├── Examples └── iris_example.ipynb ├── LICENSE ├── Notebooks ├── .ipynb_checkpoints │ ├── Pima-Prediction-checkpoint.ipynb │ ├── Pima-Prediction-with-reload-checkpoint.ipynb │ └── PythonFirstSteps-checkpoint.ipynb ├── Pima-Prediction-with-reload.ipynb ├── PythonFirstSteps.ipynb └── data │ ├── pima-data-orig.csv │ ├── pima-data-trunc.csv │ ├── pima-data.csv │ ├── pima-data.xlsx │ └── pima-trained-model.pkl └── README.md /Examples/iris_example.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [ 10 | { 11 | "data": { 12 | "text/plain": [ 13 | "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", 14 | " decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n", 15 | " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", 16 | " tol=0.001, verbose=False)" 17 | ] 18 | }, 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "output_type": "execute_result" 22 | } 23 | ], 24 | "source": [ 25 | "from sklearn import svm\n", 26 | "from sklearn import datasets\n", 27 | "\n", 28 | "# load the Support vector machine algorithm\n", 29 | "clf = svm.SVC()\n", 30 | "# iris is a classic dataset that comes with sklearn. It has Iris flower features and the resulting type of\n", 31 | "# Iris. See https://archive.ics.uci.edu/ml/datasets/Iris for the details.\n", 32 | "iris = datasets.load_iris()\n", 33 | "# X (iris.data is a list of sizes of a flower. There are 4 features of the form [5.6, 3.0, 4.1, 1.3] )\n", 34 | "# y is the type of flower (0,1,2) for (Iris Setosa, Iris Versicolour, Iris Virginica)\n", 35 | "X, y = iris.data, iris.target\n", 36 | "# create a trained model with the iris data\n", 37 | "clf.fit(X,y)\n" 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": 2, 43 | "metadata": { 44 | "collapsed": false 45 | }, 46 | "outputs": [ 47 | { 48 | "data": { 49 | "text/plain": [ 50 | "['clf_trained.pkl',\n", 51 | " 'clf_trained.pkl_01.npy',\n", 52 | " 'clf_trained.pkl_02.npy',\n", 53 | " 'clf_trained.pkl_03.npy',\n", 54 | " 'clf_trained.pkl_04.npy',\n", 55 | " 'clf_trained.pkl_05.npy',\n", 56 | " 'clf_trained.pkl_06.npy',\n", 57 | " 'clf_trained.pkl_07.npy',\n", 58 | " 'clf_trained.pkl_08.npy',\n", 59 | " 'clf_trained.pkl_09.npy',\n", 60 | " 'clf_trained.pkl_10.npy',\n", 61 | " 'clf_trained.pkl_11.npy']" 62 | ] 63 | }, 64 | "execution_count": 2, 65 | "metadata": {}, 66 | "output_type": "execute_result" 67 | } 68 | ], 69 | "source": [ 70 | "# Save the trained model to disk so we can use the model at a later time to predict the class of a new flower\n", 71 | "\n", 72 | "from sklearn.externals import joblib\n", 73 | "joblib.dump(clf, 'clf_trained.pkl')" 74 | ] 75 | }, 76 | { 77 | "cell_type": "code", 78 | "execution_count": 9, 79 | "metadata": { 80 | "collapsed": false 81 | }, 82 | "outputs": [ 83 | { 84 | "name": "stdout", 85 | "output_type": "stream", 86 | "text": [ 87 | "predicted species is: 1\n", 88 | "predicted species is: 0\n", 89 | "predicted species is: 2\n" 90 | ] 91 | }, 92 | { 93 | "name": "stderr", 94 | "output_type": "stream", 95 | "text": [ 96 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", 97 | " DeprecationWarning)\n", 98 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", 99 | " DeprecationWarning)\n", 100 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", 101 | " DeprecationWarning)\n" 102 | ] 103 | } 104 | ], 105 | "source": [ 106 | "# at some time later, or even in a different program (this is why the 2nd occurance of the import)\n", 107 | "from sklearn.externals import joblib\n", 108 | "clf_trained = joblib.load('clf_trained.pkl')\n", 109 | "\n", 110 | "# Use the trained model to predict the Iris species based on the features of \"new\" flowers\n", 111 | "new_flower_a = [ 5.3, 3.0, 4.1, 1.2]\n", 112 | "new_flower_b = [ 3.5, 2.1, 2.1, 1.5]\n", 113 | "new_flower_c = [10.5, 2.1, 2.1, 1.5]\n", 114 | "\n", 115 | "y_hat_a = clf_trained.predict( new_flower_a)\n", 116 | "# What is the predicted species of the flower based on it's feature values?\n", 117 | "print (\"predicted species is: {0:d}\".format(y_hat_a[0]))\n", 118 | "\n", 119 | "y_hat_b = clf_trained.predict( new_flower_b)\n", 120 | "# What is the predicted species of the flower based on it's feature values?\n", 121 | "print (\"predicted species is: {0:d}\".format(y_hat_b[0]))\n", 122 | "\n", 123 | "y_hat_c = clf_trained.predict( new_flower_c)\n", 124 | "# What is the predicted species of the flower based on it's feature values?\n", 125 | "print (\"predicted species is: {0:d}\".format(y_hat_c[0]))\n" 126 | ] 127 | }, 128 | { 129 | "cell_type": "code", 130 | "execution_count": null, 131 | "metadata": { 132 | "collapsed": true 133 | }, 134 | "outputs": [], 135 | "source": [] 136 | } 137 | ], 138 | "metadata": { 139 | "kernelspec": { 140 | "display_name": "Python 3", 141 | "language": "python", 142 | "name": "python3" 143 | }, 144 | "language_info": { 145 | "codemirror_mode": { 146 | "name": "ipython", 147 | "version": 3 148 | }, 149 | "file_extension": ".py", 150 | "mimetype": "text/x-python", 151 | "name": "python", 152 | "nbconvert_exporter": "python", 153 | "pygments_lexer": "ipython3", 154 | "version": "3.5.1" 155 | } 156 | }, 157 | "nbformat": 4, 158 | "nbformat_minor": 0 159 | } 160 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /Notebooks/.ipynb_checkpoints/Pima-Prediction-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Predicting Diabetes" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "## Import Libraries" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 3, 20 | "metadata": { 21 | "collapsed": false 22 | }, 23 | "outputs": [], 24 | "source": [ 25 | "import pandas as pd # pandas is a dataframe library\n", 26 | "import matplotlib.pyplot as plt # matplotlib.pyplot plots data\n", 27 | "import numpy as np # numpy provides N-dim object support\n", 28 | "\n", 29 | "# do ploting inline instead of in a separate window\n", 30 | "%matplotlib inline " 31 | ] 32 | }, 33 | { 34 | "cell_type": "markdown", 35 | "metadata": {}, 36 | "source": [ 37 | "## Load and review data" 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": 5, 43 | "metadata": { 44 | "collapsed": false 45 | }, 46 | "outputs": [], 47 | "source": [ 48 | "df = pd.read_csv(\"./data/pima-data.csv\") # load Pima data. Adjust path as necessary" 49 | ] 50 | }, 51 | { 52 | "cell_type": "code", 53 | "execution_count": 6, 54 | "metadata": { 55 | "collapsed": false 56 | }, 57 | "outputs": [ 58 | { 59 | "data": { 60 | "text/plain": [ 61 | "(768, 10)" 62 | ] 63 | }, 64 | "execution_count": 6, 65 | "metadata": {}, 66 | "output_type": "execute_result" 67 | } 68 | ], 69 | "source": [ 70 | "df.shape" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": 7, 76 | "metadata": { 77 | "collapsed": false 78 | }, 79 | "outputs": [ 80 | { 81 | "data": { 82 | "text/html": [ 83 | "
