split of 0.1\n",
"- test your trained model on the test data from the train/test split\n",
"- check the accuracy score, the confusion matrix and the classification report"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y_cat,\n",
" random_state=22,\n",
" test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense\n",
"from tensorflow.keras.optimizers import Adam"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Dense(32, input_shape=(8,), activation='relu'))\n",
"model.add(Dense(32, activation='relu'))\n",
"model.add(Dense(2, activation='softmax'))\n",
"model.compile(Adam(learning_rate=0.05),\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"32*8 + 32"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train, y_train, epochs=20, verbose=2, validation_split=0.1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_test_class = np.argmax(y_test, axis=1)\n",
"y_pred_class = np.argmax(y_pred, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import accuracy_score\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.metrics import confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pd.Series(y_test_class).value_counts() / len(y_test_class)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"accuracy_score(y_test_class, y_pred_class)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(classification_report(y_test_class, y_pred_class))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"confusion_matrix(y_test_class, y_pred_class)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 3\n",
"Compare your work with the results presented in [this notebook](https://www.kaggle.com/sheshu/pima-data-visualisation-and-machine-learning). Are your Neural Network results better or worse than the results obtained by traditional Machine Learning techniques?\n",
"\n",
"- Try training a Support Vector Machine or a Random Forest model on the exact same train/test split. Is the performance better or worse?\n",
"- Try restricting your features to only 4 features like in the suggested notebook. How does model performance change?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.svm import SVC\n",
"from sklearn.naive_bayes import GaussianNB\n",
"\n",
"for mod in [RandomForestClassifier(), SVC(), GaussianNB()]:\n",
" mod.fit(X_train, y_train[:, 1])\n",
" y_pred = mod.predict(X_test)\n",
" print(\"=\"*80)\n",
" print(mod)\n",
" print(\"-\"*80)\n",
" print(\"Accuracy score: {:0.3}\".format(accuracy_score(y_test_class,\n",
" y_pred)))\n",
" print(\"Confusion Matrix:\")\n",
" print(confusion_matrix(y_test_class, y_pred))\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 4\n",
"\n",
"[Tensorflow playground](http://playground.tensorflow.org/) is a web based neural network demo. It is really useful to develop an intuition about what happens when you change architecture, activation function or other parameters. Try playing with it for a few minutes. You don't nee do understand the meaning of every knob and button in the page, just get a sense for what happens if you change something. In the next chapter we'll explore these things in more detail.\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

518
solutions/5 Gradient Descent Exercises Solution.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Gradient Descent"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 1\n",
"\n",
"You've just been hired at a wine company and they would like you to help them build a model that predicts the quality of their wine based on several measurements. They give you a dataset with wine\n",
"\n",
"- Load the ../data/wines.csv into Pandas\n",
"- Use the column called \"Class\" as target\n",
"- Check how many classes are there in target, and if necessary use dummy columns for a multi-class classification\n",
"- Use all the other columns as features, check their range and distribution (using seaborn pairplot)\n",
"- Rescale all the features using either MinMaxScaler or StandardScaler\n",
"- Build a deep model with at least 1 hidden layer to classify the data\n",
"- Choose the cost function, what will you use? Mean Squared Error? Binary Cross-Entropy? Categorical Cross-Entropy?\n",
"- Choose an optimizer\n",
"- Choose a value for the learning rate, you may want to try with several values\n",
"- Choose a batch size\n",
"- Train your model on all the data using a `validation_split=0.2`. Can you converge to 100% validation accuracy?\n",
"- What's the minumum number of epochs to converge?\n",
