From effc56c53e266ab2ac26b6a2eb761e964469abae Mon Sep 17 00:00:00 2001 From: Ostrowski Date: Thu, 11 Jan 2018 08:42:23 -0600 Subject: [PATCH 1/2] Christina's Answers --- .../Advertising Assessment-checkpoint.ipynb | 592 ++++++++++++++++++ Advertising Assessment.ipynb | 592 ++++++++++++++++++ Advertising.csv | 201 ++++++ 3 files changed, 1385 insertions(+) create mode 100644 .ipynb_checkpoints/Advertising Assessment-checkpoint.ipynb create mode 100644 Advertising Assessment.ipynb create mode 100644 Advertising.csv diff --git a/.ipynb_checkpoints/Advertising Assessment-checkpoint.ipynb b/.ipynb_checkpoints/Advertising Assessment-checkpoint.ipynb new file mode 100644 index 0000000..a218422 --- /dev/null +++ b/.ipynb_checkpoints/Advertising Assessment-checkpoint.ipynb @@ -0,0 +1,592 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ANSWERS\n", + "Best R2 Value: 0.897\n", + "\n", + "sales = 2.9211 + 0.0458*tv + 9.2*radio\n", + "\n", + "Predicted Sales at TV=199, Radio=32, Newspaper=88\n", + "sales = 2.9211 + 0.0458*199 + 9.2*32\n", + "sales = $306.44\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task\n", + "- Download: http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv\n", + "- Find the best R2 value, by searching the feature space\n", + "- Include your code\n", + "- Include your final R2 value, also the equation for y_hat\n", + " - Such as sales = 3.5 + 7.7*tv + 9.2*newspaper, etc\n", + "- Predict the sales for TV=199, Radio=32, Newspaper=88" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import statsmodels.api as sm\n", + "from pandas.plotting import scatter_matrix \n", + "from sklearn import linear_model\n", + "from sklearn.model_selection import train_test_split\n", + "%matplotlib inline\n", + "pd.options.display.max_colwidth = 100\n", + "\n", + "from IPython.core.display import HTML\n", + "\n", + "#def short_summary(est):\n", + "# return HTML(est.summary().tables[i].as_html())\n", + "\n", + "def dummify(df,column):\n", + " dummy = pd.get_dummies(df[column]).rename(columns = lambda x: column+'_'+str(x)).iloc[:,0:len(df[column].unique())-1]\n", + " df = df.drop(column, axis =1)\n", + " return pd.concat([df,dummy],axis=1)\n", + "from itertools import combinations" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "df = pd.read_csv('Advertising.csv',index_col = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TVradionewspapersales
1230.137.869.222.1
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" + ], + "text/plain": [ + " TV radio newspaper sales\n", + "1 230.1 37.8 69.2 22.1\n", + "2 44.5 39.3 45.1 10.4\n", + "3 17.2 45.9 69.3 9.3\n", + "4 151.5 41.3 58.5 18.5\n", + "5 180.8 10.8 58.4 12.9" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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vSu/xbcRnW2PNU/VtfFt7tK+2XPJC8v0bnYrHq7+TKCuR1Q7i6EI6M6wDKm7n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ooTbIXBLClNuwTzTYswzRmHGeMw495Cg4Zt8OGNDqtdF8fQSAqJLuWPjNDtjxrmGjazKCWXDrah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\nWszjqdMVTk8U+diVVVr9mJO1Am8/X0cpxVIn4OXFHi8v9jg35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TVradioConst
1230.137.81
244.539.31
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" + ], + "text/plain": [ + " TV radio Const\n", + "1 230.1 37.8 1\n", + "2 44.5 39.3 1\n", + "3 17.2 45.9 1\n", + "4 151.5 41.3 1\n", + "5 180.8 10.8 1" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = df.sales\n", + "X = df[['TV', 'radio']].astype(float)\n", + "X['Const']= 1\n", + "model = sm.OLS(endog=y, exog= X).fit() #exogenous and endogenous (what we predict) variables\n", + "X.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: sales R-squared: 0.897
Model: OLS Adj. R-squared: 0.896
Method: Least Squares F-statistic: 859.6
Date: Thu, 11 Jan 2018 Prob (F-statistic): 4.83e-98
Time: 08:24:20 Log-Likelihood: -386.20
No. Observations: 200 AIC: 778.4
Df Residuals: 197 BIC: 788.3
Df Model: 2
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
TV 0.0458 0.001 32.909 0.000 0.043 0.048
radio 0.1880 0.008 23.382 0.000 0.172 0.204
Const 2.9211 0.294 9.919 0.000 2.340 3.502
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Omnibus: 60.022 Durbin-Watson: 2.081
Prob(Omnibus): 0.000 Jarque-Bera (JB): 148.679
Skew: -1.323 Prob(JB): 5.19e-33
Kurtosis: 6.292 Cond. No. 425.
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: sales R-squared: 0.897\n", + "Model: OLS Adj. R-squared: 0.896\n", + "Method: Least Squares F-statistic: 859.6\n", + "Date: Thu, 11 Jan 2018 Prob (F-statistic): 4.83e-98\n", + "Time: 08:24:20 Log-Likelihood: -386.20\n", + "No. Observations: 200 AIC: 778.4\n", + "Df Residuals: 197 BIC: 788.3\n", + "Df Model: 2 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "TV 0.0458 0.001 32.909 0.000 0.043 0.048\n", + "radio 0.1880 0.008 23.382 0.000 0.172 0.204\n", + "Const 2.9211 0.294 9.919 0.000 2.340 3.502\n", + "==============================================================================\n", + "Omnibus: 60.022 Durbin-Watson: 2.081\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 148.679\n", + "Skew: -1.323 Prob(JB): 5.19e-33\n", + "Kurtosis: 6.292 Cond. No. 425.\n", + "==============================================================================\n", + "\n", + "Warnings:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m 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"df1.head()\n", + "\n", + " #print('score:', score, 'columns:', com)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "list indices must be integers or slices, not str", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrows\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Score'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0midxmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m: list indices must be integers or slices, not str" + ] + } + 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CoefColumnsIntScore
