├── .ipynb_checkpoints ├── Data Pre-processing-checkpoint.ipynb ├── Data With Visualization-checkpoint.ipynb ├── Descriptive Statistics-checkpoint.ipynb ├── Feature Selection-checkpoint.ipynb ├── Python Ecosystem-checkpoint.ipynb ├── Resampling & Algorithm Performance Metrics-checkpoint.ipynb ├── Titanic Data Science-checkpoint.ipynb └── house-prices-advanced-regression-techniques-checkpoint.ipynb ├── Adrian Rosebrock - Deep Learning for Computer Vision with Python 1,Starter Bundle(2017, PyImageSearch).pdf ├── Data Pre-processing.ipynb ├── Data With Visualization.ipynb ├── Descriptive Statistics.ipynb ├── Feature Selection.ipynb ├── Python Ecosystem.ipynb ├── Resampling & Algorithm Performance Metrics.ipynb ├── Titanic Data Science.ipynb ├── diabetes.csv ├── house-prices-advanced-regression-techniques.ipynb ├── house-prices-advanced-regression-techniques ├── data_description.txt ├── sample_submission.csv ├── test.csv └── train.csv ├── submission.csv ├── test.csv └── train.csv /.ipynb_checkpoints/Data Pre-processing-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "# Rescale data (between 0 and 1)\n", 10 | "from pandas import read_csv\n", 11 | "from numpy import set_printoptions\n", 12 | "from sklearn.preprocessing import MinMaxScaler\n", 13 | "filename = 'diabetes.csv'\n", 14 | "names = ['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age','Outcome']\n", 15 | "dataframe = read_csv(filename, names=names)\n", 16 | "array = dataframe.values\n", 17 | "# separate array into input and output components\n", 18 | "X = array[:,0:8]\n", 19 | "Y = array[:,8]\n", 20 | "scaler = MinMaxScaler(feature_range=(0, 1))\n", 21 | "rescaledX = scaler.fit_transform(X)\n", 22 | "# summarize transformed data\n", 23 | "set_printoptions(precision=3)\n", 24 | "print(rescaledX[0:5,:])" 25 | ] 26 | } 27 | ], 28 | "metadata": { 29 | "kernelspec": { 30 | "display_name": "Python 3", 31 | "language": "python", 32 | "name": "python3" 33 | }, 34 | "language_info": { 35 | "codemirror_mode": { 36 | "name": "ipython", 37 | "version": 3 38 | }, 39 | "file_extension": ".py", 40 | "mimetype": "text/x-python", 41 | "name": "python", 42 | "nbconvert_exporter": "python", 43 | "pygments_lexer": "ipython3", 44 | "version": "3.7.1" 45 | } 46 | }, 47 | "nbformat": 4, 48 | "nbformat_minor": 2 49 | } 50 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Data With Visualization-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 5, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "data": { 10 | "image/png": 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\n", 11 | "text/plain": [ 12 | "
" 13 | ] 14 | }, 15 | "metadata": { 16 | "needs_background": "light" 17 | }, 18 | "output_type": "display_data" 19 | } 20 | ], 21 | "source": [ 22 | "# Univariate Histograms\n", 23 | "from matplotlib import pyplot\n", 24 | "from pandas import read_csv\n", 25 | "from pandas import set_option\n", 26 | "filename = 'diabetes.csv'\n", 27 | "names = ['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age','Outcome']\n", 28 | "data = read_csv(filename, names=names)\n", 29 | "set_option('display.width', 200)\n", 30 | "set_option('precision', 10)\n", 31 | "\n", 32 | "data.hist()\n", 33 | "pyplot.show()" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": null, 39 | "metadata": {}, 40 | "outputs": [], 41 | "source": [] 42 | } 43 | ], 44 | "metadata": { 45 | "kernelspec": { 46 | "display_name": "Python 3", 47 | "language": "python", 48 | "name": "python3" 49 | }, 50 | "language_info": { 51 | "codemirror_mode": { 52 | "name": "ipython", 53 | "version": 3 54 | }, 55 | "file_extension": ".py", 56 | "mimetype": "text/x-python", 57 | "name": "python", 58 | "nbconvert_exporter": "python", 59 | "pygments_lexer": "ipython3", 60 | "version": "3.7.1" 61 | } 62 | }, 63 | "nbformat": 4, 64 | "nbformat_minor": 2 65 | } 66 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Descriptive Statistics-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 7, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction \\\n", 13 | "0 6 148 72 35 0 33.6 0.627 \n", 14 | "1 1 85 66 29 0 26.6 0.351 \n", 15 | "2 8 183 64 0 0 23.3 0.672 \n", 16 | "3 1 89 66 23 94 28.1 0.167 \n", 17 | "4 0 137 40 35 168 43.1 2.288 \n", 18 | "5 5 116 74 0 0 25.6 0.201 \n", 19 | "6 3 78 50 32 88 31.0 0.248 \n", 20 | "7 10 115 0 0 0 35.3 0.134 \n", 21 | "8 2 197 70 45 543 30.5 0.158 \n", 22 | "9 8 125 96 0 0 0.0 0.232 \n", 23 | "10 4 110 92 0 0 37.6 0.191 \n", 24 | "11 10 168 74 0 0 38.0 0.537 \n", 25 | "12 10 139 80 0 0 27.1 1.441 \n", 26 | "13 1 189 60 23 846 30.1 0.398 \n", 27 | "14 5 166 72 19 175 25.8 0.587 \n", 28 | "15 7 100 0 0 0 30.0 0.484 \n", 29 | "16 0 118 84 47 230 45.8 0.551 \n", 30 | "17 7 107 74 0 0 29.6 0.254 \n", 31 | "18 1 103 30 38 83 43.3 0.183 \n", 32 | "19 1 115 70 30 96 34.6 0.529 \n", 33 | "\n", 34 | " Age Outcome \n", 35 | "0 50 1 \n", 36 | "1 31 0 \n", 37 | "2 32 1 \n", 38 | "3 21 0 \n", 39 | "4 33 1 \n", 40 | "5 30 0 \n", 41 | "6 26 1 \n", 42 | "7 29 0 \n", 43 | "8 53 1 \n", 44 | "9 54 1 \n", 45 | "10 30 0 \n", 46 | "11 34 1 \n", 47 | "12 57 0 \n", 48 | "13 59 1 \n", 49 | "14 51 1 \n", 50 | "15 32 1 \n", 51 | "16 31 1 \n", 52 | "17 31 1 \n", 53 | "18 33 0 \n", 54 | "19 32 1 \n" 55 | ] 56 | } 57 | ], 58 | "source": [ 59 | "# View first 20 rows\n", 60 | "from pandas import read_csv\n", 61 | "from pandas import set_option\n", 62 | "filename = 'diabetes.csv'\n", 63 | "names = ['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age','Outcome']\n", 64 | "data = read_csv(filename, names=names)\n", 65 | "set_option('display.width', 100)\n", 66 | "set_option('precision', 3)\n", 67 | "peek = data.head(20)\n", 68 | "print(peek)" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 8, 74 | "metadata": {}, 75 | "outputs": [ 76 | { 77 | "name": "stdout", 78 | "output_type": "stream", 79 | "text": [ 80 | "(768, 9)\n" 81 | ] 82 | } 83 | ], 84 | "source": [ 85 | "# Dimensions of your data\n", 86 | "shape=data.shape\n", 87 | "print(shape)" 88 | ] 89 | }, 90 | { 91 | "cell_type": "code", 92 | "execution_count": 9, 93 | "metadata": {}, 94 | "outputs": [ 95 | { 96 | "name": "stdout", 97 | "output_type": "stream", 98 | "text": [ 99 | "Pregnancies int64\n", 100 | "Glucose int64\n", 101 | "BloodPressure int64\n", 102 | "SkinThickness int64\n", 103 | "Insulin int64\n", 104 | "BMI float64\n", 105 | "DiabetesPedigreeFunction float64\n", 106 | "Age int64\n", 107 | "Outcome int64\n", 108 | "dtype: object\n" 109 | ] 110 | } 111 | ], 112 | "source": [ 113 | "# Data Types for Each Attribute\n", 114 | "types=data.dtypes\n", 115 | "print(types)" 116 | ] 117 | }, 118 | { 119 | "cell_type": "code", 120 | "execution_count": 10, 121 | "metadata": {}, 122 | "outputs": [ 123 | { 124 | "name": "stdout", 125 | "output_type": "stream", 126 | "text": [ 127 | " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", 128 | "count 768.000 768.000 768.000 768.000 768.000 768.000 \n", 129 | "mean 3.845 120.895 69.105 20.536 79.799 31.993 \n", 130 | "std 3.370 31.973 19.356 15.952 115.244 7.884 \n", 131 | "min 0.000 0.000 0.000 0.000 0.000 0.000 \n", 132 | "25% 1.000 99.000 62.000 0.000 0.000 27.300 \n", 133 | "50% 3.000 117.000 72.000 23.000 30.500 32.000 \n", 134 | "75% 6.000 140.250 80.000 32.000 127.250 36.600 \n", 135 | "max 17.000 199.000 122.000 99.000 846.000 67.100 \n", 136 | "\n", 137 | " DiabetesPedigreeFunction Age Outcome \n", 138 | "count 768.000 768.000 768.000 \n", 139 | "mean 0.472 33.241 0.349 \n", 140 | "std 0.331 11.760 0.477 \n", 141 | "min 0.078 21.000 0.000 \n", 142 | "25% 0.244 24.000 0.000 \n", 143 | "50% 0.372 29.000 0.000 \n", 144 | "75% 0.626 41.000 1.000 \n", 145 | "max 2.420 81.000 1.000 \n" 146 | ] 147 | } 148 | ], 149 | "source": [ 150 | "description = data.describe()\n", 151 | "print(description)" 152 | ] 153 | }, 154 | { 155 | "cell_type": "code", 156 | "execution_count": 12, 157 | "metadata": {}, 158 | "outputs": [ 159 | { 160 | "name": "stdout", 161 | "output_type": "stream", 162 | "text": [ 163 | "Outcome\n", 164 | "0 500\n", 165 | "1 268\n", 166 | "dtype: int64\n" 167 | ] 168 | } 169 | ], 170 | "source": [ 171 | "# Class Distribution\n", 172 | "class_counts = data.groupby('Outcome').size()\n", 173 | "print(class_counts)" 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": 13, 179 | "metadata": {}, 180 | "outputs": [ 181 | { 182 | "name": "stdout", 183 | "output_type": "stream", 184 | "text": [ 185 | " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", 186 | "Pregnancies 1.000 0.129 0.141 -0.082 -0.074 0.018 \n", 187 | "Glucose 0.129 1.000 0.153 0.057 0.331 0.221 \n", 188 | "BloodPressure 0.141 0.153 1.000 0.207 0.089 0.282 \n", 189 | "SkinThickness -0.082 0.057 0.207 1.000 0.437 0.393 \n", 190 | "Insulin -0.074 0.331 0.089 0.437 1.000 0.198 \n", 191 | "BMI 0.018 0.221 0.282 0.393 0.198 1.000 \n", 192 | "DiabetesPedigreeFunction -0.034 0.137 0.041 0.184 0.185 0.141 \n", 193 | "Age 0.544 0.264 0.240 -0.114 -0.042 0.036 \n", 194 | "Outcome 0.222 0.467 0.065 0.075 0.131 0.293 \n", 195 | "\n", 196 | " DiabetesPedigreeFunction Age Outcome \n", 197 | "Pregnancies -0.034 0.544 0.222 \n", 198 | "Glucose 0.137 0.264 0.467 \n", 199 | "BloodPressure 0.041 0.240 0.065 \n", 200 | "SkinThickness 0.184 -0.114 0.075 \n", 201 | "Insulin 0.185 -0.042 0.131 \n", 202 | "BMI 0.141 0.036 0.293 \n", 203 | "DiabetesPedigreeFunction 1.000 0.034 0.174 \n", 204 | "Age 0.034 1.000 0.238 \n", 205 | "Outcome 0.174 0.238 1.000 \n" 206 | ] 207 | } 208 | ], 209 | "source": [ 210 | "# Pairwise Pearson correlations\n", 211 | "correlations = data.corr(method='pearson')\n", 212 | "print(correlations)" 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "execution_count": null, 218 | "metadata": {}, 219 | "outputs": [], 220 | "source": [] 221 | } 222 | ], 223 | "metadata": { 224 | "kernelspec": { 225 | "display_name": "Python 3", 226 | "language": "python", 227 | "name": "python3" 228 | }, 229 | "language_info": { 230 | "codemirror_mode": { 231 | "name": "ipython", 232 | "version": 3 233 | }, 234 | "file_extension": ".py", 235 | "mimetype": "text/x-python", 236 | "name": "python", 237 | "nbconvert_exporter": "python", 238 | "pygments_lexer": "ipython3", 239 | "version": "3.7.1" 240 | } 241 | }, 242 | "nbformat": 4, 243 | "nbformat_minor": 2 244 | } 245 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Feature Selection-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | "[ 111.52 1411.887 17.605 53.108 2175.565 127.669 5.393 181.304]\n", 13 | "[[148. 0. 33.6 50. ]\n", 14 | " [ 85. 0. 26.6 31. ]\n", 15 | " [183. 0. 23.3 32. ]\n", 16 | " [ 89. 94. 28.1 21. ]\n", 17 | " [137. 168. 43.1 33. ]]\n" 18 | ] 19 | } 20 | ], 21 | "source": [ 22 | "# Feature Extraction with Univariate Statistical Tests (Chi-squared for classification)\n", 23 | "from pandas import read_csv\n", 24 | "from numpy import set_printoptions\n", 25 | "from sklearn.feature_selection import SelectKBest\n", 26 | "from sklearn.feature_selection import chi2\n", 27 | "# load data\n", 28 | "filename = 'diabetes.csv'\n", 29 | "names = ['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age','Outcome']\n", 30 | "dataframe = read_csv(filename, names=names)\n", 31 | "array = dataframe.values\n", 32 | "X = array[:,0:8]\n", 33 | "Y = array[:,8]\n", 34 | "# feature extraction\n", 35 | "test = SelectKBest(score_func=chi2, k=4)\n", 36 | "fit = test.fit(X, Y)\n", 37 | "# summarize scores\n", 38 | "set_printoptions(precision=3)\n", 39 | "print(fit.scores_)\n", 40 | "features = fit.transform(X)\n", 41 | "# summarize selected features\n", 42 | "print(features[0:5,:])" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": null, 48 | "metadata": {}, 49 | "outputs": [], 50 | "source": [ 51 | "#Recursive Feature Elimination\n", 52 | "# Feature Extraction with RFE\n", 53 | "array = dataframe.values\n", 54 | "X = array[:,0:8]\n", 55 | "Y = array[:,8]\n", 56 | "# feature extraction\n", 57 | "model = LogisticRegression()\n", 58 | "rfe = RFE(model, 3)\n", 59 | "fit = rfe.fit(X, Y)\n", 60 | "print(\"Num Features: %d\"% fit.n_features_) \n", 61 | "print(\"Selected Features: %s\"% fit.support_) \n", 62 | "print(\"Feature Ranking: %s\"% fit.ranking_) " 63 | ] 64 | } 65 | ], 66 | "metadata": { 67 | "kernelspec": { 68 | "display_name": "Python 3", 69 | "language": "python", 70 | "name": "python3" 71 | }, 72 | "language_info": { 73 | "codemirror_mode": { 74 | "name": "ipython", 75 | "version": 3 76 | }, 77 | "file_extension": ".py", 78 | "mimetype": "text/x-python", 79 | "name": "python", 80 | "nbconvert_exporter": "python", 81 | "pygments_lexer": "ipython3", 82 | "version": "3.7.1" 83 | } 84 | }, 85 | "nbformat": 4, 86 | "nbformat_minor": 2 87 | } 88 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Resampling & Algorithm Performance Metrics-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 2, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | "Accuracy: 75.591%\n" 13 | ] 14 | } 15 | ], 16 | "source": [ 17 | "# Evaluate using a train and a test set\n", 18 | "from pandas import read_csv\n", 19 | "from sklearn.model_selection import train_test_split\n", 20 | "from sklearn.linear_model import LogisticRegression\n", 21 | "filename = 'diabetes.csv'\n", 22 | "names = ['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age','Outcome']\n", 23 | "dataframe = read_csv(filename, names=names)\n", 24 | "array = dataframe.values\n", 25 | "X = array[:,0:8]\n", 26 | "Y = array[:,8]\n", 27 | "test_size = 0.33\n", 28 | "seed = 7\n", 29 | "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n", 30 | "random_state=seed)\n", 31 | "model = LogisticRegression()\n", 32 | "model.fit(X_train, Y_train)\n", 33 | "result = model.score(X_test, Y_test)\n", 34 | "print(\"Accuracy: %.3f%%\" % (result*100.0))" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 3, 40 | "metadata": {}, 41 | "outputs": [ 42 | { 43 | "name": "stdout", 44 | "output_type": "stream", 45 | "text": [ 46 | "Accuracy: 76.951% (4.841%)\n" 47 | ] 48 | } 49 | ], 50 | "source": [ 51 | "# Evaluate using Cross Validation\n", 52 | "from sklearn.model_selection import KFold\n", 53 | "from sklearn.model_selection import cross_val_score\n", 54 | "array = dataframe.values\n", 55 | "X = array[:,0:8]\n", 56 | "Y = array[:,8]\n", 57 | "num_folds = 10\n", 58 | "seed = 7\n", 59 | "kfold = KFold(n_splits=num_folds, random_state=seed)\n", 60 | "model = LogisticRegression()\n", 61 | "results = cross_val_score(model, X, Y, cv=kfold)\n", 62 | "print(\"Accuracy: %.3f%% (%.3f%%)\"% (results.mean()*100.0, results.std()*100.0)) " 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": 4, 68 | "metadata": {}, 69 | "outputs": [ 70 | { 71 | "name": "stdout", 72 | "output_type": "stream", 73 | "text": [ 74 | "Accuracy: 76.953% (42.113%)\n" 75 | ] 76 | } 77 | ], 78 | "source": [ 79 | "# Evaluate using Leave One Out Cross Validation\n", 80 | "from sklearn.model_selection import LeaveOneOut\n", 81 | "from sklearn.model_selection import cross_val_score\n", 82 | "array = dataframe.values\n", 83 | "X = array[:,0:8]\n", 84 | "Y = array[:,8]\n", 85 | "num_folds = 10\n", 86 | "loocv = LeaveOneOut()\n", 87 | "model = LogisticRegression()\n", 88 | "results = cross_val_score(model, X, Y, cv=loocv)\n", 89 | "print(\"Accuracy: %.3f%% (%.3\n", 90 | " f%%)\"% (results.mean()*100.0, results.std()*100.0)) " 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 5, 96 | "metadata": {}, 97 | "outputs": [ 98 | { 99 | "name": "stdout", 100 | "output_type": "stream", 101 | "text": [ 102 | "Accuracy: 76.496% (1.698%)\n" 103 | ] 104 | } 105 | ], 106 | "source": [ 107 | "# Evaluate using Shuffle Split Cross Validation\n", 108 | "from sklearn.model_selection import ShuffleSplit\n", 109 | "from sklearn.model_selection import cross_val_score\n", 110 | "array = dataframe.values\n", 111 | "X = array[:,0:8]\n", 112 | "Y = array[:,8]\n", 113 | "n_splits = 10\n", 114 | "test_size = 0.33\n", 115 | "seed = 7\n", 116 | "kfold = ShuffleSplit(n_splits=n_splits, test_size=test_size, random_state=seed)\n", 117 | "model = LogisticRegression()\n", 118 | "results = cross_val_score(model, X, Y, cv=kfold)\n", 119 | "print(\"Accuracy: %.3f%% (%.3f%%)\"% (results.mean()*100.0, results.std()*100.0)) " 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": 6, 125 | "metadata": {}, 126 | "outputs": [ 127 | { 128 | "name": "stdout", 129 | "output_type": "stream", 130 | "text": [ 131 | "Accuracy: 0.770 (0.048)\n" 132 | ] 133 | } 134 | ], 135 | "source": [ 136 | "# Cross Validation Classification Accuracy\n", 137 | "array = dataframe.values\n", 138 | "X = array[:,0:8]\n", 139 | "Y = array[:,8]\n", 140 | "kfold = KFold(n_splits=10, random_state=7)\n", 141 | "model = LogisticRegression()\n", 142 | "scoring = 'accuracy'\n", 143 | "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", 144 | "print(\"Accuracy: %.3f (%.3f)\"% (results.mean(), results.std())) " 145 | ] 146 | }, 147 | { 148 | "cell_type": "code", 149 | "execution_count": 8, 150 | "metadata": {}, 151 | "outputs": [ 152 | { 153 | "name": "stdout", 154 | "output_type": "stream", 155 | "text": [ 156 | "Logloss: -0.493 (0.047)\n" 157 | ] 158 | } 159 | ], 160 | "source": [ 161 | "# Cross Validation Classification LogLoss\n", 162 | "array = dataframe.values\n", 163 | "X = array[:,0:8]\n", 164 | "Y = array[:,8]\n", 165 | "kfold = KFold(n_splits=10, random_state=7)\n", 166 | "model = LogisticRegression()\n", 167 | "scoring = 'neg_log_loss'\n", 168 | "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", 169 | "print(\"Logloss: %.3f (%.3f)\"% (results.mean(), results.std())) " 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "execution_count": 9, 175 | "metadata": {}, 176 | "outputs": [ 177 | { 178 | "name": "stdout", 179 | "output_type": "stream", 180 | "text": [ 181 | "AUC: 0.823 (0.041)\n" 182 | ] 183 | } 184 | ], 185 | "source": [ 186 | "# Cross Validation Classification ROC AUC\n", 187 | "array = dataframe.values\n", 188 | "X = array[:,0:8]\n", 189 | "Y = array[:,8]\n", 190 | "kfold = KFold(n_splits=10, random_state=7)\n", 191 | "model = LogisticRegression()\n", 192 | "scoring = 'roc_auc'\n", 193 | "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", 194 | "print(\"AUC: %.3f (%.3f)\"% (results.mean(), results.std())) " 195 | ] 196 | }, 197 | { 198 | "cell_type": "code", 199 | "execution_count": 11, 200 | "metadata": {}, 201 | "outputs": [ 202 | { 203 | "name": "stdout", 204 | "output_type": "stream", 205 | "text": [ 206 | "[[141 21]\n", 207 | " [ 41 51]]\n" 208 | ] 209 | } 210 | ], 211 | "source": [ 212 | "# Cross Validation Classification Confusion Matrix\n", 213 | "from sklearn.metrics import confusion_matrix\n", 214 | "array = dataframe.values\n", 215 | "X = array[:,0:8]\n", 216 | "Y = array[:,8]\n", 217 | "test_size = 0.33\n", 218 | "seed = 7\n", 219 | "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n", 220 | "random_state=seed)\n", 221 | "model = LogisticRegression()\n", 222 | "model.fit(X_train, Y_train)\n", 223 | "predicted = model.predict(X_test)\n", 224 | "matrix = confusion_matrix(Y_test, predicted)\n", 225 | "print(matrix)" 226 | ] 227 | }, 228 | { 229 | "cell_type": "code", 230 | "execution_count": 13, 231 | "metadata": {}, 232 | "outputs": [ 233 | { 234 | "name": "stdout", 235 | "output_type": "stream", 236 | "text": [ 237 | " precision recall f1-score support\n", 238 | "\n", 239 | " 0.0 0.77 0.87 0.82 162\n", 240 | " 1.0 0.71 0.55 0.62 92\n", 241 | "\n", 242 | "avg / total 0.75 0.76 0.75 254\n", 243 | "\n" 244 | ] 245 | } 246 | ], 247 | "source": [ 248 | "# Cross Validation Classification Report\n", 249 | "from sklearn.metrics import classification_report\n", 250 | "array = dataframe.values\n", 251 | "X = array[:,0:8]\n", 252 | "Y = array[:,8]\n", 253 | "test_size = 0.33\n", 254 | "seed = 7\n", 255 | "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n", 256 | "random_state=seed)\n", 257 | "model = LogisticRegression()\n", 258 | "model.fit(X_train, Y_train)\n", 259 | "predicted = model.predict(X_test)\n", 260 | "report = classification_report(Y_test, predicted)\n", 261 | "print(report)" 262 | ] 263 | }, 264 | { 265 | "cell_type": "code", 266 | "execution_count": 18, 267 | "metadata": {}, 268 | "outputs": [ 269 | { 270 | "name": "stdout", 271 | "output_type": "stream", 272 | "text": [ 273 | "MAE: -0.337 (0.022)\n" 274 | ] 275 | } 276 | ], 277 | "source": [ 278 | "# Cross Validation Regression MAE'\n", 279 | "from sklearn.linear_model import LinearRegression\n", 280 | "array = dataframe.values\n", 281 | "X = array[:,0:8]\n", 282 | "Y = array[:,8]\n", 283 | "kfold = KFold(n_splits=10, random_state=7)\n", 284 | "model = LinearRegression()\n", 285 | "scoring = 'neg_mean_absolute_error'\n", 286 | "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", 287 | "print(\"MAE: %.3f (%.3f)\"% (results.mean(), results.std())) " 288 | ] 289 | }, 290 | { 291 | "cell_type": "code", 292 | "execution_count": 20, 293 | "metadata": {}, 294 | "outputs": [ 295 | { 296 | "name": "stdout", 297 | "output_type": "stream", 298 | "text": [ 299 | "MSE: -0.163 (0.022)\n" 300 | ] 301 | } 302 | ], 303 | "source": [ 304 | "# Cross Validation Regression MSE\n", 305 | "array = dataframe.values\n", 306 | "X = array[:,0:8]\n", 307 | "Y = array[:,8]\n", 308 | "num_folds = 10\n", 309 | "kfold = KFold(n_splits=10, random_state=7)\n", 310 | "model = LinearRegression()\n", 311 | "scoring = 'neg_mean_squared_error'\n", 312 | "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", 313 | "print(\"MSE: %.3f (%.3f)\" % (results.mean(), results.std())) " 314 | ] 315 | }, 316 | { 317 | "cell_type": "code", 318 | "execution_count": null, 319 | "metadata": {}, 320 | "outputs": [], 321 | "source": [ 322 | "# Cross Validation Regression R^2\n", 323 | "array = dataframe.values\n", 324 | "X = array[:,0:8]\n", 325 | "Y = array[:,8]" 326 | ] 327 | } 328 | ], 329 | "metadata": { 330 | "kernelspec": { 331 | "display_name": "Python 3", 332 | "language": "python", 333 | "name": "python3" 334 | }, 335 | "language_info": { 336 | "codemirror_mode": { 337 | "name": "ipython", 338 | "version": 3 339 | }, 340 | "file_extension": ".py", 341 | "mimetype": "text/x-python", 342 | "name": "python", 343 | "nbconvert_exporter": "python", 344 | "pygments_lexer": "ipython3", 345 | "version": "3.7.1" 346 | } 347 | }, 348 | "nbformat": 4, 349 | "nbformat_minor": 2 350 | } 351 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Titanic Data Science-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/house-prices-advanced-regression-techniques-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "C:\\Users\\ASUS\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n", 13 | " from numpy.core.umath_tests import inner1d\n" 14 | ] 15 | } 16 | ], 17 | "source": [ 18 | "# data analysis and wrangling\n", 19 | "import pandas as pd\n", 20 | "import numpy as np\n", 21 | "import random as rnd\n", 22 | "\n", 23 | "# visualization\n", 24 | "import seaborn as sns\n", 25 | "import matplotlib.pyplot as plt\n", 26 | "%matplotlib inline\n", 27 | "\n", 28 | "# machine learning\n", 29 | "from sklearn.linear_model import LogisticRegression\n", 30 | "from sklearn.svm import SVC, LinearSVC\n", 31 | "from sklearn.ensemble import RandomForestClassifier\n", 32 | "from sklearn.neighbors import KNeighborsClassifier\n", 33 | "from sklearn.naive_bayes import GaussianNB\n", 34 | "from sklearn.linear_model import Perceptron\n", 35 | "from sklearn.linear_model import SGDClassifier\n", 36 | "from sklearn.tree import DecisionTreeClassifier" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": null, 42 | "metadata": {}, 43 | "outputs": [], 44 | "source": [ 45 | "# read titanic training & test csv files as a pandas DataFrame\n", 46 | "train_df = pd.read_csv('/house-prices-advanced-regression-techniques/train.csv')\n", 47 | "test_df = pd.read_csv('/house-prices-advanced-regression-techniques/test.csv')" 48 | ] 49 | } 50 | ], 51 | "metadata": { 52 | "kernelspec": { 53 | "display_name": "Python 3", 54 | "language": "python", 55 | "name": "python3" 56 | }, 57 | "language_info": { 58 | "codemirror_mode": { 59 | "name": "ipython", 60 | "version": 3 61 | }, 62 | "file_extension": ".py", 63 | "mimetype": "text/x-python", 64 | "name": "python", 65 | "nbconvert_exporter": "python", 66 | "pygments_lexer": "ipython3", 67 | "version": "3.7.1" 68 | } 69 | }, 70 | "nbformat": 4, 71 | "nbformat_minor": 2 72 | } 73 | -------------------------------------------------------------------------------- /Adrian Rosebrock - Deep Learning for Computer Vision with Python 1,Starter Bundle(2017, PyImageSearch).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arunbsmrstu/Machine-Learning-Mastery-With-Python/9b64ad809caa1171d0913b43582a2e551ebd12da/Adrian Rosebrock - Deep Learning for Computer Vision with Python 1,Starter Bundle(2017, PyImageSearch).pdf -------------------------------------------------------------------------------- /Data Pre-processing.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | "[[0.353 0.744 0.59 0.354 0. 0.501 0.234 0.483]\n", 13 | " [0.059 0.427 0.541 0.293 0. 0.396 0.117 0.167]\n", 14 | " [0.471 0.92 0.525 0. 0. 0.347 0.254 0.183]\n", 15 | " [0.059 0.447 0.541 0.232 0.111 0.419 0.038 0. ]\n", 16 | " [0. 0.688 0.328 0.354 0.199 0.642 0.944 0.2 ]]\n" 17 | ] 18 | } 19 | ], 20 | "source": [ 21 | "# Rescale data (between 0 and 1)\n", 22 | "from pandas import read_csv\n", 23 | "from numpy import set_printoptions\n", 24 | "from sklearn.preprocessing import MinMaxScaler\n", 25 | "filename = 'diabetes.csv'\n", 26 | "names = ['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age','Outcome']\n", 27 | "dataframe = read_csv(filename, names=names)\n", 28 | "array = dataframe.values\n", 29 | "# separate array into input and output components\n", 30 | "X = array[:,0:8]\n", 31 | "Y = array[:,8]\n", 32 | "scaler = MinMaxScaler(feature_range=(0, 1))\n", 33 | "rescaledX = scaler.fit_transform(X)\n", 34 | "# summarize transformed data\n", 35 | "set_printoptions(precision=3)\n", 36 | "print(rescaledX[0:5,:])" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": 3, 42 | "metadata": {}, 43 | "outputs": [ 44 | { 45 | "name": "stdout", 46 | "output_type": "stream", 47 | "text": [ 48 | "[[ 0.64 0.848 0.15 0.907 -0.693 0.204 0.468 1.426]\n", 49 | " [-0.845 -1.123 -0.161 0.531 -0.693 -0.684 -0.365 -0.191]\n", 50 | " [ 1.234 1.944 -0.264 -1.288 -0.693 -1.103 0.604 -0.106]\n", 51 | " [-0.845 -0.998 -0.161 0.155 0.123 -0.494 -0.921 -1.042]\n", 52 | " [-1.142 0.504 -1.505 0.907 0.766 1.41 5.485 -0.02 ]]\n" 53 | ] 54 | } 55 | ], 56 | "source": [ 57 | "# Standardize data (0 mean, 1 stdev)\n", 58 | "from sklearn.preprocessing import StandardScaler\n", 59 | "array = dataframe.values\n", 60 | "# separate array into input and output components\n", 61 | "X = array[:,0:8]\n", 62 | "Y = array[:,8]\n", 63 | "scaler = StandardScaler().fit(X)\n", 64 | "rescaledX = scaler.transform(X)\n", 65 | "# summarize transformed data\n", 66 | "set_printoptions(precision=3)\n", 67 | "print(rescaledX[0:5,:])" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": 4, 73 | "metadata": {}, 74 | "outputs": [ 75 | { 76 | "name": "stdout", 77 | "output_type": "stream", 78 | "text": [ 79 | "[[0.034 0.828 0.403 0.196 0. 0.188 0.004 0.28 ]\n", 80 | " [0.008 0.716 0.556 0.244 0. 0.224 0.003 0.261]\n", 81 | " [0.04 0.924 0.323 0. 0. 0.118 0.003 0.162]\n", 82 | " [0.007 0.588 0.436 0.152 0.622 0.186 0.001 0.139]\n", 83 | " [0. 0.596 0.174 0.152 0.731 0.188 0.01 0.144]]\n" 84 | ] 85 | } 86 | ], 87 | "source": [ 88 | "# Normalize data (length of 1)\n", 89 | "from sklearn.preprocessing import Normalizer\n", 90 | "array = dataframe.values\n", 91 | "# separate array into input and output components\n", 92 | "X = array[:,0:8]\n", 93 | "Y = array[:,8]\n", 94 | "scaler = Normalizer().fit(X)\n", 95 | "normalizedX = scaler.transform(X)\n", 96 | "# summarize transformed data\n", 97 | "set_printoptions(precision=3)\n", 98 | "print(normalizedX[0:5,:])" 99 | ] 100 | }, 101 | { 102 | "cell_type": "code", 103 | "execution_count": 5, 104 | "metadata": {}, 105 | "outputs": [ 106 | { 107 | "name": "stdout", 108 | "output_type": "stream", 109 | "text": [ 110 | "[[1. 1. 1. 1. 0. 1. 1. 1.]\n", 111 | " [1. 1. 1. 1. 0. 1. 1. 1.]\n", 112 | " [1. 1. 1. 0. 0. 1. 1. 1.]\n", 113 | " [1. 1. 1. 1. 1. 1. 1. 1.]\n", 114 | " [0. 1. 1. 1. 1. 1. 1. 1.]]\n" 115 | ] 116 | } 117 | ], 118 | "source": [ 119 | "# binarization\n", 120 | "from sklearn.preprocessing import Binarizer\n", 121 | "array = dataframe.values\n", 122 | "# separate array into input and output components\n", 123 | "X = array[:,0:8]\n", 124 | "Y = array[:,8]\n", 125 | "binarizer = Binarizer(threshold=0.0).fit(X)\n", 126 | "binaryX = binarizer.transform(X)\n", 127 | "# summarize transformed data\n", 128 | "set_printoptions(precision=3)\n", 129 | "print(binaryX[0:5,:])" 130 | ] 131 | }, 132 | { 133 | "cell_type": "code", 134 | "execution_count": null, 135 | "metadata": {}, 136 | "outputs": [], 137 | "source": [] 138 | } 139 | ], 140 | "metadata": { 141 | "kernelspec": { 142 | "display_name": "Python 3", 143 | "language": "python", 144 | "name": "python3" 145 | }, 146 | "language_info": { 147 | "codemirror_mode": { 148 | "name": "ipython", 149 | "version": 3 150 | }, 151 | "file_extension": ".py", 152 | "mimetype": "text/x-python", 153 | "name": "python", 154 | "nbconvert_exporter": "python", 155 | "pygments_lexer": "ipython3", 156 | "version": "3.7.1" 157 | } 158 | }, 159 | "nbformat": 4, 160 | "nbformat_minor": 2 161 | } 162 | -------------------------------------------------------------------------------- /Descriptive Statistics.