"- Repeat the training several times to verify how stable your results are"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('../data/wines.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y = df['Class']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_cat = pd.get_dummies(y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_cat.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X = df.drop('Class', axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.pairplot(df, hue='Class')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sc = StandardScaler()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Xsc = sc.fit_transform(X)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense\n",
"from tensorflow.keras.optimizers import SGD, Adam, Adadelta, RMSprop\n",
"import tensorflow.keras.backend as K"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"K.clear_session()\n",
"model = Sequential()\n",
"model.add(Dense(5, input_shape=(13,),\n",
" kernel_initializer='he_normal',\n",
" activation='relu'))\n",
"model.add(Dense(3, activation='softmax'))\n",
"\n",
"model.compile(RMSprop(learning_rate=0.1),\n",
" 'categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"model.fit(Xsc, y_cat.values,\n",
" batch_size=8,\n",
" epochs=10,\n",
" verbose=1,\n",
" validation_split=0.2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 2\n",
"\n",
"Since this dataset has 13 features we can only visualize pairs of features like we did in the Paired plot. We could however exploit the fact that a neural network is a function to extract 2 high level features to represent our data.\n",
"\n",
"- Build a deep fully connected network with the following structure:\n",
" - Layer 1: 8 nodes\n",
" - Layer 2: 5 nodes\n",
" - Layer 3: 2 nodes\n",
" - Output : 3 nodes\n",
"- Choose activation functions, inizializations, optimizer and learning rate so that it converges to 100% accuracy within 20 epochs (not easy)\n",
"- Remember to train the model on the scaled data\n",
"- Define a Feature Function like we did above between the input of the 1st layer and the output of the 3rd layer\n",
"- Calculate the features and plot them on a 2-dimensional scatter plot\n",
"- Can we distinguish the 3 classes well?\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"K.clear_session()\n",
"model = Sequential()\n",
"model.add(Dense(8, input_shape=(13,),\n",
" kernel_initializer='he_normal', activation='tanh'))\n",
"model.add(Dense(5, kernel_initializer='he_normal', activation='tanh'))\n",
"model.add(Dense(2, kernel_initializer='he_normal', activation='tanh'))\n",
"model.add(Dense(3, activation='softmax'))\n",
"\n",
"model.compile(RMSprop(learning_rate=0.05),\n",
" 'categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"model.fit(Xsc, y_cat.values,\n",
" batch_size=16,\n",
" epochs=20,\n",
" verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"inp = model.layers[0].input\n",
"out = model.layers[2].output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"features_function = K.function([inp], [out])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"features = features_function([Xsc])[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"features.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.scatter(features[:, 0], features[:, 1], c=y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 3\n",
"\n",
"Keras functional API. So far we've always used the Sequential model API in Keras. However, Keras also offers a Functional API, which is much more powerful. You can find its [documentation here](https://keras.io/getting-started/functional-api-guide/). Let's see how we can leverage it.\n",
"\n",
"- define an input layer called `inputs`\n",
"- define two hidden layers as before, one with 8 nodes, one with 5 nodes\n",
"- define a `second_to_last` layer with 2 nodes\n",
"- define an output layer with 3 nodes\n",
"- create a model that connect input and output\n",
"- train it and make sure that it converges\n",
"- define a function between inputs and second_to_last layer\n",
"- recalculate the features and plot them"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import Input\n",
"from tensorflow.keras.models import Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"K.clear_session()\n",
"\n",
"inputs = Input(shape=(13,))\n",
"x = Dense(8, kernel_initializer='he_normal', activation='tanh')(inputs)\n",
"x = Dense(5, kernel_initializer='he_normal', activation='tanh')(x)\n",
"second_to_last = Dense(2, kernel_initializer='he_normal',\n",
" activation='tanh')(x)\n",
"outputs = Dense(3, activation='softmax')(second_to_last)\n",
"\n",
"model = Model(inputs=inputs, outputs=outputs)\n",