0[0.0482245128152]TV7.0665830.623689
1[0.223774515044]radio9.2904170.081757
2[0.0658344742479]newspaper12.602571-0.111407
3[0.0447396196487, 0.199355464099]TV, radio2.8673550.859348
4[0.0472036046549, 0.0507723040181]TV, newspaper5.6733100.643582
5[0.216625857637, 0.0156987409941]radio, newspaper8.9803430.071047
6[0.0446651206327, 0.196630062826, 0.00607438654789]TV, radio, newspaper2.7580720.855557
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" + ], + "text/plain": [ + " Coef Columns \\\n", + "0 [0.0482245128152] TV \n", + "1 [0.223774515044] radio \n", + "2 [0.0658344742479] newspaper \n", + "3 [0.0447396196487, 0.199355464099] TV, radio \n", + "4 [0.0472036046549, 0.0507723040181] TV, newspaper \n", + "5 [0.216625857637, 0.0156987409941] radio, newspaper \n", + "6 [0.0446651206327, 0.196630062826, 0.00607438654789] TV, radio, newspaper \n", + "\n", + " Int Score \n", + "0 7.066583 0.623689 \n", + "1 9.290417 0.081757 \n", + "2 12.602571 -0.111407 \n", + "3 2.867355 0.859348 \n", + "4 5.673310 0.643582 \n", + "5 8.980343 0.071047 \n", + "6 2.758072 0.855557 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Advertising Assessment.ipynb b/Advertising Assessment.ipynb new file mode 100644 index 0000000..a218422 --- /dev/null +++ b/Advertising Assessment.ipynb @@ -0,0 +1,592 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ANSWERS\n", + "Best R2 Value: 0.897\n", + "\n", + "sales = 2.9211 + 0.0458*tv + 9.2*radio\n", + "\n", + "Predicted Sales at TV=199, Radio=32, Newspaper=88\n", + "sales = 2.9211 + 0.0458*199 + 9.2*32\n", + "sales = $306.44\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task\n", + "- Download: http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv\n", + "- Find the best R2 value, by searching the feature space\n", + "- Include your code\n", + "- Include your final R2 value, also the equation for y_hat\n", + " - Such as sales = 3.5 + 7.7*tv + 9.2*newspaper, etc\n", + "- Predict the sales for TV=199, Radio=32, Newspaper=88" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import statsmodels.api as sm\n", + "from pandas.plotting import scatter_matrix \n", + "from sklearn import linear_model\n", + "from sklearn.model_selection import train_test_split\n", + "%matplotlib inline\n", + "pd.options.display.max_colwidth = 100\n", + "\n", + "from IPython.core.display import HTML\n", + "\n", + "#def short_summary(est):\n", + "# return HTML(est.summary().tables[i].as_html())\n", + "\n", + "def dummify(df,column):\n", + " dummy = pd.get_dummies(df[column]).rename(columns = lambda x: column+'_'+str(x)).iloc[:,0:len(df[column].unique())-1]\n", + " df = df.drop(column, axis =1)\n", + " return pd.concat([df,dummy],axis=1)\n", + "from itertools import combinations" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "df = pd.read_csv('Advertising.csv',index_col = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TVradionewspapersales
1230.137.869.222.1
244.539.345.110.4
317.245.969.39.3
4151.541.358.518.5
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vSu/xbcRnW2PNU/VtfFt7tK+2XPJC8v0bnYrHq7+TKCuR1Q7i6EI6M6wDKm7n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ooTbIXBLClNuwTzTYswzRmHGeMw495Cg4Zt8OGNDqtdF8fQSAqJLuWPjNDtjxrmGjazKCWXDrah\nn+so4Fk6O5OIftMmSHMatqIjfbDcIisKvPJZm2va/ML7C/ixeo2Ukmmc4Zo6pq4x17T57uaEkZ8y\nCTOWWi4d79MLmj8A1oCn5c+3uMT2XAhxB2Wu9j0gkVL+C0KIvwL8y+V7//nSkO2NoTJjMw3Be4ut\nE4EzztQMc9s1TtRqDv9cZVQ/3JkRJjmOqfHeUgvX0rlfkrI3RiH7swRNgw+WWhi6RssxWWw7KnAb\ngpu90wVHqgyr2pq3HIM9Q1AUoCEIkuxSzY7VrsveLMaz9EO+0K+Xld2eO1k/3J7GDGYJQF3HmsVZ\nXQ6o6kzPBwGjIMUxNR4sNutrUU2QqPE5h41xyPZYmejV2acmmG/ZPN6b8WIQ0nB0fupW79pg7x3D\nLM54sleOOHYcHFM/czgjSnM+3p5h6kLR68YxAPMtrayTHn0mDV2rA+Gz/YBxmGLogg+WWhRS4lk6\n4zBFE2AbgnGQ0rD118o4L/rUzQHfE0JUHfOvAr8jhPg/AKSUf+YVjvn/SCn/dQAhxALwS1LKnxNC\n/LvAv4KSnTsXO5OI7UlEJiWmpuGYGrf63oVmx6vaWppJ/DhTX8xUiQDcmW/weM8nzSQDX+OD5Zfz\nEauglpwy9lj9riiU4IChg1HeDNOyyxueITiy1vcYBkm9zbQNnc8tt9mfxWyMIphQZ1xnIUxyxmGq\nCu1lYd4ytAvVLivnR13A/VN4mX6csT4KsY3TlabMcjESQhGPAdJDkm/VtakMuYZ+SpIV2KbOtBZ3\n0Lgz5yGEGtWbxglbE7VAuZZef98vBiF7s4R9Hz633L7unF8R8kIyChJcU2d7GvN4z6dpG7y/1LyQ\nWEeSFQz8hDDN6vFgTQj6Dauc3inYHIXsTGNajsntOY8Xg4CdSYwQlLoSF6e6Jbm6l7JcycuZusbn\nltrc6LrMN22eDQLiVJWEXodrfNG76z+89BFO4pdKn/P/DfgY+Iflv/8W8K9ygaA5CBJ2ZzHrw4iG\nrbPW92g56anjVcex0nHYKpVSng0CdqcRtqH4ftPo4MtN8pyH21MA1ubODshrcx7b44i55smbaKXr\noE9UVldtp+ebtlJpmhQ82Q/ICqnMqY51DBu2cWpAzA5ta7KXFLif7Ptkuepafn5F1U3jLOfZvuoq\n3p5rnDlN88nOjO9uTAC1mh/PJjdGIUGcE6enK01VZGNL1+rP3vVM4qygkLLO1Fc6Dt9ZH5MVkkd7\nPu8vtRgFSlszSgu1PbMMJOAYBlkhSTJVR11o6fV7xFlBwzJqPuk1Xh/rw5BxmJLmBbksGAcpUZrT\n88wLBc1vPh2wN03oeSY3ei7TMGMQJEyilPeWmrwYhDwfBgz9lFt9NbhgGTrzTYskL7gzr+Tn0vxi\nFMKbvZPych3PrBku1Q7ldRtDF+ZpUhLchRDvA58D/q9LbKU3gfeBGPh7QBvYLn83BnrH/0AI8avA\nrwKsra0Bqj6yOQqZb1qkeYEQ4sKqKa6pqD5hkjGJMpqOQZCorXLTNvDmG0zClCQv6kmFcZjSdtQ2\noygkLecgc4tS1UlPxhEN2zjSCbcNnVt9j6JcsSuV9rajSLhhombPzVcgjM83bQpZNju8828kFUCO\n8jrVjV/Un+sscYPDf3M8EO1MVIlj4CfcW2jiHAu8aV4w9BOajnGEeiKEYLnjIKWqy1aL1WLbIYiV\n1UVeyHprXpGmBermNzVopwaGLo6Q3O8uNGl7Zh2c07y48MDANc5GJQZs6oKWaTGwU1q2caEAluUF\n01Dt5CZxys/MzbM5Vk1IKZXq0dN9n61JhK6pEUdTF8w3HRxLU01VccC3re6Z85wOHFM/13jv9lyD\nUZDU45aXxUWD5j8Gfl4I0QP+X1QQ/RVe0TdIShmjAiZCiL8PTFBmbaAC6OiUv/lbwN8C+OijjyTA\nQsvmvaUWT/Z8ltsO9xaaR5oIYZLzYhhgGRq3et6R31Wy+pYh6DdMCmmy0nGO1DgcUydKD/QZbUPj\nhzsznu4HNGydhZZdqr0IgvhgS5Bkpz+sL8oVW9Pg/XL0zzN1bq11WG67uJZOnOXMooy2a577wOva\nxTU+qzHNwwtKyzHZncWKpuGc/fXfX2yiawJdE9zsHT2en+S0HJOGbXB33jtRH3o2CAjinJ1pzOdX\n2ifqxluTiEc7PsMw4Us3Oqx2XDbHIYOy07nW92r5v1mkspNZlGEagp+81T1RCtA1wWLLYXsS8ofb\nPi3HuFBp5arxoybmcaPnMvATPEstbvcWmjzanTEI1IJ4vAwipeTFMCQs6WsPlppsjiPW+m79HVWs\nEk2oXY+UlM0+xY/uNyxcU+fJXsAOiboXPLNmfexOI5baDrfmvFcetLB0JSX5uloFr6LcHggh/iLw\n16WUf00I8QevejAhREtKOS1//Fngr6O25H8N+GXg/7vI+0gpGfopHdciySX7fsIwSJhv2vQbFnuz\nmCgtiNKCWSMjiJX4w3LHqWtoSSZZXnTP7KA7ps7nV9SDFyTqyxWo4CgOEY0W2zZZUeCY+pn1xawo\nyvOGWSmWa5s6utDqTOyglprwXmmLOgqSWjvzOHG32oYstk6S3YMkY2+a0HaNEyIIrqXXAb9CnOVs\njSMsQzsSkM+aG15q20gp8SwD99CDUxSS58OA5wOfpm2WGpgzuq55ZFY+L4nOaS7ZmUbc7HnMNW38\nOCfNJNuTiFmkvqftaXRwHXMlnXfaDjwvJN/fmjKYpfhxxu0fEdvitwlT147oN8zirN6lDPxE1SuT\nnJWuS5IVbE1CpqFqUO5NY+4vNLk330AIwd5M6QdUkn/VhNxiyyHNj45NHt4+x3kOmBSFqpGOw4y2\nm7MzUYMReSF5Uaos3ey55zZ4Hu3NSDMVL86zHn4ZLhw0hRA/jcos/2L5b5fhvPy8EOI/QWWbX5dS\nfkMI8Y+FEF8HngH/xUXeZBSkTKIEDY07Cx7bkwgplbhov6G0Nqsumiwku1PVgZNE2IZGXkgWW/ZL\nKUdVYGnYBitdh5ZrYBsaPc+qf+eYeq26fhZu9jz2/RjPMmhYaqVLsuJIpldRMKqSZVGoVVvKcjRQ\nVwrnd+cbbI8jVe+L81PdJteHIVGqtClbjnnicx7P1HYmcZ1VtxyzzkxncUaaFXQ988jfeJZx6mee\nxkp4o+OoQv8kTNjf8ek1THqHFG+W2w5rfY9pnDHXUN9DdV3SvKDfsChkQhCrSaK2Y7I3i+l4Z08t\nFYVkGqalSK13zdO8AuyXqlpzTbuc6z74jhxDZ7OU5tsah4RJQS4le35MJ5cstNR9KYQgSvN6Uisv\nJHdKVf+v3ukzDNSWX0KtkdnzTDUMImXN713pOug6CCExdb3+fodlYgGq13FaElGhzF3qssP+LGZz\nHL2yIPJFX/2XgX8f+N+llN8t3Sn/wSsdCZBS/iZKWu7wv/1V4K++yvusj0LajpohXem4xGnBNDqY\ni+54Jk2njSZU40TT1AUb+WqqRIijLnlprgJQ01H8r7yQZMVRJffzBIUPfRbCNMc29DpQSanea6nl\noGmCoZ9g6oLFtnuktnJ3vsEkzOiWdUoh1EqfZAVZXiClCjiTMMWzDaI0wTFP55rahk6UFpiGOJU4\nfhyqRqTKB5auFpXdaczmKMTQNfw4Y6lzvg0FKH6doQuE0FjpOPwzPyZMc4yImilg6gJD1/jwRodZ\nnNZjr4au2AoVZ7XjWnU2Mo1SskKe2/hK8oKFtkPTNrnZc69pR68JKaViaQBJHtIpy0bVdySlClJx\nWtBxLaRUVhgNS1dDG2GGZ6doCIQ4eAarSTRQYuKnNZSEUJNgpi7QNBV0NSG40fVY7bgU8qDm7ll6\nvfNwTZ2NkRqIWOm4J7bhd+cbjMO0fsaGJYl+Gr3apN1Fg+aLw7Si0rr3L73Ska4QrqUTxHm9Zb0z\n3zhR/K8uqllyttJcsj4MmMW5ytoOvd8nuypt98pO/MPtGXkhWe06F9atHPgJH29NMXVF9q622M8G\nAZMww7VUh/yP1seI8phfuzdXBw2tFBiuzlsIwf2FBmEpU/d0ENR80EXXYq5hYemnCxXc6rv0YhNT\nE8RZ8VIi/HzTqk3NBPC9zQl+rEYXl9oOD3diRmHK7Tnv3Nl+U9dqfpyqhXq0nZSltsP2JGZ3qpS1\n319q8Y3H+0zDjDvzHh8cmoiqPo+igsXoQjAqt/KzKDtzRt4tuX8bWUgm5anK99e4OIRQEmthkuOZ\nxonfCQHvLTbJC4mha/Q9k0mcYuiQ5Ur7tTJAXGrZrPQc+g373KwuyQqyomAWZ2yPY3RNsNCy2BrH\nZEVByzbolkMjO4d0C6r6dZjm7JfcYF2LcE2d4FBJ4PhMfL9hs5mFbyzT/DtCiBvA76GaQr8tpXxr\nE0H35hu1ukmFwwHzuPqJoWvM4oTtacQwSPna3T6mrjp0e7OY/VlM27HIckmU5vVWOUhy5i5wPklW\n8GTPZ3Mc4ppGPf2jaaIetxz4KUGcEyQZ25OYtb7H1jii5ZhnEu4NXaNVfq75ps36MOTZIOTB4vle\n6UIIHFOvg/9Sx663LWlekOUSx9TY9xMEytsnTHI6nmISPNnzcUxF/WjZavKp0kd8mSCKpgmEpBal\nTQuJY+o8LsnNFQVqZ6L8mdaH0ZGgWWH3EDm+ipGOqZXXNMOPFfWlqmFpmlp0DtdFr/F6uDffKL2z\nTt9hqGdLfTlPBgF+nNVaky3bIEiV0IYfp0zjjJ+8pcpboyClaR9lVhz2x8qLAl3TSj6zygIrTdUw\nLUBIkKoJ2/MOGqeFPMhodSHqTDkrZD3XfhiXdW64qMrRLwghLBSp/RdRTpRNKWX/lY94BaiCwll4\nuq8EJJqOUV+s/VnMzkQ9hB9vzxgGilw+i/N6NNI2NLYnEbapYWgHXiObY9X9Xmw5p17kOMvrrmHL\nUZy0KhtquwY/2JypLYGQ5bZDY7nj4JUr3GHCfZoX6KfI0VW+0FIqP5TzPn+U5vxga8rmOGS54/Ki\nFNTouSabk6iW8QoTtUDMkoyOYzINMwoJSx2bOC344o0OnmWwPgzZGofsTNXi9DI/pif7AbMoO2Jc\ntdx22CbCs1U9aqXjMAxS7iyc3rCptlZCwJ15D02ohWh7ErI1jtGEwI+zeuKjyiYqNferNMn7cYWm\niQtJIxaFJIiVCtgPtqb0Gxa3ex6b04h9P8Y1dVZ7gu+uj9F1Qccx0TRlgZHkhRotPuSP1XJN9DLT\n7TcsNkcRWVGgawJNg4ZlMI3yujwlpWQYpFiGVstEGpqojf6uWtn/ooIdPwf8fPm/LvD3gd++0jO5\nJKI0V0T1odq+3up5dRDy44NaRb9h41gaeV7wbOCTFgWuaeCaeum1rSgPhZRoAh6sdtDK+ubeVAXb\nnWl0atBMc8nNnkucWtzsu0deE6cF/aaqzzmGiWMV9BoWa31FhRoHKSsdtX1tWGdnkPNNW1lj6OJM\nEeJxkPJ8GDCNMzxTx9Q0oiTD0DWCOGcapuglJzQuWQS6Jlgu3TIXmg5ZUbDvJywu2nVWudp1GIdp\n3WybxVlJfXLw4wzH0DEO3ZjVdZ8duv6upR/pxv+J2z1yKU8EtyDJiNOCnmdiG1opkKK6rd/fndUz\n7cttl1wqFXtQNKbPLbd5sNgkK+S1he8V4fCuLS8kj/d8krzg9tyBupemKf7tJ7tTdE0wDlMeFWqw\nou/ZdFyDKMmZa1js+onSGRCCvEiRqEGJB4stFlo2SZbTa1g07QPjtLU5j7U5j2mkuL2WodXuCkII\nHu3O2BhHtGxFNaueoQeLTeLs8hbfZ+Gi7/aPUNzM/wz4TSllcqVncUlU29pCqi2nZegMgoQbXZd9\nP6k7y2muAtXP3J/nk52Z4gTOUn7yllcHOE2oVezFIMQxdB7v+9xfaBKlOVuTkDgt+IlbpxtDdV0T\n19J4uDMlL1QHuipwt2yDP96YoJV1oKo+lOQF2wP1wN/ouUe2D36cYZQq5hVMXTuXuAuqMO8nGXle\nEKG2yF3P5PkgICvgg2V1Y8aZol9Fiar7ebZeb3GGviruvxiGNZFYE4JZnBImyiZgFKgpkR9sThiV\n9dpf+mARu7xZb3RdBkFyame/gqFrJ26+OMt5tOsrhZs058ahcc+qBq1rmhqLa9n0PKvmhFYB/ir9\ni8eL0uYAACAASURBVK6hqHB+aWnRa1j4sfJmGgUJTdsgy1UG2HYNvnyri2vp+HHOziQiySRSwN2m\nh0Cw5yes9Tz6TQtTE3z72YjtScS9hSYrHbcMvDOe7AW0XYOltuJ1Vtv4w6Whw4MMT/Z9xkFG5ORH\nqERKA/bqhxxeZfb8Z4FfAP6SEKIAfkdK+R9c+Rm9AqqMcqNM35u2wVq/T+fQmNeTPV9tTRsmN3se\nq123noVe6ytidiUaoWmw2LSxDL2W3B+HKa6pn2vgVdUuLV1nt6RpVMdveya3+h6aEFiGpjIoUymy\nVDis+LI3i9kcRQihVspXCQJpVrA1UiZlX7mlxEOyvGAUpniWwTRO+dLNg8BvGzpP932eDVTX/lbf\nq3msh8sAe35M0zbxLEnLMZiEGS+GATuTqMxcLfwkr4Nmr2FdylJZykNe8MdUcAxN4Jo6fpJxb+Eg\nm7g33yAr5PUE0BtCcEgPteMabI4D8qLURvATXgzDmsKjCcGXb/WYRimPd00mUcrteQ9b1wBB2zX5\n4g11/z3e87EMRTZ3DB2/9Mqq9Ff3ZwnTKGN/pvyw3ls8XdatkCqbNYRGv2meED9+E7hoTXMkhHiE\nUje6CfwMryuNcwVYbjtsjUNcU2eu6WHoR0cLpTwoJE/CDHrKXqHlqGZN9SXEh4Q17sw3yiCrHnrX\n1Bn6Skdzf6q4lqdNIvQ9S42IcdTQzTZ0VjpOTXXQheA762O6nslCyyrrb4LHez4d16yDtZQqG32V\noGkZakY8THJsS2e+aTMK1KZgGqVowis/b16fW8Vxm0RqZHShZZOXNap9P1b6lOU2qerw9zyLXKos\ndnMccXfBu5C1wcvgmIq9EGV57QVfYRSkBElOmOR8b2PCvcUmHVfxR49rKV7j6lDtGuYaamG82VM7\nIiGo7x2lean4u+Mwoeta3Og5rEiHe/MNPtn1mUQpd+cOWC62odF1TdJc0nKUvGKQZCy0bDVCq6kp\noEE5dtn1zFMn4WxD5+5CgyhVGfCz/YD51uVN0y6Ci9Y0P0HJw30d+JvAX3jbW/QD61eDL9xoM6rU\n0A9BCMFSxz7xu4qqtD4KGQUJHcfE0jWajpqgOUwzajkG9xYb+HHGdzcmvBhGfPGGUk45TPe5Pddg\nvmXjmoqj+WIYkOYHtKVBkLA1jvlkd4qUgs1RyM2eR9Mx2BirLHUWZby/1Kyz2lcZE9saR0RZzv4s\nwjR0NkYRHyy3MDTBnTk1TrnYsplGKU/2grLB0qi3REUh2RyHzDVU0HwxUpYdH2/PWG47+In6+0rL\ncK1QN+qfvDt3pVvijmfSOWU9Nsssf3sSMd+0eT4I6Nx4fR/tTwtnjVi+q+OVaV6wPgzRNcHduYZq\nCplq0aosKSxDWSl3XBfLEAz8hJ1JxPc3p9ydb/DF1Q6b45DNccgkTBVzIlTTOKtdl4Zt0HJjnuz7\nDGcpTUfnJ252ub/QREqlQxAm2UvNBzuuScPS+d6mGjaMs7ym/L0JXDQcv1e6Ub4z2J5E+HHOxiji\n9pzLe4e0GA+j51nkhWQcJmyMFCfr9pynyLmzhCDNCOKMj+70j/z9YYOu9xZbPNmdqYZKkvPd9TFD\nP6XftOq6m6aJuuYyDtNa7GN3GrPQskmzSvxAU7UeqKd8hn6KH4cstm1yKU/MwheFZLOk0Ky0nRM8\nxUoJfmsUsTUJ69LA+0tqJn8/iGti+L4f11qclSf7ziRi30/4ZMdHLiiyb1oKLmR5we40Is4L2o5Z\nj6ueJfRRoZorPi2gxpni0zVOEX9Q31V6RBkKlDnWg8UmmlBTU455vR1/k9ibxfUurWkbeLZiKax2\nnTqLm4RqnHehbbM212JnEvO9zSm6pmGXakW//2zEo90paSGxdI1+w6o1GqqhklGQ4McZWVHUpTMh\nBHcXGqx0HbJC4pk6QaKajqfxdIMkI8oyHMN443XtiwbNB0KIvwEsSSk/FEL8BPBnpJT/6Rs8t3Ph\nWQZP9n3+4NmI5wObn30wz9opYrgfb00JkpzdWcxiy1a+NEmGZegIIdkYRvQ8k93pgZd4VVcEZdDl\nWQZr8w12ZjFJJmve2qhsOh1HEGdsTdQUxU1LKbXPNdXN8ovvL5IUBY6usVPWP8PSvvTx/owwzVnp\nuLVaO8C+n7A/i5lFGbKQ3DzWEJJIdsYxTwcBmlALykLTxk9yBn4CUmBoQsngmUplvWEr5sAoSJBI\n9mcJLccgL4NrJbc22lciygstt9Y4TLKCrXGEoasO+vHF6smez3fWx7Qdky/f7p7ImNeHIX6szs1b\nbh3JIr6zPmYSpvQaJl9Y6Rx5QFxL5/MrbYI0L7UWr/GmoAJjor5zS+cbjwaMw4TFtsNXb/fYHMf8\nk4e7GLrG9iTmVtdFAqsdj4+3x8w1TP7Rw11e7EdsjMJyBl1R8KqdnmNozOIUz9SZhcqocFj62h8e\nU4YDse+GfXJseRqlPN0PMTVldHhcYOaqcdGg+beBvwL8NwBSyj8UQvxd4K0FzYWWjbOjK8rCOOLr\nn+zxk1HG/cVmueJNar+eWaSI24YmyKUkKyR5ktN0TB6UKuv5ocbMaaZMjqnztXtz5FIymCUMguRU\n9eksL9ibJSw0HUxdEKaKw+nHavql4RhUof12Wfh+vOtjluOKzwcBhqZMpqJUTdbYpsbeLGYcqK76\nQts+QdW51fcopOTbzwaEqSIHr3ZcNF3QsDSiTNJxLdJcstR26DVMHu/5deOlug6dhslixyaIM763\nOaVhm7Qcg/eXVYfT1DXWR4q3CtTZYl5IBJI/eD7mH/5gh6ZjkhVqWKBlG2yOlZHaStfB0DTSPEXX\njsrOTaKU9ZFiKsDpvkWapiT8wiQnLYpXVrq5xsXQcU0+WG4hBPVO7fkwZHsSM9cw+c76lI1JiC4E\nD5ZaPN73eTEM2J3F9Bo2UgjStKBAYpmCpZ7N3bJLXmEUppil3UnXsyikqIdLjGN16qrWH6XKaHBj\nFGLoghtdt54pV01JySTK3qj2wEWDpiel/N1jGcXrWyO+Jm70XFYHDuvDkDgpeLg7o+OZ/HB3Wgeg\nuwsN+g1D1RstQwk7RBmWobKkwM3J8uJIzXOhZSORGJp25OIbukYUZ+z7SgezajolWYFE1jPnlqGx\nP1OjX1WQ/c7GhKcDn6/e6R9ZKV1L56t3e2S5xE9SFtsOEnWT7kzUSn9/oclaz2PkKJ5acaxQognB\n2pyHRBJkGRuDiCTNeT4MadgaLddi1VHb24GfUEjoOAY747ic44VCFiVRWBXXNSHoNUzCUgS46x7U\nlcIkY3cWMVeOtA38hPVhiKTg8Z5P2zEYRQlfvtVBK3l00yhXUnOTmLZr8HwY1Flr1fEUKAWlaZhx\ns3egQKWcQDMW23apQ6qmR4Aj007XuFpUbBFDkyy1HIZ+ymrXZRapwGZpGpmUaKjEZXMSYQqNlmvg\nGBq9+QbjOEVKizQt1Ohv+Z55IfnB1oTNUYxjqmGPrOQ7G7qmdmBJzlxT3Xe3esrJoFeqmFWlg5at\nRIZXCocgyRgHqVJ+RyUSh9W1rgoXDZp7Qoj7lIu/EOLPoQSF3ypWuw7vLyn6SZwW6AhsQ2Ol7bI5\nimi5peafEKQ5PHw+ZqXj4Foad0tHx9PqH6dpVo7DlEmpYi2lykz9Uji3ythuzyuNv6W2zcOdCUkm\na3Owtmtg6Tp+UnXHZS08EGeS95db7EwiOp5Ju8zcolQFmkrFencW4xgnPaWB2sgtl6r+gxT0m8r+\ntgoquZT1TRSlOXGWMwpTXEs1oQxNQ9c0mittZRWw3KblmAz9lGeDgOWOw5O9GU/2Qhq2TttVwr+V\n2o2UgqajowmHP3mnz2L5+jiTJHnOfMPBs5SYSJUhhml+hIf3YFFNdPTKBamSravO//5Cs5bag6N0\nrc8iPgsanEIIfup2j7ZnEqcFmgaOqZIG29AxNMHGMERIME3BrZ5HLgs808DWdZyGzjBI+ePNCZMw\nZbmk/c2inDQv6LgGX7ndq5OOJCtqd4Eozbkz3ziiwC6lZCPL610YqOGPWawzDjL2ZopAb03i+h69\nSlw0aP46Sgj4c0KIdeAxryhA/CZQbUNv9FzCRF3c+abNXMNmpWPzfKgetspyVy/5lE/2fQazlK/c\n7tJ2z+YTBkmm+JUll1NKVT+sOuRtx6gnZQCiJKftmIqQaxqkWQYIvrDaKaeCJO+VIsSf7M7Icsmt\nvodrqqC12nNZK3/enUZM4hRTU/QMQ9fOFR9OSz7mYsvGMZRQQcsxuN1v4JeNn8Nb+jgraNjqhhqG\nCYamiMC6dqCMJIQ44E1KydN9nxfDkJ1pxIrm1pmnZ2lMI0m/YfETNztkecHzYcjTQcDvPtpnqe3y\nYLHBe0uKd5rmRb0gHKcqHd9WmZqGaQjSTNZ1zJZjstJ1SPPiOsv8lFAtolvjSDlFNmzajkWcqaAH\nEscwWe7a3F9UAuHf35wyjVUdvt+w8OOM7WlMWkjuzXuYuqKwrXTdurmkrKtTVH4mTlXxKkrh4uO/\naZYSjlVCYuiibixdJS4aNNeB/w4lB9dHKa7/G8B/fOVn9ApwTL1eae6tNuk1rJrMrtTdm6UjnaF4\nYvMNfv/ZED/O2RNKNOKsoDnwE54PAuIs54srnVqmTQhY6br1aFbPs5hEKaMwrRtEXc/kSzc67M1U\np3prHLHYduh5FtuTiDDNeDEIsQyNrqdI90p6S9Rd87SQLDZVQLgIX3NrHDEKUvZnyrFxd5qwPgz4\nYLnF50vR4WmkrC6U5qhRj3c+WFTKRNX0xeEyTNdVHfOFlq34q56NJgS35xrc6LqMAlVGSHPVYBsG\nKf2Gqp2GSUacSSZRQpa79Wcwda0eqYyznCLjTFKypikRkzjLj3DvXibTd403A03A430fQ9P4U3f7\nNGyD9VGIrmvEacHNnldKucEkikGqMlpVD/csAynh0V7AQstB06DnmsSpGo54tDtjvxQ3frDYOLXR\nWllww0kdhkrC8bAM4VXjokHz76GsKL4NbFz5WVwSuiZ4b7FZb3PzQvJwe4afZPhJxtc6BxpFbUcJ\nm1q6KhZnRXEqbWYcpiVxN+Xbz4bkheqWf3G1U9tWPN71uTOvZNJUd1dgCK2sISpTp8W2Q9Mx+GRH\nqfvEacHjXZ9pnDLwE+I0J87UlMskSkmz4sjM+mLLoSguztesakUdz0RDzcm3bINng7CuoT7ZO9jy\n3Op7p96QlYNgy1F+Ry9GIQLBKEi5O+eRZAULTYuVrlO/HlTdMUoKkkzNfS+2bF4MAm72HGVbER84\nY1YIkqwem6wsfQ9jFKiJk4ZtcOdaif2dQC4ld+ZUJ1zTlKiGqQsMTcPx1DjuNx7v87hkUDTKcsxP\nrfXIC8nTQYBraYRJgaGroY5nhDzZn/HlWz38JGN7EiEQTKKMW2WmGKW50sx1jXoA4zwdhjc5GXTR\noHlTSvkvvrGzeAVkecGer1RuFpqKmpDnBfuzGMdUIri6UGl5JWhbQaAUVL64qkjRx/leVUNDvVgi\nCzCE0qTc9xO+uz5iEmXcnW+cW0uTUrJTqsUvtq2apvSH6yMGs4Slts2Nnqck5KTk0Y5PUQojVPa6\nlqGxdk6gOP7ZDE2w0LbwTIOsKIjygp1xjGdqTKKUhqUDkiQvKKRqwJw2FvrDnSmP9nwE8AvvL6Br\ngixXddlJnJHlapv+cHfG7b7Hhzc6pIUkzfOyJqq2VPt+zFzTZpZkNCyj5vQdDppxWtTb/2oqq/ps\nAz/hk90ZtqFKF+fpgh6/Ftd4Oc76/isUhZJN1DVxZNij51nMogztUHmqaenkUtK0DMJU0fuCJGPP\nT5hvNplv2TRsgz/eGPHJtmrg/dRaDynUGKUf53x/a0ZWqNp8t6xDVju3cZjy3fUxDdtg4Ot8sNx6\nqQ7Dm8RFg+Y/FUJ86W1qaIKqq/3u4wE701hN5KBqIs8GPmGiCtT3Fhpld1zjO+sTPFuvfUoMXePB\nYpOn++r1T/YCbs+5bIwjNsoZ2igruNFVquqfX20TxBl9z+bh9pRHewGaBvOxIvg+3J7WtJd+06Tn\nWRi6VsrQqaC50nW41bd5XPqTmLpgtetyd0EpsDza8fn+1qRsEqmpm/O2FIfroZUosKJgRCRZzizJ\nmASqudNxdT7ZnbE9jfnwRhtDF2yMYrbGEZNQiXYcz7ZnpR6lrqns8s5cg2mkFNbTXLEEJlGKaQg2\nJ1Gdsa52HGZxhlPWe2WZ/a/1XL7xeJ9hkFJIyY2uW2cBXU915/NCMtewahpTlhf88eaEp3sBlin4\nyq3emZqOFaf28Pd8jfNRSQeudJ0zyxx7s7hWkDL0AxaJY+r1tM0kSnm2H5TJhk+Q5HQ9ZTWzP024\n0XGUF1dbKYj90x/u872tSd2c+eKNDn/idpdvPRvRTAz8OKfjSr52b444V5bMu9OIh9szdqcxcZaz\n2n37O46LBs2fA/68EOIxyt9HAFJK+RNv7MxOQSFVeXgaZgythPvaSZ+a1Y7D0EzZKlP8IM5LIVX1\noFbNIL/0n9mdxvzOD/dZHyl6zpdv9WiXjYauZ9JyTCZhyiiIMXVBxzXpuDrf35oQpwW7s4imZbLQ\ntmlYBuvD6ZEHN4gztnJJ1zVpuwY9z2S+qRSsgzLIVIrplb5gVqiMUDWVDrKoaZTycHvGsPRCqTyA\nKmyMldPmOExZaFqEacEwUFYR+zM14TQKMrYnIRKlTelZOmnZoFrquHxhpU1a1m67rhqVqzINy9D4\n0o0OLdvgyX5A95AIsBAHE1FSSkVXSnJc0+YPXoxpWCogH45pyg/GIs5yHm7P6qDrWVpJZDZYbKpF\nZBof2JlUAs+gZtLVdT76PV/jbFTZvR9nzDdtojRnFme1pQUc3YWd5aVVzQgO/JhvPxvhxzmOKfjK\nWo+mYzBvKEWtMM15uucTJGrix9CU5cw0ypDo9DyTpmUQZwVrfQ+7VFx/tDurg3fT0Zlv2dwuJeJM\nXatLcq/SHT9871wWFw2af/q1jnJFqDq7LVeNdTVsdbHW+o0ygBhsjCMmoXKg9GydnmcdeZC2xpHS\n8pMF800Lt9xaOIaGkCpALLVtHm4rFemVrmS+qTqC8y0HXRPsTmOSrGBnGqOVtAc/ztgch5RaGNwu\n63+KjqNUhL56t8/eNC6V3ZVcnZrO0clzRbrXNVEf2zEV8dcyNO4vNNmZxqRFwSRSNKK50nTKNDSW\n2ja5LOg4GU/3Z2ilF3yc5az1Pe4vKA+iQZAgcQjLhePhTkKWSwZ+ykd3KMWRdXQh2PcTWse62Yau\n8WCpxXJZRmjaRr2dNsqJjK1JxN5UcUyX2i5fu9fnyb7PF5Y7R27wjVHI7jTm2TCgW5rh3Z5r0PEs\nPrxhsF3qmyq1m4QHiw2GQco0UpzNpbajLC7GajzWNvTrrfoFYOgCz9ZLS13Jo12fvJCMgrQecphv\n2piahqZxph5lxzO5IV0Eku9sjAGwLY1BkHK777LvpyXrIqDt6ix1LCxT4wsrLbqehW0Idf9bJoWp\nfH16DbWIPt7z2ZlGpOVARMcx+GC5zdY05MV+hGmU7gaadm7GXKGyFx4FR8efL3X9LvIiKeXTSx/h\nCiFQzo6eZSg70SSvRUnnmxbDIK2Fb5ul9/XxQX+zFECda9j0myqg/ks/sczXH+4z37ToeRYFB6tx\nNYlwWO4sSHKSrOCD5RYrHYdvPxviaBp7s4QsL1huK1Mn29TYmlBvVaWEWZTx8daMhqOsdG/1vVp+\ny7V08kMK1vt+TMNS3LgwzdE12Bgqq92+Z9ZTEwJYbDt0PMWp/KnbXZ4PlISXY2nc7jewDI3FtqTp\nqiD38faU9WFluarRtA3yQvLx1pSn+2ocs182fE7L3g4/SDtTtUWeRikfrLRIM6mCcpJyq+fy+ZUO\nn185Ka4RJEodh0JNFjUcg+WOw1xDuX2udl3WRyHP9wM2xxF5oRwKTV1n6Cv/osP8vcd7PrMoO7Xs\ncI0DmLpahEEFE1nPXh2t03fKrXZVnjkN/YZFxzHU4jcIWJtTNi4SjYW2TZ7bCA0Wmw535wzarsGt\nnsfeLAahdgpxWlBI2J7E7PsJ9+cbbE+U46oSDPFYaDlI4OG2z2CWYJmCjm2i64IwOb9RujtVJamN\ncchK2zlz/PmieCfkrYUQ/znwEfBtKeVfPu+1q12XjVLL8ffzgp99MI+pa+zOYrbHMXkh6XoGi+2T\n7onKDz1noWWVK50KBvNNp/apidKcW47HYlvNqS+dMlFwb6FRu1dmRUHXtUjzAj9KWWxZbI1DskLN\nm99baJBkBR3XZBJlZIVyrGw4ivA717RZLuuBlYrQSlfNfS/pNk/3fHoNC8/U6boWd+Y9xmHCw50Z\noyg9IrpqGzquqTLgfsMuO+9GvYXWNVHfLI/3fBaaDnfnG6z1PRxTp+UY/HAnY75p8XjPZ7nt8mIY\n1g8YqHrjrNQ+PHx9d6ZKQMXZD7g312BjHDKNMnRtxJ+6N3dq4F3tKsX6j+708CyjdgM9jJW2gx+l\nCE1xX7cmIWESn2gEpHnBrJwSGZUUqWu8HEII7s43mEVZvfhMItW0k1ISlkLV1eDGafjh7owkV9a8\ny6UjaJTm3J1XHGFdUxQ1UAF7ZxLV9dJbPZeGY9R9hixX2+cbXVdpKLRsFttK3yDOcjrlM2frGo/3\nfOVt9ZKRyUoe0TY0ELz2vfHWg6YQ4itAQ0r580KIvyGE+KqU8vfOer2uCZRgkCjVgZR0VLVI6pqg\n37RP6OklWVGLcGSFznLn4CE2dEWQn0YHEnKnBcsKpq7R8apApLPccZiESvhCIMgP1YxWuy4Vk6bj\nmtxbaKgOvyaYb6pfLLSOqgZV5/Dx9hTHVLw2TROlt44SADF1jaI4oPxU2BiHZLnK9L642j51qzqL\nVaMs1HK6rnVE6ORW36UTqvKHqem1lmaFJ+XNbRla7QK42LJZbNmEdqGI/7oot+3KmTDJTq81epbB\n3fnzb0FNE9xfbLE5VqyGygPo+McydcV5nUbZhR1E33V8WtNCnmUceV42R0onYBgktEu/njw/nS2S\n5QVb45ggyZVVdceh6eYUhWSl655aDz38b7ouMHWNG12VfbbKRd4wBJaplc6X6vW2oXOz79FrKB3a\n3WlC0zZfasE737TZHEfcX2heSdf9rQdN4KeB3yr/+7eAr6FcLwEQQvwq8KsAa2trANyd90izgoZj\n1NvEhZaNpola6+84DE0cTJacMYZ4GWe66tgLLVVQD5NczeeWnfDjOMvr+TRUwiGVaIgQyoul6ykH\nS8fUa8WYCg1L0UCOk9QPw9LVdtwzDZY6RxeH6vwW26ru2T62ilfncniUUQjBhzc6SrXIVt/Jhzc6\nrI8CltrOSx0sX4bKDhigaSfszZJTv6u3SUP5tHFeQD0LFw20nqUoacttm66nSiVnKfHrmmCpbaty\nTsMq75/z33+ubO7ppfgKqNLU4e9PFxp9zy4XyYM6dUVel1IyCGKCOOdzK+drZ17WSeAsvAtBswt8\nUv73GPji4V9KKf8WaoSTjz76SAKsdj36DbvuoIF6cM8rBmua4MFCkyQv3piq8+FZ9qUreL878x6j\n4MDc/vBx7pxiSQoqU1zM7DMpOkDt2pflpy8g1TFO60renlPndHxLVBH6Kyx3HJY7Vz/i+CqLzjUu\nh1t9j4WWMi57WadZCMF7S4o36b0CofxlW+q1vldnuqct/kIIvrL2VsxwEYe9at7KCQjx68CulPI3\nhBB/FkWk/69Oe+38/Ly8c+fOp3p+7wIKediiQjsipwbw5MkTfhyvy2FIlMOmlIpNYGjijV+XrJAl\nPUt8pkSRr++Xk/jWt74lpZQX+hLfhUzzd4BfA34D+GXg75z1wjt37vDNb37zUzqtdweHRZGX2jb9\nhnWEAP/RRx/9WFyXygLhNPK/H6uRTFBCt7fnGm/8ulTdelB6pJcd3Tvvc70J/LjcL68CIcS3L/ra\ntx40pZTfFkJEQojfBv6ZlPJ33/Y5vWvouiazKKOQygpiexK/NtfsswYpJZ/szgiTgqW2fUIn0bN0\neg2TKD1dU+BNYLFlkxcFrmVcOmDmhfpccVrUflLXeLfx1oMmwMtoRj8uOG1aoSgzkDvzDbK84JtP\nhtimxvg1uWafNYzChP1ZohwPg/RE0BRCNYvOm/i4immQw2jYBg8WW6e+98uOFZT20wJRK9VPPsOd\n/8+CLuhV4Z0ImtdQ1qPj8Oi0QqXyU00EPR0EjMKU5+s+X7rZJc7yH4uxwXGY8o1HQ9ZHAY6hc3+h\nSXsSHaGFSanEoP04P5GJVtlcUukKXGEnNcsLPtn1SfOCW6W7aHWsm72Tyk3V56lEdtfm3HoG/5pb\n+tnAZ6d6/SOEtCSIH27CVWIV43KWuvo3KaknghTfUbDQcmhYBkGcf+rn/jYQJpkiMTsmmlAZ3ujQ\ndQLVlJmEKUGa1WTmCpMwZVT6Z1fX+crOrfxeqveO0rxWbzrrWIe5tZUQ9ftLrTPHFa/xbuH6W/qU\nkReSH+4olaJew6z5h4ttm2GQ1PPkoPhsUVrgmBoNS03szGKDrmvi2ernH1XkhWTfV3J/iy2Hu/Me\nsySj71lomjiRlelCMIlSxkFG8xClLM5y1kcBozAhlyZ3F06nal0WDcug5SixibmmhWfptF2jFnve\nncYY2lGe41zDIisKBOLS3OBrvD386D517yjyQtak9cMakktt58h2c3Mcsj9LamX3UZAw9FMcw7iQ\nQMFnHRujsM4m31tq8v5yiyd7gZpFnm+c0IIspKTn2fQ8u1bzB0XGl1J5PnU988qzOU0TJzizt+ca\nFIXkW08HvBhGLLVtPrzZqYcutFM8qK7x2cF10HzDGIdKqb3jmAyCmDgraLsGAsFi++zAN/ATpFSC\nBjd7nBBTfj5QNdDz3uOzhKf7yqZkpaM6yFo9tKB+Pw5T8kKWHjIZfeNohmboGvMti3/ycI+ma9TT\nWE3bYKltq1n+K3AmlKVqzyw+ONfTEGcFYdXgCTOGfsJgpmbij09xvS6KQvJkX+lZ3up59Qz5gAs+\nfwAAIABJREFUNd4MroPmG8aLYUBRwO40wjXV5daEODIylheSYZDgHhqLnG/a7M3ievvWcU3W+sqm\nt+WYbIwmgAqun3UkWcEkVN3kgZ8w17RZaTs4hoZj6hQlj1HX1Iz5WWWJWZShaxphrIzdKlzExnUS\npapk4p0+gVKfa17Us87DIDkzaDqmxs2ei6VrLHXs+vOleVGL+F4V4qzAL+vbgyC5DppvGNdB8w2j\nkvPvehZaqdRyfCyy2ooKAe8vtbAM7cR2HTjyMFQK2T8KNTHL0EoVqIPPo5U2C1le8P2tKVIekNbP\nQmVjnBWvllXO4oynpX9SlhfnBllL12i7BtMoo984O8sXQnBvocm9hWYpxTc74vF+lXBMjYatE6Y5\n/esR0zeO66D5BlEUknapfj4NMzRD0HJ0PtnxWe069cN5mUnWW32PW1d8vm8TZ/khSRT1auAnLLcd\n1kr90c1Sfm+t72HpGuMwpWEb/POfX1S2y+fM3p84hpQEScb2RFkqLLRK76myHHD4vUTpxLk5Dtka\nR+SFPJUq5McZSVbQLTPXB4vNE46aV4UqQF/j08F10HyDWC8zyN1ZRM+zSIuC77zwkcDuLOKn788z\n9BNajo5jadi6xsYoJC45fldd+3qXMfQTtiYRIGnayhJEOR1qeKVAsmVoPN0P2BxFjKOEG12PgZ+Q\n5rI0+4LPL7dfmcDeckxcS6frqQUuSHIsQ6tZDscndaSU7E1VWWR3Gp8ImlGqlMelVJSk1VIi7XjA\njLOcnYnSeM0KSc+z3ojIyTWuFj8+T+VbQFGmkA3roJYZZzlxVtBrmDwubQYGPrRdk41AZU+2obM/\nS36sgubuLCZOc/5ofYxn6qz0HH7m/gIAN7ouO5qyXn685+OZOlkhEULVeitBWymPa49fHGt9D02o\nOujzYUBeyNpgTvlJKXFnIWCppeT5RkFKr3Gyfijlwe7hPNfSrdKa5fHejNWu0kldatvXdh3vOH58\nnspPEeMwxdI1VrsujpngmB5tx+DFMORWX9GHbvU8Hu7MCJOcxZZVKxdFaY5j6udKZ42ChO1JTNs1\n3mnqyjhQBnctx6itic9C1zWZhSmb4/+/vTcPkmxNy/t+79nz5FZZWVvv3bdv992Ge4e5MwMDszCA\nIkAgJDCLMLJDwhbCko1GFhjJWA5QyLYQIDAmxGiwiAkEQgaLTSCxGjTDaGaYjdnufvt23+6urj33\nzLN//uM7mV3VVdXd1bX3zSeio6uycvnyZJ73vN/7Pu/zDPAsCxEhTjNsU9d3Jwo2Ly12cS2DQZLy\njkfqVAs23TBhtuLSy5X0tzMBuxcmfIdqweZmc0CjF6NQOLbgWiYlz7ztglrzMPNG3uma9kZ6caFD\nzbdH5ZaCY3J20idIUqZK2qO7E8T4jrVhqz/8uVKwMXOR6XHAPPoYB809xlInYLEVkinF5dnyhoaE\nYxmUPZtKwcZ3tOJ7EKfM5Se9mAZPn5mg5FgbtphhkhLEGRVPB5OlTsggSgnj9EjzNW+1BvTDhLVc\nNHgrfc40UxiiO9wF26QxiHObAwdr3TEYitaWPZuSZ1IvuSOVIcsUHp8rbxlwlFLEqd7aJ2l2VyUh\nEaFS0Bnk0EfHNg2WO+HI46kfpSObDxFhsa0tVhbb4agWCrppV+W2d9FqV2fDz56rje5zolqg5Fpc\nnivlJmbjgHkcMA6ae4w4ybjR6BPEGSXX5OLMbXrJbMWj4Jg4ptZ7DJKMsmsyiDPSTDFTcTepzg8n\niLJMd8zPTPqoTJ+I5YLFE4esh7odWv2YxU7AC/MdTtU8bjT6I3GLIYYXmIKjA1TJs7g8W+Z0rcCZ\nSX9DEDQN3Ux5dblLJ0i4ttojzdXj00yRKTC3iDl//OKSnhJyTeaqBabKzl2z84pn8+SJCiK3ubET\neRY5zJjXz5NXChaNnnZC3S5L7IcJ19cGiGiPq/VZ925V7cc4eBxo0BSRLwN+EkiBTyql/p6I/ADw\nl4FrwF9XSu3tcPABo15y8WyDkmexVTmr4tl0gphGP+HkhIcgvLigvdIX2gHd3N+7km8VBR0UBL1d\nBe0weWHKxzQMwm28Ww4b7SCmVnCwTSGIM15d6iKiDbM826TZj7iy3MOzTAZRNvIsv5tlhWMZZEof\ni/Yg4dJsSdsMb7Mtz5Si1U9IM8X1Rp+5aoH2IGG9MWZrELPc0SWEKNF+88M1DmGbBhfWTf30o4TW\nIKbmO5yu+cxV7p7BzlU9FjoBRcca1bnHOL446EzzGvDVSqlARH5JRN4FvFcp9U4R+UHgrwC/esBr\n2jXiNCNMMoqOtoh4bK6ivbnLmzuhw8wxTjNWuhHTJZeSZxElGb0oox+lXFvrY4lQzL28a/m89dCI\nbaqkJ1w8y6S4D7y/3WIQpXQCPQk1WdT+MYMkox+mLLUDRITXVrSD4SBKuTxXvqda0yBKcx91j9Vu\nRK1o49nmXeXxDBHO1gsstkJKToH51oBLMxupOQstbSK20unjOdpIbrmz2e1yPV5b6ZFletLnsbky\npiF0wwTXMjY5oIK+kD5zeoIoybb0jQJN8F/rRRRdc5x9HnEcaNBUSi2s+zUBngb+JP/9D4H/kjuC\n5lbGaoeFfpRgGcaGYn6aKV5e7JJmaiTrNtx+jexu15mctQcxn7q6hgwN4OYqFByTx+bKzDcHXG/0\nCaMML9/2napZzFa8Da9ZdK0N1r1HDYvtgDTLDa1KDlmq6Ea3J2Ka/YTP3miRpBmXZ8vUizb9KNmW\nw9gO4hH5/PyUP3LBBB1MDYNtg+5bztZ4abHL8wttCoY58pgfouRZrHX1FI1S+vO813y6aQhZPqEE\ncKOhqWWWKTw2W95UmwzilMVWQGmdnfKduNHo0x4kRGnKm09P4GxR/z2u2E5r87jqbB5KTVNEngam\ngCZ6qw7aVK125323MlY7DAzrb+undkC7MmraUEijH1HzbXzHIssUryx3iROFZxtYpp7aWOmEVAoO\ngzjFz0+MXpiw3AmZKjpESYoBeLbFxVmH6ZL3wB3hw0LRtbi6omehv+LiJL6ru8OZUvTChEY/ppmP\njXaDhBdudTANYwMfMk4z5psDTEOwjduBJkwyhiFzrRdxs6FrhY/OlLZsNCmln8u3tcNiMed8rvZC\nCnmmOlVycEyDTEGUpHTChG6YbBs8y67NUhQwVSqM1rTcDYgTxZnJAiV3Y6b43HyLa6sDDNHlma30\nPE1DmG8OiNKMyeLm+u8YRwcHHjRFZBL4GeDbgWeBU/mfKuggeiQxVNdWuclZO9Ad1mrBZqrssNAO\nmPTtkb9yqjTPD+B6Y8B0yaXZiygXLOYqHhmKN5+eoBMmrHYjFloB11a1qLBjmVyY8ikd022abQrN\nQUyaKp671eFt52tEuXXyhK/l0x6fK9PsJ9RK9igzC9apPq10w9G8dskzMQxNS1o/JhjE+nqrP5Ns\ny6BpGMKZmk85t3ueLDq8stSlFyZYpsGl2dIoSzUFFtshnSBBJOSxufKm7fZwG20ZBivdiAnfoeJZ\npKm+WDR68aagGWeKVhBRdm2MbUqfp2s+Nxp9XMskSsZ1z6OMg24EWcAvAj+glFoQkU8Afxv4Z2hT\ntY8d5HruF2mmRrzJ4cTIUk6ovjDlU3FtTtU80pSRjalt6szp2mqPOE2Zb2ri+mmjwJl6gRNV3Wxw\nbGMkgTbkapqG4BxjRXZBcEyDThITpxkvLXZpDWIypThV85kuuzw+VyXJMp48UaHR1/ebKNgjNXrf\ntoCIXpiMbjMMIc4yXEMfm+mymxuSCZW7aItWfXs0t39ttcdrKz0Gccq5SR9hYxZ/ZwM8STOSTI0C\nspVnzNdWe0yWHM7VfSZ8h5M1jyy7/fkrpUZaqJ5lMl3yqBZsqoXt/cPfdGqCZj/aU2X5MfYeexI0\nRcQASkqp9j3u+m3A24AfzWt8/xD4kIj8KfA68FN7sZ69RJopXlrskKSaEjRb8VhsB6O/X28MSHIe\n4COz/oZsp15yWWyHnKyaNPohZc+mFyasdEJ6YcqjMyV8x+LclE+cZFQLNs1BjCnCUd+Rp5kiTrfO\n7qq+zcWZIq8u9bAtIUhS2gM95tjN5d/Kno1lCJZpMFc16YUJr61oN8lzdS1vdtkp0Rkk3GoFBHHK\nq0tdVgoO56Z8Kp6NbRp3bdjciRuNPi/c6uiJrILWKb1zRv10zafhRviOiVLw0lJHm57VPOYqhVxI\nxCHOu/1auMPhsdnyhuB6dbVPN0gouiaubegZ+XvMw+ugejx3F28kPHDQFJF/A3wvuib5KaAqIv9c\nKfVj2z1GKfXLwC/fcfNHgR990HXsN+I0I0m1cEOzHzNb8agWLNqDmFquzA3kVhS3T4osUzQHMb5j\n0g0TLkyVMAQ+d7OFoO0ZhtM/67mZhgg3GgMMQ3eK+2FKrWgfmY5qJ4hZ7oSs9SJcyxxZCt9qBdim\nMZqd9h2LM5P+yEDsRNUlVeggYhkbOI1BnGfiqR5bHMQpZc/GtUzcsollCrdaAU6iHzOI0k181q2Q\nKsXrq30Ktkk/Sriy0sV3dVC7OFtial0nu9mPMPLm3HBgoBsmNHr6/Tb7EZO+i2MZTBYd2oME05AN\ndc/1fvTD992P9MWxHcTjgPiQYDeZ5pNKqbaIfBfwH4AfRAfPbYPmcYRnm0wWbZ67pUflVrshS52Q\nJFU0ehEzZZfX1/qcqBZY60UsdUKqBZtMKRo9Lfc2rJsttAYstwNag3jbrGJ4smUZXFnuUrAtOmHM\nUyerm+57GLi+NqAXJtxsDrg4XaIXpVxfa3J1Vauqv/nsBOfrRWYqLr0woRclTJVcZsoec1WPThDT\nHiQESaq1M4sOK92IKM3ohQlnJv1N8madQCsGdYKEs5NF6ve5fY2SjNYg5vmFNo5psNAKKBdsnjk9\nwYlKgfmmbiLZhnCrpS9+wywWtIBxwTZyCwubINFCHoYIlikY+Y4gTNLRAMLZSZ0lT/q6zn2y6nF9\nrY8I1MaybQ8FdhM0bRGx0dzKn1FKxSJy7CvYS50g5xc6I55l1XeYy8che2E6IihnClbzpkBroOeV\nk1Sx2o1GQrmNfsRzN9tUfZuldkBrkKCUPum2miCZLrvEqcI2BS8yCOLsSDlOurZBmpnMVdxczCLm\n5cUONxoBJc9kpVPgZLUwIqrH6fBYqQ3UoeGFY7ETIAimaCX7rSTielGCIUK14HC6VthE6ekEMfPN\ngIJtcmayMDquw8yv5FgESUrRtThT86mXHFZ6ITcbA1qDmHrJwco7NCrb+NpvOl3l2kqfdhgTxil4\nNo1+tMF21xQhH06iGyWs9AKeu9VhyndZaIejdbQG8ZEeex3j/rCboPkvgavAZ9F1yXPAvWqaRx5L\n7ZA4zVhKg1HQLDomkyWHME6ZqbhMKb09m/DtUR0uzRT1ksNSO6TsWZyuFbjR6NPs6/pWqWdRckxO\n1wokmeLc5NZiuq5ljqZP0kzrPA5VklqDGNvcLDG2lwiTdLT93WoW+kK9SC/nVF5Z7nKzEdEOE3xX\nb8195/Z0TtG1ODNZIEoy6iWXTnB72KuUb5Mniy4TBZtOkGypGJRlitmyx2pP+yVttabVbkSUZERJ\nxlTsjI6Paxmcn/LxbZNulNILYnzXwncs4kSx1AmJEj3uenKqgGXKJtVz19IDC2GSsdAKKbk2gi4p\nlDyLoqvHYid8myRTVD2L+caAJFE0BiFT5TJRqk3USq6FylXo7zZBNMbRxgOffUqpnwZ+et1N10Tk\nvbtf0uEiyTKurvSZKjlEccrNXGj2zGRhQ8Y3PDHP14s0BxHVguZnTuf+NlmmmKsUaAcJa/0IlCJV\nikuzJS5MFSmuo6W0BzGvLHWo+Q7np4qjTGkoUAFat/FmY8BiO+BUzePS7L2naHaKNFN8/NU12kHM\nmVqBM/Uia70QEE7XdLffMIROoLfnUaJViE5UPc7UCsxVC9RL7gZe6fo57QnfIckUmVJMl1yUYhQE\nC7bJ9UafOM04XfPzQJXy6lKPTCnO1v1t65iVPOh6trHpmJQ9m+VOmDt9Okz4Tl6TDZnwrVzFyGIm\nl2RrBzELrYCia1EvOoRJhm3e9ivSY5cRrq2bUMPXGzaklNJDDnGaUfVtLkwVR8cjU4wU3O/U6FyP\nRi9iuavLPHvha3RUsR3pHY428X03jaBZ4H8HTiqlvl5EngTeAfyrvVrcQaEb6i1WwdEjbMMv+mov\notWPMQ3RTpB2ynInpOrboyy04JgUnNujfIYhXF3p8fytNjXf5tGZEqcnCqz0IoI4RSnRnEVb5UFC\ncW21S3uQcrMRMF12t+RnDg3F+lFKJ9D1wL2WhQvjlLW+NnT73M0W3Sjl6kqPCd8mzRSPzpRIsozX\nlrt8Yb5NwTb52qdmecqu4FrmyMqhNYhZageUPXuTqO767amIDjJKQSdMRrzM4RjjIEpHepTdQM/k\n90J9n/Vao5NFh4nC1lko6AklpWChHWCZBh+7sopSMFtxuTSrGQwi+kL356839UikadCLEioFm0uz\nZc5N+biWMfKlF4Qkvb2Xn2/qWu9c1WO67HKy4lG4gxwfRMnI87w5iLcNmosdTZRfisPRRXiMo4Pd\n7BE+CPwecDL//SXgfbtd0EFjrRfx2nJvRHieKbsUXb0d70UJV9d6LHf0CNzNxoD55oAXFzpkW6hx\nBHFKox/x+lqf5iDmU6+v8dy8FuM4UfUwRLSCt2vmHt0x/TAZWfnKXcYBp8suM2UH09DBZi+saLNM\nsdAcsJLLlhUck/NTRaq+xamJAqYBrX5ElCi+MN/k09caNHrRqKaXKVjrRni2ucH7ZrEdEMQZy52Q\nKElZ6gQsd0LUHWIVYZLy/K0Oz91qkymV13kZZZQVz6bsWRQck8miQ2sQc2W5x5XlHq3BRl2XuwWW\nYX05SRWvrXTxbIOF1oDn5lvcaAxGBPZulJCqjDDOeL2h3THXuhGDMEHQgXKq5GrqWdUdZdFBnLLa\njQjijOdvtXltucerK70R+X4IP1eH70cJq52IV5a2/h4N33/RNccB8whiN2felFLqV0TkHwIopRIR\nSe/1oMPGsBkhorg0Wx4pB4GmF034zmhL/IWbLS7Uixii61GJ0iIbSZZxbbXHhdyXpRsmZJkmPCsF\nSZry8mKHMEm5NK0Vcc5PFXniRAVBn+DdIOHaqu6qvvnsBGmqmCja2Ntw+TpBzNXVPogmzu92a55l\nik9eW9PZbcXlS04aVH2bZ05X87HQiC/cbDJVdumFWgnIwMAyhS+7UCdMMuIsox8mvHCrTS3f+lYL\nOtCFcUTBMWkNYhbzzrRpyAYjuF54O5MM4pTHZsuo/H6w2VO8m2eZw89KP0dCJ9D1ZUOEm80BliEb\nhDzO1YusdANuNbWF8kTBxnMMio7Ny4tdTk8UuNEcsNwJaPRiUIp60aE9SLFMwTSF15Z7xFnGbNlj\nquxu4KcawFpPTzBlSsvUVXxrgzISaKm5M5M+UarFS4bqTp6x8bM8OVFgpuyO655HFLsJmj0RqZM7\nDIjIl6Pnx4805psDXl3uohTYpsmjMyVdmM+Vs4dQSnMG+2EyCo6nJ3xWOiGtfsJSJ2Sy5BLEejro\nRrNPqx9RcCxmKw5PnCjT6icEaTbiA66v9Q3ilLN1H6UUZde+p+1qaxBrs69Ur223CUiYZKNxvaG/\nDugT2zIl7zbbIyrQIM64vtbniRMl4kxxcbpEox/hWCbLnZAgzkZybSeqBaZKLqYwygqruTr5elQ8\ni4ZrkuX+OPfKqiZ9hyTvxteLDlmmRl48nUAbq3Vze931Nr+9MBl1sF3L5FRN16dfXe4iorjRGPDK\ncpflTkjFszhfL5IBZyYNKp7FtdU+jV7MIEpQCnpRukE0pBUkOKZBmKS0goiZsodrGdvalUyVXBbS\nAD9XxdoK44B5dLGboPk/Ar8FXBSRjwDTwLfuyar2EUXXwhCwLMEydCA7OVGgFyY8d6uNZRg8Ml2k\n0YtIUkXBsXBt/QWul1wemS6x1A7xHf08wxqVlnZL6UY6Y4oqUC9mfOnZiS230vWiSz9KsQyhdJcR\nwNH9Sy6nooQkVVyaKe36pPLyKRXP1rYc68nzWaY5psvdMPfh0epNp2oFJvN1m4ZBzXeo+BaIwhQD\nkds0H9s0WGoHDOKMlW5IlKbMVl3g9utYuTr6/cIw5I4aqcIQIVUKw9BSeqvdCMNgFIzmmwNWu5H+\nnKse1xt95pvadmTYlGr0tUla1bMpeRbTFY+SY2qRaM+iOdCe6IbooHtnbC+6JqnSHfFTE7phdTdv\n8/Hkz/HGbrrnnxaR9wCPAQK8eJQFhNNM17OCOOPZ8xN4lkXBNkdTOa1BTJZBlGU6W1l3Yox4dv2Y\nsmdTKziYpoymVgAemfIRoORqR8OvuFhHwZb6ioAWoPBt0lSNpovuhpJr8eSJ3RPc00xxba2HIVrI\nYiteZGMQ5R10n6JrMlfxuN7Q294J36FSUJhGSMExmSq5o+knzzY22d2mWYZS2lyu2Y+31BgFTep3\nTOOeFwOlFNdW+3RDPY55caZIL0yp5LJrj58oI9zO1Ib14jRTIBCnisV2n14YM1l0CWLtRf70qQnC\nVFvurv/MlFLUiy6+Y1Hzba43BmRKZ9/DTNJ3LN5+fpKFvOE0XXbx878NCf6TvjPOHh8S7Dhoisi3\nbPOnyyKCUurXdrmmfUE/ShhE+QmUCr5v8epylyBOeWS6RM13aAcxliGUPYtBnNINYwqOiWsafPb1\nBldWtEjD43MVZov65HcsTT05M+lzYsKnGySjreh68nqaKaIkGzVMXl/ts9gOWe2FzJQ9RBTTZU/X\nUPep+L/QCvjM6w1uNPrUfL29PZtnXN46mbr5RpB3hxUnJwr4jsXlWYsXF9r8/nML1Hybt56fHF0w\njHW1yiBOtZybaTBV0j4/rq3nuLeb5Blmg7YlXJ7ZrEe5HlGqJ4NADw7US+6G+u6dF6kTVY8lCfFd\nk6pn89x8m5eX21Q9my89O8kj00W6YcJ8e8D5enHD44NY7wSGddVGLxwWo0Y+60M4tslMxeOVpS7t\nIOF0rUDJs3htJXcc7UY8to2PEWwWBhnj6OJBMs2/dJe/KeBIBs2iY+FYkk/0ZDT6IUudgFZfd6/f\n8Uidx+cqo/s3W8FI4utzN5u8tNChF+qAcOfXvtXX00BKwQsLbWzD4NHZEufqt0nqLy91iBO1pUdN\no6+1HfthyiBORydjo6czvnrJHW3xkzTj6mqPONUBb6u6WS9MWGoHNPsxjm0wU3apF11WuiH9SAtj\n3LQC4jTjykqXmZLHyZquQw7P6WrB1hmTownZryx1+LPX1rQfT6ZoDxKmyxtP8GY/GnnhDPUta0Xn\nrll0nGbMtwagIEwgSNK7kvddSzt1dvJM8V7wbJOzdZ8oyXhhocXrjR6DIENUgkLXltNUsdwP8R2T\ns5NF4jRjoRWMhIWH5ZDVXsRSJ8CxtKTc8D23cvpQmqqRde9SRw9JKKW42exjioHvWFtm9lGS8fJS\nhyyDExPevkwNhUmKIbLtzmeM+8eOg6ZS6m/sx0L2G2GSEecjjlGqrSks0fXEomORZArHEBbbAavd\nCNcSDENrQ6bKYLrsEWcDTlS8ke0E6ID5+poeDexFCUGcMVC3syHQhPnOQAdnzzG0+EPebe5FHs/f\nanNluQ8IT53UgTvLdINiuPahUnsv77qCzrS2CprzzQGNfsTz8x0AUpXx5Mkq5+tFVkoul2dK9KKM\ndhDTDVNsw7wd2BSs9AJMER6f04FBZ8mKubwmWC85VAqbX3eoiq6U1h+9n6zp9bU+KoNraz2mSy7X\nVvv3rNluFXjuhU4QEySKqZKLb5lMlvVo7KmJAp+4ukYvSkmXFUXH5FYr5MpyFzGEqmdTLzqs9iKu\nr/WZrXg0+3p+vmCb3GgMUEoH38dmy9RLDs1+RD9M8mkji5JrU/Fs+nGy5dqiNBuNYQ6ivSegtAYx\nr+dMjUdn7r+GfJg4ysT3XZH9ROQbgKeAUaFKKfWPd7uovUSWKVqDmEGUjlS8vzjfpFpwePelaXpR\nSsm97Ue93NFb5pVOyLPnJjlVK9AJEmxTb9s9x2K1FzOdd8SV3q8xiBP8PKuD2yd2mul59EGc0I8y\n7C4EUUacZjwyXcQxTVY7Wg0pVWqkFxnEKfOtAZlSPDZbHvESh1JjQ3rUnVBKESYZz823+MKNFnGW\nUnIdemHKhbrPW89NcmqiwNWVHp0wwbUE3zFH7+eV5S7zjYBBlHKi6vHoTHnUEHp0psw7L01vyxHV\nc/O6hrnUCXQgLLubMus0/0yG8muerbfOE3l3PLqH1e6DQKE5uScmPE5P+FycLlF0LTpBTKZgpRNy\ns9FnoaX9mRaaIaHKODdZxDF1J7zs6rlzxxC+MN+iG2rCexBrv6YhH9exhFvNMP+8LN50qjqacd8K\nJddiquwQxhkzlY1ZZpxmLLYDHNMY+arvFEMRGKX2Jyi/0bCbiaD3Az7wXuD/RnfO/2yP1rVrtPox\nt9oDOkGMZ1koFL5tYhqQpoow1v49j0yXiJKMRi+i6Ji0BhHP32wjAh96eZmL00Xedr7OmUl/JDax\nnqQ94TtcXenx4kKHSd/mXZenqeRCs0ma8eJCh5Wu9sY+NVEgTlNWugGfvNrg8zdcHj9RwbH1pMmT\npfJIOKIxiJkte0RJhiG6BgpaRedu/kDXG31eutXmT15YJk4yDEMoF1zKnsULCx1WuhFxmnJptswz\nZyYQEerF23QfpbS6kmVog7F6yWOlo7vLlYJWP4/ilAw2ZZK2aXCuXuTFhQ4vLnQwDE0Iny17G+qU\nQz8cw4BHpkp0gpiTEx6tga4h72S2fvg5F3Mpuu3wwq023SChG8UYNXjuVovVbsStVsBkQYupnKwW\neHWlTy9ICJKMC1M+1YJD0TNRSpGpjDBOeb0d4pgGtiE8fWYCxzRGEoAffXUVpRQnJzxmK9pKQ2Qj\nP3UrbDfdtdQJNXcU8BzzviTx7sRUySWMM8w7aHVjPBh2k2l+hVLqaRH5nFLqR0TkJziAvR0vAAAd\ntklEQVQC9UylFEmmWO6GxImi2Uuol7TPeJhk9ELNq5wqM8ouX13ujrJBzzapl11uNPp59xeurHSJ\nk4woVcxVNtecUqUouTZRCs1BMgqaYZJxqxXQyXmQl+dsZssl/v1nb5Fl0BokrHRDnpirkKF48kRl\nxGWsFmwavQjPsfAck06Qjl7rbmj0YpZ7WkQjyxSnqi4nqh4Xpnxsy9D0nySjXoo5OymjDHMIw9Dz\n2nGaMYgzHMsgyTM/1zRZ64V85JVVskzx9kcmtzzZM6UoezqLq24x3jgktGuurIwyqK3GCuM0wzK2\nVoQCbn/OScxMJd2W9D/MYBu9mI9fWdMZo2kwSFLSLMXPaUM1z8ISg0kRLk1XePJkmZrv8NytNi8t\ndUdWypYpNPoxQZyOJN8W28GIJ5qkbDq2D4KhRqsIOA+YfdumsWFIYDe427b5jYLdBM2hfHlfRE4C\na8CF3S/pwZGkGX/43CI3mn3OTPqcrPqcrReoeJoGdKMxYK0X4lg5N9G18qwnRqGL+lMll8mS9rHp\nBQlJmnF9tU83TDlV87AtGQWB4QjcxekSvTCl4JgjCTnQY3Ml16TVj5kpu0yXXWzL5OkzVdb6IYYY\nPHuuRjFX3llP/i65Fk+drJAzEjBFnzC1e5DgH5ku8tx8k8szRTzLZKKox/36UYaTW0Po6T7FYntA\nkmVMl1x6oQ4cXq6ylCjFdNlhqR2QKoVvaQWgV5Y6I4L5SifcMmierxc1h9OzRtSb9Thd80d2tVtt\nw5faAc28HNHsxbSCiMszZc7W/U3Bs1qwGUT62G8XVNpBTKVgs9oL8CwDJdAZxLTDhJmKx5kJn1LB\nYbJos9qNuNHo49kGT5+uMFst8PJiZ6Qj6ljGSIjDMgRjXVtw0neol2zdpLtL3bXVj1nMvdbvpR8w\nVXIp2CamIePO+hHBboLmvxeRCbTo8KfRZaOfu9sD8uD628CTaHuMRER+Engr8Gml1N/dxXpo9CPN\n4YsSGtebnJ8scnn2Ns3jxlpfy5q5mnP34mKHm42ATGnHSFMMFtsB5+pFlNJZq++YNPox/WjAfEsX\n/UuuRZbB1VUtC/fIdJF3X57e6v0yV/UYxBmWZWDm63h0psxM2cU2DQp32YoO1y2yOSPcDrdafZY7\nWszjqdMVTk8U+diVVVr9mJO1Am8/X0cpxVIn4OXFHi8v9jg35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TVradioConst
1230.137.81
244.539.31
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" + ], + "text/plain": [ + " TV radio Const\n", + "1 230.1 37.8 1\n", + "2 44.5 39.3 1\n", + "3 17.2 45.9 1\n", + "4 151.5 41.3 1\n", + "5 180.8 10.8 1" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = df.sales\n", + "X = df[['TV', 'radio']].astype(float)\n", + "X['Const']= 1\n", + "model = sm.OLS(endog=y, exog= X).fit() #exogenous and endogenous (what we predict) variables\n", + "X.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: sales R-squared: 0.897
Model: OLS Adj. R-squared: 0.896
Method: Least Squares F-statistic: 859.6
Date: Thu, 11 Jan 2018 Prob (F-statistic): 4.83e-98
Time: 08:24:20 Log-Likelihood: -386.20
No. Observations: 200 AIC: 778.4
Df Residuals: 197 BIC: 788.3
Df Model: 2
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
TV 0.0458 0.001 32.909 0.000 0.043 0.048
radio 0.1880 0.008 23.382 0.000 0.172 0.204
Const 2.9211 0.294 9.919 0.000 2.340 3.502
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Omnibus: 60.022 Durbin-Watson: 2.081
Prob(Omnibus): 0.000 Jarque-Bera (JB): 148.679
Skew: -1.323 Prob(JB): 5.19e-33
Kurtosis: 6.292 Cond. No. 425.