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 7, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction \\\n", 13 | "0 6 148 72 35 0 33.6 0.627 \n", 14 | "1 1 85 66 29 0 26.6 0.351 \n", 15 | "2 8 183 64 0 0 23.3 0.672 \n", 16 | "3 1 89 66 23 94 28.1 0.167 \n", 17 | "4 0 137 40 35 168 43.1 2.288 \n", 18 | "5 5 116 74 0 0 25.6 0.201 \n", 19 | "6 3 78 50 32 88 31.0 0.248 \n", 20 | "7 10 115 0 0 0 35.3 0.134 \n", 21 | "8 2 197 70 45 543 30.5 0.158 \n", 22 | "9 8 125 96 0 0 0.0 0.232 \n", 23 | "10 4 110 92 0 0 37.6 0.191 \n", 24 | "11 10 168 74 0 0 38.0 0.537 \n", 25 | "12 10 139 80 0 0 27.1 1.441 \n", 26 | "13 1 189 60 23 846 30.1 0.398 \n", 27 | "14 5 166 72 19 175 25.8 0.587 \n", 28 | "15 7 100 0 0 0 30.0 0.484 \n", 29 | "16 0 118 84 47 230 45.8 0.551 \n", 30 | "17 7 107 74 0 0 29.6 0.254 \n", 31 | "18 1 103 30 38 83 43.3 0.183 \n", 32 | "19 1 115 70 30 96 34.6 0.529 \n", 33 | "\n", 34 | " Age Outcome \n", 35 | "0 50 1 \n", 36 | "1 31 0 \n", 37 | "2 32 1 \n", 38 | "3 21 0 \n", 39 | "4 33 1 \n", 40 | "5 30 0 \n", 41 | "6 26 1 \n", 42 | "7 29 0 \n", 43 | "8 53 1 \n", 44 | "9 54 1 \n", 45 | "10 30 0 \n", 46 | "11 34 1 \n", 47 | "12 57 0 \n", 48 | "13 59 1 \n", 49 | "14 51 1 \n", 50 | "15 32 1 \n", 51 | "16 31 1 \n", 52 | "17 31 1 \n", 53 | "18 33 0 \n", 54 | "19 32 1 \n" 55 | ] 56 | } 57 | ], 58 | "source": [ 59 | "# View first 20 rows\n", 60 | "from pandas import read_csv\n", 61 | "from pandas import set_option\n", 62 | "filename = 'diabetes.csv'\n", 63 | "names = ['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age','Outcome']\n", 64 | "data = read_csv(filename, names=names)\n", 65 | "set_option('display.width', 100)\n", 66 | "set_option('precision', 3)\n", 67 | "peek = data.head(20)\n", 68 | "print(peek)" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 8, 74 | "metadata": {}, 75 | "outputs": [ 76 | { 77 | "name": "stdout", 78 | "output_type": "stream", 79 | "text": [ 80 | "(768, 9)\n" 81 | ] 82 | } 83 | ], 84 | "source": [ 85 | "# Dimensions of your data\n", 86 | "shape=data.shape\n", 87 | "print(shape)" 88 | ] 89 | }, 90 | { 91 | "cell_type": "code", 92 | "execution_count": 9, 93 | "metadata": {}, 94 | "outputs": [ 95 | { 96 | "name": "stdout", 97 | "output_type": "stream", 98 | "text": [ 99 | "Pregnancies int64\n", 100 | "Glucose int64\n", 101 | "BloodPressure int64\n", 102 | "SkinThickness int64\n", 103 | "Insulin int64\n", 104 | "BMI float64\n", 105 | "DiabetesPedigreeFunction float64\n", 106 | "Age int64\n", 107 | "Outcome int64\n", 108 | "dtype: object\n" 109 | ] 110 | } 111 | ], 112 | "source": [ 113 | "# Data Types for Each Attribute\n", 114 | "types=data.dtypes\n", 115 | "print(types)" 116 | ] 117 | }, 118 | { 119 | "cell_type": "code", 120 | "execution_count": 10, 121 | "metadata": {}, 122 | "outputs": [ 123 | { 124 | "name": "stdout", 125 | "output_type": "stream", 126 | "text": [ 127 | " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", 128 | "count 768.000 768.000 768.000 768.000 768.000 768.000 \n", 129 | "mean 3.845 120.895 69.105 20.536 79.799 31.993 \n", 130 | "std 3.370 31.973 19.356 15.952 115.244 7.884 \n", 131 | "min 0.000 0.000 0.000 0.000 0.000 0.000 \n", 132 | "25% 1.000 99.000 62.000 0.000 0.000 27.300 \n", 133 | "50% 3.000 117.000 72.000 23.000 30.500 32.000 \n", 134 | "75% 6.000 140.250 80.000 32.000 127.250 36.600 \n", 135 | "max 17.000 199.000 122.000 99.000 846.000 67.100 \n", 136 | "\n", 137 | " DiabetesPedigreeFunction Age Outcome \n", 138 | "count 768.000 768.000 768.000 \n", 139 | "mean 0.472 33.241 0.349 \n", 140 | "std 0.331 11.760 0.477 \n", 141 | "min 0.078 21.000 0.000 \n", 142 | "25% 0.244 24.000 0.000 \n", 143 | "50% 0.372 29.000 0.000 \n", 144 | "75% 0.626 41.000 1.000 \n", 145 | "max 2.420 81.000 1.000 \n" 146 | ] 147 | } 148 | ], 149 | "source": [ 150 | "description = data.describe()\n", 151 | "print(description)" 152 | ] 153 | }, 154 | { 155 | "cell_type": "code", 156 | "execution_count": 12, 157 | "metadata": {}, 158 | "outputs": [ 159 | { 160 | "name": "stdout", 161 | "output_type": "stream", 162 | "text": [ 163 | "Outcome\n", 164 | "0 500\n", 165 | "1 268\n", 166 | "dtype: int64\n" 167 | ] 168 | } 169 | ], 170 | "source": [ 171 | "# Class Distribution\n", 172 | "class_counts = data.groupby('Outcome').size()\n", 173 | "print(class_counts)" 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": 13, 179 | "metadata": {}, 180 | "outputs": [ 181 | { 182 | "name": "stdout", 183 | "output_type": "stream", 184 | "text": [ 185 | " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", 186 | "Pregnancies 1.000 0.129 0.141 -0.082 -0.074 0.018 \n", 187 | "Glucose 0.129 1.000 0.153 0.057 0.331 0.221 \n", 188 | "BloodPressure 0.141 0.153 1.000 0.207 0.089 0.282 \n", 189 | "SkinThickness -0.082 0.057 0.207 1.000 0.437 0.393 \n", 190 | "Insulin -0.074 0.331 0.089 0.437 1.000 0.198 \n", 191 | "BMI 0.018 0.221 0.282 0.393 0.198 1.000 \n", 192 | "DiabetesPedigreeFunction -0.034 0.137 0.041 0.184 0.185 0.141 \n", 193 | "Age 0.544 0.264 0.240 -0.114 -0.042 0.036 \n", 194 | "Outcome 0.222 0.467 0.065 0.075 0.131 0.293 \n", 195 | "\n", 196 | " DiabetesPedigreeFunction Age Outcome \n", 197 | "Pregnancies -0.034 0.544 0.222 \n", 198 | "Glucose 0.137 0.264 0.467 \n", 199 | "BloodPressure 0.041 0.240 0.065 \n", 200 | "SkinThickness 0.184 -0.114 0.075 \n", 201 | "Insulin 0.185 -0.042 0.131 \n", 202 | "BMI 0.141 0.036 0.293 \n", 203 | "DiabetesPedigreeFunction 1.000 0.034 0.174 \n", 204 | "Age 0.034 1.000 0.238 \n", 205 | "Outcome 0.174 0.238 1.000 \n" 206 | ] 207 | } 208 | ], 209 | "source": [ 210 | "# Pairwise Pearson correlations\n", 211 | "correlations = data.corr(method='pearson')\n", 212 | "print(correlations)" 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "execution_count": 14, 218 | "metadata": {}, 219 | "outputs": [ 220 | { 221 | "name": "stdout", 222 | "output_type": "stream", 223 | "text": [ 224 | "Pregnancies 0.902\n", 225 | "Glucose 0.174\n", 226 | "BloodPressure -1.844\n", 227 | "SkinThickness 0.109\n", 228 | "Insulin 2.272\n", 229 | "BMI -0.429\n", 230 | "DiabetesPedigreeFunction 1.920\n", 231 | "Age 1.130\n", 232 | "Outcome 0.635\n", 233 | "dtype: float64\n" 234 | ] 235 | } 236 | ], 237 | "source": [ 238 | "#Skew of Univariate Distributions\n", 239 | "skew = data.skew()\n", 240 | "print(skew)" 241 | ] 242 | }, 243 | { 244 | "cell_type": "code", 245 | "execution_count": null, 246 | "metadata": {}, 247 | "outputs": [], 248 | "source": [] 249 | } 250 | ], 251 | "metadata": { 252 | "kernelspec": { 253 | "display_name": "Python 3", 254 | "language": "python", 255 | "name": "python3" 256 | }, 257 | "language_info": { 258 | "codemirror_mode": { 259 | "name": "ipython", 260 | "version": 3 261 | }, 262 | "file_extension": ".py", 263 | "mimetype": "text/x-python", 264 | "name": "python", 265 | "nbconvert_exporter": "python", 266 | "pygments_lexer": "ipython3", 267 | "version": "3.7.1" 268 | } 269 | }, 270 | "nbformat": 4, 271 | "nbformat_minor": 2 272 | } 273 | -------------------------------------------------------------------------------- /Feature Selection.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | "[ 111.52 1411.887 17.605 53.108 2175.565 127.669 5.393 181.304]\n", 13 | "[[148. 0. 33.6 50. ]\n", 14 | " [ 85. 0. 26.6 31. ]\n", 15 | " [183. 0. 23.3 32. ]\n", 16 | " [ 89. 94. 28.1 21. ]\n", 17 | " [137. 168. 43.1 33. ]]\n" 18 | ] 19 | } 20 | ], 21 | "source": [ 22 | "# Feature Extraction with Univariate Statistical Tests (Chi-squared for classification)\n", 23 | "from pandas import read_csv\n", 24 | "from numpy import set_printoptions\n", 25 | "from sklearn.feature_selection import SelectKBest\n", 26 | "from sklearn.feature_selection import chi2\n", 27 | "# load data\n", 28 | "filename = 'diabetes.csv'\n", 29 | "names = ['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age','Outcome']\n", 30 | "dataframe = read_csv(filename, names=names)\n", 31 | "array = dataframe.values\n", 32 | "X = array[:,0:8]\n", 33 | "Y = array[:,8]\n", 34 | "# feature extraction\n", 35 | "test = SelectKBest(score_func=chi2, k=4)\n", 36 | "fit = test.fit(X, Y)\n", 37 | "# summarize scores\n", 38 | "set_printoptions(precision=3)\n", 39 | "print(fit.scores_)\n", 40 | "features = fit.transform(X)\n", 41 | "# summarize selected features\n", 42 | "print(features[0:5,:])" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 3, 48 | "metadata": {}, 49 | "outputs": [ 50 | { 51 | "name": "stdout", 52 | "output_type": "stream", 53 | "text": [ 54 | "Num Features: 3\n", 55 | "Selected Features: [ True False False False False True True False]\n", 56 | "Feature Ranking: [1 2 3 5 6 1 1 4]\n" 57 | ] 58 | } 59 | ], 60 | "source": [ 61 | "#Recursive Feature Elimination\n", 62 | "# Feature Extraction with RFE\n", 63 | "from sklearn.feature_selection import RFE\n", 64 | "from sklearn.linear_model import LogisticRegression\n", 65 | "array = dataframe.values\n", 66 | "X = array[:,0:8]\n", 67 | "Y = array[:,8]\n", 68 | "# feature extraction\n", 69 | "model = LogisticRegression()\n", 70 | "rfe = RFE(model, 3)\n", 71 | "fit = rfe.fit(X, Y)\n", 72 | "print(\"Num Features: %d\"% fit.n_features_) \n", 73 | "print(\"Selected Features: %s\"% fit.support_) \n", 74 | "print(\"Feature Ranking: %s\"% fit.ranking_) " 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": 4, 80 | "metadata": {}, 81 | "outputs": [ 82 | { 83 | "name": "stdout", 84 | "output_type": "stream", 85 | "text": [ 86 | "Explained Variance: [0.889 0.062 0.026]\n", 87 | "[[-2.022e-03 9.781e-02 1.609e-02 6.076e-02 9.931e-01 1.401e-02\n", 88 | " 5.372e-04 -3.565e-03]\n", 89 | " [-2.265e-02 -9.722e-01 -1.419e-01 5.786e-02 9.463e-02 -4.697e-02\n", 90 | " -8.168e-04 -1.402e-01]\n", 91 | " [-2.246e-02 1.434e-01 -9.225e-01 -3.070e-01 2.098e-02 -1.324e-01\n", 92 | " -6.400e-04 -1.255e-01]]\n" 93 | ] 94 | } 95 | ], 96 | "source": [ 97 | "#Principal Component Analysis\n", 98 | "# Feature Extraction with PCA\n", 99 | "from sklearn.decomposition import PCA\n", 100 | "array = dataframe.values\n", 101 | "X = array[:,0:8]\n", 102 | "Y = array[:,8]\n", 103 | "# feature extraction\n", 104 | "pca = PCA(n_components=3)\n", 105 | "fit = pca.fit(X)\n", 106 | "# summarize components\n", 107 | "print(\"Explained Variance: %s\" % fit.explained_variance_ratio_) \n", 108 | "print(fit.components_)" 109 | ] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "execution_count": 6, 114 | "metadata": {}, 115 | "outputs": [ 116 | { 117 | "name": "stdout", 118 | "output_type": "stream", 119 | "text": [ 120 | "[0.113 0.246 0.095 0.075 0.07 0.149 0.115 0.138]\n" 121 | ] 122 | }, 123 | { 124 | "name": "stderr", 125 | "output_type": "stream", 126 | "text": [ 127 | "C:\\Users\\ASUS\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n", 128 | " from numpy.core.umath_tests import inner1d\n" 129 | ] 130 | } 131 | ], 132 | "source": [ 133 | "#Feature Importance\n", 134 | "# Feature Importance with Extra Trees Classifier\n", 135 | "from sklearn.ensemble import ExtraTreesClassifier\n", 136 | "array = dataframe.values\n", 137 | "X = array[:,0:8]\n", 138 | "Y = array[:,8]\n", 139 | "# feature extraction\n", 140 | "model = ExtraTreesClassifier()\n", 141 | "model.fit(X, Y)\n", 142 | "print(model.feature_importances_)" 143 | ] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "execution_count": null, 148 | "metadata": {}, 149 | "outputs": [], 150 | "source": [] 151 | } 152 | ], 153 | "metadata": { 154 | "kernelspec": { 155 | "display_name": "Python 3", 156 | "language": "python", 157 | "name": "python3" 158 | }, 159 | "language_info": { 160 | "codemirror_mode": { 161 | "name": "ipython", 162 | "version": 3 163 | }, 164 | "file_extension": ".py", 165 | "mimetype": "text/x-python", 166 | "name": "python", 167 | "nbconvert_exporter": "python", 168 | "pygments_lexer": "ipython3", 169 | "version": "3.7.1" 170 | } 171 | }, 172 | "nbformat": 4, 173 | "nbformat_minor": 2 174 | } 175 | -------------------------------------------------------------------------------- /Resampling & Algorithm Performance Metrics.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 2, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | "Accuracy: 75.591%\n" 13 | ] 14 | } 15 | ], 16 | "source": [ 17 | "# Evaluate using a train and a test set\n", 18 | "from pandas import read_csv\n", 19 | "from sklearn.model_selection import train_test_split\n", 20 | "from sklearn.linear_model import LogisticRegression\n", 21 | "filename = 'diabetes.csv'\n", 22 | "names = ['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age','Outcome']\n", 23 | "dataframe = read_csv(filename, names=names)\n", 24 | "array = dataframe.values\n", 25 | "X = array[:,0:8]\n", 26 | "Y = array[:,8]\n", 27 | "test_size = 0.33\n", 28 | "seed = 7\n", 29 | "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n", 30 | "random_state=seed)\n", 31 | "model = LogisticRegression()\n", 32 | "model.fit(X_train, Y_train)\n", 33 | "result = model.score(X_test, Y_test)\n", 34 | "print(\"Accuracy: %.3f%%\" % (result*100.0))" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 3, 40 | "metadata": {}, 41 | "outputs": [ 42 | { 43 | "name": "stdout", 44 | "output_type": "stream", 45 | "text": [ 46 | "Accuracy: 76.951% (4.841%)\n" 47 | ] 48 | } 49 | ], 50 | "source": [ 51 | "# Evaluate using Cross Validation\n", 52 | "from sklearn.model_selection import KFold\n", 53 | "from sklearn.model_selection import cross_val_score\n", 54 | "array = dataframe.values\n", 55 | "X = array[:,0:8]\n", 56 | "Y = array[:,8]\n", 57 | "num_folds = 10\n", 58 | "seed = 7\n", 59 | "kfold = KFold(n_splits=num_folds, random_state=seed)\n", 60 | "model = LogisticRegression()\n", 61 | "results = cross_val_score(model, X, Y, cv=kfold)\n", 62 | "print(\"Accuracy: %.3f%% (%.3f%%)\"% (results.mean()*100.0, results.std()*100.0)) " 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": 4, 68 | "metadata": {}, 69 | "outputs": [ 70 | { 71 | "name": "stdout", 72 | "output_type": "stream", 73 | "text": [ 74 | "Accuracy: 76.953% (42.113%)\n" 75 | ] 76 | } 77 | ], 78 | "source": [ 79 | "# Evaluate using Leave One Out Cross Validation\n", 80 | "from sklearn.model_selection import LeaveOneOut\n", 81 | "from sklearn.model_selection import cross_val_score\n", 82 | "array = dataframe.values\n", 83 | "X = array[:,0:8]\n", 84 | "Y = array[:,8]\n", 85 | "num_folds = 10\n", 86 | "loocv = LeaveOneOut()\n", 87 | "model = LogisticRegression()\n", 88 | "results = cross_val_score(model, X, Y, cv=loocv)\n", 89 | "print(\"Accuracy: %.3f%% (%.3\n", 90 | " f%%)\"% (results.mean()*100.0, results.std()*100.0)) " 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 5, 96 | "metadata": {}, 97 | "outputs": [ 98 | { 99 | "name": "stdout", 100 | "output_type": "stream", 101 | "text": [ 102 | "Accuracy: 76.496% (1.698%)\n" 103 | ] 104 | } 105 | ], 106 | "source": [ 107 | "# Evaluate using Shuffle Split Cross Validation\n", 108 | "from sklearn.model_selection import ShuffleSplit\n", 109 | "from sklearn.model_selection import cross_val_score\n", 110 | "array = dataframe.values\n", 111 | "X = array[:,0:8]\n", 112 | "Y = array[:,8]\n", 113 | "n_splits = 10\n", 114 | "test_size = 0.33\n", 115 | "seed = 7\n", 116 | "kfold = ShuffleSplit(n_splits=n_splits, test_size=test_size, random_state=seed)\n", 117 | "model = LogisticRegression()\n", 118 | "results = cross_val_score(model, X, Y, cv=kfold)\n", 119 | "print(\"Accuracy: %.3f%% (%.3f%%)\"% (results.mean()*100.0, results.std()*100.0)) " 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": 6, 125 | "metadata": {}, 126 | "outputs": [ 127 | { 128 | "name": "stdout", 129 | "output_type": "stream", 130 | "text": [ 131 | "Accuracy: 0.770 (0.048)\n" 132 | ] 133 | } 134 | ], 135 | "source": [ 136 | "# Cross Validation Classification Accuracy\n", 137 | "array = dataframe.values\n", 138 | "X = array[:,0:8]\n", 139 | "Y = array[:,8]\n", 140 | "kfold = KFold(n_splits=10, random_state=7)\n", 141 | "model = LogisticRegression()\n", 142 | "scoring = 'accuracy'\n", 143 | "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", 144 | "print(\"Accuracy: %.3f (%.3f)\"% (results.mean(), results.std())) " 145 | ] 146 | }, 147 | { 148 | "cell_type": "code", 149 | "execution_count": 8, 150 | "metadata": {}, 151 | "outputs": [ 152 | { 153 | "name": "stdout", 154 | "output_type": "stream", 155 | "text": [ 156 | "Logloss: -0.493 (0.047)\n" 157 | ] 158 | } 159 | ], 160 | "source": [ 161 | "# Cross Validation Classification LogLoss\n", 162 | "array = dataframe.values\n", 163 | "X = array[:,0:8]\n", 164 | "Y = array[:,8]\n", 165 | "kfold = KFold(n_splits=10, random_state=7)\n", 166 | "model = LogisticRegression()\n", 167 | "scoring = 'neg_log_loss'\n", 168 | "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", 169 | "print(\"Logloss: %.3f (%.3f)\"% (results.mean(), results.std())) " 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "execution_count": 9, 175 | "metadata": {}, 176 | "outputs": [ 177 | { 178 | "name": "stdout", 179 | "output_type": "stream", 180 | "text": [ 181 | "AUC: 0.823 (0.041)\n" 182 | ] 183 | } 184 | ], 185 | "source": [ 186 | "# Cross Validation Classification ROC AUC\n", 187 | "array = dataframe.values\n", 188 | "X = array[:,0:8]\n", 189 | "Y = array[:,8]\n", 190 | "kfold = KFold(n_splits=10, random_state=7)\n", 191 | "model = LogisticRegression()\n", 192 | "scoring = 'roc_auc'\n", 193 | "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", 194 | "print(\"AUC: %.3f (%.3f)\"% (results.mean(), results.std())) " 195 | ] 196 | }, 197 | { 198 | "cell_type": "code", 199 | "execution_count": 11, 200 | "metadata": {}, 201 | "outputs": [ 202 | { 203 | "name": "stdout", 204 | "output_type": "stream", 205 | "text": [ 206 | "[[141 21]\n", 207 | " [ 41 51]]\n" 208 | ] 209 | } 210 | ], 211 | "source": [ 212 | "# Cross Validation Classification Confusion Matrix\n", 213 | "from sklearn.metrics import confusion_matrix\n", 214 | "array = dataframe.values\n", 215 | "X = array[:,0:8]\n", 216 | "Y = array[:,8]\n", 217 | "test_size = 0.33\n", 218 | "seed = 7\n", 219 | "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n", 220 | "random_state=seed)\n", 221 | "model = LogisticRegression()\n", 222 | "model.fit(X_train, Y_train)\n", 223 | "predicted = model.predict(X_test)\n", 224 | "matrix = confusion_matrix(Y_test, predicted)\n", 225 | "print(matrix)" 226 | ] 227 | }, 228 | { 229 | "cell_type": "code", 230 | "execution_count": 13, 231 | "metadata": {}, 232 | "outputs": [ 233 | { 234 | "name": "stdout", 235 | "output_type": "stream", 236 | "text": [ 237 | " precision recall f1-score support\n", 238 | "\n", 239 | " 0.0 0.77 0.87 0.82 162\n", 240 | " 1.0 0.71 0.55 0.62 92\n", 241 | "\n", 242 | "avg / total 0.75 0.76 0.75 254\n", 243 | "\n" 244 | ] 245 | } 246 | ], 247 | "source": [ 248 | "# Cross Validation Classification Report\n", 249 | "from sklearn.metrics import classification_report\n", 250 | "array = dataframe.values\n", 251 | "X = array[:,0:8]\n", 252 | "Y = array[:,8]\n", 253 | "test_size = 0.33\n", 254 | "seed = 7\n", 255 | "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n", 256 | "random_state=seed)\n", 257 | "model = LogisticRegression()\n", 258 | "model.fit(X_train, Y_train)\n", 259 | "predicted = model.predict(X_test)\n", 260 | "report = classification_report(Y_test, predicted)\n", 261 | "print(report)" 262 | ] 263 | }, 264 | { 265 | "cell_type": "code", 266 | "execution_count": 18, 267 | "metadata": {}, 268 | "outputs": [ 269 | { 270 | "name": "stdout", 271 | "output_type": "stream", 272 | "text": [ 273 | "MAE: -0.337 (0.022)\n" 274 | ] 275 | } 276 | ], 277 | "source": [ 278 | "# Cross Validation Regression MAE'\n", 279 | "from sklearn.linear_model import LinearRegression\n", 280 | "array = dataframe.values\n", 281 | "X = array[:,0:8]\n", 282 | "Y = array[:,8]\n", 283 | "kfold = KFold(n_splits=10, random_state=7)\n", 284 | "model = LinearRegression()\n", 285 | "scoring = 'neg_mean_absolute_error'\n", 286 | "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", 287 | "print(\"MAE: %.3f (%.3f)\"% (results.mean(), results.std())) " 288 | ] 289 | }, 290 | { 291 | "cell_type": "code", 292 | "execution_count": 20, 293 | "metadata": {}, 294 | "outputs": [ 295 | { 296 | "name": "stdout", 297 | "output_type": "stream", 298 | "text": [ 299 | "MSE: -0.163 (0.022)\n" 300 | ] 301 | } 302 | ], 303 | "source": [ 304 | "# Cross Validation Regression MSE\n", 305 | "array = dataframe.values\n", 306 | "X = array[:,0:8]\n", 307 | "Y = array[:,8]\n", 308 | "num_folds = 10\n", 309 | "kfold = KFold(n_splits=10, random_state=7)\n", 310 | "model = LinearRegression()\n", 311 | "scoring = 'neg_mean_squared_error'\n", 312 | "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", 313 | "print(\"MSE: %.3f (%.3f)\" % (results.mean(), results.std())) " 314 | ] 315 | }, 316 | { 317 | "cell_type": "code", 318 | "execution_count": 22, 319 | "metadata": {}, 320 | "outputs": [ 321 | { 322 | "name": "stdout", 323 | "output_type": "stream", 324 | "text": [ 325 | "R^2: 0.258 (0.118)\n" 326 | ] 327 | } 328 | ], 329 | "source": [ 330 | "# Cross Validation Regression R^2\n", 331 | "array = dataframe.values\n", 332 | "X = array[:,0:8]\n", 333 | "Y = array[:,8]\n", 334 | "kfold = KFold(n_splits=10, random_state=7)\n", 335 | "model = LinearRegression()\n", 336 | "scoring = 'r2'\n", 337 | "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", 338 | "print(\"R^2: %.3f (%.3f)\" % (results.mean(), results.std()))" 339 | ] 340 | }, 341 | { 342 | "cell_type": "code", 343 | "execution_count": null, 344 | "metadata": {}, 345 | "outputs": [], 346 | "source": [] 347 | } 348 | ], 349 | "metadata": { 350 | "kernelspec": { 351 | "display_name": "Python 3", 352 | "language": "python", 353 | "name": "python3" 354 | }, 355 | "language_info": { 356 | "codemirror_mode": { 357 | "name": "ipython", 358 | "version": 3 359 | }, 360 | "file_extension": ".py", 361 | "mimetype": "text/x-python", 362 | "name": "python", 363 | "nbconvert_exporter": "python", 364 | "pygments_lexer": "ipython3", 365 | "version": "3.7.1" 366 | } 367 | }, 368 | "nbformat": 4, 369 | "nbformat_minor": 2 370 | } 371 | -------------------------------------------------------------------------------- /diabetes.csv: -------------------------------------------------------------------------------- 1 | 6,148,72,35,0,33.6,0.627,50,1 2 | 1,85,66,29,0,26.6,0.351,31,0 3 | 8,183,64,0,0,23.3,0.672,32,1 4 | 1,89,66,23,94,28.1,0.167,21,0 5 | 0,137,40,35,168,43.1,2.288,33,1 6 | 5,116,74,0,0,25.6,0.201,30,0 7 | 3,78,50,32,88,31,0.248,26,1 8 | 10,115,0,0,0,35.3,0.134,29,0 9 | 2,197,70,45,543,30.5,0.158,53,1 10 | 8,125,96,0,0,0,0.232,54,1 11 | 4,110,92,0,0,37.6,0.191,30,0 12 | 10,168,74,0,0,38,0.537,34,1 13 | 10,139,80,0,0,27.1,1.441,57,0 14 | 1,189,60,23,846,30.1,0.398,59,1 15 | 5,166,72,19,175,25.8,0.587,51,1 16 | 7,100,0,0,0,30,0.484,32,1 17 | 0,118,84,47,230,45.8,0.551,31,1 18 | 7,107,74,0,0,29.6,0.254,31,1 19 | 1,103,30,38,83,43.3,0.183,33,0 20 | 1,115,70,30,96,34.6,0.529,32,1 21 | 3,126,88,41,235,39.3,0.704,27,0 22 | 8,99,84,0,0,35.4,0.388,50,0 23 | 7,196,90,0,0,39.8,0.451,41,1 24 | 9,119,80,35,0,29,0.263,29,1 25 | 11,143,94,33,146,36.6,0.254,51,1 26 | 10,125,70,26,115,31.1,0.205,41,1 27 | 7,147,76,0,0,39.4,0.257,43,1 28 | 1,97,66,15,140,23.2,0.487,22,0 29 | 13,145,82,19,110,22.2,0.245,57,0 30 | 5,117,92,0,0,34.1,0.337,38,0 31 | 5,109,75,26,0,36,0.546,60,0 32 | 3,158,76,36,245,31.6,0.851,28,1 33 | 3,88,58,11,54,24.8,0.267,22,0 34 | 6,92,92,0,0,19.9,0.188,28,0 35 | 10,122,78,31,0,27.6,0.512,45,0 36 | 4,103,60,33,192,24,0.966,33,0 37 | 11,138,76,0,0,33.2,0.42,35,0 38 | 9,102,76,37,0,32.9,0.665,46,1 39 | 2,90,68,42,0,38.2,0.503,27,1 40 | 4,111,72,47,207,37.1,1.39,56,1 41 | 3,180,64,25,70,34,0.271,26,0 42 | 7,133,84,0,0,40.2,0.696,37,0 43 | 7,106,92,18,0,22.7,0.235,48,0 44 | 9,171,110,24,240,45.4,0.721,54,1 45 | 7,159,64,0,0,27.4,0.294,40,0 46 | 0,180,66,39,0,42,1.893,25,1 47 | 1,146,56,0,0,29.7,0.564,29,0 48 | 2,71,70,27,0,28,0.586,22,0 49 | 7,103,66,32,0,39.1,0.344,31,1 50 | 7,105,0,0,0,0,0.305,24,0 51 | 1,103,80,11,82,19.4,0.491,22,0 52 | 1,101,50,15,36,24.2,0.526,26,0 53 | 5,88,66,21,23,24.4,0.342,30,0 54 | 8,176,90,34,300,33.7,0.467,58,1 55 | 7,150,66,42,342,34.7,0.718,42,0 56 | 1,73,50,10,0,23,0.248,21,0 57 | 7,187,68,39,304,37.7,0.254,41,1 58 | 0,100,88,60,110,46.8,0.962,31,0 59 | 0,146,82,0,0,40.5,1.781,44,0 60 | 0,105,64,41,142,41.5,0.173,22,0 61 | 2,84,0,0,0,0,0.304,21,0 62 | 8,133,72,0,0,32.9,0.27,39,1 63 | 5,44,62,0,0,25,0.587,36,0 64 | 2,141,58,34,128,25.4,0.699,24,0 65 | 7,114,66,0,0,32.8,0.258,42,1 66 | 5,99,74,27,0,29,0.203,32,0 67 | 0,109,88,30,0,32.5,0.855,38,1 68 | 2,109,92,0,0,42.7,0.845,54,0 69 | 1,95,66,13,38,19.6,0.334,25,0 70 | 4,146,85,27,100,28.9,0.189,27,0 71 | 2,100,66,20,90,32.9,0.867,28,1 72 | 5,139,64,35,140,28.6,0.411,26,0 73 | 13,126,90,0,0,43.4,0.583,42,1 74 | 4,129,86,20,270,35.1,0.231,23,0 75 | 1,79,75,30,0,32,0.396,22,0 76 | 1,0,48,20,0,24.7,0.14,22,0 77 | 7,62,78,0,0,32.6,0.391,41,0 78 | 5,95,72,33,0,37.7,0.37,27,0 79 | 0,131,0,0,0,43.2,0.27,26,1 80 | 2,112,66,22,0,25,0.307,24,0 81 | 3,113,44,13,0,22.4,0.14,22,0 82 | 2,74,0,0,0,0,0.102,22,0 83 | 7,83,78,26,71,29.3,0.767,36,0 84 | 0,101,65,28,0,24.6,0.237,22,0 85 | 5,137,108,0,0,48.8,0.227,37,1 86 | 2,110,74,29,125,32.4,0.698,27,0 87 | 13,106,72,54,0,36.6,0.178,45,0 88 | 2,100,68,25,71,38.5,0.324,26,0 89 | 15,136,70,32,110,37.1,0.153,43,1 90 | 1,107,68,19,0,26.5,0.165,24,0 91 | 1,80,55,0,0,19.1,0.258,21,0 92 | 4,123,80,15,176,32,0.443,34,0 93 | 7,81,78,40,48,46.7,0.261,42,0 94 | 4,134,72,0,0,23.8,0.277,60,1 95 | 2,142,82,18,64,24.7,0.761,21,0 96 | 6,144,72,27,228,33.9,0.255,40,0 97 | 2,92,62,28,0,31.6,0.13,24,0 98 | 1,71,48,18,76,20.4,0.323,22,0 99 | 6,93,50,30,64,28.7,0.356,23,0 100 | 1,122,90,51,220,49.7,0.325,31,1 101 | 1,163,72,0,0,39,1.222,33,1 102 | 1,151,60,0,0,26.1,0.179,22,0 103 | 0,125,96,0,0,22.5,0.262,21,0 104 | 1,81,72,18,40,26.6,0.283,24,0 105 | 2,85,65,0,0,39.6,0.93,27,0 106 | 1,126,56,29,152,28.7,0.801,21,0 107 | 1,96,122,0,0,22.4,0.207,27,0 108 | 4,144,58,28,140,29.5,0.287,37,0 109 | 3,83,58,31,18,34.3,0.336,25,0 110 | 0,95,85,25,36,37.4,0.247,24,1 111 | 3,171,72,33,135,33.3,0.199,24,1 112 | 8,155,62,26,495,34,0.543,46,1 113 | 1,89,76,34,37,31.2,0.192,23,0 114 | 4,76,62,0,0,34,0.391,25,0 115 | 7,160,54,32,175,30.5,0.588,39,1 116 | 4,146,92,0,0,31.2,0.539,61,1 117 | 5,124,74,0,0,34,0.22,38,1 118 | 5,78,48,0,0,33.7,0.654,25,0 119 | 4,97,60,23,0,28.2,0.443,22,0 120 | 4,99,76,15,51,23.2,0.223,21,0 121 | 0,162,76,56,100,53.2,0.759,25,1 122 | 6,111,64,39,0,34.2,0.26,24,0 123 | 2,107,74,30,100,33.6,0.404,23,0 124 | 5,132,80,0,0,26.8,0.186,69,0 125 | 0,113,76,0,0,33.3,0.278,23,1 126 | 1,88,30,42,99,55,0.496,26,1 127 | 3,120,70,30,135,42.9,0.452,30,0 128 | 1,118,58,36,94,33.3,0.261,23,0 129 | 1,117,88,24,145,34.5,0.403,40,1 130 | 0,105,84,0,0,27.9,0.741,62,1 131 | 4,173,70,14,168,29.7,0.361,33,1 132 | 9,122,56,0,0,33.3,1.114,33,1 133 | 3,170,64,37,225,34.5,0.356,30,1 134 | 8,84,74,31,0,38.3,0.457,39,0 135 | 2,96,68,13,49,21.1,0.647,26,0 136 | 2,125,60,20,140,33.8,0.088,31,0 137 | 0,100,70,26,50,30.8,0.597,21,0 138 | 0,93,60,25,92,28.7,0.532,22,0 139 | 0,129,80,0,0,31.2,0.703,29,0 140 | 5,105,72,29,325,36.9,0.159,28,0 141 | 3,128,78,0,0,21.1,0.268,55,0 142 | 5,106,82,30,0,39.5,0.286,38,0 143 | 2,108,52,26,63,32.5,0.318,22,0 144 | 10,108,66,0,0,32.4,0.272,42,1 145 | 4,154,62,31,284,32.8,0.237,23,0 146 | 0,102,75,23,0,0,0.572,21,0 147 | 9,57,80,37,0,32.8,0.096,41,0 148 | 2,106,64,35,119,30.5,1.4,34,0 149 | 5,147,78,0,0,33.7,0.218,65,0 150 | 2,90,70,17,0,27.3,0.085,22,0 151 | 1,136,74,50,204,37.4,0.399,24,0 152 | 4,114,65,0,0,21.9,0.432,37,0 153 | 9,156,86,28,155,34.3,1.189,42,1 154 | 1,153,82,42,485,40.6,0.687,23,0 155 | 8,188,78,0,0,47.9,0.137,43,1 156 | 7,152,88,44,0,50,0.337,36,1 157 | 2,99,52,15,94,24.6,0.637,21,0 158 | 1,109,56,21,135,25.2,0.833,23,0 159 | 2,88,74,19,53,29,0.229,22,0 160 | 17,163,72,41,114,40.9,0.817,47,1 161 | 4,151,90,38,0,29.7,0.294,36,0 162 | 7,102,74,40,105,37.2,0.204,45,0 163 | 0,114,80,34,285,44.2,0.167,27,0 164 | 2,100,64,23,0,29.7,0.368,21,0 165 | 0,131,88,0,0,31.6,0.743,32,1 166 | 6,104,74,18,156,29.9,0.722,41,1 167 | 3,148,66,25,0,32.5,0.256,22,0 168 | 4,120,68,0,0,29.6,0.709,34,0 169 | 4,110,66,0,0,31.9,0.471,29,0 170 | 3,111,90,12,78,28.4,0.495,29,0 171 | 6,102,82,0,0,30.8,0.18,36,1 172 | 6,134,70,23,130,35.4,0.542,29,1 173 | 2,87,0,23,0,28.9,0.773,25,0 174 | 1,79,60,42,48,43.5,0.678,23,0 175 | 2,75,64,24,55,29.7,0.37,33,0 176 | 8,179,72,42,130,32.7,0.719,36,1 177 | 6,85,78,0,0,31.2,0.382,42,0 178 | 0,129,110,46,130,67.1,0.319,26,1 179 | 5,143,78,0,0,45,0.19,47,0 180 | 5,130,82,0,0,39.1,0.956,37,1 181 | 6,87,80,0,0,23.2,0.084,32,0 182 | 0,119,64,18,92,34.9,0.725,23,0 183 | 1,0,74,20,23,27.7,0.299,21,0 184 | 5,73,60,0,0,26.8,0.268,27,0 185 | 4,141,74,0,0,27.6,0.244,40,0 186 | 7,194,68,28,0,35.9,0.745,41,1 187 | 8,181,68,36,495,30.1,0.615,60,1 188 | 1,128,98,41,58,32,1.321,33,1 189 | 8,109,76,39,114,27.9,0.64,31,1 190 | 5,139,80,35,160,31.6,0.361,25,1 191 | 3,111,62,0,0,22.6,0.142,21,0 192 | 9,123,70,44,94,33.1,0.374,40,0 193 | 7,159,66,0,0,30.4,0.383,36,1 194 | 11,135,0,0,0,52.3,0.578,40,1 195 | 8,85,55,20,0,24.4,0.136,42,0 196 | 5,158,84,41,210,39.4,0.395,29,1 197 | 1,105,58,0,0,24.3,0.187,21,0 198 | 3,107,62,13,48,22.9,0.678,23,1 199 | 4,109,64,44,99,34.8,0.905,26,1 200 | 4,148,60,27,318,30.9,0.15,29,1 201 | 0,113,80,16,0,31,0.874,21,0 202 | 1,138,82,0,0,40.1,0.236,28,0 203 | 0,108,68,20,0,27.3,0.787,32,0 204 | 2,99,70,16,44,20.4,0.235,27,0 205 | 6,103,72,32,190,37.7,0.324,55,0 206 | 5,111,72,28,0,23.9,0.407,27,0 207 | 8,196,76,29,280,37.5,0.605,57,1 208 | 5,162,104,0,0,37.7,0.151,52,1 209 | 1,96,64,27,87,33.2,0.289,21,0 210 | 7,184,84,33,0,35.5,0.355,41,1 211 | 2,81,60,22,0,27.7,0.29,25,0 212 | 0,147,85,54,0,42.8,0.375,24,0 213 | 7,179,95,31,0,34.2,0.164,60,0 214 | 0,140,65,26,130,42.6,0.431,24,1 215 | 9,112,82,32,175,34.2,0.26,36,1 216 | 12,151,70,40,271,41.8,0.742,38,1 217 | 5,109,62,41,129,35.8,0.514,25,1 218 | 6,125,68,30,120,30,0.464,32,0 219 | 5,85,74,22,0,29,1.224,32,1 220 | 5,112,66,0,0,37.8,0.261,41,1 221 | 0,177,60,29,478,34.6,1.072,21,1 222 | 2,158,90,0,0,31.6,0.805,66,1 223 | 7,119,0,0,0,25.2,0.209,37,0 224 | 7,142,60,33,190,28.8,0.687,61,0 225 | 1,100,66,15,56,23.6,0.666,26,0 226 | 1,87,78,27,32,34.6,0.101,22,0 227 | 0,101,76,0,0,35.7,0.198,26,0 228 | 3,162,52,38,0,37.2,0.652,24,1 229 | 4,197,70,39,744,36.7,2.329,31,0 230 | 0,117,80,31,53,45.2,0.089,24,0 231 | 4,142,86,0,0,44,0.645,22,1 232 | 6,134,80,37,370,46.2,0.238,46,1 233 | 1,79,80,25,37,25.4,0.583,22,0 234 | 4,122,68,0,0,35,0.394,29,0 235 | 3,74,68,28,45,29.7,0.293,23,0 236 | 4,171,72,0,0,43.6,0.479,26,1 237 | 7,181,84,21,192,35.9,0.586,51,1 238 | 0,179,90,27,0,44.1,0.686,23,1 239 | 9,164,84,21,0,30.8,0.831,32,1 240 | 0,104,76,0,0,18.4,0.582,27,0 241 | 1,91,64,24,0,29.2,0.192,21,0 242 | 4,91,70,32,88,33.1,0.446,22,0 243 | 3,139,54,0,0,25.6,0.402,22,1 244 | 6,119,50,22,176,27.1,1.318,33,1 245 | 2,146,76,35,194,38.2,0.329,29,0 246 | 9,184,85,15,0,30,1.213,49,1 247 | 10,122,68,0,0,31.2,0.258,41,0 248 | 0,165,90,33,680,52.3,0.427,23,0 249 | 9,124,70,33,402,35.4,0.282,34,0 250 | 1,111,86,19,0,30.1,0.143,23,0 251 | 9,106,52,0,0,31.2,0.38,42,0 252 | 2,129,84,0,0,28,0.284,27,0 253 | 2,90,80,14,55,24.4,0.249,24,0 254 | 0,86,68,32,0,35.8,0.238,25,0 255 | 12,92,62,7,258,27.6,0.926,44,1 256 | 1,113,64,35,0,33.6,0.543,21,1 257 | 3,111,56,39,0,30.1,0.557,30,0 258 | 2,114,68,22,0,28.7,0.092,25,0 259 | 1,193,50,16,375,25.9,0.655,24,0 260 | 11,155,76,28,150,33.3,1.353,51,1 261 | 3,191,68,15,130,30.9,0.299,34,0 262 | 3,141,0,0,0,30,0.761,27,1 263 | 4,95,70,32,0,32.1,0.612,24,0 264 | 3,142,80,15,0,32.4,0.2,63,0 265 | 4,123,62,0,0,32,0.226,35,1 266 | 5,96,74,18,67,33.6,0.997,43,0 267 | 0,138,0,0,0,36.3,0.933,25,1 268 | 2,128,64,42,0,40,1.101,24,0 269 | 0,102,52,0,0,25.1,0.078,21,0 270 | 2,146,0,0,0,27.5,0.24,28,1 271 | 10,101,86,37,0,45.6,1.136,38,1 272 | 2,108,62,32,56,25.2,0.128,21,0 273 | 3,122,78,0,0,23,0.254,40,0 274 | 1,71,78,50,45,33.2,0.422,21,0 275 | 13,106,70,0,0,34.2,0.251,52,0 276 | 2,100,70,52,57,40.5,0.677,25,0 277 | 7,106,60,24,0,26.5,0.296,29,1 278 | 0,104,64,23,116,27.8,0.454,23,0 279 | 5,114,74,0,0,24.9,0.744,57,0 280 | 2,108,62,10,278,25.3,0.881,22,0 281 | 0,146,70,0,0,37.9,0.334,28,1 282 | 10,129,76,28,122,35.9,0.28,39,0 283 | 7,133,88,15,155,32.4,0.262,37,0 284 | 7,161,86,0,0,30.4,0.165,47,1 285 | 2,108,80,0,0,27,0.259,52,1 286 | 7,136,74,26,135,26,0.647,51,0 287 | 5,155,84,44,545,38.7,0.619,34,0 288 | 1,119,86,39,220,45.6,0.808,29,1 289 | 4,96,56,17,49,20.8,0.34,26,0 290 | 5,108,72,43,75,36.1,0.263,33,0 291 | 0,78,88,29,40,36.9,0.434,21,0 292 | 0,107,62,30,74,36.6,0.757,25,1 293 | 2,128,78,37,182,43.3,1.224,31,1 294 | 1,128,48,45,194,40.5,0.613,24,1 295 | 0,161,50,0,0,21.9,0.254,65,0 296 | 6,151,62,31,120,35.5,0.692,28,0 297 | 2,146,70,38,360,28,0.337,29,1 298 | 0,126,84,29,215,30.7,0.52,24,0 299 | 14,100,78,25,184,36.6,0.412,46,1 300 | 8,112,72,0,0,23.6,0.84,58,0 301 | 0,167,0,0,0,32.3,0.839,30,1 302 | 2,144,58,33,135,31.6,0.422,25,1 303 | 5,77,82,41,42,35.8,0.156,35,0 304 | 5,115,98,0,0,52.9,0.209,28,1 305 | 3,150,76,0,0,21,0.207,37,0 306 | 2,120,76,37,105,39.7,0.215,29,0 307 | 10,161,68,23,132,25.5,0.326,47,1 308 | 0,137,68,14,148,24.8,0.143,21,0 309 | 0,128,68,19,180,30.5,1.391,25,1 310 | 2,124,68,28,205,32.9,0.875,30,1 311 | 6,80,66,30,0,26.2,0.313,41,0 312 | 0,106,70,37,148,39.4,0.605,22,0 313 | 2,155,74,17,96,26.6,0.433,27,1 314 | 3,113,50,10,85,29.5,0.626,25,0 315 | 7,109,80,31,0,35.9,1.127,43,1 316 | 2,112,68,22,94,34.1,0.315,26,0 317 | 3,99,80,11,64,19.3,0.284,30,0 318 | 3,182,74,0,0,30.5,0.345,29,1 319 | 3,115,66,39,140,38.1,0.15,28,0 320 | 6,194,78,0,0,23.5,0.129,59,1 321 | 4,129,60,12,231,27.5,0.527,31,0 322 | 3,112,74,30,0,31.6,0.197,25,1 323 | 0,124,70,20,0,27.4,0.254,36,1 324 | 13,152,90,33,29,26.8,0.731,43,1 325 | 2,112,75,32,0,35.7,0.148,21,0 326 | 1,157,72,21,168,25.6,0.123,24,0 327 | 1,122,64,32,156,35.1,0.692,30,1 328 | 10,179,70,0,0,35.1,0.2,37,0 329 | 2,102,86,36,120,45.5,0.127,23,1 330 | 6,105,70,32,68,30.8,0.122,37,0 331 | 8,118,72,19,0,23.1,1.476,46,0 332 | 2,87,58,16,52,32.7,0.166,25,0 333 | 1,180,0,0,0,43.3,0.282,41,1 334 | 12,106,80,0,0,23.6,0.137,44,0 335 | 1,95,60,18,58,23.9,0.26,22,0 336 | 0,165,76,43,255,47.9,0.259,26,0 337 | 0,117,0,0,0,33.8,0.932,44,0 338 | 5,115,76,0,0,31.2,0.343,44,1 339 | 9,152,78,34,171,34.2,0.893,33,1 340 | 7,178,84,0,0,39.9,0.331,41,1 341 | 1,130,70,13,105,25.9,0.472,22,0 342 | 1,95,74,21,73,25.9,0.673,36,0 343 | 1,0,68,35,0,32,0.389,22,0 344 | 5,122,86,0,0,34.7,0.29,33,0 345 | 8,95,72,0,0,36.8,0.485,57,0 346 | 8,126,88,36,108,38.5,0.349,49,0 347 | 1,139,46,19,83,28.7,0.654,22,0 348 | 3,116,0,0,0,23.5,0.187,23,0 349 | 3,99,62,19,74,21.8,0.279,26,0 350 | 5,0,80,32,0,41,0.346,37,1 351 | 4,92,80,0,0,42.2,0.237,29,0 352 | 4,137,84,0,0,31.2,0.252,30,0 353 | 3,61,82,28,0,34.4,0.243,46,0 354 | 1,90,62,12,43,27.2,0.58,24,0 355 | 3,90,78,0,0,42.7,0.559,21,0 356 | 9,165,88,0,0,30.4,0.302,49,1 357 | 1,125,50,40,167,33.3,0.962,28,1 358 | 13,129,0,30,0,39.9,0.569,44,1 359 | 12,88,74,40,54,35.3,0.378,48,0 360 | 1,196,76,36,249,36.5,0.875,29,1 361 | 5,189,64,33,325,31.2,0.583,29,1 362 | 5,158,70,0,0,29.8,0.207,63,0 363 | 5,103,108,37,0,39.2,0.305,65,0 364 | 4,146,78,0,0,38.5,0.52,67,1 365 | 4,147,74,25,293,34.9,0.385,30,0 366 | 5,99,54,28,83,34,0.499,30,0 367 | 6,124,72,0,0,27.6,0.368,29,1 368 | 0,101,64,17,0,21,0.252,21,0 369 | 3,81,86,16,66,27.5,0.306,22,0 370 | 1,133,102,28,140,32.8,0.234,45,1 371 | 3,173,82,48,465,38.4,2.137,25,1 372 | 0,118,64,23,89,0,1.731,21,0 373 | 0,84,64,22,66,35.8,0.545,21,0 374 | 2,105,58,40,94,34.9,0.225,25,0 375 | 2,122,52,43,158,36.2,0.816,28,0 376 | 12,140,82,43,325,39.2,0.528,58,1 377 | 0,98,82,15,84,25.2,0.299,22,0 378 | 1,87,60,37,75,37.2,0.509,22,0 379 | 4,156,75,0,0,48.3,0.238,32,1 380 | 0,93,100,39,72,43.4,1.021,35,0 381 | 1,107,72,30,82,30.8,0.821,24,0 382 | 0,105,68,22,0,20,0.236,22,0 383 | 1,109,60,8,182,25.4,0.947,21,0 384 | 1,90,62,18,59,25.1,1.268,25,0 385 | 1,125,70,24,110,24.3,0.221,25,0 386 | 1,119,54,13,50,22.3,0.205,24,0 387 | 5,116,74,29,0,32.3,0.66,35,1 388 | 8,105,100,36,0,43.3,0.239,45,1 389 | 5,144,82,26,285,32,0.452,58,1 390 | 3,100,68,23,81,31.6,0.949,28,0 391 | 1,100,66,29,196,32,0.444,42,0 392 | 5,166,76,0,0,45.7,0.34,27,1 393 | 1,131,64,14,415,23.7,0.389,21,0 394 | 4,116,72,12,87,22.1,0.463,37,0 395 | 4,158,78,0,0,32.9,0.803,31,1 396 | 2,127,58,24,275,27.7,1.6,25,0 397 | 3,96,56,34,115,24.7,0.944,39,0 398 | 0,131,66,40,0,34.3,0.196,22,1 399 | 3,82,70,0,0,21.1,0.389,25,0 400 | 3,193,70,31,0,34.9,0.241,25,1 401 | 4,95,64,0,0,32,0.161,31,1 402 | 6,137,61,0,0,24.2,0.151,55,0 403 | 5,136,84,41,88,35,0.286,35,1 404 | 9,72,78,25,0,31.6,0.28,38,0 405 | 5,168,64,0,0,32.9,0.135,41,1 406 | 2,123,48,32,165,42.1,0.52,26,0 407 | 4,115,72,0,0,28.9,0.376,46,1 408 | 0,101,62,0,0,21.9,0.336,25,0 409 | 8,197,74,0,0,25.9,1.191,39,1 410 | 1,172,68,49,579,42.4,0.702,28,1 411 | 6,102,90,39,0,35.7,0.674,28,0 412 | 1,112,72,30,176,34.4,0.528,25,0 413 | 1,143,84,23,310,42.4,1.076,22,0 414 | 1,143,74,22,61,26.2,0.256,21,0 415 | 0,138,60,35,167,34.6,0.534,21,1 416 | 3,173,84,33,474,35.7,0.258,22,1 417 | 1,97,68,21,0,27.2,1.095,22,0 418 | 4,144,82,32,0,38.5,0.554,37,1 419 | 1,83,68,0,0,18.2,0.624,27,0 420 | 3,129,64,29,115,26.4,0.219,28,1 421 | 1,119,88,41,170,45.3,0.507,26,0 422 | 2,94,68,18,76,26,0.561,21,0 423 | 0,102,64,46,78,40.6,0.496,21,0 424 | 2,115,64,22,0,30.8,0.421,21,0 425 | 8,151,78,32,210,42.9,0.516,36,1 426 | 4,184,78,39,277,37,0.264,31,1 427 | 0,94,0,0,0,0,0.256,25,0 428 | 1,181,64,30,180,34.1,0.328,38,1 429 | 0,135,94,46,145,40.6,0.284,26,0 430 | 1,95,82,25,180,35,0.233,43,1 431 | 2,99,0,0,0,22.2,0.108,23,0 432 | 3,89,74,16,85,30.4,0.551,38,0 433 | 1,80,74,11,60,30,0.527,22,0 434 | 2,139,75,0,0,25.6,0.167,29,0 435 | 1,90,68,8,0,24.5,1.138,36,0 436 | 0,141,0,0,0,42.4,0.205,29,1 437 | 12,140,85,33,0,37.4,0.244,41,0 438 | 5,147,75,0,0,29.9,0.434,28,0 439 | 1,97,70,15,0,18.2,0.147,21,0 440 | 6,107,88,0,0,36.8,0.727,31,0 441 | 0,189,104,25,0,34.3,0.435,41,1 442 | 2,83,66,23,50,32.2,0.497,22,0 443 | 4,117,64,27,120,33.2,0.23,24,0 444 | 8,108,70,0,0,30.5,0.955,33,1 445 | 4,117,62,12,0,29.7,0.38,30,1 446 | 0,180,78,63,14,59.4,2.42,25,1 447 | 1,100,72,12,70,25.3,0.658,28,0 448 | 0,95,80,45,92,36.5,0.33,26,0 449 | 0,104,64,37,64,33.6,0.51,22,1 450 | 0,120,74,18,63,30.5,0.285,26,0 451 | 1,82,64,13,95,21.2,0.415,23,0 452 | 2,134,70,0,0,28.9,0.542,23,1 453 | 0,91,68,32,210,39.9,0.381,25,0 454 | 2,119,0,0,0,19.6,0.832,72,0 455 | 2,100,54,28,105,37.8,0.498,24,0 456 | 14,175,62,30,0,33.6,0.212,38,1 457 | 1,135,54,0,0,26.7,0.687,62,0 458 | 5,86,68,28,71,30.2,0.364,24,0 459 | 10,148,84,48,237,37.6,1.001,51,1 460 | 9,134,74,33,60,25.9,0.46,81,0 461 | 9,120,72,22,56,20.8,0.733,48,0 462 | 1,71,62,0,0,21.8,0.416,26,0 463 | 8,74,70,40,49,35.3,0.705,39,0 464 | 5,88,78,30,0,27.6,0.258,37,0 465 | 10,115,98,0,0,24,1.022,34,0 466 | 0,124,56,13,105,21.8,0.452,21,0 467 | 0,74,52,10,36,27.8,0.269,22,0 468 | 0,97,64,36,100,36.8,0.6,25,0 469 | 8,120,0,0,0,30,0.183,38,1 470 | 6,154,78,41,140,46.1,0.571,27,0 471 | 1,144,82,40,0,41.3,0.607,28,0 472 | 0,137,70,38,0,33.2,0.17,22,0 473 | 0,119,66,27,0,38.8,0.259,22,0 474 | 7,136,90,0,0,29.9,0.21,50,0 475 | 4,114,64,0,0,28.9,0.126,24,0 476 | 0,137,84,27,0,27.3,0.231,59,0 477 | 2,105,80,45,191,33.7,0.711,29,1 478 | 7,114,76,17,110,23.8,0.466,31,0 479 | 8,126,74,38,75,25.9,0.162,39,0 480 | 4,132,86,31,0,28,0.419,63,0 481 | 3,158,70,30,328,35.5,0.344,35,1 482 | 0,123,88,37,0,35.2,0.197,29,0 483 | 4,85,58,22,49,27.8,0.306,28,0 484 | 0,84,82,31,125,38.2,0.233,23,0 485 | 0,145,0,0,0,44.2,0.63,31,1 486 | 0,135,68,42,250,42.3,0.365,24,1 487 | 1,139,62,41,480,40.7,0.536,21,0 488 | 0,173,78,32,265,46.5,1.159,58,0 489 | 4,99,72,17,0,25.6,0.294,28,0 490 | 8,194,80,0,0,26.1,0.551,67,0 491 | 2,83,65,28,66,36.8,0.629,24,0 492 | 2,89,90,30,0,33.5,0.292,42,0 493 | 4,99,68,38,0,32.8,0.145,33,0 494 | 4,125,70,18,122,28.9,1.144,45,1 495 | 3,80,0,0,0,0,0.174,22,0 496 | 6,166,74,0,0,26.6,0.304,66,0 497 | 5,110,68,0,0,26,0.292,30,0 498 | 2,81,72,15,76,30.1,0.547,25,0 499 | 7,195,70,33,145,25.1,0.163,55,1 500 | 6,154,74,32,193,29.3,0.839,39,0 501 | 2,117,90,19,71,25.2,0.313,21,0 502 | 3,84,72,32,0,37.2,0.267,28,0 503 | 6,0,68,41,0,39,0.727,41,1 504 | 7,94,64,25,79,33.3,0.738,41,0 505 | 3,96,78,39,0,37.3,0.238,40,0 506 | 10,75,82,0,0,33.3,0.263,38,0 507 | 0,180,90,26,90,36.5,0.314,35,1 508 | 1,130,60,23,170,28.6,0.692,21,0 509 | 2,84,50,23,76,30.4,0.968,21,0 510 | 8,120,78,0,0,25,0.409,64,0 511 | 12,84,72,31,0,29.7,0.297,46,1 512 | 0,139,62,17,210,22.1,0.207,21,0 513 | 9,91,68,0,0,24.2,0.2,58,0 514 | 2,91,62,0,0,27.3,0.525,22,0 515 | 3,99,54,19,86,25.6,0.154,24,0 516 | 3,163,70,18,105,31.6,0.268,28,1 517 | 9,145,88,34,165,30.3,0.771,53,1 518 | 7,125,86,0,0,37.6,0.304,51,0 519 | 13,76,60,0,0,32.8,0.18,41,0 520 | 6,129,90,7,326,19.6,0.582,60,0 521 | 2,68,70,32,66,25,0.187,25,0 522 | 3,124,80,33,130,33.2,0.305,26,0 523 | 6,114,0,0,0,0,0.189,26,0 524 | 9,130,70,0,0,34.2,0.652,45,1 525 | 3,125,58,0,0,31.6,0.151,24,0 526 | 3,87,60,18,0,21.8,0.444,21,0 527 | 1,97,64,19,82,18.2,0.299,21,0 528 | 3,116,74,15,105,26.3,0.107,24,0 529 | 0,117,66,31,188,30.8,0.493,22,0 530 | 0,111,65,0,0,24.6,0.66,31,0 531 | 2,122,60,18,106,29.8,0.717,22,0 532 | 0,107,76,0,0,45.3,0.686,24,0 533 | 1,86,66,52,65,41.3,0.917,29,0 534 | 6,91,0,0,0,29.8,0.501,31,0 535 | 1,77,56,30,56,33.3,1.251,24,0 536 | 4,132,0,0,0,32.9,0.302,23,1 537 | 0,105,90,0,0,29.6,0.197,46,0 538 | 0,57,60,0,0,21.7,0.735,67,0 539 | 0,127,80,37,210,36.3,0.804,23,0 540 | 3,129,92,49,155,36.4,0.968,32,1 541 | 8,100,74,40,215,39.4,0.661,43,1 542 | 3,128,72,25,190,32.4,0.549,27,1 543 | 10,90,85,32,0,34.9,0.825,56,1 544 | 4,84,90,23,56,39.5,0.159,25,0 545 | 1,88,78,29,76,32,0.365,29,0 546 | 8,186,90,35,225,34.5,0.423,37,1 547 | 5,187,76,27,207,43.6,1.034,53,1 548 | 4,131,68,21,166,33.1,0.16,28,0 549 | 1,164,82,43,67,32.8,0.341,50,0 550 | 4,189,110,31,0,28.5,0.68,37,0 551 | 1,116,70,28,0,27.4,0.204,21,0 552 | 3,84,68,30,106,31.9,0.591,25,0 553 | 6,114,88,0,0,27.8,0.247,66,0 554 | 1,88,62,24,44,29.9,0.422,23,0 555 | 1,84,64,23,115,36.9,0.471,28,0 556 | 7,124,70,33,215,25.5,0.161,37,0 557 | 1,97,70,40,0,38.1,0.218,30,0 558 | 8,110,76,0,0,27.8,0.237,58,0 559 | 11,103,68,40,0,46.2,0.126,42,0 560 | 11,85,74,0,0,30.1,0.3,35,0 561 | 6,125,76,0,0,33.8,0.121,54,1 562 | 0,198,66,32,274,41.3,0.502,28,1 563 | 1,87,68,34,77,37.6,0.401,24,0 564 | 6,99,60,19,54,26.9,0.497,32,0 565 | 0,91,80,0,0,32.4,0.601,27,0 566 | 2,95,54,14,88,26.1,0.748,22,0 567 | 1,99,72,30,18,38.6,0.412,21,0 568 | 6,92,62,32,126,32,0.085,46,0 569 | 4,154,72,29,126,31.3,0.338,37,0 570 | 0,121,66,30,165,34.3,0.203,33,1 571 | 3,78,70,0,0,32.5,0.27,39,0 572 | 2,130,96,0,0,22.6,0.268,21,0 573 | 3,111,58,31,44,29.5,0.43,22,0 574 | 2,98,60,17,120,34.7,0.198,22,0 575 | 1,143,86,30,330,30.1,0.892,23,0 576 | 1,119,44,47,63,35.5,0.28,25,0 577 | 6,108,44,20,130,24,0.813,35,0 578 | 2,118,80,0,0,42.9,0.693,21,1 579 | 10,133,68,0,0,27,0.245,36,0 580 | 2,197,70,99,0,34.7,0.575,62,1 581 | 0,151,90,46,0,42.1,0.371,21,1 582 | 6,109,60,27,0,25,0.206,27,0 583 | 12,121,78,17,0,26.5,0.259,62,0 584 | 8,100,76,0,0,38.7,0.19,42,0 585 | 8,124,76,24,600,28.7,0.687,52,1 586 | 1,93,56,11,0,22.5,0.417,22,0 587 | 8,143,66,0,0,34.9,0.129,41,1 588 | 6,103,66,0,0,24.3,0.249,29,0 589 | 3,176,86,27,156,33.3,1.154,52,1 590 | 0,73,0,0,0,21.1,0.342,25,0 591 | 11,111,84,40,0,46.8,0.925,45,1 592 | 2,112,78,50,140,39.4,0.175,24,0 593 | 3,132,80,0,0,34.4,0.402,44,1 594 | 2,82,52,22,115,28.5,1.699,25,0 595 | 6,123,72,45,230,33.6,0.733,34,0 596 | 0,188,82,14,185,32,0.682,22,1 597 | 0,67,76,0,0,45.3,0.194,46,0 598 | 1,89,24,19,25,27.8,0.559,21,0 599 | 1,173,74,0,0,36.8,0.088,38,1 600 | 1,109,38,18,120,23.1,0.407,26,0 601 | 1,108,88,19,0,27.1,0.4,24,0 602 | 6,96,0,0,0,23.7,0.19,28,0 603 | 1,124,74,36,0,27.8,0.1,30,0 604 | 7,150,78,29,126,35.2,0.692,54,1 605 | 4,183,0,0,0,28.4,0.212,36,1 606 | 1,124,60,32,0,35.8,0.514,21,0 607 | 1,181,78,42,293,40,1.258,22,1 608 | 1,92,62,25,41,19.5,0.482,25,0 609 | 0,152,82,39,272,41.5,0.27,27,0 610 | 1,111,62,13,182,24,0.138,23,0 611 | 3,106,54,21,158,30.9,0.292,24,0 612 | 3,174,58,22,194,32.9,0.593,36,1 613 | 7,168,88,42,321,38.2,0.787,40,1 614 | 6,105,80,28,0,32.5,0.878,26,0 615 | 11,138,74,26,144,36.1,0.557,50,1 616 | 3,106,72,0,0,25.8,0.207,27,0 617 | 6,117,96,0,0,28.7,0.157,30,0 618 | 2,68,62,13,15,20.1,0.257,23,0 619 | 9,112,82,24,0,28.2,1.282,50,1 620 | 0,119,0,0,0,32.4,0.141,24,1 621 | 2,112,86,42,160,38.4,0.246,28,0 622 | 2,92,76,20,0,24.2,1.698,28,0 623 | 6,183,94,0,0,40.8,1.461,45,0 624 | 0,94,70,27,115,43.5,0.347,21,0 625 | 2,108,64,0,0,30.8,0.158,21,0 626 | 4,90,88,47,54,37.7,0.362,29,0 627 | 0,125,68,0,0,24.7,0.206,21,0 628 | 0,132,78,0,0,32.4,0.393,21,0 629 | 5,128,80,0,0,34.6,0.144,45,0 630 | 4,94,65,22,0,24.7,0.148,21,0 631 | 7,114,64,0,0,27.4,0.732,34,1 632 | 0,102,78,40,90,34.5,0.238,24,0 633 | 2,111,60,0,0,26.2,0.343,23,0 634 | 1,128,82,17,183,27.5,0.115,22,0 635 | 10,92,62,0,0,25.9,0.167,31,0 636 | 13,104,72,0,0,31.2,0.465,38,1 637 | 5,104,74,0,0,28.8,0.153,48,0 638 | 2,94,76,18,66,31.6,0.649,23,0 639 | 7,97,76,32,91,40.9,0.871,32,1 640 | 1,100,74,12,46,19.5,0.149,28,0 641 | 0,102,86,17,105,29.3,0.695,27,0 642 | 4,128,70,0,0,34.3,0.303,24,0 643 | 6,147,80,0,0,29.5,0.178,50,1 644 | 4,90,0,0,0,28,0.61,31,0 645 | 3,103,72,30,152,27.6,0.73,27,0 646 | 2,157,74,35,440,39.4,0.134,30,0 647 | 1,167,74,17,144,23.4,0.447,33,1 648 | 0,179,50,36,159,37.8,0.455,22,1 649 | 11,136,84,35,130,28.3,0.26,42,1 650 | 0,107,60,25,0,26.4,0.133,23,0 651 | 1,91,54,25,100,25.2,0.234,23,0 652 | 1,117,60,23,106,33.8,0.466,27,0 653 | 5,123,74,40,77,34.1,0.269,28,0 654 | 2,120,54,0,0,26.8,0.455,27,0 655 | 1,106,70,28,135,34.2,0.142,22,0 656 | 2,155,52,27,540,38.7,0.24,25,1 657 | 2,101,58,35,90,21.8,0.155,22,0 658 | 1,120,80,48,200,38.9,1.162,41,0 659 | 11,127,106,0,0,39,0.19,51,0 660 | 3,80,82,31,70,34.2,1.292,27,1 661 | 10,162,84,0,0,27.7,0.182,54,0 662 | 1,199,76,43,0,42.9,1.394,22,1 663 | 8,167,106,46,231,37.6,0.165,43,1 664 | 9,145,80,46,130,37.9,0.637,40,1 665 | 6,115,60,39,0,33.7,0.245,40,1 666 | 1,112,80,45,132,34.8,0.217,24,0 667 | 4,145,82,18,0,32.5,0.235,70,1 668 | 10,111,70,27,0,27.5,0.141,40,1 669 | 6,98,58,33,190,34,0.43,43,0 670 | 9,154,78,30,100,30.9,0.164,45,0 671 | 6,165,68,26,168,33.6,0.631,49,0 672 | 1,99,58,10,0,25.4,0.551,21,0 673 | 10,68,106,23,49,35.5,0.285,47,0 674 | 3,123,100,35,240,57.3,0.88,22,0 675 | 8,91,82,0,0,35.6,0.587,68,0 676 | 6,195,70,0,0,30.9,0.328,31,1 677 | 9,156,86,0,0,24.8,0.23,53,1 678 | 0,93,60,0,0,35.3,0.263,25,0 679 | 3,121,52,0,0,36,0.127,25,1 680 | 2,101,58,17,265,24.2,0.614,23,0 681 | 2,56,56,28,45,24.2,0.332,22,0 682 | 0,162,76,36,0,49.6,0.364,26,1 683 | 0,95,64,39,105,44.6,0.366,22,0 684 | 4,125,80,0,0,32.3,0.536,27,1 685 | 5,136,82,0,0,0,0.64,69,0 686 | 2,129,74,26,205,33.2,0.591,25,0 687 | 3,130,64,0,0,23.1,0.314,22,0 688 | 1,107,50,19,0,28.3,0.181,29,0 689 | 1,140,74,26,180,24.1,0.828,23,0 690 | 1,144,82,46,180,46.1,0.335,46,1 691 | 8,107,80,0,0,24.6,0.856,34,0 692 | 13,158,114,0,0,42.3,0.257,44,1 693 | 2,121,70,32,95,39.1,0.886,23,0 694 | 7,129,68,49,125,38.5,0.439,43,1 695 | 2,90,60,0,0,23.5,0.191,25,0 696 | 7,142,90,24,480,30.4,0.128,43,1 697 | 3,169,74,19,125,29.9,0.268,31,1 698 | 0,99,0,0,0,25,0.253,22,0 699 | 4,127,88,11,155,34.5,0.598,28,0 700 | 4,118,70,0,0,44.5,0.904,26,0 701 | 2,122,76,27,200,35.9,0.483,26,0 702 | 6,125,78,31,0,27.6,0.565,49,1 703 | 1,168,88,29,0,35,0.905,52,1 704 | 2,129,0,0,0,38.5,0.304,41,0 705 | 4,110,76,20,100,28.4,0.118,27,0 706 | 6,80,80,36,0,39.8,0.177,28,0 707 | 10,115,0,0,0,0,0.261,30,1 708 | 2,127,46,21,335,34.4,0.176,22,0 709 | 9,164,78,0,0,32.8,0.148,45,1 710 | 2,93,64,32,160,38,0.674,23,1 711 | 3,158,64,13,387,31.2,0.295,24,0 712 | 5,126,78,27,22,29.6,0.439,40,0 713 | 10,129,62,36,0,41.2,0.441,38,1 714 | 0,134,58,20,291,26.4,0.352,21,0 715 | 3,102,74,0,0,29.5,0.121,32,0 716 | 7,187,50,33,392,33.9,0.826,34,1 717 | 3,173,78,39,185,33.8,0.97,31,1 718 | 10,94,72,18,0,23.1,0.595,56,0 719 | 1,108,60,46,178,35.5,0.415,24,0 720 | 5,97,76,27,0,35.6,0.378,52,1 721 | 4,83,86,19,0,29.3,0.317,34,0 722 | 1,114,66,36,200,38.1,0.289,21,0 723 | 1,149,68,29,127,29.3,0.349,42,1 724 | 5,117,86,30,105,39.1,0.251,42,0 725 | 1,111,94,0,0,32.8,0.265,45,0 726 | 4,112,78,40,0,39.4,0.236,38,0 727 | 1,116,78,29,180,36.1,0.496,25,0 728 | 0,141,84,26,0,32.4,0.433,22,0 729 | 2,175,88,0,0,22.9,0.326,22,0 730 | 2,92,52,0,0,30.1,0.141,22,0 731 | 3,130,78,23,79,28.4,0.323,34,1 732 | 8,120,86,0,0,28.4,0.259,22,1 733 | 2,174,88,37,120,44.5,0.646,24,1 734 | 2,106,56,27,165,29,0.426,22,0 735 | 2,105,75,0,0,23.3,0.56,53,0 736 | 4,95,60,32,0,35.4,0.284,28,0 737 | 0,126,86,27,120,27.4,0.515,21,0 738 | 8,65,72,23,0,32,0.6,42,0 739 | 2,99,60,17,160,36.6,0.453,21,0 740 | 1,102,74,0,0,39.5,0.293,42,1 741 | 11,120,80,37,150,42.3,0.785,48,1 742 | 3,102,44,20,94,30.8,0.4,26,0 743 | 1,109,58,18,116,28.5,0.219,22,0 744 | 9,140,94,0,0,32.7,0.734,45,1 745 | 13,153,88,37,140,40.6,1.174,39,0 746 | 12,100,84,33,105,30,0.488,46,0 747 | 1,147,94,41,0,49.3,0.358,27,1 748 | 1,81,74,41,57,46.3,1.096,32,0 749 | 3,187,70,22,200,36.4,0.408,36,1 750 | 6,162,62,0,0,24.3,0.178,50,1 751 | 4,136,70,0,0,31.2,1.182,22,1 752 | 1,121,78,39,74,39,0.261,28,0 753 | 3,108,62,24,0,26,0.223,25,0 754 | 0,181,88,44,510,43.3,0.222,26,1 755 | 8,154,78,32,0,32.4,0.443,45,1 756 | 1,128,88,39,110,36.5,1.057,37,1 757 | 7,137,90,41,0,32,0.391,39,0 758 | 0,123,72,0,0,36.3,0.258,52,1 759 | 1,106,76,0,0,37.5,0.197,26,0 760 | 6,190,92,0,0,35.5,0.278,66,1 761 | 2,88,58,26,16,28.4,0.766,22,0 762 | 9,170,74,31,0,44,0.403,43,1 763 | 9,89,62,0,0,22.5,0.142,33,0 764 | 10,101,76,48,180,32.9,0.171,63,0 765 | 2,122,70,27,0,36.8,0.34,27,0 766 | 5,121,72,23,112,26.2,0.245,30,0 767 | 1,126,60,0,0,30.1,0.349,47,1 768 | 1,93,70,31,0,30.4,0.315,23,0 -------------------------------------------------------------------------------- /house-prices-advanced-regression-techniques.