\n", 84 | "\n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | "
num_pregglucose_concdiastolic_bpthicknessinsulinbmidiab_predageskindiabetes
061487235033.60.627501.3790True
11856629026.60.351311.1426False
28183640023.30.672320.0000True
318966239428.10.167210.9062False
40137403516843.12.288331.3790True
\n", 168 | "
" 169 | ], 170 | "text/plain": [ 171 | " num_preg glucose_conc diastolic_bp thickness insulin bmi diab_pred \\\n", 172 | "0 6 148 72 35 0 33.6 0.627 \n", 173 | "1 1 85 66 29 0 26.6 0.351 \n", 174 | "2 8 183 64 0 0 23.3 0.672 \n", 175 | "3 1 89 66 23 94 28.1 0.167 \n", 176 | "4 0 137 40 35 168 43.1 2.288 \n", 177 | "\n", 178 | " age skin diabetes \n", 179 | "0 50 1.3790 True \n", 180 | "1 31 1.1426 False \n", 181 | "2 32 0.0000 True \n", 182 | "3 21 0.9062 False \n", 183 | "4 33 1.3790 True " 184 | ] 185 | }, 186 | "execution_count": 7, 187 | "metadata": {}, 188 | "output_type": "execute_result" 189 | } 190 | ], 191 | "source": [ 192 | "df.head(5)" 193 | ] 194 | }, 195 | { 196 | "cell_type": "code", 197 | "execution_count": 8, 198 | "metadata": { 199 | "collapsed": false 200 | }, 201 | "outputs": [ 202 | { 203 | "data": { 204 | "text/html": [ 205 | "
\n", 206 | "\n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | "
num_pregglucose_concdiastolic_bpthicknessinsulinbmidiab_predageskindiabetes
76310101764818032.90.171631.8912False
76421227027036.80.340271.0638False
7655121722311226.20.245300.9062False
7661126600030.10.349470.0000True
7671937031030.40.315231.2214False
\n", 290 | "
" 291 | ], 292 | "text/plain": [ 293 | " num_preg glucose_conc diastolic_bp thickness insulin bmi \\\n", 294 | "763 10 101 76 48 180 32.9 \n", 295 | "764 2 122 70 27 0 36.8 \n", 296 | "765 5 121 72 23 112 26.2 \n", 297 | "766 1 126 60 0 0 30.1 \n", 298 | "767 1 93 70 31 0 30.4 \n", 299 | "\n", 300 | " diab_pred age skin diabetes \n", 301 | "763 0.171 63 1.8912 False \n", 302 | "764 0.340 27 1.0638 False \n", 303 | "765 0.245 30 0.9062 False \n", 304 | "766 0.349 47 0.0000 True \n", 305 | "767 0.315 23 1.2214 False " 306 | ] 307 | }, 308 | "execution_count": 8, 309 | "metadata": {}, 310 | "output_type": "execute_result" 311 | } 312 | ], 313 | "source": [ 314 | "df.tail(5)" 315 | ] 316 | }, 317 | { 318 | "cell_type": "markdown", 319 | "metadata": {}, 320 | "source": [ 321 | "### Definition of features \n", 322 | "\n", 323 | "From the metadata on the data source we have the following definition of the features.\n", 324 | "\n", 325 | "| Feature | Description | Comments |\n", 326 | "|--------------|-------------|--------|\n", 327 | "| num_preg | number of pregnancies |\n", 328 | "| glucose_conc | Plasma glucose concentration a 2 hours in an oral glucose tolerance test |\n", 329 | "| diastolic_bp | Diastolic blood pressure (mm Hg) |\n", 330 | "| thickness | Triceps skin fold thickness (mm) |\n", 331 | "|insulin | 2-Hour serum insulin (mu U/ml) |\n", 332 | "| bmi | Body mass index (weight in kg/(height in m)^2) |\n", 333 | "| diab_pred | Diabetes pedigree function |\n", 334 | "| Age (years) | Age (years)|\n", 335 | "| skin | ???? | What is this? |\n", 336 | "| diabetes | Class variable (1=True, 0=False) | Why is our data boolean (True/False)? |\n", 337 | "\n" 338 | ] 339 | }, 340 | { 341 | "cell_type": "markdown", 342 | "metadata": {}, 343 | "source": [ 344 | "## Handle null values" 345 | ] 346 | }, 347 | { 348 | "cell_type": "markdown", 349 | "metadata": {}, 350 | "source": [ 351 | "*Pandas makes it easy to see if there are any null values in the data frame.*\n", 352 | "*The isnull() method will check each value in the data frame for null values, and then .any() will return if any nulls are found.*" 353 | ] 354 | }, 355 | { 356 | "cell_type": "code", 357 | "execution_count": 32, 358 | "metadata": { 359 | "collapsed": false 360 | }, 361 | "outputs": [ 362 | { 363 | "data": { 364 | "text/plain": [ 365 | "False" 366 | ] 367 | }, 368 | "execution_count": 32, 369 | "metadata": {}, 370 | "output_type": "execute_result" 371 | } 372 | ], 373 | "source": [ 374 | "df.isnull().values.any()" 375 | ] 376 | }, 377 | { 378 | "cell_type": "code", 379 | "execution_count": 15, 380 | "metadata": { 381 | "collapsed": true 382 | }, 383 | "outputs": [], 384 | "source": [ 385 | "def plot_corr(df, size=10):\n", 386 | " \"\"\"\n", 387 | " Function plots a graphical correlation matrix for each pair of columns in the dataframe.\n", 388 | "\n", 389 | " Input:\n", 390 | " df: pandas DataFrame\n", 391 | " size: vertical and horizontal size of the plot\n", 392 | "\n", 393 | " Displays:\n", 394 | " matrix of correlation between columns. Blue-cyan-yellow-red-darkred => less to more correlated\n", 395 | " 0 ------------------> 1\n", 396 | " Expect a darkred line running from top left to bottom right\n", 397 | " \"\"\"\n", 398 | "\n", 399 | " corr = df.corr() # data frame correlation function\n", 400 | " fig, ax = plt.subplots(figsize=(size, size))\n", 401 | " ax.matshow(corr) # color code the rectangles by correlation value\n", 402 | " plt.xticks(range(len(corr.columns)), corr.columns) # draw x tick marks\n", 403 | " plt.yticks(range(len(corr.columns)), corr.columns) # draw y tick marks\n" 404 | ] 405 | }, 406 | { 407 | "cell_type": "code", 408 | "execution_count": 14, 409 | "metadata": { 410 | "collapsed": false 411 | }, 412 | "outputs": [ 413 | { 414 | "data": { 415 | "image/png": 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num_pregglucose_concdiastolic_bpthicknessinsulinbmidiab_predageskindiabetes