"\n",
"model.compile(RMSprop(learning_rate=0.05),\n",
" 'categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"model.fit(Xsc, y_cat.values, batch_size=16, epochs=20, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"features_function = K.function([inputs], [second_to_last])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"features = features_function([Xsc])[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.scatter(features[:, 0], features[:, 1], c=y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 4 \n",
"\n",
"Keras offers the possibility to call a function at each epoch. These are Callbacks, and their [documentation is here](https://keras.io/callbacks/). Callbacks allow us to add some neat functionality. In this exercise we'll explore a few of them.\n",
"\n",
"- Split the data into train and test sets with a test_size = 0.3 and random_state=42\n",
"- Reset and recompile your model\n",
"- train the model on the train data using `validation_data=(X_test, y_test)`\n",
"- Use the `EarlyStopping` callback to stop your training if the `val_loss` doesn't improve\n",
"- Use the `ModelCheckpoint` callback to save the trained model to disk once training is finished\n",
"- Use the `TensorBoard` callback to output your training information to a `/tmp/` subdirectory\n",
"- Watch the next video for an overview of tensorboard"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, TensorBoard"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"checkpointer = ModelCheckpoint(filepath=\"/tmp/udemy/weights.hdf5\",\n",
" verbose=1, save_best_only=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"earlystopper = EarlyStopping(monitor='val_loss', min_delta=0,\n",
" patience=1, verbose=1, mode='auto')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tensorboard = TensorBoard(log_dir='/tmp/udemy/tensorboard/')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(Xsc, y_cat.values,\n",
" test_size=0.3,\n",
" random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"K.clear_session()\n",
"\n",
"inputs = Input(shape=(13,))\n",
"\n",
"x = Dense(8, kernel_initializer='he_normal', activation='tanh')(inputs)\n",
"x = Dense(5, kernel_initializer='he_normal', activation='tanh')(x)\n",
"second_to_last = Dense(2, kernel_initializer='he_normal',\n",
" activation='tanh')(x)\n",
"outputs = Dense(3, activation='softmax')(second_to_last)\n",
"\n",
"model = Model(inputs=inputs, outputs=outputs)\n",
"\n",
"model.compile(RMSprop(learning_rate=0.05), 'categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"model.fit(X_train, y_train, batch_size=32,\n",
" epochs=20, verbose=2,\n",
" validation_data=(X_test, y_test),\n",
" callbacks=[checkpointer, earlystopper, tensorboard])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run Tensorboard with the command:\n",
"\n",
" tensorboard --logdir /tmp/udemy/tensorboard/\n",
" \n",
"and open your browser at http://localhost:6006"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

365
solutions/6 Convolutional Neural Networks Exercises Solution.ipynb Normal file
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@@ -0,0 +1,365 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Convolutional Neural Networks Exercises Solution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.utils import to_categorical"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten\n",
"import tensorflow.keras.backend as K"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Exercise 1\n",
"\n",
"You've been hired by a shipping company to overhaul the way they route mail, parcels and packages. They want to build an image recognition system capable of recognizing the digits in the zipcode on a package, so that it can be automatically routed to the correct location.\n",
"You are tasked to build the digit recognition system. Luckily, you can rely on the MNIST dataset for the intial training of your model!\n",
"\n",
"Build a deep convolutional neural network with at least two convolutional and two pooling layers before the fully connected layer.\n",
"\n",
"- Start from the network we have just built\n",
"- Insert a `Conv2D` layer after the first `MaxPool2D`, give it 64 filters.\n",
"- Insert a `MaxPool2D` after that one\n",
"- Insert an `Activation` layer\n",
"- retrain the model\n",
"- does performance improve?\n",
"- how many parameters does this new model have? More or less than the previous model? Why?\n",
"- how long did this second model take to train? Longer or shorter than the previous model? Why?\n",