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "C:\\Users\\ASUS\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n", 13 | " from numpy.core.umath_tests import inner1d\n" 14 | ] 15 | } 16 | ], 17 | "source": [ 18 | "# data analysis and wrangling\n", 19 | "import pandas as pd\n", 20 | "import numpy as np\n", 21 | "import random as rnd\n", 22 | "\n", 23 | "# visualization\n", 24 | "import seaborn as sns\n", 25 | "import matplotlib.pyplot as plt\n", 26 | "%matplotlib inline\n", 27 | "\n", 28 | "# machine learning\n", 29 | "from sklearn.linear_model import LogisticRegression\n", 30 | "from sklearn.svm import SVC, LinearSVC\n", 31 | "from sklearn.ensemble import RandomForestClassifier\n", 32 | "from sklearn.neighbors import KNeighborsClassifier\n", 33 | "from sklearn.naive_bayes import GaussianNB\n", 34 | "from sklearn.linear_model import Perceptron\n", 35 | "from sklearn.linear_model import SGDClassifier\n", 36 | "from sklearn.tree import DecisionTreeClassifier" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": 3, 42 | "metadata": {}, 43 | "outputs": [], 44 | "source": [ 45 | "# read titanic training & test csv files as a pandas DataFrame\n", 46 | "train_df = pd.read_csv('house-prices-advanced-regression-techniques/train.csv')\n", 47 | "test_df = pd.read_csv('house-prices-advanced-regression-techniques/test.csv')" 48 | ] 49 | }, 50 | { 51 | "cell_type": "code", 52 | "execution_count": 5, 53 | "metadata": {}, 54 | "outputs": [ 55 | { 56 | "name": "stdout", 57 | "output_type": "stream", 58 | "text": [ 59 | "['Id' 'MSSubClass' 'MSZoning' 'LotFrontage' 'LotArea' 'Street' 'Alley'\n", 60 | " 'LotShape' 'LandContour' 'Utilities' 'LotConfig' 'LandSlope'\n", 61 | " 'Neighborhood' 'Condition1' 'Condition2' 'BldgType' 'HouseStyle'\n", 62 | " 'OverallQual' 'OverallCond' 'YearBuilt' 'YearRemodAdd' 'RoofStyle'\n", 63 | " 'RoofMatl' 'Exterior1st' 'Exterior2nd' 'MasVnrType' 'MasVnrArea'\n", 64 | " 'ExterQual' 'ExterCond' 'Foundation' 'BsmtQual' 'BsmtCond' 'BsmtExposure'\n", 65 | " 'BsmtFinType1' 'BsmtFinSF1' 'BsmtFinType2' 'BsmtFinSF2' 'BsmtUnfSF'\n", 66 | " 'TotalBsmtSF' 'Heating' 'HeatingQC' 'CentralAir' 'Electrical' '1stFlrSF'\n", 67 | " '2ndFlrSF' 'LowQualFinSF' 'GrLivArea' 'BsmtFullBath' 'BsmtHalfBath'\n", 68 | " 'FullBath' 'HalfBath' 'BedroomAbvGr' 'KitchenAbvGr' 'KitchenQual'\n", 69 | " 'TotRmsAbvGrd' 'Functional' 'Fireplaces' 'FireplaceQu' 'GarageType'\n", 70 | " 'GarageYrBlt' 'GarageFinish' 'GarageCars' 'GarageArea' 'GarageQual'\n", 71 | " 'GarageCond' 'PavedDrive' 'WoodDeckSF' 'OpenPorchSF' 'EnclosedPorch'\n", 72 | " '3SsnPorch' 'ScreenPorch' 'PoolArea' 'PoolQC' 'Fence' 'MiscFeature'\n", 73 | " 'MiscVal' 'MoSold' 'YrSold' 'SaleType' 'SaleCondition' 'SalePrice']\n" 74 | ] 75 | } 76 | ], 77 | "source": [ 78 | "print(train_df.columns.values)" 79 | ] 80 | }, 81 | { 82 | "cell_type": "code", 83 | "execution_count": 6, 84 | "metadata": {}, 85 | "outputs": [ 86 | { 87 | "data": { 88 | "text/html": [ 89 | "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilities...PoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
0160RL65.08450PaveNaNRegLvlAllPub...0NaNNaNNaN022008WDNormal208500
1220RL80.09600PaveNaNRegLvlAllPub...0NaNNaNNaN052007WDNormal181500
2360RL68.011250PaveNaNIR1LvlAllPub...0NaNNaNNaN092008WDNormal223500
3470RL60.09550PaveNaNIR1LvlAllPub...0NaNNaNNaN022006WDAbnorml140000
4560RL84.014260PaveNaNIR1LvlAllPub...0NaNNaNNaN0122008WDNormal250000
\n", 253 | "

5 rows × 81 columns

\n", 254 | "
" 255 | ], 256 | "text/plain": [ 257 | " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", 258 | "0 1 60 RL 65.0 8450 Pave NaN Reg \n", 259 | "1 2 20 RL 80.0 9600 Pave NaN Reg \n", 260 | "2 3 60 RL 68.0 11250 Pave NaN IR1 \n", 261 | "3 4 70 RL 60.0 9550 Pave NaN IR1 \n", 262 | "4 5 60 RL 84.0 14260 Pave NaN IR1 \n", 263 | "\n", 264 | " LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal \\\n", 265 | "0 Lvl AllPub ... 0 NaN NaN NaN 0 \n", 266 | "1 Lvl AllPub ... 0 NaN NaN NaN 0 \n", 267 | "2 Lvl AllPub ... 0 NaN NaN NaN 0 \n", 268 | "3 Lvl AllPub ... 0 NaN NaN NaN 0 \n", 269 | "4 Lvl AllPub ... 0 NaN NaN NaN 0 \n", 270 | "\n", 271 | " MoSold YrSold SaleType SaleCondition SalePrice \n", 272 | "0 2 2008 WD Normal 208500 \n", 273 | "1 5 2007 WD Normal 181500 \n", 274 | "2 9 2008 WD Normal 223500 \n", 275 | "3 2 2006 WD Abnorml 140000 \n", 276 | "4 12 2008 WD Normal 250000 \n", 277 | "\n", 278 | "[5 rows x 81 columns]" 279 | ] 280 | }, 281 | "execution_count": 6, 282 | "metadata": {}, 283 | "output_type": "execute_result" 284 | } 285 | ], 286 | "source": [ 287 | "# preview the data\n", 288 | "train_df.head()" 289 | ] 290 | }, 291 | { 292 | "cell_type": "code", 293 | "execution_count": null, 294 | "metadata": {}, 295 | "outputs": [], 296 | "source": [] 297 | } 298 | ], 299 | "metadata": { 300 | "kernelspec": { 301 | "display_name": "Python 3", 302 | "language": "python", 303 | "name": "python3" 304 | }, 305 | "language_info": { 306 | "codemirror_mode": { 307 | "name": "ipython", 308 | "version": 3 309 | }, 310 | "file_extension": ".py", 311 | "mimetype": "text/x-python", 312 | "name": "python", 313 | "nbconvert_exporter": "python", 314 | "pygments_lexer": "ipython3", 315 | "version": "3.7.1" 316 | } 317 | }, 318 | "nbformat": 4, 319 | "nbformat_minor": 2 320 | } 321 | -------------------------------------------------------------------------------- /house-prices-advanced-regression-techniques/data_description.txt: -------------------------------------------------------------------------------- 1 | MSSubClass: Identifies the type of dwelling involved in the sale. 2 | 3 | 20 1-STORY 1946 & NEWER ALL STYLES 4 | 30 1-STORY 1945 & OLDER 5 | 40 1-STORY W/FINISHED ATTIC ALL AGES 6 | 45 1-1/2 STORY - UNFINISHED ALL AGES 7 | 50 1-1/2 STORY FINISHED ALL AGES 8 | 60 2-STORY 1946 & NEWER 9 | 70 2-STORY 1945 & OLDER 10 | 75 2-1/2 STORY ALL AGES 11 | 80 SPLIT OR MULTI-LEVEL 12 | 85 SPLIT FOYER 13 | 90 DUPLEX - ALL STYLES AND AGES 14 | 120 1-STORY PUD (Planned Unit Development) - 1946 & NEWER 15 | 150 1-1/2 STORY PUD - ALL AGES 16 | 160 2-STORY PUD - 1946 & NEWER 17 | 180 PUD - MULTILEVEL - INCL SPLIT LEV/FOYER 18 | 190 2 FAMILY CONVERSION - ALL STYLES AND AGES 19 | 20 | MSZoning: Identifies the general zoning classification of the sale. 21 | 22 | A Agriculture 23 | C Commercial 24 | FV Floating Village Residential 25 | I Industrial 26 | RH Residential High Density 27 | RL Residential Low Density 28 | RP Residential Low Density Park 29 | RM Residential Medium Density 30 | 31 | LotFrontage: Linear feet of street connected to property 32 | 33 | LotArea: Lot size in square feet 34 | 35 | Street: Type of road access to property 36 | 37 | Grvl Gravel 38 | Pave Paved 39 | 40 | Alley: Type of alley access to property 41 | 42 | Grvl Gravel 43 | Pave Paved 44 | NA No alley access 45 | 46 | LotShape: General shape of property 47 | 48 | Reg Regular 49 | IR1 Slightly irregular 50 | IR2 Moderately Irregular 51 | IR3 Irregular 52 | 53 | LandContour: Flatness of the property 54 | 55 | Lvl Near Flat/Level 56 | Bnk Banked - Quick and significant rise from street grade to building 57 | HLS Hillside - Significant slope from side to side 58 | Low Depression 59 | 60 | Utilities: Type of utilities available 61 | 62 | AllPub All public Utilities (E,G,W,& S) 63 | NoSewr Electricity, Gas, and Water (Septic Tank) 64 | NoSeWa Electricity and Gas Only 65 | ELO Electricity only 66 | 67 | LotConfig: Lot configuration 68 | 69 | Inside Inside lot 70 | Corner Corner lot 71 | CulDSac Cul-de-sac 72 | FR2 Frontage on 2 sides of property 73 | FR3 Frontage on 3 sides of property 74 | 75 | LandSlope: Slope of property 76 | 77 | Gtl Gentle slope 78 | Mod Moderate Slope 79 | Sev Severe Slope 80 | 81 | Neighborhood: Physical locations within Ames city limits 82 | 83 | Blmngtn Bloomington Heights 84 | Blueste Bluestem 85 | BrDale Briardale 86 | BrkSide Brookside 87 | ClearCr Clear Creek 88 | CollgCr College Creek 89 | Crawfor Crawford 90 | Edwards Edwards 91 | Gilbert Gilbert 92 | IDOTRR Iowa DOT and Rail Road 93 | MeadowV Meadow Village 94 | Mitchel Mitchell 95 | Names North Ames 96 | NoRidge Northridge 97 | NPkVill Northpark Villa 98 | NridgHt Northridge Heights 99 | NWAmes Northwest Ames 100 | OldTown Old Town 101 | SWISU South & West of Iowa State University 102 | Sawyer Sawyer 103 | SawyerW Sawyer West 104 | Somerst Somerset 105 | StoneBr Stone Brook 106 | Timber Timberland 107 | Veenker Veenker 108 | 109 | Condition1: Proximity to various conditions 110 | 111 | Artery Adjacent to arterial street 112 | Feedr Adjacent to feeder street 113 | Norm Normal 114 | RRNn Within 200' of North-South Railroad 115 | RRAn Adjacent to North-South Railroad 116 | PosN Near positive off-site feature--park, greenbelt, etc. 117 | PosA Adjacent to postive off-site feature 118 | RRNe Within 200' of East-West Railroad 119 | RRAe Adjacent to East-West Railroad 120 | 121 | Condition2: Proximity to various conditions (if more than one is present) 122 | 123 | Artery Adjacent to arterial street 124 | Feedr Adjacent to feeder street 125 | Norm Normal 126 | RRNn Within 200' of North-South Railroad 127 | RRAn Adjacent to North-South Railroad 128 | PosN Near positive off-site feature--park, greenbelt, etc. 129 | PosA Adjacent to postive off-site feature 130 | RRNe Within 200' of East-West Railroad 131 | RRAe Adjacent to East-West Railroad 132 | 133 | BldgType: Type of dwelling 134 | 135 | 1Fam Single-family Detached 136 | 2FmCon Two-family Conversion; originally built as one-family dwelling 137 | Duplx Duplex 138 | TwnhsE Townhouse End Unit 139 | TwnhsI Townhouse Inside Unit 140 | 141 | HouseStyle: Style of dwelling 142 | 143 | 1Story One story 144 | 1.5Fin One and one-half story: 2nd level finished 145 | 1.5Unf One and one-half story: 2nd level unfinished 146 | 2Story Two story 147 | 2.5Fin Two and one-half story: 2nd level finished 148 | 2.5Unf Two and one-half story: 2nd level unfinished 149 | SFoyer Split Foyer 150 | SLvl Split Level 151 | 152 | OverallQual: Rates the overall material and finish of the house 153 | 154 | 10 Very Excellent 155 | 9 Excellent 156 | 8 Very Good 157 | 7 Good 158 | 6 Above Average 159 | 5 Average 160 | 4 Below Average 161 | 3 Fair 162 | 2 Poor 163 | 1 Very Poor 164 | 165 | OverallCond: Rates the overall condition of the house 166 | 167 | 10 Very Excellent 168 | 9 Excellent 169 | 8 Very Good 170 | 7 Good 171 | 6 Above Average 172 | 5 Average 173 | 4 Below Average 174 | 3 Fair 175 | 2 Poor 176 | 1 Very Poor 177 | 178 | YearBuilt: Original construction date 179 | 180 | YearRemodAdd: Remodel date (same as construction date if no remodeling or additions) 181 | 182 | RoofStyle: Type of roof 183 | 184 | Flat Flat 185 | Gable Gable 186 | Gambrel Gabrel (Barn) 187 | Hip Hip 188 | Mansard Mansard 189 | Shed Shed 190 | 191 | RoofMatl: Roof material 192 | 193 | ClyTile Clay or Tile 194 | CompShg Standard (Composite) Shingle 195 | Membran Membrane 196 | Metal Metal 197 | Roll Roll 198 | Tar&Grv Gravel & Tar 199 | WdShake Wood Shakes 200 | WdShngl Wood Shingles 201 | 202 | Exterior1st: Exterior covering on house 203 | 204 | AsbShng Asbestos Shingles 205 | AsphShn Asphalt Shingles 206 | BrkComm Brick Common 207 | BrkFace Brick Face 208 | CBlock Cinder Block 209 | CemntBd Cement Board 210 | HdBoard Hard Board 211 | ImStucc Imitation Stucco 212 | MetalSd Metal Siding 213 | Other Other 214 | Plywood Plywood 215 | PreCast PreCast 216 | Stone Stone 217 | Stucco Stucco 218 | VinylSd Vinyl Siding 219 | Wd Sdng Wood Siding 220 | WdShing Wood Shingles 221 | 222 | Exterior2nd: Exterior covering on house (if more than one material) 223 | 224 | AsbShng Asbestos Shingles 225 | AsphShn Asphalt Shingles 226 | BrkComm Brick Common 227 | BrkFace Brick Face 228 | CBlock Cinder Block 229 | CemntBd Cement Board 230 | HdBoard Hard Board 231 | ImStucc Imitation Stucco 232 | MetalSd Metal Siding 233 | Other Other 234 | Plywood Plywood 235 | PreCast PreCast 236 | Stone Stone 237 | Stucco Stucco 238 | VinylSd Vinyl Siding 239 | Wd Sdng Wood Siding 240 | WdShing Wood Shingles 241 | 242 | MasVnrType: Masonry veneer type 243 | 244 | BrkCmn Brick Common 245 | BrkFace Brick Face 246 | CBlock Cinder Block 247 | None None 248 | Stone Stone 249 | 250 | MasVnrArea: Masonry veneer area in square feet 251 | 252 | ExterQual: Evaluates the quality of the material on the exterior 253 | 254 | Ex Excellent 255 | Gd Good 256 | TA Average/Typical 257 | Fa Fair 258 | Po Poor 259 | 260 | ExterCond: Evaluates the present condition of the material on the exterior 261 | 262 | Ex Excellent 263 | Gd Good 264 | TA Average/Typical 265 | Fa Fair 266 | Po Poor 267 | 268 | Foundation: Type of foundation 269 | 270 | BrkTil Brick & Tile 271 | CBlock Cinder Block 272 | PConc Poured Contrete 273 | Slab Slab 274 | Stone Stone 275 | Wood Wood 276 | 277 | BsmtQual: Evaluates the height of the basement 278 | 279 | Ex Excellent (100+ inches) 280 | Gd Good (90-99 inches) 281 | TA Typical (80-89 inches) 282 | Fa Fair (70-79 inches) 283 | Po Poor (<70 inches 284 | NA No Basement 285 | 286 | BsmtCond: Evaluates the general condition of the basement 287 | 288 | Ex Excellent 289 | Gd Good 290 | TA Typical - slight dampness allowed 291 | Fa Fair - dampness or some cracking or settling 292 | Po Poor - Severe cracking, settling, or wetness 293 | NA No Basement 294 | 295 | BsmtExposure: Refers to walkout or garden level walls 296 | 297 | Gd Good Exposure 298 | Av Average Exposure (split levels or foyers typically score average or above) 299 | Mn Mimimum Exposure 300 | No No Exposure 301 | NA No Basement 302 | 303 | BsmtFinType1: Rating of basement finished area 304 | 305 | GLQ Good Living Quarters 306 | ALQ Average Living Quarters 307 | BLQ Below Average Living Quarters 308 | Rec Average Rec Room 309 | LwQ Low Quality 310 | Unf Unfinshed 311 | NA No Basement 312 | 313 | BsmtFinSF1: Type 1 finished square feet 314 | 315 | BsmtFinType2: Rating of basement finished area (if multiple types) 316 | 317 | GLQ Good Living Quarters 318 | ALQ Average Living Quarters 319 | BLQ Below Average Living Quarters 320 | Rec Average Rec Room 321 | LwQ Low Quality 322 | Unf Unfinshed 323 | NA No Basement 324 | 325 | BsmtFinSF2: Type 2 finished square feet 326 | 327 | BsmtUnfSF: Unfinished square feet of basement area 328 | 329 | TotalBsmtSF: Total square feet of basement area 330 | 331 | Heating: Type of heating 332 | 333 | Floor Floor Furnace 334 | GasA Gas forced warm air furnace 335 | GasW Gas hot water or steam heat 336 | Grav Gravity furnace 337 | OthW Hot water or steam heat other than gas 338 | Wall Wall furnace 339 | 340 | HeatingQC: Heating quality and condition 341 | 342 | Ex Excellent 343 | Gd Good 344 | TA Average/Typical 345 | Fa Fair 346 | Po Poor 347 | 348 | CentralAir: Central air conditioning 349 | 350 | N No 351 | Y Yes 352 | 353 | Electrical: Electrical system 354 | 355 | SBrkr Standard Circuit Breakers & Romex 356 | FuseA Fuse Box over 60 AMP and all Romex wiring (Average) 357 | FuseF 60 AMP Fuse Box and mostly Romex wiring (Fair) 358 | FuseP 60 AMP Fuse Box and mostly knob & tube wiring (poor) 359 | Mix Mixed 360 | 361 | 1stFlrSF: First Floor square feet 362 | 363 | 2ndFlrSF: Second floor square feet 364 | 365 | LowQualFinSF: Low quality finished square feet (all floors) 366 | 367 | GrLivArea: Above grade (ground) living area square feet 368 | 369 | BsmtFullBath: Basement full bathrooms 370 | 371 | BsmtHalfBath: Basement half bathrooms 372 | 373 | FullBath: Full bathrooms above grade 374 | 375 | HalfBath: Half baths above grade 376 | 377 | Bedroom: Bedrooms above grade (does NOT include basement bedrooms) 378 | 379 | Kitchen: Kitchens above grade 380 | 381 | KitchenQual: Kitchen quality 382 | 383 | Ex Excellent 384 | Gd Good 385 | TA Typical/Average 386 | Fa Fair 387 | Po Poor 388 | 389 | TotRmsAbvGrd: Total rooms above grade (does not include bathrooms) 390 | 391 | Functional: Home functionality (Assume typical unless deductions are warranted) 392 | 393 | Typ Typical Functionality 394 | Min1 Minor Deductions 1 395 | Min2 Minor Deductions 2 396 | Mod Moderate Deductions 397 | Maj1 Major Deductions 1 398 | Maj2 Major Deductions 2 399 | Sev Severely Damaged 400 | Sal Salvage only 401 | 402 | Fireplaces: Number of fireplaces 403 | 404 | FireplaceQu: Fireplace quality 405 | 406 | Ex Excellent - Exceptional Masonry Fireplace 407 | Gd Good - Masonry Fireplace in main level 408 | TA Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basement 409 | Fa Fair - Prefabricated Fireplace in basement 410 | Po Poor - Ben Franklin Stove 411 | NA No Fireplace 412 | 413 | GarageType: Garage location 414 | 415 | 2Types More than one type of garage 416 | Attchd Attached to home 417 | Basment Basement Garage 418 | BuiltIn Built-In (Garage part of house - typically has room above garage) 419 | CarPort Car Port 420 | Detchd Detached from home 421 | NA No Garage 422 | 423 | GarageYrBlt: Year garage was built 424 | 425 | GarageFinish: Interior finish of the garage 426 | 427 | Fin Finished 428 | RFn Rough Finished 429 | Unf Unfinished 430 | NA No Garage 431 | 432 | GarageCars: Size of garage in car capacity 433 | 434 | GarageArea: Size of garage in square feet 435 | 436 | GarageQual: Garage quality 437 | 438 | Ex Excellent 439 | Gd Good 440 | TA Typical/Average 441 | Fa Fair 442 | Po Poor 443 | NA No Garage 444 | 445 | GarageCond: Garage condition 446 | 447 | Ex Excellent 448 | Gd Good 449 | TA Typical/Average 450 | Fa Fair 451 | Po Poor 452 | NA No Garage 453 | 454 | PavedDrive: Paved driveway 455 | 456 | Y Paved 457 | P Partial Pavement 458 | N Dirt/Gravel 459 | 460 | WoodDeckSF: Wood deck area in square feet 461 | 462 | OpenPorchSF: Open porch area in square feet 463 | 464 | EnclosedPorch: Enclosed porch area in square feet 465 | 466 | 3SsnPorch: Three season porch area in square feet 467 | 468 | ScreenPorch: Screen porch area in square feet 469 | 470 | PoolArea: Pool area in square feet 471 | 472 | PoolQC: Pool quality 473 | 474 | Ex Excellent 475 | Gd Good 476 | TA Average/Typical 477 | Fa Fair 478 | NA No Pool 479 | 480 | Fence: Fence quality 481 | 482 | GdPrv Good Privacy 483 | MnPrv Minimum Privacy 484 | GdWo Good Wood 485 | MnWw Minimum Wood/Wire 486 | NA No Fence 487 | 488 | MiscFeature: Miscellaneous feature not covered in other categories 489 | 490 | Elev Elevator 491 | Gar2 2nd Garage (if not described in garage section) 492 | Othr Other 493 | Shed Shed (over 100 SF) 494 | TenC Tennis Court 495 | NA None 496 | 497 | MiscVal: $Value of miscellaneous feature 498 | 499 | MoSold: Month Sold (MM) 500 | 501 | YrSold: Year Sold (YYYY) 502 | 503 | SaleType: Type of sale 504 | 505 | WD Warranty Deed - Conventional 506 | CWD Warranty Deed - Cash 507 | VWD Warranty Deed - VA Loan 508 | New Home just constructed and sold 509 | COD Court Officer Deed/Estate 510 | Con Contract 15% Down payment regular terms 511 | ConLw Contract Low Down payment and low interest 512 | ConLI Contract Low Interest 513 | ConLD Contract Low Down 514 | Oth Other 515 | 516 | SaleCondition: Condition of sale 517 | 518 | Normal Normal Sale 519 | Abnorml Abnormal Sale - trade, foreclosure, short sale 520 | AdjLand Adjoining Land Purchase 521 | Alloca Allocation - two linked properties with separate deeds, typically condo with a garage unit 522 | Family Sale between family members 523 | Partial Home was not completed when last assessed (associated with New Homes) 524 | -------------------------------------------------------------------------------- /house-prices-advanced-regression-techniques/sample_submission.csv: -------------------------------------------------------------------------------- 1 | Id,SalePrice 2 | 1461,169277.0524984 3 | 1462,187758.393988768 4 | 1463,183583.683569555 5 | 1464,179317.47751083 6 | 1465,150730.079976501 7 | 1466,177150.989247307 8 | 1467,172070.659229164 9 | 1468,175110.956519547 10 | 1469,162011.698831665 11 | 1470,160726.247831419 12 | 1471,157933.279456005 13 | 1472,145291.245020389 14 | 1473,159672.017631819 15 | 1474,164167.518301885 16 | 1475,150891.638244053 17 | 1476,179460.96518734 18 | 1477,185034.62891405 19 | 1478,182352.192644656 20 | 1479,183053.458213802 21 | 1480,187823.339254278 22 | 1481,186544.114327568 23 | 1482,158230.77520516 24 | 1483,190552.829321091 25 | 1484,147183.67487199 26 | 1485,185855.300905493 27 | 1486,174350.470676986 28 | 1487,201740.620690863 29 | 1488,162986.378895754 30 | 1489,162330.199085679 31 | 1490,165845.938616539 32 | 1491,180929.622876974 33 | 1492,163481.501519718 34 | 1493,187798.076714233 35 | 1494,198822.198942566 36 | 1495,194868.409899858 37 | 1496,152605.298564403 38 | 1497,147797.702836811 39 | 1498,150521.96899297 40 | 1499,146991.630153739 41 | 1500,150306.307814534 42 | 1501,151164.372534604 43 | 1502,151133.706960953 44 | 1503,156214.042540726 45 | 1504,171992.760735142 46 | 1505,173214.912549738 47 | 1506,192429.187345783 48 | 1507,190878.69508543 49 | 1508,194542.544135519 50 | 1509,191849.439072822 51 | 1510,176363.773907793 52 | 1511,176954.185412429 53 | 1512,176521.216975696 54 | 1513,179436.704810176 55 | 1514,220079.756777048 56 | 1515,175502.918109444 57 | 1516,188321.073833569 58 | 1517,163276.324450004 59 | 1518,185911.366293097 60 | 1519,171392.830997252 61 | 1520,174418.207020775 62 | 1521,179682.709603774 63 | 1522,179423.751581665 64 | 1523,171756.918091777 65 | 1524,166849.638174419 66 | 1525,181122.168676666 67 | 1526,170934.462746566 68 | 1527,159738.292580329 69 | 1528,174445.759557658 70 | 1529,174706.363659627 71 | 1530,164507.672539365 72 | 1531,163602.512172832 73 | 1532,154126.270249525 74 | 1533,171104.853481351 75 | 1534,167735.39270528 76 | 1535,183003.613338104 77 | 1536,172580.381161499 78 | 1537,165407.889104689 79 | 1538,176363.773907793 80 | 1539,175182.950898522 81 | 1540,190757.177789246 82 | 1541,167186.995771991 83 | 1542,167839.376779276 84 | 1543,173912.421165137 85 | 1544,154034.917445551 86 | 1545,156002.955794336 87 | 1546,168173.94329857 88 | 1547,168882.437104132 89 | 1548,168173.94329857 90 | 1549,157580.177551642 91 | 1550,181922.15256011 92 | 1551,155134.227842592 93 | 1552,188885.573319552 94 | 1553,183963.193012381 95 | 1554,161298.762306335 96 | 1555,188613.66763056 97 | 1556,175080.111822945 98 | 1557,174744.400305232 99 | 1558,168175.911336919 100 | 1559,182333.472575006 101 | 1560,158307.206742274 102 | 1561,193053.055502348 103 | 1562,175031.089987177 104 | 1563,160713.294602908 105 | 1564,173186.215014436 106 | 1565,191736.7598055 107 | 1566,170401.630997116 108 | 1567,164626.577880222 109 | 1568,205469.409444832 110 | 1569,209561.784211885 111 | 1570,182271.503072356 112 | 1571,178081.549427793 113 | 1572,178425.956138831 114 | 1573,162015.318511503 115 | 1574,181722.420373045 116 | 1575,156705.730169433 117 | 1576,182902.420342386 118 | 1577,157574.595395085 119 | 1578,184380.739100813 120 | 1579,169364.469225677 121 | 1580,175846.179822063 122 | 1581,189673.295302136 123 | 1582,174401.317715566 124 | 1583,179021.448718583 125 | 1584,189196.845337149 126 | 1585,139647.095720655 127 | 1586,161468.198288911 128 | 1587,171557.32317862 129 | 1588,179447.36804185 130 | 1589,169611.619017694 131 | 1590,172088.872655744 132 | 1591,171190.624128768 133 | 1592,154850.508361878 134 | 1593,158617.655719941 135 | 1594,209258.33693701 136 | 1595,177939.027626751 137 | 1596,194631.100299584 138 | 1597,213618.871562568 139 | 1598,198342.504228533 140 | 1599,138607.971472497 141 | 1600,150778.958976731 142 | 1601,146966.230339786 143 | 1602,162182.59620952 144 | 1603,176825.940961269 145 | 1604,152799.812402444 146 | 1605,180322.322067129 147 | 1606,177508.027228367 148 | 1607,208029.642652019 149 | 1608,181987.282510201 150 | 1609,160172.72797397 151 | 1610,176761.317654248 152 | 1611,176515.497545231 153 | 1612,176270.453065471 154 | 1613,183050.846258475 155 | 1614,150011.102062216 156 | 1615,159270.537808667 157 | 1616,163419.663729346 158 | 1617,163399.983345859 159 | 1618,173364.161505756 160 | 1619,169556.835902417 161 | 1620,183690.595995738 162 | 1621,176980.914909382 163 | 1622,204773.36222471 164 | 1623,174728.655998442 165 | 1624,181873.458244461 166 | 1625,177322.000823979 167 | 1626,193927.939041863 168 | 1627,181715.622732304 169 | 1628,199270.841200324 170 | 1629,177109.589956218 171 | 1630,153909.578271486 172 | 1631,162931.203336223 173 | 1632,166386.7567182 174 | 1633,173719.30379824 175 | 1634,179757.925656704 176 | 1635,179007.601964376 177 | 1636,180370.808623106 178 | 1637,185102.616730563 179 | 1638,198825.563452058 180 | 1639,184294.576009142 181 | 1640,200443.7920562 182 | 1641,181294.784484153 183 | 1642,174354.336267919 184 | 1643,172023.677781517 185 | 1644,181666.922855025 186 | 1645,179024.491269586 187 | 1646,178324.191575907 188 | 1647,184534.676687694 189 | 1648,159397.250378784 190 | 1649,178430.966728182 191 | 1650,177743.799385967 192 | 1651,179395.305519087 193 | 1652,151713.38474815 194 | 1653,151713.38474815 195 | 1654,168434.977996215 196 | 1655,153999.100311019 197 | 1656,164096.097354123 198 | 1657,166335.403036551 199 | 1658,163020.725375757 200 | 1659,155862.510668829 201 | 1660,182760.651095509 202 | 1661,201912.270622883 203 | 1662,185988.233987516 204 | 1663,183778.44888032 205 | 1664,170935.85921771 206 | 1665,184468.908382254 207 | 1666,191569.089663229 208 | 1667,232991.025583822 209 | 1668,180980.721388278 210 | 1669,164279.13048219 211 | 1670,183859.460411109 212 | 1671,185922.465682076 213 | 1672,191742.778119363 214 | 1673,199954.072465842 215 | 1674,180690.274752587 216 | 1675,163099.3096358 217 | 1676,140791.922472443 218 | 1677,166481.86647592 219 | 1678,172080.434496773 220 | 1679,191719.161659178 221 | 1680,160741.098612515 222 | 1681,157829.546854733 223 | 1682,196896.748596341 224 | 1683,159675.423990355 225 | 1684,182084.790901946 226 | 1685,179233.926374487 227 | 1686,155774.270901623 228 | 1687,181354.326716058 229 | 1688,179605.563663918 230 | 1689,181609.34866147 231 | 1690,178221.531623281 232 | 1691,175559.920735795 233 | 1692,200328.822792041 234 | 1693,178630.060559899 235 | 1694,177174.535221728 236 | 1695,172515.687368714 237 | 1696,204032.992922943 238 | 1697,176023.232787689 239 | 1698,202202.073341595 240 | 1699,181734.480075862 241 | 1700,183982.158993126 242 | 1701,188007.94241481 243 | 1702,185922.966763517 244 | 1703,183978.544874918 245 | 1704,177199.618638821 246 | 1705,181878.647956764 247 | 1706,173622.088728263 248 | 1707,180728.168562655 249 | 1708,176477.026606328 250 | 1709,184282.266697609 251 | 1710,162062.47538448 252 | 1711,182550.070992189 253 | 1712,180987.949624695 254 | 1713,178173.79762147 255 | 1714,179980.635948606 256 | 1715,173257.637826205 257 | 1716,177271.291059307 258 | 1717,175338.355442312 259 | 1718,177548.140549508 260 | 1719,175969.91662932 261 | 1720,175011.481953462 262 | 1721,185199.372568143 263 | 1722,188514.050228937 264 | 1723,185080.145268797 265 | 1724,157304.402574096 266 | 1725,194260.859481297 267 | 1726,181262.329995106 268 | 1727,157003.292706732 269 | 1728,182924.499359899 270 | 1729,181902.586375439 271 | 1730,188985.371708134 272 | 1731,185290.904495068 273 | 1732,177304.425752748 274 | 1733,166274.900490809 275 | 1734,177807.420530107 276 | 1735,180330.624816201 277 | 1736,179069.112234629 278 | 1737,175943.371816948 279 | 1738,185199.050609653 280 | 1739,167350.910824524 281 | 1740,149315.311876449 282 | 1741,139010.847766793 283 | 1742,155412.151845447 284 | 1743,171308.313985441 285 | 1744,176220.543265638 286 | 1745,177643.434991809 287 | 1746,187222.653264601 288 | 1747,185635.132083154 289 | 1748,206492.534215854 290 | 1749,181681.021081956 291 | 1750,180500.198072685 292 | 1751,206486.17086841 293 | 1752,161334.301195429 294 | 1753,176156.558313965 295 | 1754,191642.223478994 296 | 1755,191945.808027777 297 | 1756,164146.306037354 298 | 1757,179883.057071096 299 | 1758,178071.137668844 300 | 1759,188241.637896875 301 | 1760,174559.656173171 302 | 1761,182347.363042264 303 | 1762,191507.251872857 304 | 1763,199751.865597358 305 | 1764,162106.416145131 306 | 1765,164575.982314367 307 | 1766,179176.352180931 308 | 1767,177327.403857584 309 | 1768,177818.083761781 310 | 1769,186965.204048443 311 | 1770,178762.742169197 312 | 1771,183322.866146283 313 | 1772,178903.295931891 314 | 1773,186570.129421778 315 | 1774,199144.242829024 316 | 1775,172154.713310956 317 | 1776,177444.019201603 318 | 1777,166200.938073485 319 | 1778,158995.770555632 320 | 1779,168273.282454755 321 | 1780,189680.453052788 322 | 1781,181681.021081956 323 | 1782,160277.142643643 324 | 1783,197318.54715833 325 | 1784,162228.935604196 326 | 1785,187340.455456083 327 | 1786,181065.347037275 328 | 1787,190233.609102705 329 | 1788,157929.594852031 330 | 1789,168557.001935469 331 | 1790,160805.584645628 332 | 1791,221648.391978216 333 | 1792,180539.88079815 334 | 1793,182105.616283853 335 | 1794,166380.852603154 336 | 1795,178942.155617426 337 | 1796,162804.747800461 338 | 1797,183077.684392615 339 | 1798,171728.4720292 340 | 1799,164786.741540638 341 | 1800,177427.267170302 342 | 1801,197318.54715833 343 | 1802,178658.114178223 344 | 1803,185437.320523764 345 | 1804,169759.652489529 346 | 1805,173986.635055186 347 | 1806,168607.664289468 348 | 1807,194138.519145183 349 | 1808,192502.440921994 350 | 1809,176746.969818601 351 | 1810,177604.891703134 352 | 1811,193283.746584832 353 | 1812,181627.061006609 354 | 1813,169071.62025834 355 | 1814,167398.006470987 356 | 1815,150106.505141704 357 | 1816,159650.304285848 358 | 1817,179471.23597476 359 | 1818,177109.589956218 360 | 1819,166558.113328453 361 | 1820,153796.714319583 362 | 1821,174520.152570658 363 | 1822,196297.95829524 364 | 1823,169100.681601175 365 | 1824,176911.319164431 366 | 1825,169234.6454828 367 | 1826,172386.297919134 368 | 1827,156031.904802362 369 | 1828,168202.892306596 370 | 1829,166505.984017547 371 | 1830,176507.37022149 372 | 1831,180116.752553161 373 | 1832,183072.740591406 374 | 1833,189595.964677698 375 | 1834,167523.919076265 376 | 1835,210817.775863413 377 | 1836,172942.930813351 378 | 1837,145286.278144089 379 | 1838,176468.653371492 380 | 1839,159040.069562187 381 | 1840,178518.204332507 382 | 1841,169163.980786825 383 | 1842,189786.685274579 384 | 1843,181246.728523853 385 | 1844,176349.927153587 386 | 1845,205266.631009142 387 | 1846,187397.993362224 388 | 1847,208943.427726113 389 | 1848,165014.532907657 390 | 1849,182492.037566236 391 | 1850,161718.71259042 392 | 1851,180084.118941162 393 | 1852,178534.950802179 394 | 1853,151217.259961305 395 | 1854,156342.717587562 396 | 1855,188511.443835239 397 | 1856,183570.337896789 398 | 1857,225810.160292177 399 | 1858,214217.401131694 400 | 1859,187665.64101603 401 | 1860,161157.177744039 402 | 1861,187643.992594193 403 | 1862,228156.372839158 404 | 1863,220449.534665317 405 | 1864,220522.352084222 406 | 1865,156647.763531624 407 | 1866,187388.833374873 408 | 1867,178640.723791573 409 | 1868,180847.216739049 410 | 1869,159505.170529478 411 | 1870,164305.538020654 412 | 1871,180181.19673723 413 | 1872,184602.734989972 414 | 1873,193440.372174434 415 | 1874,184199.788209911 416 | 1875,196241.892907637 417 | 1876,175588.618271096 418 | 1877,179503.046546829 419 | 1878,183658.076582555 420 | 1879,193700.976276404 421 | 1880,165399.62450704 422 | 1881,186847.944787446 423 | 1882,198127.73287817 424 | 1883,183320.898107934 425 | 1884,181613.606696657 426 | 1885,178298.791761954 427 | 1886,185733.534000593 428 | 1887,180008.188485489 429 | 1888,175127.59621604 430 | 1889,183467.176862723 431 | 1890,182705.546021743 432 | 1891,152324.943593181 433 | 1892,169878.515981342 434 | 1893,183735.975076576 435 | 1894,224118.280105941 436 | 1895,169355.202465146 437 | 1896,180054.276407441 438 | 1897,174081.601977368 439 | 1898,168494.985022146 440 | 1899,181871.598843299 441 | 1900,173554.489658383 442 | 1901,169805.382165577 