num_preg1.0000000.1294590.141282-0.081672-0.0735350.017683-0.0335230.544341-0.0816720.221898
glucose_conc0.1294591.0000000.1525900.0573280.3313570.2210710.1373370.2635140.0573280.466581
diastolic_bp0.1412820.1525901.0000000.2073710.0889330.2818050.0412650.2395280.2073710.065068
thickness-0.0816720.0573280.2073711.0000000.4367830.3925730.183928-0.1139701.0000000.074752
insulin-0.0735350.3313570.0889330.4367831.0000000.1978590.185071-0.0421630.4367830.130548
bmi0.0176830.2210710.2818050.3925730.1978591.0000000.1406470.0362420.3925730.292695
diab_pred-0.0335230.1373370.0412650.1839280.1850710.1406471.0000000.0335610.1839280.173844
age0.5443410.2635140.239528-0.113970-0.0421630.0362420.0335611.000000-0.1139700.238356
skin-0.0816720.0573280.2073711.0000000.4367830.3925730.183928-0.1139701.0000000.074752
diabetes0.2218980.4665810.0650680.0747520.1305480.2926950.1738440.2383560.0747521.000000
\n", 588 | "
" 589 | ], 590 | "text/plain": [ 591 | " num_preg glucose_conc diastolic_bp thickness insulin \\\n", 592 | "num_preg 1.000000 0.129459 0.141282 -0.081672 -0.073535 \n", 593 | "glucose_conc 0.129459 1.000000 0.152590 0.057328 0.331357 \n", 594 | "diastolic_bp 0.141282 0.152590 1.000000 0.207371 0.088933 \n", 595 | "thickness -0.081672 0.057328 0.207371 1.000000 0.436783 \n", 596 | "insulin -0.073535 0.331357 0.088933 0.436783 1.000000 \n", 597 | "bmi 0.017683 0.221071 0.281805 0.392573 0.197859 \n", 598 | "diab_pred -0.033523 0.137337 0.041265 0.183928 0.185071 \n", 599 | "age 0.544341 0.263514 0.239528 -0.113970 -0.042163 \n", 600 | "skin -0.081672 0.057328 0.207371 1.000000 0.436783 \n", 601 | "diabetes 0.221898 0.466581 0.065068 0.074752 0.130548 \n", 602 | "\n", 603 | " bmi diab_pred age skin diabetes \n", 604 | "num_preg 0.017683 -0.033523 0.544341 -0.081672 0.221898 \n", 605 | "glucose_conc 0.221071 0.137337 0.263514 0.057328 0.466581 \n", 606 | "diastolic_bp 0.281805 0.041265 0.239528 0.207371 0.065068 \n", 607 | "thickness 0.392573 0.183928 -0.113970 1.000000 0.074752 \n", 608 | "insulin 0.197859 0.185071 -0.042163 0.436783 0.130548 \n", 609 | "bmi 1.000000 0.140647 0.036242 0.392573 0.292695 \n", 610 | "diab_pred 0.140647 1.000000 0.033561 0.183928 0.173844 \n", 611 | "age 0.036242 0.033561 1.000000 -0.113970 0.238356 \n", 612 | "skin 0.392573 0.183928 -0.113970 1.000000 0.074752 \n", 613 | "diabetes 0.292695 0.173844 0.238356 0.074752 1.000000 " 614 | ] 615 | }, 616 | "execution_count": 16, 617 | "metadata": {}, 618 | "output_type": "execute_result" 619 | } 620 | ], 621 | "source": [ 622 | "df.corr()" 623 | ] 624 | }, 625 | { 626 | "cell_type": "code", 627 | "execution_count": 17, 628 | "metadata": { 629 | "collapsed": false 630 | }, 631 | "outputs": [ 632 | { 633 | "data": { 634 | "text/html": [ 635 | "
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num_pregglucose_concdiastolic_bpthicknessinsulinbmidiab_predageskindiabetes
061487235033.60.627501.3790True
11856629026.60.351311.1426False
28183640023.30.672320.0000True
318966239428.10.167210.9062False
40137403516843.12.288331.3790True
\n", 720 | "
" 721 | ], 722 | "text/plain": [ 723 | " num_preg glucose_conc diastolic_bp thickness insulin bmi diab_pred \\\n", 724 | "0 6 148 72 35 0 33.6 0.627 \n", 725 | "1 1 85 66 29 0 26.6 0.351 \n", 726 | "2 8 183 64 0 0 23.3 0.672 \n", 727 | "3 1 89 66 23 94 28.1 0.167 \n", 728 | "4 0 137 40 35 168 43.1 2.288 \n", 729 | "\n", 730 | " age skin diabetes \n", 731 | "0 50 1.3790 True \n", 732 | "1 31 1.1426 False \n", 733 | "2 32 0.0000 True \n", 734 | "3 21 0.9062 False \n", 735 | "4 33 1.3790 True " 736 | ] 737 | }, 738 | "execution_count": 17, 739 | "metadata": {}, 740 | "output_type": "execute_result" 741 | } 742 | ], 743 | "source": [ 744 | "df.head()" 745 | ] 746 | }, 747 | { 748 | "cell_type": "code", 749 | "execution_count": 18, 750 | "metadata": { 751 | "collapsed": true 752 | }, 753 | "outputs": [], 754 | "source": [ 755 | "del df['skin']" 756 | ] 757 | }, 758 | { 759 | "cell_type": "code", 760 | "execution_count": 19, 761 | "metadata": { 762 | "collapsed": false 763 | }, 764 | "outputs": [ 765 | { 766 | "data": { 767 | "text/html": [ 768 | "
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num_pregglucose_concdiastolic_bpthicknessinsulinbmidiab_predagediabetes
061487235033.60.62750True
11856629026.60.35131False
28183640023.30.67232True
318966239428.10.16721False
40137403516843.12.28833True
\n", 847 | "
" 848 | ], 849 | "text/plain": [ 850 | " num_preg glucose_conc diastolic_bp thickness insulin bmi diab_pred \\\n", 851 | "0 6 148 72 35 0 33.6 0.627 \n", 852 | "1 1 85 66 29 0 26.6 0.351 \n", 853 | "2 8 183 64 0 0 23.3 0.672 \n", 854 | "3 1 89 66 23 94 28.1 0.167 \n", 855 | "4 0 137 40 35 168 43.1 2.288 \n", 856 | "\n", 857 | " age diabetes \n", 858 | "0 50 True \n", 859 | "1 31 False \n", 860 | "2 32 True \n", 861 | "3 21 False \n", 862 | "4 33 True " 863 | ] 864 | }, 865 | "execution_count": 19, 866 | "metadata": {}, 867 | "output_type": "execute_result" 868 | } 869 | ], 870 | "source": [ 871 | "df.head()" 872 | ] 873 | }, 874 | { 875 | "cell_type": "code", 876 | "execution_count": 25, 877 | "metadata": { 878 | "collapsed": false 879 | }, 880 | "outputs": [ 881 | { 882 | "data": { 883 | "image/png": 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DkUHNWkn+mu66mNGAfHpVHTdoYQPqT62+h1UBL3Q/YfSiydeSNFOSvILutOjoH9+Lqmpj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\naphhUJIkqWGGQUm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884 | "text/plain": [ 885 | "" 886 | ] 887 | }, 888 | "metadata": {}, 889 | "output_type": "display_data" 890 | } 891 | ], 892 | "source": [ 893 | "plot_corr(df)" 894 | ] 895 | }, 896 | { 897 | "cell_type": "markdown", 898 | "metadata": {}, 899 | "source": [ 900 | "## Check Data Types" 901 | ] 902 | }, 903 | { 904 | "cell_type": "code", 905 | "execution_count": 21, 906 | "metadata": { 907 | "collapsed": false 908 | }, 909 | "outputs": [ 910 | { 911 | "data": { 912 | "text/html": [ 913 | "
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num_pregglucose_concdiastolic_bpthicknessinsulinbmidiab_predagediabetes