"- did it perform better or worse than the previous model?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.datasets import mnist"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(X_train, y_train), (X_test, y_test) = mnist.load_data(('/tmp/mnist.npz'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train = X_train.astype('float32') / 255.0\n",
"X_test = X_test.astype('float32') / 255.0\n",
"\n",
"X_train = X_train.reshape(-1, 28, 28, 1)\n",
"X_test = X_test.reshape(-1, 28, 28, 1)\n",
"\n",
"y_train_cat = to_categorical(y_train, 10)\n",
"y_test_cat = to_categorical(y_test, 10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"K.clear_session()\n",
"\n",
"model = Sequential()\n",
"\n",
"model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
"model.add(MaxPool2D(pool_size=(2, 2)))\n",
"\n",
"model.add(Conv2D(64, (3, 3), activation='relu'))\n",
"model.add(MaxPool2D(pool_size=(2, 2)))\n",
"\n",
"model.add(Flatten())\n",
"\n",
"model.add(Dense(128, activation='relu'))\n",
"\n",
"model.add(Dense(10, activation='softmax'))\n",
"\n",
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='rmsprop',\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train, y_train_cat, batch_size=128,\n",
" epochs=2, verbose=1, validation_split=0.3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.evaluate(X_test, y_test_cat)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 2\n",
"\n",
"Pleased with your performance with the digits recognition task, your boss decides to challenge you with a harder task. Their online branch allows people to upload images to a website that generates and prints a postcard that is shipped to destination. Your boss would like to know what images people are loading on the site in order to provide targeted advertising on the same page, so he asks you to build an image recognition system capable of recognizing a few objects. Luckily for you, there's a dataset ready made with a collection of labeled images. This is the [Cifar 10 Dataset](http://www.cs.toronto.edu/~kriz/cifar.html), a very famous dataset that contains images for 10 different categories:\n",
"\n",
"- airplane \t\t\t\t\t\t\t\t\t\t\n",
"- automobile \t\t\t\t\t\t\t\t\t\t\n",
"- bird \t\t\t\t\t\t\t\t\t\t\n",
"- cat \t\t\t\t\t\t\t\t\t\t\n",
"- deer \t\t\t\t\t\t\t\t\t\t\n",
"- dog \t\t\t\t\t\t\t\t\t\t\n",
"- frog \t\t\t\t\t\t\t\t\t\t\n",
"- horse \t\t\t\t\t\t\t\t\t\t\n",
"- ship \t\t\t\t\t\t\t\t\t\t\n",
"- truck\n",
"\n",
"In this exercise we will reach the limit of what you can achieve on your laptop and get ready for the next session on cloud GPUs.\n",
"\n",
"Here's what you have to do:\n",
"- load the cifar10 dataset using `keras.datasets.cifar10.load_data()`\n",
"- display a few images, see how hard/easy it is for you to recognize an object with such low resolution\n",
"- check the shape of X_train, does it need reshape?\n",
"- check the scale of X_train, does it need rescaling?\n",
"- check the shape of y_train, does it need reshape?\n",
"- build a model with the following architecture, and choose the parameters and activation functions for each of the layers:\n",
" - conv2d\n",
" - conv2d\n",
" - maxpool\n",
" - conv2d\n",
" - conv2d\n",
" - maxpool\n",
" - flatten\n",
" - dense\n",
" - output\n",
"- compile the model and check the number of parameters\n",
"- attempt to train the model with the optimizer of your choice. How fast does training proceed?\n",
"- If training is too slow (as expected) stop the execution and move to the next session!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.datasets import cifar10"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.imshow(X_train[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train = X_train.astype('float32') / 255.0\n",
"X_test = X_test.astype('float32') / 255.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_train.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_train_cat = to_categorical(y_train, 10)\n",
"y_test_cat = to_categorical(y_test, 10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_train_cat.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Conv2D(32, (3, 3),\n",
" padding='same',\n",
" input_shape=(32, 32, 3),\n",
" activation='relu'))\n",
"model.add(Conv2D(32, (3, 3), activation='relu'))\n",
"model.add(MaxPool2D(pool_size=(2, 2)))\n",
"\n",
"model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
"model.add(Conv2D(64, (3, 3), activation='relu'))\n",
"model.add(MaxPool2D(pool_size=(2, 2)))\n",
"\n",
"model.add(Flatten())\n",
"model.add(Dense(512, activation='relu'))\n",