443 | 1902,176192.990728755 444 | 1903,204264.39284654 445 | 1904,169630.906956928 446 | 1905,185724.838807268 447 | 1906,195699.036281861 448 | 1907,189494.276162169 449 | 1908,149607.905673439 450 | 1909,154650.199045978 451 | 1910,151579.558140433 452 | 1911,185147.380531144 453 | 1912,196314.53120359 454 | 1913,210802.395364155 455 | 1914,166271.2863726 456 | 1915,154865.359142973 457 | 1916,173575.5052865 458 | 1917,179399.563554274 459 | 1918,164280.776562049 460 | 1919,171247.48948121 461 | 1920,166878.587182445 462 | 1921,188129.459710994 463 | 1922,183517.34369691 464 | 1923,175522.026925727 465 | 1924,190060.105331152 466 | 1925,174179.824771856 467 | 1926,171059.523675194 468 | 1927,183004.186769318 469 | 1928,183601.647387418 470 | 1929,163539.327185998 471 | 1930,164677.676391525 472 | 1931,162395.073865424 473 | 1932,182207.6323195 474 | 1933,192223.939790304 475 | 1934,176391.829390125 476 | 1935,181913.179121348 477 | 1936,179136.097888261 478 | 1937,196595.568243212 479 | 1938,194822.365690957 480 | 1939,148356.669440918 481 | 1940,160387.604263899 482 | 1941,181276.500571809 483 | 1942,192474.817899346 484 | 1943,157699.907796437 485 | 1944,215785.540813051 486 | 1945,181824.300998793 487 | 1946,221813.00948166 488 | 1947,165281.292597397 489 | 1948,255629.49047034 490 | 1949,173154.590990955 491 | 1950,183884.65246539 492 | 1951,200210.353608489 493 | 1952,186599.221265342 494 | 1953,192718.532696106 495 | 1954,178628.665952764 496 | 1955,180650.342418406 497 | 1956,206003.107947263 498 | 1957,166457.67844853 499 | 1958,202916.221653487 500 | 1959,192463.969983091 501 | 1960,171775.497189898 502 | 1961,175249.222149411 503 | 1962,147086.59893993 504 | 1963,149709.672100371 505 | 1964,171411.404533743 506 | 1965,178188.964799425 507 | 1966,156491.711373235 508 | 1967,180953.241201168 509 | 1968,203909.759061135 510 | 1969,175470.149087545 511 | 1970,205578.333622415 512 | 1971,199428.857699441 513 | 1972,187599.163869476 514 | 1973,192265.198109864 515 | 1974,196666.554897677 516 | 1975,155537.862252682 517 | 1976,169543.240620935 518 | 1977,202487.010170501 519 | 1978,208232.716273485 520 | 1979,173621.195202569 521 | 1980,172414.608571812 522 | 1981,164400.75641556 523 | 1982,160480.424024781 524 | 1983,156060.853810389 525 | 1984,157437.192820581 526 | 1985,158163.720929772 527 | 1986,154849.043268978 528 | 1987,152186.609341561 529 | 1988,180340.215399228 530 | 1989,178344.62451356 531 | 1990,190170.382266827 532 | 1991,168092.975480832 533 | 1992,178757.912566805 534 | 1993,174518.256882082 535 | 1994,198168.490116289 536 | 1995,176882.693978902 537 | 1996,183801.672896251 538 | 1997,196400.046680661 539 | 1998,172281.605004025 540 | 1999,196380.366297173 541 | 2000,198228.354306682 542 | 2001,195556.581268962 543 | 2002,186453.264469043 544 | 2003,181869.381196234 545 | 2004,175610.840124147 546 | 2005,183438.730800145 547 | 2006,179584.488673295 548 | 2007,182386.152242034 549 | 2008,160750.367237054 550 | 2009,182477.505046008 551 | 2010,187720.359207171 552 | 2011,187201.942081511 553 | 2012,176385.102235149 554 | 2013,175901.787841278 555 | 2014,182584.280198283 556 | 2015,195664.686104237 557 | 2016,181420.346494222 558 | 2017,176676.04995228 559 | 2018,181594.678867334 560 | 2019,178521.747964951 561 | 2020,175895.883726231 562 | 2021,168468.005916477 563 | 2022,200973.129447888 564 | 2023,197030.641992202 565 | 2024,192867.417844592 566 | 2025,196449.247639381 567 | 2026,141684.196398607 568 | 2027,153353.334123901 569 | 2028,151143.549016705 570 | 2029,163753.087114229 571 | 2030,158682.460013921 572 | 2031,144959.835250915 573 | 2032,160144.390548579 574 | 2033,156286.534303521 575 | 2034,165726.707619571 576 | 2035,182427.481047359 577 | 2036,173310.56154032 578 | 2037,173310.56154032 579 | 2038,151556.01403002 580 | 2039,158908.146068683 581 | 2040,209834.383092536 582 | 2041,192410.516550815 583 | 2042,174026.247294886 584 | 2043,195499.830115336 585 | 2044,200918.018812493 586 | 2045,207243.616023976 587 | 2046,196149.783851876 588 | 2047,192097.914850217 589 | 2048,178570.948923671 590 | 2049,228617.968325428 591 | 2050,199929.884438451 592 | 2051,160206.365612859 593 | 2052,179854.431885567 594 | 2053,185987.340461822 595 | 2054,161122.505607926 596 | 2055,175949.342720138 597 | 2056,183683.590595324 598 | 2057,176401.34762338 599 | 2058,205832.532527897 600 | 2059,177799.799849436 601 | 2060,167565.362080406 602 | 2061,186348.958436557 603 | 2062,179782.759465081 604 | 2063,169837.623333323 605 | 2064,178817.275675758 606 | 2065,174444.479149339 607 | 2066,192834.968917174 608 | 2067,196564.717984981 609 | 2068,206977.567039357 610 | 2069,157054.253944128 611 | 2070,175142.948078577 612 | 2071,159932.1643654 613 | 2072,182801.408333628 614 | 2073,181510.375176825 615 | 2074,181613.035129451 616 | 2075,186920.512597635 617 | 2076,157950.170625222 618 | 2077,176115.159022876 619 | 2078,182744.514344465 620 | 2079,180660.683691591 621 | 2080,160775.629777099 622 | 2081,186711.715848082 623 | 2082,223581.758190888 624 | 2083,172330.943236652 625 | 2084,163474.633393212 626 | 2085,175308.263299874 627 | 2086,187462.725306432 628 | 2087,180655.101535034 629 | 2088,152121.98603454 630 | 2089,159856.233909727 631 | 2090,186559.854936737 632 | 2091,183962.550959411 633 | 2092,162107.168699296 634 | 2093,162582.288981283 635 | 2094,154407.701597409 636 | 2095,181625.666399474 637 | 2096,164810.609473548 638 | 2097,176429.401241704 639 | 2098,179188.089925259 640 | 2099,145997.635377703 641 | 2100,218676.768270367 642 | 2101,188323.861214226 643 | 2102,168690.0722914 644 | 2103,165088.746797705 645 | 2104,191435.007885166 646 | 2105,168864.404664512 647 | 2106,176041.882371574 648 | 2107,215911.674390325 649 | 2108,167388.238629016 650 | 2109,163854.786753017 651 | 2110,163299.477980171 652 | 2111,178298.214633119 653 | 2112,176376.586164775 654 | 2113,170211.043976522 655 | 2114,170818.344786366 656 | 2115,174388.867432503 657 | 2116,161112.987374671 658 | 2117,172179.082325307 659 | 2118,157798.309713876 660 | 2119,169106.151422924 661 | 2120,170129.531364292 662 | 2121,157680.227412949 663 | 2122,162690.209131977 664 | 2123,146968.379365095 665 | 2124,181507.721372455 666 | 2125,191215.589752983 667 | 2126,189432.689844522 668 | 2127,207271.484957719 669 | 2128,170030.807488363 670 | 2129,148409.806476335 671 | 2130,193850.613979055 672 | 2131,193808.319298263 673 | 2132,166300.235380627 674 | 2133,163474.633393212 675 | 2134,177473.606564978 676 | 2135,157443.925537187 677 | 2136,180681.007992057 678 | 2137,183463.17030026 679 | 2138,182481.763081195 680 | 2139,193717.15117887 681 | 2140,182782.55099007 682 | 2141,175530.651633287 683 | 2142,177804.057884623 684 | 2143,159448.670848577 685 | 2144,181338.976717529 686 | 2145,178553.558537021 687 | 2146,162820.928264556 688 | 2147,188832.479997186 689 | 2148,164682.185899437 690 | 2149,181549.735943801 691 | 2150,199158.097008868 692 | 2151,152889.520990566 693 | 2152,181150.551679116 694 | 2153,181416.732376013 695 | 2154,164391.238182305 696 | 2155,185421.046498812 697 | 2156,193981.327550004 698 | 2157,178824.324789223 699 | 2158,209270.051606246 700 | 2159,177801.266806344 701 | 2160,179053.762236101 702 | 2161,178762.170601992 703 | 2162,184655.300458183 704 | 2163,191284.655779772 705 | 2164,179598.085818785 706 | 2165,167517.628078595 707 | 2166,182873.903794044 708 | 2167,177484.91371363 709 | 2168,188444.597319524 710 | 2169,179184.153848562 711 | 2170,184365.175780982 712 | 2171,184479.322005212 713 | 2172,182927.863869391 714 | 2173,178611.639373646 715 | 2174,181943.343613558 716 | 2175,175080.614768394 717 | 2176,190720.794649138 718 | 2177,198422.868144723 719 | 2178,184482.11308349 720 | 2179,139214.952187861 721 | 2180,169233.113601757 722 | 2181,180664.118686848 723 | 2182,178818.742632666 724 | 2183,180422.049969947 725 | 2184,178601.93645581 726 | 2185,183083.159775993 727 | 2186,173163.101499699 728 | 2187,185968.161159774 729 | 2188,171226.050683054 730 | 2189,281643.976116786 731 | 2190,160031.711281258 732 | 2191,162775.979779394 733 | 2192,160735.445970193 734 | 2193,166646.109048572 735 | 2194,188384.548444549 736 | 2195,165830.697255197 737 | 2196,182138.358533039 738 | 2197,171595.397975647 739 | 2198,160337.079183809 740 | 2199,191215.088671543 741 | 2200,166956.093232213 742 | 2201,186581.830878692 743 | 2202,176450.548582099 744 | 2203,193743.194909801 745 | 2204,198882.566078408 746 | 2205,176385.102235149 747 | 2206,162447.639333636 748 | 2207,193782.555676777 749 | 2208,183653.890897141 750 | 2209,210578.623546866 751 | 2210,158527.164107319 752 | 2211,163081.025723456 753 | 2212,174388.867432503 754 | 2213,191905.870131966 755 | 2214,174388.867432503 756 | 2215,161642.711648983 757 | 2216,186939.507215101 758 | 2217,172482.165792649 759 | 2218,159695.999763546 760 | 2219,157230.369671007 761 | 2220,179188.089925259 762 | 2221,157972.82120994 763 | 2222,156804.951429181 764 | 2223,211491.972463654 765 | 2224,186537.246201062 766 | 2225,200468.161070551 767 | 2226,182241.340444154 768 | 2227,157342.225898399 769 | 2228,182022.387105998 770 | 2229,181244.510876788 771 | 2230,178556.671573788 772 | 2231,189547.199876284 773 | 2232,187948.65165563 774 | 2233,194107.287565956 775 | 2234,183521.710369283 776 | 2235,183682.123638416 777 | 2236,178483.353073443 778 | 2237,184003.879764736 779 | 2238,171318.59033449 780 | 2239,162039.754313997 781 | 2240,154846.252190699 782 | 2241,194822.365690957 783 | 2242,169788.738771463 784 | 2243,178891.554489941 785 | 2244,152084.772428865 786 | 2245,139169.86642879 787 | 2246,192439.536044606 788 | 2247,161067.859766557 789 | 2248,158762.648504781 790 | 2249,175569.690441774 791 | 2250,183659.795012187 792 | 2251,280618.132617258 793 | 2252,180051.809151659 794 | 2253,176519.18031559 795 | 2254,179028.429210291 796 | 2255,177161.583857224 797 | 2256,180081.508849842 798 | 2257,205895.254584712 799 | 2258,183389.78131415 800 | 2259,178543.647859512 801 | 2260,194798.320499104 802 | 2261,162845.613675766 803 | 2262,148103.867006579 804 | 2263,201016.171121215 805 | 2264,277936.12694354 806 | 2265,249768.279823405 807 | 2266,161596.052159825 808 | 2267,158011.114889899 809 | 2268,194089.683858004 810 | 2269,181733.336941451 811 | 2270,182852.32772198 812 | 2271,189893.003058465 813 | 2272,194650.210979875 814 | 2273,187904.461286262 815 | 2274,171774.925622692 816 | 2275,177998.685921479 817 | 2276,175648.484325498 818 | 2277,196918.071362067 819 | 2278,184299.838071218 820 | 2279,182379.855682734 821 | 2280,184050.725802482 822 | 2281,158296.975970284 823 | 2282,175053.355553278 824 | 2283,162293.376090644 825 | 2284,186328.880047186 826 | 2285,151422.116936538 827 | 2286,181969.358707768 828 | 2287,189122.67702416 829 | 2288,185645.475220346 830 | 2289,182829.898109257 831 | 2290,195848.788183328 832 | 2291,198785.059550672 833 | 2292,181676.126555428 834 | 2293,194131.012663328 835 | 2294,201416.004864508 836 | 2295,185096.577205616 837 | 2296,195158.972598372 838 | 2297,184795.783735112 839 | 2298,189168.263864671 840 | 2299,216855.260149095 841 | 2300,184946.642483576 842 | 2301,189317.51282069 843 | 2302,180803.277842406 844 | 2303,175061.18585763 845 | 2304,179074.839090732 846 | 2305,145708.764336107 847 | 2306,142398.022752011 848 | 2307,161474.534863641 849 | 2308,157025.945155458 850 | 2309,163424.037827357 851 | 2310,164692.778645345 852 | 2311,152163.2443541 853 | 2312,192383.215486656 854 | 2313,182520.230322476 855 | 2314,187254.507549722 856 | 2315,176489.659740359 857 | 2316,181520.466841293 858 | 2317,186414.978214721 859 | 2318,185197.764639705 860 | 2319,178657.794083741 861 | 2320,179731.198023759 862 | 2321,161748.271317074 863 | 2322,158608.749069322 864 | 2323,178807.370559878 865 | 2324,184187.158803897 866 | 2325,181686.10402108 867 | 2326,190311.050228337 868 | 2327,192252.496354076 869 | 2328,193954.849525775 870 | 2329,181044.201560887 871 | 2330,180258.131219792 872 | 2331,199641.657313834 873 | 2332,197530.775205517 874 | 2333,191777.196949138 875 | 2334,195779.543033588 876 | 2335,202112.046522999 877 | 2336,192343.34807661 878 | 2337,185191.359443218 879 | 2338,186760.207965688 880 | 2339,177733.78193528 881 | 2340,164430.391189608 882 | 2341,185299.601552401 883 | 2342,186414.012339254 884 | 2343,176401.921054593 885 | 2344,182381.322639642 886 | 2345,176334.184710805 887 | 2346,184901.735847457 888 | 2347,180085.766885029 889 | 2348,184901.735847457 890 | 2349,183967.561548763 891 | 2350,193046.301574659 892 | 2351,168538.969495849 893 | 2352,170157.842016969 894 | 2353,196559.709259637 895 | 2354,177133.709361852 896 | 2355,181553.279576244 897 | 2356,185770.606634739 898 | 2357,177017.595099274 899 | 2358,184123.358536806 900 | 2359,165970.357492196 901 | 2360,158151.985049452 902 | 2361,177086.476441481 903 | 2362,196373.896176551 904 | 2363,172465.707083115 905 | 2364,168590.782409896 906 | 2365,158820.474171061 907 | 2366,151611.37057651 908 | 2367,152125.028585543 909 | 2368,158404.073081048 910 | 2369,160692.078640755 911 | 2370,170175.22684199 912 | 2371,169854.436591138 913 | 2372,183410.785819008 914 | 2373,180347.194026928 915 | 2374,178930.528374292 916 | 2375,153346.220086301 917 | 2376,182675.204270589 918 | 2377,180770.649792036 919 | 2378,188714.148087543 920 | 2379,191393.608594076 921 | 2380,174016.157494425 922 | 2381,183189.685319552 923 | 2382,183621.508757866 924 | 2383,168991.29635758 925 | 2384,185306.650665866 926 | 2385,189030.680303208 927 | 2386,179208.665698449 928 | 2387,174901.452792889 929 | 2388,168337.406544343 930 | 2389,158234.96461859 931 | 2390,179562.453368834 932 | 2391,174176.391640607 933 | 2392,173931.531845427 934 | 2393,184111.729429665 935 | 2394,179374.482001188 936 | 2395,207348.811884535 937 | 2396,186983.419339031 938 | 2397,206779.094049527 939 | 2398,177472.074683935 940 | 2399,156727.948324862 941 | 2400,157090.568462479 942 | 2401,160387.032696693 943 | 2402,172410.28005086 944 | 2403,191603.365657467 945 | 2404,182152.207151253 946 | 2405,180161.697340702 947 | 2406,169652.235284283 948 | 2407,182503.520140218 949 | 2408,179714.630677039 950 | 2409,180282.570719908 951 | 2410,192600.338060371 952 | 2411,166115.491248565 953 | 2412,186379.553524443 954 | 2413,184361.992258449 955 | 2414,186220.965458121 956 | 2415,198176.47090687 957 | 2416,168437.776500131 958 | 2417,178003.582312015 959 | 2418,179180.469244588 960 | 2419,191930.561104806 961 | 2420,175590.266214964 962 | 2421,176713.19307219 963 | 2422,180159.090947005 964 | 2423,188090.100808026 965 | 2424,186184.717727913 966 | 2425,223055.588672278 967 | 2426,158270.753116401 968 | 2427,184733.12846644 969 | 2428,199926.378957429 970 | 2429,175075.785166001 971 | 2430,180917.925148076 972 | 2431,182067.760625207 973 | 2432,178238.60191545 974 | 2433,173454.944606532 975 | 2434,176821.936262814 976 | 2435,183642.191304235 977 | 2436,177254.582741058 978 | 2437,168715.950111702 979 | 2438,180096.931198144 980 | 2439,160620.728178758 981 | 2440,175286.544392273 982 | 2441,153494.783276297 983 | 2442,156407.65915545 984 | 2443,162162.525245786 985 | 2444,166809.886827197 986 | 2445,172929.156408918 987 | 2446,193514.330894137 988 | 2447,181612.141603756 989 | 2448,191745.386377068 990 | 2449,171369.325038261 991 | 2450,184425.470567051 992 | 2451,170563.252355189 993 | 2452,184522.369240168 994 | 2453,164968.947931153 995 | 2454,157939.621592364 996 | 2455,151520.381580069 997 | 2456,176129.508722531 998 | 2457,171112.978971478 999 | 2458,169762.081624282 1000 | 2459,162246.828936295 1001 | 2460,171339.303381589 1002 | 2461,189034.753653813 1003 | 2462,175758.873595981 1004 | 2463,163351.721489893 1005 | 2464,189806.546645026 1006 | 2465,175370.990918319 1007 | 2466,196895.599900301 1008 | 2467,176905.917994834 1009 | 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