061487235033.60.62750True
11856629026.60.35131False
28183640023.30.67232True
318966239428.10.16721False
40137403516843.12.28833True
\n", 992 | "
" 993 | ], 994 | "text/plain": [ 995 | " num_preg glucose_conc diastolic_bp thickness insulin bmi diab_pred \\\n", 996 | "0 6 148 72 35 0 33.6 0.627 \n", 997 | "1 1 85 66 29 0 26.6 0.351 \n", 998 | "2 8 183 64 0 0 23.3 0.672 \n", 999 | "3 1 89 66 23 94 28.1 0.167 \n", 1000 | "4 0 137 40 35 168 43.1 2.288 \n", 1001 | "\n", 1002 | " age diabetes \n", 1003 | "0 50 True \n", 1004 | "1 31 False \n", 1005 | "2 32 True \n", 1006 | "3 21 False \n", 1007 | "4 33 True " 1008 | ] 1009 | }, 1010 | "execution_count": 21, 1011 | "metadata": {}, 1012 | "output_type": "execute_result" 1013 | } 1014 | ], 1015 | "source": [ 1016 | "df.head(5)" 1017 | ] 1018 | }, 1019 | { 1020 | "cell_type": "markdown", 1021 | "metadata": {}, 1022 | "source": [ 1023 | "Change True to 1, False to 0" 1024 | ] 1025 | }, 1026 | { 1027 | "cell_type": "code", 1028 | "execution_count": 26, 1029 | "metadata": { 1030 | "collapsed": true 1031 | }, 1032 | "outputs": [], 1033 | "source": [ 1034 | "diabetes_map = {True : 1, False : 0}" 1035 | ] 1036 | }, 1037 | { 1038 | "cell_type": "code", 1039 | "execution_count": 27, 1040 | "metadata": { 1041 | "collapsed": true 1042 | }, 1043 | "outputs": [], 1044 | "source": [ 1045 | "df['diabetes'] = df['diabetes'].map(diabetes_map)" 1046 | ] 1047 | }, 1048 | { 1049 | "cell_type": "code", 1050 | "execution_count": 28, 1051 | "metadata": { 1052 | "collapsed": false 1053 | }, 1054 | "outputs": [ 1055 | { 1056 | "data": { 1057 | "text/html": [ 1058 | "
\n", 1059 | "\n", 1060 | " \n", 1061 | " \n", 1062 | " \n", 1063 | " \n", 1064 | " \n", 1065 | " \n", 1066 | " \n", 1067 | " \n", 1068 | " \n", 1069 | " \n", 1070 | " \n", 1071 | " \n", 1072 | " \n", 1073 | " \n", 1074 | " \n", 1075 | " \n", 1076 | " \n", 1077 | " \n", 1078 | " \n", 1079 | " \n", 1080 | " \n", 1081 | " \n", 1082 | " \n", 1083 | " \n", 1084 | " \n", 1085 | " \n", 1086 | " \n", 1087 | " \n", 1088 | " \n", 1089 | " \n", 1090 | " \n", 1091 | " \n", 1092 | " \n", 1093 | " \n", 1094 | " \n", 1095 | " \n", 1096 | " \n", 1097 | " \n", 1098 | " \n", 1099 | " \n", 1100 | " \n", 1101 | " \n", 1102 | " \n", 1103 | " \n", 1104 | " \n", 1105 | " \n", 1106 | " \n", 1107 | " \n", 1108 | " \n", 1109 | " \n", 1110 | " \n", 1111 | " \n", 1112 | " \n", 1113 | " \n", 1114 | " \n", 1115 | " \n", 1116 | " \n", 1117 | " \n", 1118 | " \n", 1119 | " \n", 1120 | " \n", 1121 | " \n", 1122 | " \n", 1123 | " \n", 1124 | " \n", 1125 | " \n", 1126 | " \n", 1127 | " \n", 1128 | " \n", 1129 | " \n", 1130 | " \n", 1131 | " \n", 1132 | " \n", 1133 | " \n", 1134 | " \n", 1135 | " \n", 1136 | "
num_pregglucose_concdiastolic_bpthicknessinsulinbmidiab_predagediabetes
061487235033.60.627501
11856629026.60.351310
28183640023.30.672321
318966239428.10.167210
40137403516843.12.288331
\n", 1137 | "
" 1138 | ], 1139 | "text/plain": [ 1140 | " num_preg glucose_conc diastolic_bp thickness insulin bmi diab_pred \\\n", 1141 | "0 6 148 72 35 0 33.6 0.627 \n", 1142 | "1 1 85 66 29 0 26.6 0.351 \n", 1143 | "2 8 183 64 0 0 23.3 0.672 \n", 1144 | "3 1 89 66 23 94 28.1 0.167 \n", 1145 | "4 0 137 40 35 168 43.1 2.288 \n", 1146 | "\n", 1147 | " age diabetes \n", 1148 | "0 50 1 \n", 1149 | "1 31 0 \n", 1150 | "2 32 1 \n", 1151 | "3 21 0 \n", 1152 | "4 33 1 " 1153 | ] 1154 | }, 1155 | "execution_count": 28, 1156 | "metadata": {}, 1157 | "output_type": "execute_result" 1158 | } 1159 | ], 1160 | "source": [ 1161 | "df.head(5)" 1162 | ] 1163 | }, 1164 | { 1165 | "cell_type": "markdown", 1166 | "metadata": {}, 1167 | "source": [ 1168 | "## Check true/false ratio" 1169 | ] 1170 | }, 1171 | { 1172 | "cell_type": "code", 1173 | "execution_count": 31, 1174 | "metadata": { 1175 | "collapsed": false 1176 | }, 1177 | "outputs": [ 1178 | { 1179 | "name": "stdout", 1180 | "output_type": "stream", 1181 | "text": [ 1182 | "Number of True cases: 268 (34.90%)\n", 1183 | "Number of False cases: 500 (65.10%)\n" 1184 | ] 1185 | } 1186 | ], 1187 | "source": [ 1188 | "num_true = len(df.loc[df['diabetes'] == True])\n", 1189 | "num_false = len(df.loc[df['diabetes'] == False])\n", 1190 | "print(\"Number of True cases: {0} ({1:2.2f}%)\".format(num_true, (num_true/ (num_true + num_false)) * 100))\n", 1191 | "print(\"Number of False cases: {0} ({1:2.2f}%)\".format(num_false, (num_false/ (num_true + num_false)) * 100))" 1192 | ] 1193 | }, 1194 | { 1195 | "cell_type": "code", 1196 | "execution_count": null, 1197 | "metadata": { 1198 | "collapsed": true 1199 | }, 1200 | "outputs": [], 1201 | "source": [] 1202 | } 1203 | ], 1204 | "metadata": { 1205 | "kernelspec": { 1206 | "display_name": "Python 3", 1207 | "language": "python", 1208 | "name": "python3" 1209 | }, 1210 | "language_info": { 1211 | "codemirror_mode": { 1212 | "name": "ipython", 1213 | "version": 3 1214 | }, 1215 | "file_extension": ".py", 1216 | "mimetype": "text/x-python", 1217 | "name": "python", 1218 | "nbconvert_exporter": "python", 1219 | "pygments_lexer": "ipython3", 1220 | "version": "3.5.1" 1221 | } 1222 | }, 1223 | "nbformat": 4, 1224 | "nbformat_minor": 0 1225 | } 1226 | -------------------------------------------------------------------------------- /Notebooks/.ipynb_checkpoints/PythonFirstSteps-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Learning\n", 8 | "\n", 9 | "This notebook contains some simple examples of using *Jupyter Notebook* and *Python3*" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "## Example 1: *Hello, World*" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 1, 22 | "metadata": { 23 | "collapsed": false 24 | }, 25 | "outputs": [ 26 | { 27 | "name": "stdout", 28 | "output_type": "stream", 29 | "text": [ 30 | "Hello, Jerry\n", 31 | "\n" 32 | ] 33 | } 34 | ], 35 | "source": [ 36 | "my_name = \"Jerry\"\n", 37 | "hello_statement = \"Hello, \" + my_name\n", 38 | "print(hello_statement, end=\"\\n\\n\")\n", 39 | "x = 10" 40 | ] 41 | }, 42 | { 43 | "cell_type": "markdown", 44 | "metadata": {}, 45 | "source": [ 