"model.add(Dense(10, activation='softmax'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='rmsprop',\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train, y_train_cat,\n",
" batch_size=32,\n",
" epochs=2,\n",
" validation_data=(X_test, y_test_cat),\n",
" shuffle=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

333
solutions/8 Recurrent Neural Networks Exercises Solutions.ipynb Normal file
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@@ -0,0 +1,333 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Recurrent Neural Networks"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Time series forecasting"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pandas.tseries.offsets import MonthEnd\n",
"\n",
"df = pd.read_csv('../data/cansim-0800020-eng-6674700030567901031.csv',\n",
" skiprows=6, skipfooter=9,\n",
" engine='python')\n",
"\n",
"df['Adjustments'] = pd.to_datetime(df['Adjustments']) + MonthEnd(1)\n",
"df = df.set_index('Adjustments')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"split_date = pd.Timestamp('01-01-2011')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train = df.loc[:split_date, ['Unadjusted']]\n",
"test = df.loc[split_date:, ['Unadjusted']]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import MinMaxScaler\n",
"\n",
"sc = MinMaxScaler()\n",
"\n",
"train_sc = sc.fit_transform(train)\n",
"test_sc = sc.transform(test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_sc_df = pd.DataFrame(train_sc, columns=['Scaled'], index=train.index)\n",
"test_sc_df = pd.DataFrame(test_sc, columns=['Scaled'], index=test.index)\n",
"\n",
"for s in range(1, 13):\n",
" train_sc_df['shift_{}'.format(s)] = train_sc_df['Scaled'].shift(s)\n",
" test_sc_df['shift_{}'.format(s)] = test_sc_df['Scaled'].shift(s)\n",
"\n",
"X_train = train_sc_df.dropna().drop('Scaled', axis=1)\n",
"y_train = train_sc_df.dropna()[['Scaled']]\n",
"\n",
"X_test = test_sc_df.dropna().drop('Scaled', axis=1)\n",
"y_test = test_sc_df.dropna()[['Scaled']]\n",
"\n",
"X_train = X_train.values\n",
"X_test= X_test.values\n",
"\n",
"y_train = y_train.values\n",
"y_test = y_test.values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 1\n",
"\n",
"In the model above we reshaped the input shape to: `(num_samples, 1, 12)`, i.e. we treated a window of 12 months as a vector of 12 coordinates that we simultaneously passed to all the LSTM nodes. An alternative way to look at the problem is to reshape the input to `(num_samples, 12, 1)`. This means we consider each input window as a sequence of 12 values that we will pass in sequence to the LSTM. In principle this looks like a more accurate description of our situation. But does it yield better predictions? Let's check it.\n",
"\n",
"- Reshape `X_train` and `X_test` so that they represent a set of univariate sequences\n",
"- retrain the same LSTM(6) model, you'll have to adapt the `input_shape`\n",
"- check the performance of this new model, is it better at predicting the test data?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train_t = X_train.reshape(X_train.shape[0], 12, 1)\n",
"X_test_t = X_test.reshape(X_test.shape[0], 12, 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train_t.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import LSTM, Dense\n",
"import tensorflow.keras.backend as K\n",
"from tensorflow.keras.callbacks import EarlyStopping"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"K.clear_session()\n",
"model = Sequential()\n",
"\n",
"model.add(LSTM(6, input_shape=(12, 1)))\n",
"\n",
"model.add(Dense(1))\n",
"\n",
"model.compile(loss='mean_squared_error', optimizer='adam')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"early_stop = EarlyStopping(monitor='loss', patience=1, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train_t, y_train, epochs=600,\n",
" batch_size=32, verbose=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(X_test_t)\n",
"plt.plot(y_test)\n",
"plt.plot(y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Exercise 2\n",
"\n",
"RNN models can be applied to images too. In general we can apply them to any data where there's a connnection between nearby units. Let's see how we can easily build a model that works with images.\n",
"\n",
"- Load the MNIST data, by now you should be able to do it blindfolded :)\n",
"- reshape it so that an image looks like a long sequence of pixels\n",
"- create a recurrent model and train it on the training data\n",
"- how does it perform compared to a fully connected? How does it compare to Convolutional Neural Networks?\n",
"\n",
"(feel free to run this exercise on a cloud GPU if it's too slow on your laptop)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.datasets import mnist\n",
"from tensorflow.keras.utils import to_categorical"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
"X_train = X_train.astype('float32') / 255.0\n",
"X_test = X_test.astype('float32') / 255.0\n",
"y_train_cat = to_categorical(y_train, 10)\n",
"y_test_cat = to_categorical(y_test, 10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train = X_train.reshape(X_train.shape[0], -1, 1)\n",
"X_test = X_test.reshape(X_test.shape[0], -1, 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(X_train.shape)\n",
"print(X_test.shape)\n",
"print(y_train_cat.shape)\n",
"print(y_test_cat.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# define the model\n",
"K.clear_session()\n",
"model = Sequential()\n",
"model.add(LSTM(32, input_shape=X_train.shape[1:]))\n",
"model.add(Dense(10, activation='softmax'))\n",
"\n",
"# compile the model\n",
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='rmsprop',\n",
" metrics=['accuracy'])\n",
"\n",
"model.fit(X_train, y_train_cat,\n",
" batch_size=32,\n",
" epochs=100,\n",
" validation_split=0.3,\n",
" shuffle=True,\n",
" verbose=2,\n",
" )\n",
"\n",
"model.evaluate(X_test, y_test_cat)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

520
solutions/9 Improving performance Exercises Solutions.ipynb Normal file
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@@ -0,0 +1,520 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 9 Improving performance Exercises Solutions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 1\n",
"\n",
"- Reload the IMDB data keeping only the first 20000 most common words\n",
"- pad the reviews to a shorter length (eg. 70 or 80), this time make sure you keep the first part of the review if it's longer than the maximum length\n",
"- re run the model (remember to set max_features correctly)\n",
"- does it train faster this time?\n",
"- do you get a better performance?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.datasets import imdb\n",
"from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Embedding, LSTM, Dense"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"max_features = 20000\n",
"skip_top = 200"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(X_train, y_train), (X_test, y_test) = imdb.load_data('/tmp/imdb.npz',\n",
" num_words=max_features,\n",
" start_char=1,\n",
" oov_char=2,\n",
" index_from=3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"maxlen = 80"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train_pad = pad_sequences(X_train, maxlen=maxlen, truncating='post')\n",
"X_test_pad = pad_sequences(X_test, maxlen=maxlen, truncating='post')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Embedding(max_features, 128))\n",
"model.add(LSTM(64, dropout=0.2, recurrent_dropout=0.2))\n",
"model.add(Dense(1, activation='sigmoid'))\n",
"\n",
"model.compile(loss='binary_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train_pad, y_train,\n",
" batch_size=32,\n",
" epochs=2,\n",
" validation_split=0.3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"score, acc = model.evaluate(X_test_pad, y_test)\n",
"print('Test score:', score)\n",
"print('Test accuracy:', acc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 2\n",
"\n",
"- Reload the digits data as above\n",
"- define a function repeated_training_reg_dropout that adds regularization and dropout to a fully connected network\n",
"- compare the performance with/witouth dropout and regularization like we did for batch normalization\n",
"- do you get a better performance?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import load_digits\n",
"from tensorflow.keras.utils import to_categorical\n",
"from sklearn.model_selection import train_test_split\n",
"from tensorflow.keras.layers import Dropout\n",
"import tensorflow.keras.backend as K"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"digits = load_digits()\n",
"X, y = digits.data, digits.target\n",
"y_cat = to_categorical(y)\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y_cat, test_size=0.3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def repeated_training_reg_dropout(X_train,\n",
" y_train,\n",
" X_test,\n",
" y_test,\n",
" units=512,\n",
" activation='sigmoid',\n",
" optimizer='sgd',\n",
" do_dropout=False,\n",
" rate=0.3,\n",
" kernel_regularizer='l2',\n",
" epochs=10,\n",
" repeats=3):\n",
" histories = []\n",
" \n",
" for repeat in range(repeats):\n",