46 | "Example 2: World's *best* loop is here" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": 2, 52 | "metadata": { 53 | "collapsed": false 54 | }, 55 | "outputs": [ 56 | { 57 | "name": "stdout", 58 | "output_type": "stream", 59 | "text": [ 60 | "j=1 x=11\n", 61 | "j=2 x=13\n", 62 | "j=3 x=16\n", 63 | "j=4 x=20\n" 64 | ] 65 | } 66 | ], 67 | "source": [ 68 | "for j in range(1, 5):\n", 69 | " x = x + j\n", 70 | " print(\"j={0} x={1}\".format(j, x))\n", 71 | " " 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "execution_count": null, 77 | "metadata": { 78 | "collapsed": true 79 | }, 80 | "outputs": [], 81 | "source": [] 82 | } 83 | ], 84 | "metadata": { 85 | "kernelspec": { 86 | "display_name": "Python 3", 87 | "language": "python", 88 | "name": "python3" 89 | }, 90 | "language_info": { 91 | "codemirror_mode": { 92 | "name": "ipython", 93 | "version": 3 94 | }, 95 | "file_extension": ".py", 96 | "mimetype": "text/x-python", 97 | "name": "python", 98 | "nbconvert_exporter": "python", 99 | "pygments_lexer": "ipython3", 100 | "version": "3.5.1" 101 | } 102 | }, 103 | "nbformat": 4, 104 | "nbformat_minor": 0 105 | } 106 | -------------------------------------------------------------------------------- /Notebooks/PythonFirstSteps.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Learning\n", 8 | "\n", 9 | "This notebook contains some simple examples of using *Jupyter Notebook* and *Python3*" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "## Example 1: *Hello, World*" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 1, 22 | "metadata": { 23 | "collapsed": false 24 | }, 25 | "outputs": [ 26 | { 27 | "name": "stdout", 28 | "output_type": "stream", 29 | "text": [ 30 | "Hello, Jerry\n", 31 | "\n" 32 | ] 33 | } 34 | ], 35 | "source": [ 36 | "my_name = \"Jerry\"\n", 37 | "hello_statement = \"Hello, \" + my_name\n", 38 | "print(hello_statement, end=\"\\n\\n\")\n", 39 | "x = 10" 40 | ] 41 | }, 42 | { 43 | "cell_type": "markdown", 44 | "metadata": {}, 45 | "source": [ 46 | "Example 2: World's *best* loop is here" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": 2, 52 | "metadata": { 53 | "collapsed": false 54 | }, 55 | "outputs": [ 56 | { 57 | "name": "stdout", 58 | "output_type": "stream", 59 | "text": [ 60 | "j=1 x=11\n", 61 | "j=2 x=13\n", 62 | "j=3 x=16\n", 63 | "j=4 x=20\n" 64 | ] 65 | } 66 | ], 67 | "source": [ 68 | "for j in range(1, 5):\n", 69 | " x = x + j\n", 70 | " print(\"j={0} x={1}\".format(j, x))\n", 71 | " " 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "execution_count": null, 77 | "metadata": { 78 | "collapsed": true 79 | }, 80 | "outputs": [], 81 | "source": [] 82 | } 83 | ], 84 | "metadata": { 85 | "kernelspec": { 86 | "display_name": "Python 3", 87 | "language": "python", 88 | "name": "python3" 89 | }, 90 | "language_info": { 91 | "codemirror_mode": { 92 | "name": "ipython", 93 | "version": 3 94 | }, 95 | "file_extension": ".py", 96 | "mimetype": "text/x-python", 97 | "name": "python", 98 | "nbconvert_exporter": "python", 99 | "pygments_lexer": "ipython3", 100 | "version": "3.5.1" 101 | } 102 | }, 103 | "nbformat": 4, 104 | "nbformat_minor": 0 105 | } 106 | -------------------------------------------------------------------------------- /Notebooks/data/pima-data-orig.csv: -------------------------------------------------------------------------------- 1 | num_preg,glucose_conc,diastolic_bp,skin_thickness,insulin,bmi,diab_pred,age,diabetes 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 | 2,96,68,13,49,21.1,0.647,26,0 137 | 2,125,60,20,140,33.8,0.088,31,0 138 | 0,100,70,26,50,30.8,0.597,21,0 139 | 0,93,60,25,92,28.7,0.532,22,0 140 | 0,129,80,0,0,31.2,0.703,29,0 141 | 5,105,72,29,325,36.9,0.159,28,0 142 | 3,128,78,0,0,21.1,0.268,55,0 143 | 5,106,82,30,0,39.5,0.286,38,0 144 | 2,108,52,26,63,32.5,0.318,22,0 145 | 10,108,66,0,0,32.4,0.272,42,1 146 | 4,154,62,31,284,32.8,0.237,23,0 147 | 0,102,75,23,0,0,0.572,21,0 148 | 9,57,80,37,0,32.8,0.096,41,0 149 | 2,106,64,35,119,30.5,1.4,34,0 150 | 5,147,78,0,0,33.7,0.218,65,0 151 | 2,90,70,17,0,27.3,0.085,22,0 152 | 1,136,74,50,204,37.4,0.399,24,0 153 | 4,114,65,0,0,21.9,0.432,37,0 154 | 9,156,86,28,155,34.3,1.189,42,1 155 | 1,153,82,42,485,40.6,0.687,23,0 156 | 8,188,78,0,0,47.9,0.137,43,1 157 | 7,152,88,44,0,50,0.337,36,1 158 | 2,99,52,15,94,24.6,0.637,21,0 159 | 1,109,56,21,135,25.2,0.833,23,0 160 | 2,88,74,19,53,29,0.229,22,0 161 | 17,163,72,41,114,40.9,0.817,47,1 162 | 4,151,90,38,0,29.7,0.294,36,0 163 | 7,102,74,40,105,37.2,0.204,45,0 164 | 0,114,80,34,285,44.2,0.167,27,0 165 | 2,100,64,23,0,29.7,0.368,21,0 166 | 0,131,88,0,0,31.6,0.743,32,1 167 | 6,104,74,18,156,29.9,0.722,41,1 168 | 3,148,66,25,0,32.5,0.256,22,0 169 | 4,120,68,0,0,29.6,0.709,34,0 170 | 4,110,66,0,0,31.9,0.471,29,0 171 | 3,111,90,12,78,28.4,0.495,29,0 172 | 6,102,82,0,0,30.8,0.18,36,1 173 | 6,134,70,23,130,35.4,0.542,29,1 174 | 2,87,0,23,0,28.9,0.773,25,0 175 | 1,79,60,42,48,43.5,0.678,23,0 176 | 2,75,64,24,55,29.7,0.37,33,0 177 | 8,179,72,42,130,32.7,0.719,36,1 178 | 6,85,78,0,0,31.2,0.382,42,0 179 | 0,129,110,46,130,67.1,0.319,26,1 180 | 5,143,78,0,0,45,0.19,47,0 181 | 5,130,82,0,0,39.1,0.956,37,1 182 | 6,87,80,0,0,23.2,0.084,32,0 183 | 0,119,64,18,92,34.9,0.725,23,0 184 | 1,0,74,20,23,27.7,0.299,21,0 185 | 5,73,60,0,0,26.8,0.268,27,0 186 | 4,141,74,0,0,27.6,0.244,40,0 187 | 7,194,68,28,0,35.9,0.745,41,1 188 | 8,181,68,36,495,30.1,0.615,60,1 189 | 1,128,98,41,58,32,1.321,33,1 190 | 8,109,76,39,114,27.9,0.64,31,1 191 | 5,139,80,35,160,31.6,0.361,25,1 192 | 3,111,62,0,0,22.6,0.142,21,0 193 | 9,123,70,44,94,33.1,0.374,40,0 194 | 7,159,66,0,0,30.4,0.383,36,1 195 | 11,135,0,0,0,52.3,0.578,40,1 196 | 8,85,55,20,0,24.4,0.136,42,0 197 | 5,158,84,41,210,39.4,0.395,29,1 198 | 1,105,58,0,0,24.3,0.187,21,0 199 | 3,107,62,13,48,22.9,0.678,23,1 200 | 4,109,64,44,99,34.8,0.905,26,1 201 | 4,148,60,27,318,30.9,0.15,29,1 202 | 0,113,80,16,0,31,0.874,21,0 203 | 1,138,82,0,0,40.1,0.236,28,0 204 | 0,108,68,20,0,27.3,0.787,32,0 205 | 2,99,70,16,44,20.4,0.235,27,0 206 | 6,103,72,32,190,37.7,0.324,55,0 207 | 5,111,72,28,0,23.9,0.407,27,0 