" K.clear_session()\n",
"\n",
" model = Sequential()\n",
" \n",
" # first fully connected layer\n",
" model.add(Dense(units,\n",
" input_shape=X_train.shape[1:],\n",
" kernel_initializer='normal',\n",
" kernel_regularizer=kernel_regularizer,\n",
" activation=activation))\n",
" if do_dropout:\n",
" model.add(Dropout(rate))\n",
"\n",
" # second fully connected layer\n",
" model.add(Dense(units,\n",
" kernel_initializer='normal',\n",
" kernel_regularizer=kernel_regularizer,\n",
" activation=activation))\n",
" if do_dropout:\n",
" model.add(Dropout(rate))\n",
"\n",
" # third fully connected layer\n",
" model.add(Dense(units,\n",
" kernel_initializer='normal',\n",
" kernel_regularizer=kernel_regularizer,\n",
" activation=activation))\n",
" if do_dropout:\n",
" model.add(Dropout(rate))\n",
"\n",
" # output layer\n",
" model.add(Dense(10, activation='softmax'))\n",
" \n",
" model.compile(optimizer,\n",
" 'categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
" h = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=epochs, verbose=0)\n",
" histories.append([h.history['accuracy'], h.history['val_accuracy']])\n",
" print(repeat, end=' ')\n",
"\n",
" histories = np.array(histories)\n",
" \n",
" # calculate mean and standard deviation across repeats:\n",
" mean_acc = histories.mean(axis=0)\n",
" std_acc = histories.std(axis=0)\n",
" print()\n",
" \n",
" return mean_acc[0], std_acc[0], mean_acc[1], std_acc[1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mean_acc, std_acc, mean_acc_val, std_acc_val = repeated_training_reg_dropout(X_train,\n",
" y_train,\n",
" X_test,\n",
" y_test,\n",
" do_dropout=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mean_acc_do, std_acc_do, mean_acc_val_do, std_acc_val_do = repeated_training_reg_dropout(X_train,\n",
" y_train,\n",
" X_test,\n",
" y_test,\n",
" do_dropout=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def plot_mean_std(m, s):\n",
" plt.plot(m)\n",
" plt.fill_between(range(len(m)), m-s, m+s, alpha=0.1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot_mean_std(mean_acc, std_acc)\n",
"plot_mean_std(mean_acc_val, std_acc_val)\n",
"plot_mean_std(mean_acc_do, std_acc_do)\n",
"plot_mean_std(mean_acc_val_do, std_acc_val_do)\n",
"plt.ylim(0, 1.01)\n",
"plt.title(\"Dropout and Regularization Accuracy\")\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Accuracy')\n",
"plt.legend(['Train', 'Test', 'Train with Dropout and Regularization', 'Test with Dropout and Regularization'], loc='best')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 3\n",
"\n",
"This is a very long and complex exercise, that should give you an idea of a real world scenario. Feel free to look at the solution if you feel lost. Also, feel free to run this with a GPU, in which case you don't need to download the data.\n",
"\n",
"If you are running this locally, download and unpack the male/female pictures from [here](https://www.dropbox.com/s/nov493om2jmh2gp/male_female.tgz?dl=0). These images and labels were obtained from [Crowdflower](https://www.crowdflower.com/data-for-everyone/).\n",
"\n",
"Your goal is to build an image classifier that will recognize the gender of a person from pictures.\n",
"\n",
"- Have a look at the directory structure and inspect a couple of pictures\n",
"- Design a model that will take a color image of size 64x64 as input and return a binary output (female=0/male=1)\n",
"- Feel free to introduce any regularization technique in your model (Dropout, Batch Normalization, Weight Regularization)\n",
"- Compile your model with an optimizer of your choice\n",
"- Using `ImageDataGenerator`, define a train generator that will augment your images with some geometric transformations. Feel free to choose the parameters that make sense to you.\n",
"- Define also a test generator, whose only purpose is to rescale the pixels by 1./255\n",
"- use the function `flow_from_directory` to generate batches from the train and test folders. Make sure you set the `target_size` to 64x64.\n",
"- Use the `model.fit_generator` function to fit the model on the batches generated from the ImageDataGenerator. Since you are streaming and augmenting the data in real time you will have to decide how many batches make an epoch and how many epochs you want to run\n",
"- Train your model (you should get to at least 85% accuracy)\n",
"- Once you are satisfied with your training, check a few of the misclassified pictures. Are those sensible errors?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# If you are running this locally\n",
"# uncomment the next 4 lines to download, extract and set the data path:\n",
"# !wget 'https://www.dropbox.com/s/nov493om2jmh2gp/male_female.tgz?dl=1' -O ../data/male_female.tgz\n",
"# data_path = '../data/male_female'\n",
"# !mkdir -p {data_path}\n",
"# !tar -xzvf ../data/male_female.tgz --directory {data_path}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import Conv2D\n",
"from tensorflow.keras.layers import MaxPooling2D\n",
"from tensorflow.keras.layers import Flatten\n",
"from tensorflow.keras.layers import BatchNormalization\n",
"from itertools import islice\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"K.clear_session()\n",
"\n",
"model = Sequential()\n",
"model.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))\n",
"model.add(MaxPooling2D(pool_size = (2, 2)))\n",
"model.add(BatchNormalization())\n",
"\n",
"model.add(Conv2D(64, (3, 3), activation = 'relu'))\n",
"model.add(MaxPooling2D(pool_size = (2, 2)))\n",
"model.add(BatchNormalization())\n",
"\n",
"model.add(Conv2D(64, (3, 3), activation = 'relu'))\n",
"model.add(MaxPooling2D(pool_size = (2, 2)))\n",
"model.add(BatchNormalization())\n",
"\n",
"model.add(Flatten())\n",
"\n",
"model.add(Dense(128, activation = 'relu'))\n",
"model.add(Dense(1, activation = 'sigmoid'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.compile(optimizer = 'adam',\n",
" loss = 'binary_crossentropy',\n",
" metrics = ['accuracy'])\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_gen = ImageDataGenerator(rescale = 1./255,\n",
" width_shift_range=0.1,\n",
" height_shift_range=0.1,\n",
" rotation_range = 10,\n",
" shear_range = 0.2,\n",
" zoom_range = 0.2,\n",
" horizontal_flip = True)\n",
"\n",
"test_gen = ImageDataGenerator(rescale = 1./255)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train = train_gen.flow_from_directory(data_path + '/train',\n",
" target_size = (64, 64),\n",
" batch_size = 16,\n",
" class_mode = 'binary')\n",
"\n",
"test = test_gen.flow_from_directory(data_path + '/test',\n",
" target_size = (64, 64),\n",
" batch_size = 16,\n",
" class_mode = 'binary')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(train,\n",
" steps_per_epoch = 800,\n",
" epochs = 200,\n",
" validation_data = test,\n",
" validation_steps = 200)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_test = []\n",
"y_test = []\n",
"for ts in islice(test, 50):\n",
" X_test.append(ts[0])\n",
" y_test.append(ts[1])\n",
"\n",
"X_test = np.concatenate(X_test)\n",
"y_test = np.concatenate(y_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict_classes(X_test).ravel()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.argwhere(y_test != y_pred).ravel()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.imshow(X_test[14])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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tests/test_nb.py Normal file
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# tests that too long to execute on Travis are temporarily commented out
# TODO: find a way to fix this
import subprocess
import tempfile
def _exec_notebook(path):
with tempfile.NamedTemporaryFile(suffix=".ipynb") as fout:
args = ["jupyter", "nbconvert", "--to", "notebook", "--execute",
"--ExecutePreprocessor.timeout=1000",
"--output", fout.name, path]
subprocess.check_call(args)
def test_0():
_exec_notebook('course/0_Check_Environment.ipynb')
def test_1():
_exec_notebook('course/1 First Deep Learning Model.ipynb')
def test_2():
_exec_notebook('course/2 Data.ipynb')
def test_3():
_exec_notebook('course/3 Machine Learning.ipynb')
def test_4():
_exec_notebook('course/4 Deep Learning Intro.ipynb')
def test_5():
_exec_notebook('course/5 Gradient Descent.ipynb')
def test_6():
_exec_notebook('course/6 Convolutional Neural Networks.ipynb')
def test_8():
_exec_notebook('course/8 Recurrent Neural Networks.ipynb')
def test_9():
_exec_notebook('course/9 Improving performance.ipynb')
def test_2_sol():
_exec_notebook('solutions/2 Data exploration Exercises Solution.ipynb')
def test_3_sol():
_exec_notebook('solutions/3 Machine Learning Exercises Solution.ipynb')
def test_4_sol():
_exec_notebook('solutions/4 Deep Learning Intro Exercises Solution.ipynb')
def test_5_sol():
_exec_notebook('solutions/5 Gradient Descent Exercises Solution.ipynb')
def test_6_sol():
_exec_notebook('solutions/6 Convolutional Neural Networks Exercises Solution.ipynb')
def test_8_sol():
_exec_notebook('solutions/8 Recurrent Neural Networks Exercises Solutions.ipynb')
def test_9_sol():
_exec_notebook('solutions/9 Improving performance Exercises Solutions.ipynb')