208 | 8,196,76,29,280,37.5,0.605,57,1 209 | 5,162,104,0,0,37.7,0.151,52,1 210 | 1,96,64,27,87,33.2,0.289,21,0 211 | 7,184,84,33,0,35.5,0.355,41,1 212 | 2,81,60,22,0,27.7,0.29,25,0 213 | 0,147,85,54,0,42.8,0.375,24,0 214 | 7,179,95,31,0,34.2,0.164,60,0 215 | 0,140,65,26,130,42.6,0.431,24,1 216 | 9,112,82,32,175,34.2,0.26,36,1 217 | 12,151,70,40,271,41.8,0.742,38,1 218 | 5,109,62,41,129,35.8,0.514,25,1 219 | 6,125,68,30,120,30,0.464,32,0 220 | 5,85,74,22,0,29,1.224,32,1 221 | 5,112,66,0,0,37.8,0.261,41,1 222 | 0,177,60,29,478,34.6,1.072,21,1 223 | 2,158,90,0,0,31.6,0.805,66,1 224 | 7,119,0,0,0,25.2,0.209,37,0 225 | 7,142,60,33,190,28.8,0.687,61,0 226 | 1,100,66,15,56,23.6,0.666,26,0 227 | 1,87,78,27,32,34.6,0.101,22,0 228 | 0,101,76,0,0,35.7,0.198,26,0 229 | 3,162,52,38,0,37.2,0.652,24,1 230 | 4,197,70,39,744,36.7,2.329,31,0 231 | 0,117,80,31,53,45.2,0.089,24,0 232 | 4,142,86,0,0,44,0.645,22,1 233 | 6,134,80,37,370,46.2,0.238,46,1 234 | 1,79,80,25,37,25.4,0.583,22,0 235 | 4,122,68,0,0,35,0.394,29,0 236 | 3,74,68,28,45,29.7,0.293,23,0 237 | 4,171,72,0,0,43.6,0.479,26,1 238 | 7,181,84,21,192,35.9,0.586,51,1 239 | 0,179,90,27,0,44.1,0.686,23,1 240 | 9,164,84,21,0,30.8,0.831,32,1 241 | 0,104,76,0,0,18.4,0.582,27,0 242 | 1,91,64,24,0,29.2,0.192,21,0 243 | 4,91,70,32,88,33.1,0.446,22,0 244 | 3,139,54,0,0,25.6,0.402,22,1 245 | 6,119,50,22,176,27.1,1.318,33,1 246 | 2,146,76,35,194,38.2,0.329,29,0 247 | 9,184,85,15,0,30,1.213,49,1 248 | 10,122,68,0,0,31.2,0.258,41,0 249 | 0,165,90,33,680,52.3,0.427,23,0 250 | 9,124,70,33,402,35.4,0.282,34,0 251 | 1,111,86,19,0,30.1,0.143,23,0 252 | 9,106,52,0,0,31.2,0.38,42,0 253 | 2,129,84,0,0,28,0.284,27,0 254 | 2,90,80,14,55,24.4,0.249,24,0 255 | 0,86,68,32,0,35.8,0.238,25,0 256 | 12,92,62,7,258,27.6,0.926,44,1 257 | 1,113,64,35,0,33.6,0.543,21,1 258 | 3,111,56,39,0,30.1,0.557,30,0 259 | 2,114,68,22,0,28.7,0.092,25,0 260 | 1,193,50,16,375,25.9,0.655,24,0 261 | 11,155,76,28,150,33.3,1.353,51,1 262 | 3,191,68,15,130,30.9,0.299,34,0 263 | 3,141,0,0,0,30,0.761,27,1 264 | 4,95,70,32,0,32.1,0.612,24,0 265 | 3,142,80,15,0,32.4,0.2,63,0 266 | 4,123,62,0,0,32,0.226,35,1 267 | 5,96,74,18,67,33.6,0.997,43,0 268 | 0,138,0,0,0,36.3,0.933,25,1 269 | 2,128,64,42,0,40,1.101,24,0 270 | 0,102,52,0,0,25.1,0.078,21,0 271 | 2,146,0,0,0,27.5,0.24,28,1 272 | 10,101,86,37,0,45.6,1.136,38,1 273 | 2,108,62,32,56,25.2,0.128,21,0 274 | 3,122,78,0,0,23,0.254,40,0 275 | 1,71,78,50,45,33.2,0.422,21,0 276 | 13,106,70,0,0,34.2,0.251,52,0 277 | 2,100,70,52,57,40.5,0.677,25,0 278 | 7,106,60,24,0,26.5,0.296,29,1 279 | 0,104,64,23,116,27.8,0.454,23,0 280 | 5,114,74,0,0,24.9,0.744,57,0 281 | 2,108,62,10,278,25.3,0.881,22,0 282 | 0,146,70,0,0,37.9,0.334,28,1 283 | 10,129,76,28,122,35.9,0.28,39,0 284 | 7,133,88,15,155,32.4,0.262,37,0 285 | 7,161,86,0,0,30.4,0.165,47,1 286 | 2,108,80,0,0,27,0.259,52,1 287 | 7,136,74,26,135,26,0.647,51,0 288 | 5,155,84,44,545,38.7,0.619,34,0 289 | 1,119,86,39,220,45.6,0.808,29,1 290 | 4,96,56,17,49,20.8,0.34,26,0 291 | 5,108,72,43,75,36.1,0.263,33,0 292 | 0,78,88,29,40,36.9,0.434,21,0 293 | 0,107,62,30,74,36.6,0.757,25,1 294 | 2,128,78,37,182,43.3,1.224,31,1 295 | 1,128,48,45,194,40.5,0.613,24,1 296 | 0,161,50,0,0,21.9,0.254,65,0 297 | 6,151,62,31,120,35.5,0.692,28,0 298 | 2,146,70,38,360,28,0.337,29,1 299 | 0,126,84,29,215,30.7,0.52,24,0 300 | 14,100,78,25,184,36.6,0.412,46,1 301 | 8,112,72,0,0,23.6,0.84,58,0 302 | 0,167,0,0,0,32.3,0.839,30,1 303 | 2,144,58,33,135,31.6,0.422,25,1 304 | 5,77,82,41,42,35.8,0.156,35,0 305 | 5,115,98,0,0,52.9,0.209,28,1 306 | 3,150,76,0,0,21,0.207,37,0 307 | 2,120,76,37,105,39.7,0.215,29,0 308 | 10,161,68,23,132,25.5,0.326,47,1 309 | 0,137,68,14,148,24.8,0.143,21,0 310 | 0,128,68,19,180,30.5,1.391,25,1 311 | 2,124,68,28,205,32.9,0.875,30,1 312 | 6,80,66,30,0,26.2,0.313,41,0 313 | 0,106,70,37,148,39.4,0.605,22,0 314 | 2,155,74,17,96,26.6,0.433,27,1 315 | 3,113,50,10,85,29.5,0.626,25,0 316 | 7,109,80,31,0,35.9,1.127,43,1 317 | 2,112,68,22,94,34.1,0.315,26,0 318 | 3,99,80,11,64,19.3,0.284,30,0 319 | 3,182,74,0,0,30.5,0.345,29,1 320 | 3,115,66,39,140,38.1,0.15,28,0 321 | 6,194,78,0,0,23.5,0.129,59,1 322 | 4,129,60,12,231,27.5,0.527,31,0 323 | 3,112,74,30,0,31.6,0.197,25,1 324 | 0,124,70,20,0,27.4,0.254,36,1 325 | 13,152,90,33,29,26.8,0.731,43,1 326 | 2,112,75,32,0,35.7,0.148,21,0 327 | 1,157,72,21,168,25.6,0.123,24,0 328 | 1,122,64,32,156,35.1,0.692,30,1 329 | 10,179,70,0,0,35.1,0.2,37,0 330 | 2,102,86,36,120,45.5,0.127,23,1 331 | 6,105,70,32,68,30.8,0.122,37,0 332 | 8,118,72,19,0,23.1,1.476,46,0 333 | 2,87,58,16,52,32.7,0.166,25,0 334 | 1,180,0,0,0,43.3,0.282,41,1 335 | 12,106,80,0,0,23.6,0.137,44,0 336 | 1,95,60,18,58,23.9,0.26,22,0 337 | 0,165,76,43,255,47.9,0.259,26,0 338 | 0,117,0,0,0,33.8,0.932,44,0 339 | 5,115,76,0,0,31.2,0.343,44,1 340 | 9,152,78,34,171,34.2,0.893,33,1 341 | 7,178,84,0,0,39.9,0.331,41,1 342 | 1,130,70,13,105,25.9,0.472,22,0 343 | 1,95,74,21,73,25.9,0.673,36,0 344 | 1,0,68,35,0,32,0.389,22,0 345 | 5,122,86,0,0,34.7,0.29,33,0 346 | 8,95,72,0,0,36.8,0.485,57,0 347 | 8,126,88,36,108,38.5,0.349,49,0 348 | 1,139,46,19,83,28.7,0.654,22,0 349 | 3,116,0,0,0,23.5,0.187,23,0 350 | 3,99,62,19,74,21.8,0.279,26,0 351 | 5,0,80,32,0,41,0.346,37,1 352 | 4,92,80,0,0,42.2,0.237,29,0 353 | 4,137,84,0,0,31.2,0.252,30,0 354 | 3,61,82,28,0,34.4,0.243,46,0 355 | 1,90,62,12,43,27.2,0.58,24,0 356 | 3,90,78,0,0,42.7,0.559,21,0 357 | 9,165,88,0,0,30.4,0.302,49,1 358 | 1,125,50,40,167,33.3,0.962,28,1 359 | 13,129,0,30,0,39.9,0.569,44,1 360 | 12,88,74,40,54,35.3,0.378,48,0 361 | 1,196,76,36,249,36.5,0.875,29,1 362 | 5,189,64,33,325,31.2,0.583,29,1 363 | 5,158,70,0,0,29.8,0.207,63,0 364 | 5,103,108,37,0,39.2,0.305,65,0 365 | 4,146,78,0,0,38.5,0.52,67,1 366 | 4,147,74,25,293,34.9,0.385,30,0 367 | 5,99,54,28,83,34,0.499,30,0 368 | 6,124,72,0,0,27.6,0.368,29,1 369 | 0,101,64,17,0,21,0.252,21,0 370 | 3,81,86,16,66,27.5,0.306,22,0 371 | 1,133,102,28,140,32.8,0.234,45,1 372 | 3,173,82,48,465,38.4,2.137,25,1 373 | 0,118,64,23,89,0,1.731,21,0 374 | 0,84,64,22,66,35.8,0.545,21,0 375 | 2,105,58,40,94,34.9,0.225,25,0 376 | 2,122,52,43,158,36.2,0.816,28,0 377 | 12,140,82,43,325,39.2,0.528,58,1 378 | 0,98,82,15,84,25.2,0.299,22,0 379 | 1,87,60,37,75,37.2,0.509,22,0 380 | 4,156,75,0,0,48.3,0.238,32,1 381 | 0,93,100,39,72,43.4,1.021,35,0 382 | 1,107,72,30,82,30.8,0.821,24,0 383 | 0,105,68,22,0,20,0.236,22,0 384 | 1,109,60,8,182,25.4,0.947,21,0 385 | 1,90,62,18,59,25.1,1.268,25,0 386 | 1,125,70,24,110,24.3,0.221,25,0 387 | 1,119,54,13,50,22.3,0.205,24,0 388 | 5,116,74,29,0,32.3,0.66,35,1 389 | 8,105,100,36,0,43.3,0.239,45,1 390 | 5,144,82,26,285,32,0.452,58,1 391 | 3,100,68,23,81,31.6,0.949,28,0 392 | 1,100,66,29,196,32,0.444,42,0 393 | 5,166,76,0,0,45.7,0.34,27,1 394 | 1,131,64,14,415,23.7,0.389,21,0 395 | 4,116,72,12,87,22.1,0.463,37,0 396 | 4,158,78,0,0,32.9,0.803,31,1 397 | 2,127,58,24,275,27.7,1.6,25,0 398 | 3,96,56,34,115,24.7,0.944,39,0 399 | 0,131,66,40,0,34.3,0.196,22,1 400 | 3,82,70,0,0,21.1,0.389,25,0 401 | 3,193,70,31,0,34.9,0.241,25,1 402 | 4,95,64,0,0,32,0.161,31,1 403 | 6,137,61,0,0,24.2,0.151,55,0 404 | 5,136,84,41,88,35,0.286,35,1 405 | 9,72,78,25,0,31.6,0.28,38,0 406 | 5,168,64,0,0,32.9,0.135,41,1 407 | 2,123,48,32,165,42.1,0.52,26,0 408 | 4,115,72,0,0,28.9,0.376,46,1 409 | 0,101,62,0,0,21.9,0.336,25,0 410 | 8,197,74,0,0,25.9,1.191,39,1 411 | 1,172,68,49,579,42.4,0.702,28,1 412 | 6,102,90,39,0,35.7,0.674,28,0 413 | 1,112,72,30,176,34.4,0.528,25,0 414 | 1,143,84,23,310,42.4,1.076,22,0 415 | 1,143,74,22,61,26.2,0.256,21,0 416 | 0,138,60,35,167,34.6,0.534,21,1 417 | 3,173,84,33,474,35.7,0.258,22,1 418 | 1,97,68,21,0,27.2,1.095,22,0 419 | 4,144,82,32,0,38.5,0.554,37,1 420 | 1,83,68,0,0,18.2,0.624,27,0 421 | 3,129,64,29,115,26.4,0.219,28,1 422 | 1,119,88,41,170,45.3,0.507,26,0 423 | 2,94,68,18,76,26,0.561,21,0 424 | 0,102,64,46,78,40.6,0.496,21,0 425 | 2,115,64,22,0,30.8,0.421,21,0 426 | 8,151,78,32,210,42.9,0.516,36,1 427 | 4,184,78,39,277,37,0.264,31,1 428 | 0,94,0,0,0,0,0.256,25,0 429 | 1,181,64,30,180,34.1,0.328,38,1 430 | 0,135,94,46,145,40.6,0.284,26,0 431 | 1,95,82,25,180,35,0.233,43,1 432 | 2,99,0,0,0,22.2,0.108,23,0 433 | 3,89,74,16,85,30.4,0.551,38,0 434 | 1,80,74,11,60,30,0.527,22,0 435 | 2,139,75,0,0,25.6,0.167,29,0 436 | 1,90,68,8,0,24.5,1.138,36,0 437 | 0,141,0,0,0,42.4,0.205,29,1 438 | 12,140,85,33,0,37.4,0.244,41,0 439 | 5,147,75,0,0,29.9,0.434,28,0 440 | 1,97,70,15,0,18.2,0.147,21,0 441 | 6,107,88,0,0,36.8,0.727,31,0 442 | 0,189,104,25,0,34.3,0.435,41,1 443 | 2,83,66,23,50,32.2,0.497,22,0 444 | 4,117,64,27,120,33.2,0.23,24,0 445 | 8,108,70,0,0,30.5,0.955,33,1 446 | 4,117,62,12,0,29.7,0.38,30,1 447 | 0,180,78,63,14,59.4,2.42,25,1 448 | 1,100,72,12,70,25.3,0.658,28,0 449 | 0,95,80,45,92,36.5,0.33,26,0 450 | 0,104,64,37,64,33.6,0.51,22,1 451 | 0,120,74,18,63,30.5,0.285,26,0 452 | 1,82,64,13,95,21.2,0.415,23,0 453 | 2,134,70,0,0,28.9,0.542,23,1 454 | 0,91,68,32,210,39.9,0.381,25,0 455 | 2,119,0,0,0,19.6,0.832,72,0 456 | 2,100,54,28,105,37.8,0.498,24,0 457 | 14,175,62,30,0,33.6,0.212,38,1 458 | 1,135,54,0,0,26.7,0.687,62,0 459 | 5,86,68,28,71,30.2,0.364,24,0 460 | 10,148,84,48,237,37.6,1.001,51,1 461 | 9,134,74,33,60,25.9,0.46,81,0 462 | 9,120,72,22,56,20.8,0.733,48,0 463 | 1,71,62,0,0,21.8,0.416,26,0 464 | 8,74,70,40,49,35.3,0.705,39,0 465 | 5,88,78,30,0,27.6,0.258,37,0 466 | 10,115,98,0,0,24,1.022,34,0 467 | 0,124,56,13,105,21.8,0.452,21,0 468 | 0,74,52,10,36,27.8,0.269,22,0 469 | 0,97,64,36,100,36.8,0.6,25,0 470 | 8,120,0,0,0,30,0.183,38,1 471 | 6,154,78,41,140,46.1,0.571,27,0 472 | 1,144,82,40,0,41.3,0.607,28,0 473 | 0,137,70,38,0,33.2,0.17,22,0 474 | 0,119,66,27,0,38.8,0.259,22,0 475 | 7,136,90,0,0,29.9,0.21,50,0 476 | 4,114,64,0,0,28.9,0.126,24,0 477 | 0,137,84,27,0,27.3,0.231,59,0 478 | 2,105,80,45,191,33.7,0.711,29,1 479 | 7,114,76,17,110,23.8,0.466,31,0 480 | 8,126,74,38,75,25.9,0.162,39,0 481 | 4,132,86,31,0,28,0.419,63,0 482 | 3,158,70,30,328,35.5,0.344,35,1 483 | 0,123,88,37,0,35.2,0.197,29,0 484 | 4,85,58,22,49,27.8,0.306,28,0 485 | 0,84,82,31,125,38.2,0.233,23,0 486 | 0,145,0,0,0,44.2,0.63,31,1 487 | 0,135,68,42,250,42.3,0.365,24,1 488 | 1,139,62,41,480,40.7,0.536,21,0 489 | 0,173,78,32,265,46.5,1.159,58,0 490 | 4,99,72,17,0,25.6,0.294,28,0 491 | 8,194,80,0,0,26.1,0.551,67,0 492 | 2,83,65,28,66,36.8,0.629,24,0 493 | 2,89,90,30,0,33.5,0.292,42,0 494 | 4,99,68,38,0,32.8,0.145,33,0 495 | 4,125,70,18,122,28.9,1.144,45,1 496 | 3,80,0,0,0,0,0.174,22,0 497 | 6,166,74,0,0,26.6,0.304,66,0 498 | 5,110,68,0,0,26,0.292,30,0 499 | 2,81,72,15,76,30.1,0.547,25,0 500 | 7,195,70,33,145,25.1,0.163,55,1 501 | 6,154,74,32,193,29.3,0.839,39,0 502 | 2,117,90,19,71,25.2,0.313,21,0 503 | 3,84,72,32,0,37.2,0.267,28,0 504 | 6,0,68,41,0,39,0.727,41,1 505 | 7,94,64,25,79,33.3,0.738,41,0 506 | 3,96,78,39,0,37.3,0.238,40,0 507 | 10,75,82,0,0,33.3,0.263,38,0 508 | 0,180,90,26,90,36.5,0.314,35,1 509 | 1,130,60,23,170,28.6,0.692,21,0 510 | 2,84,50,23,76,30.4,0.968,21,0 511 | 8,120,78,0,0,25,0.409,64,0 512 | 12,84,72,31,0,29.7,0.297,46,1 513 | 0,139,62,17,210,22.1,0.207,21,0 514 | 9,91,68,0,0,24.2,0.2,58,0 515 | 2,91,62,0,0,27.3,0.525,22,0 516 | 3,99,54,19,86,25.6,0.154,24,0 517 | 3,163,70,18,105,31.6,0.268,28,1 518 | 9,145,88,34,165,30.3,0.771,53,1 519 | 7,125,86,0,0,37.6,0.304,51,0 520 | 13,76,60,0,0,32.8,0.18,41,0 521 | 6,129,90,7,326,19.6,0.582,60,0 522 | 2,68,70,32,66,25,0.187,25,0 523 | 3,124,80,33,130,33.2,0.305,26,0 524 | 6,114,0,0,0,0,0.189,26,0 525 | 9,130,70,0,0,34.2,0.652,45,1 526 | 3,125,58,0,0,31.6,0.151,24,0 527 | 3,87,60,18,0,21.8,0.444,21,0 528 | 1,97,64,19,82,18.2,0.299,21,0 529 | 3,116,74,15,105,26.3,0.107,24,0 530 | 0,117,66,31,188,30.8,0.493,22,0 531 | 0,111,65,0,0,24.6,0.66,31,0 532 | 2,122,60,18,106,29.8,0.717,22,0 533 | 0,107,76,0,0,45.3,0.686,24,0 534 | 1,86,66,52,65,41.3,0.917,29,0 535 | 6,91,0,0,0,29.8,0.501,31,0 536 | 1,77,56,30,56,33.3,1.251,24,0 537 | 4,132,0,0,0,32.9,0.302,23,1 538 | 0,105,90,0,0,29.6,0.197,46,0 539 | 0,57,60,0,0,21.7,0.735,67,0 540 | 0,127,80,37,210,36.3,0.804,23,0 541 | 3,129,92,49,155,36.4,0.968,32,1 542 | 8,100,74,40,215,39.4,0.661,43,1 543 | 3,128,72,25,190,32.4,0.549,27,1 544 | 10,90,85,32,0,34.9,0.825,56,1 545 | 4,84,90,23,56,39.5,0.159,25,0 546 | 1,88,78,29,76,32,0.365,29,0 547 | 8,186,90,35,225,34.5,0.423,37,1 548 | 5,187,76,27,207,43.6,1.034,53,1 549 | 4,131,68,21,166,33.1,0.16,28,0 550 | 1,164,82,43,67,32.8,0.341,50,0 551 | 4,189,110,31,0,28.5,0.68,37,0 552 | 1,116,70,28,0,27.4,0.204,21,0 553 | 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609 | 1,92,62,25,41,19.5,0.482,25,0 610 | 0,152,82,39,272,41.5,0.27,27,0 611 | 1,111,62,13,182,24,0.138,23,0 612 | 3,106,54,21,158,30.9,0.292,24,0 613 | 3,174,58,22,194,32.9,0.593,36,1 614 | 7,168,88,42,321,38.2,0.787,40,1 615 | 6,105,80,28,0,32.5,0.878,26,0 616 | 11,138,74,26,144,36.1,0.557,50,1 617 | 3,106,72,0,0,25.8,0.207,27,0 618 | 6,117,96,0,0,28.7,0.157,30,0 619 | 2,68,62,13,15,20.1,0.257,23,0 620 | 9,112,82,24,0,28.2,1.282,50,1 621 | 0,119,0,0,0,32.4,0.141,24,1 622 | 2,112,86,42,160,38.4,0.246,28,0 623 | 2,92,76,20,0,24.2,1.698,28,0 624 | 6,183,94,0,0,40.8,1.461,45,0 625 | 0,94,70,27,115,43.5,0.347,21,0 626 | 2,108,64,0,0,30.8,0.158,21,0 627 | 4,90,88,47,54,37.7,0.362,29,0 628 | 0,125,68,0,0,24.7,0.206,21,0 629 | 0,132,78,0,0,32.4,0.393,21,0 630 | 5,128,80,0,0,34.6,0.144,45,0 631 | 4,94,65,22,0,24.7,0.148,21,0 632 | 7,114,64,0,0,27.4,0.732,34,1 633 | 0,102,78,40,90,34.5,0.238,24,0 634 | 2,111,60,0,0,26.2,0.343,23,0 635 | 1,128,82,17,183,27.5,0.115,22,0 636 | 10,92,62,0,0,25.9,0.167,31,0 637 | 13,104,72,0,0,31.2,0.465,38,1 638 | 5,104,74,0,0,28.8,0.153,48,0 639 | 2,94,76,18,66,31.6,0.649,23,0 640 | 7,97,76,32,91,40.9,0.871,32,1 641 | 1,100,74,12,46,19.5,0.149,28,0 642 | 0,102,86,17,105,29.3,0.695,27,0 643 | 4,128,70,0,0,34.3,0.303,24,0 644 | 6,147,80,0,0,29.5,0.178,50,1 645 | 4,90,0,0,0,28,0.61,31,0 646 | 3,103,72,30,152,27.6,0.73,27,0 647 | 2,157,74,35,440,39.4,0.134,30,0 648 | 1,167,74,17,144,23.4,0.447,33,1 649 | 0,179,50,36,159,37.8,0.455,22,1 650 | 11,136,84,35,130,28.3,0.26,42,1 651 | 0,107,60,25,0,26.4,0.133,23,0 652 | 1,91,54,25,100,25.2,0.234,23,0 653 | 1,117,60,23,106,33.8,0.466,27,0 654 | 5,123,74,40,77,34.1,0.269,28,0 655 | 2,120,54,0,0,26.8,0.455,27,0 656 | 1,106,70,28,135,34.2,0.142,22,0 657 | 2,155,52,27,540,38.7,0.24,25,1 658 | 2,101,58,35,90,21.8,0.155,22,0 659 | 1,120,80,48,200,38.9,1.162,41,0 660 | 11,127,106,0,0,39,0.19,51,0 661 | 3,80,82,31,70,34.2,1.292,27,1 662 | 10,162,84,0,0,27.7,0.182,54,0 663 | 1,199,76,43,0,42.9,1.394,22,1 664 | 8,167,106,46,231,37.6,0.165,43,1 665 | 9,145,80,46,130,37.9,0.637,40,1 666 | 6,115,60,39,0,33.7,0.245,40,1 667 | 1,112,80,45,132,34.8,0.217,24,0 668 | 4,145,82,18,0,32.5,0.235,70,1 669 | 10,111,70,27,0,27.5,0.141,40,1 670 | 6,98,58,33,190,34,0.43,43,0 671 | 9,154,78,30,100,30.9,0.164,45,0 672 | 6,165,68,26,168,33.6,0.631,49,0 673 | 1,99,58,10,0,25.4,0.551,21,0 674 | 10,68,106,23,49,35.5,0.285,47,0 675 | 3,123,100,35,240,57.3,0.88,22,0 676 | 8,91,82,0,0,35.6,0.587,68,0 677 | 6,195,70,0,0,30.9,0.328,31,1 678 | 9,156,86,0,0,24.8,0.23,53,1 679 | 0,93,60,0,0,35.3,0.263,25,0 680 | 3,121,52,0,0,36,0.127,25,1 681 | 2,101,58,17,265,24.2,0.614,23,0 682 | 2,56,56,28,45,24.2,0.332,22,0 683 | 0,162,76,36,0,49.6,0.364,26,1 684 | 0,95,64,39,105,44.6,0.366,22,0 685 | 4,125,80,0,0,32.3,0.536,27,1 686 | 5,136,82,0,0,0,0.64,69,0 687 | 2,129,74,26,205,33.2,0.591,25,0 688 | 3,130,64,0,0,23.1,0.314,22,0 689 | 1,107,50,19,0,28.3,0.181,29,0 690 | 1,140,74,26,180,24.1,0.828,23,0 691 | 1,144,82,46,180,46.1,0.335,46,1 692 | 8,107,80,0,0,24.6,0.856,34,0 693 | 13,158,114,0,0,42.3,0.257,44,1 694 | 2,121,70,32,95,39.1,0.886,23,0 695 | 7,129,68,49,125,38.5,0.439,43,1 696 | 2,90,60,0,0,23.5,0.191,25,0 697 | 7,142,90,24,480,30.4,0.128,43,1 698 | 3,169,74,19,125,29.9,0.268,31,1 699 | 0,99,0,0,0,25,0.253,22,0 700 | 4,127,88,11,155,34.5,0.598,28,0 701 | 4,118,70,0,0,44.5,0.904,26,0 702 | 2,122,76,27,200,35.9,0.483,26,0 703 | 6,125,78,31,0,27.6,0.565,49,1 704 | 1,168,88,29,0,35,0.905,52,1 705 | 2,129,0,0,0,38.5,0.304,41,0 706 | 4,110,76,20,100,28.4,0.118,27,0 707 | 6,80,80,36,0,39.8,0.177,28,0 708 | 10,115,0,0,0,0,0.261,30,1 709 | 2,127,46,21,335,34.4,0.176,22,0 710 | 9,164,78,0,0,32.8,0.148,45,1 711 | 2,93,64,32,160,38,0.674,23,1 712 | 3,158,64,13,387,31.2,0.295,24,0 713 | 5,126,78,27,22,29.6,0.439,40,0 714 | 10,129,62,36,0,41.2,0.441,38,1 715 | 0,134,58,20,291,26.4,0.352,21,0 716 | 3,102,74,0,0,29.5,0.121,32,0 717 | 7,187,50,33,392,33.9,0.826,34,1 718 | 3,173,78,39,185,33.8,0.97,31,1 719 | 10,94,72,18,0,23.1,0.595,56,0 720 | 1,108,60,46,178,35.5,0.415,24,0 721 | 5,97,76,27,0,35.6,0.378,52,1 722 | 4,83,86,19,0,29.3,0.317,34,0 723 | 1,114,66,36,200,38.1,0.289,21,0 724 | 1,149,68,29,127,29.3,0.349,42,1 725 | 5,117,86,30,105,39.1,0.251,42,0 726 | 1,111,94,0,0,32.8,0.265,45,0 727 | 4,112,78,40,0,39.4,0.236,38,0 728 | 1,116,78,29,180,36.1,0.496,25,0 729 | 0,141,84,26,0,32.4,0.433,22,0 730 | 2,175,88,0,0,22.9,0.326,22,0 731 | 2,92,52,0,0,30.1,0.141,22,0 732 | 3,130,78,23,79,28.4,0.323,34,1 733 | 8,120,86,0,0,28.4,0.259,22,1 734 | 2,174,88,37,120,44.5,0.646,24,1 735 | 2,106,56,27,165,29,0.426,22,0 736 | 2,105,75,0,0,23.3,0.56,53,0 737 | 4,95,60,32,0,35.4,0.284,28,0 738 | 0,126,86,27,120,27.4,0.515,21,0 739 | 8,65,72,23,0,32,0.6,42,0 740 | 2,99,60,17,160,36.6,0.453,21,0 741 | 1,102,74,0,0,39.5,0.293,42,1 742 | 11,120,80,37,150,42.3,0.785,48,1 743 | 3,102,44,20,94,30.8,0.4,26,0 744 | 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https://raw.githubusercontent.com/JerryKurata/MachineLearningWithPython/8cb5f24eb6dc25b70d4bc8ae70e1fa0f58faff29/Notebooks/data/pima-data.xlsx -------------------------------------------------------------------------------- /Notebooks/data/pima-trained-model.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JerryKurata/MachineLearningWithPython/8cb5f24eb6dc25b70d4bc8ae70e1fa0f58faff29/Notebooks/data/pima-trained-model.pkl -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MachineLearningWithPython 2 | Starter files for Pluralsight course: Understanding Machine Learning with Python 3 | 4 | 5 | ## Edit history 6 | Jan 05, 2019 - Updated references to deprecated functions in Pima-Prediction-with-reload.ipynb 7 | --------------------------------------------------------------------------------