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: sales R-squared: 0.897\n", + "Model: OLS Adj. R-squared: 0.896\n", + "Method: Least Squares F-statistic: 859.6\n", + "Date: Thu, 11 Jan 2018 Prob (F-statistic): 4.83e-98\n", + "Time: 08:24:20 Log-Likelihood: -386.20\n", + "No. Observations: 200 AIC: 778.4\n", + "Df Residuals: 197 BIC: 788.3\n", + "Df Model: 2 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "TV 0.0458 0.001 32.909 0.000 0.043 0.048\n", + "radio 0.1880 0.008 23.382 0.000 0.172 0.204\n", + "Const 2.9211 0.294 9.919 0.000 2.340 3.502\n", + "==============================================================================\n", + "Omnibus: 60.022 Durbin-Watson: 2.081\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 148.679\n", + "Skew: -1.323 Prob(JB): 5.19e-33\n", + "Kurtosis: 6.292 Cond. No. 425.\n", + "==============================================================================\n", + "\n", + "Warnings:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.33\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m42\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlinear_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLinearRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfit_intercept\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0mensure_2d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 573\u001b[1;33m ensure_min_features, warn_on_dtype, estimator)\n\u001b[0m\u001b[0;32m 574\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 575\u001b[0m y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m 451\u001b[0m % (array.ndim, estimator_name))\n\u001b[0;32m 452\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 453\u001b[1;33m \u001b[0m_assert_all_finite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 454\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 455\u001b[0m \u001b[0mshape_repr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X)\u001b[0m\n\u001b[0;32m 42\u001b[0m and not np.isfinite(X).all()):\n\u001b[0;32m 43\u001b[0m raise ValueError(\"Input contains NaN, infinity\"\n\u001b[1;32m---> 44\u001b[1;33m \" or a value too large for %r.\" % X.dtype)\n\u001b[0m\u001b[0;32m 45\u001b[0m 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"df1.head()\n", + "\n", + " #print('score:', score, 'columns:', com)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "list indices must be integers or slices, not str", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrows\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Score'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0midxmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m: list indices must be integers or slices, not str" + ] + } + ], + "source": [ + "df1.loc[rows['Score'].idxmax()]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CoefColumnsIntScore
0[0.0482245128152]TV7.0665830.623689
1[0.223774515044]radio9.2904170.081757
2[0.0658344742479]newspaper12.602571-0.111407
3[0.0447396196487, 0.199355464099]TV, radio2.8673550.859348
4[0.0472036046549, 0.0507723040181]TV, newspaper5.6733100.643582
5[0.216625857637, 0.0156987409941]radio, newspaper8.9803430.071047
6[0.0446651206327, 0.196630062826, 0.00607438654789]TV, radio, newspaper2.7580720.855557
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" + ], + "text/plain": [ + " Coef Columns \\\n", + "0 [0.0482245128152] TV \n", + "1 [0.223774515044] radio \n", + "2 [0.0658344742479] newspaper \n", + "3 [0.0447396196487, 0.199355464099] TV, radio \n", + "4 [0.0472036046549, 0.0507723040181] TV, newspaper \n", + "5 [0.216625857637, 0.0156987409941] radio, newspaper \n", + "6 [0.0446651206327, 0.196630062826, 0.00607438654789] TV, radio, newspaper \n", + "\n", + " Int Score \n", + "0 7.066583 0.623689 \n", + "1 9.290417 0.081757 \n", + "2 12.602571 -0.111407 \n", + "3 2.867355 0.859348 \n", + "4 5.673310 0.643582 \n", + "5 8.980343 0.071047 \n", + "6 2.758072 0.855557 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + 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+190,18.7,12.1,23.4,6.7 +191,39.5,41.1,5.8,10.8 +192,75.5,10.8,6,9.9 +193,17.2,4.1,31.6,5.9 +194,166.8,42,3.6,19.6 +195,149.7,35.6,6,17.3 +196,38.2,3.7,13.8,7.6 +197,94.2,4.9,8.1,9.7 +198,177,9.3,6.4,12.8 +199,283.6,42,66.2,25.5 +200,232.1,8.6,8.7,13.4 From 91f3f7b2865078ac636b634da047914b2ca30783 Mon Sep 17 00:00:00 2001 From: Ostrowski Date: Thu, 11 Jan 2018 09:01:03 -0600 Subject: [PATCH 2/2] 2nd Version --- .../Advertising Assessment-checkpoint.ipynb | 217 ++++++++++-------- Advertising Assessment.ipynb | 217 ++++++++++-------- 2 files changed, 254 insertions(+), 180 deletions(-) diff --git a/.ipynb_checkpoints/Advertising Assessment-checkpoint.ipynb b/.ipynb_checkpoints/Advertising Assessment-checkpoint.ipynb index a218422..5018765 100644 --- a/.ipynb_checkpoints/Advertising Assessment-checkpoint.ipynb +++ b/.ipynb_checkpoints/Advertising Assessment-checkpoint.ipynb @@ -5,13 +5,14 @@ "metadata": {}, "source": [ "ANSWERS\n", - "Best R2 Value: 0.897\n", + "Best R2 Value: 0.859348\n", + "\n", + "sales = 2.867355 + 0.0447396196487*tv + 0.199355464099*radio\n", "\n", - "sales = 2.9211 + 0.0458*tv + 9.2*radio\n", "\n", "Predicted Sales at TV=199, Radio=32, Newspaper=88\n", - "sales = 2.9211 + 0.0458*199 + 9.2*32\n", - "sales = $306.44\n" + "sales = 2.867355 + 0.0447396196487*199 + 0.199355464099*32\n", + "sales = $18.15\n" ] }, { @@ -179,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -205,7 +206,6 @@ " \n", " TV\n", " radio\n", - " Const\n", " \n", " \n", " \n", @@ -213,46 +213,41 @@ " 1\n", " 230.1\n", " 37.8\n", - " 1\n", " \n", " \n", " 2\n", " 44.5\n", " 39.3\n", - " 1\n", " \n", " \n", " 3\n", " 17.2\n", " 45.9\n", - " 1\n", " \n", " \n", " 4\n", " 151.5\n", " 41.3\n", - " 1\n", " \n", " \n", " 5\n", " 180.8\n", " 10.8\n", - " 1\n", " \n", " \n", "\n", "" ], "text/plain": [ - " TV radio Const\n", - "1 230.1 37.8 1\n", - "2 44.5 39.3 1\n", - "3 17.2 45.9 1\n", - "4 151.5 41.3 1\n", - "5 180.8 10.8 1" + " TV radio\n", + "1 230.1 37.8\n", + "2 44.5 39.3\n", + "3 17.2 45.9\n", + "4 151.5 41.3\n", + "5 180.8 10.8" ] }, - "execution_count": 6, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -260,14 +255,14 @@ "source": [ "y = df.sales\n", "X = df[['TV', 'radio']].astype(float)\n", - "X['Const']= 1\n", + "#X['Const']= 1\n", "model = sm.OLS(endog=y, exog= X).fit() #exogenous and endogenous (what we predict) variables\n", "X.head()" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -276,31 +271,31 @@ "\n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
OLS Regression Results
Dep. Variable: sales R-squared: 0.897Dep. Variable: sales R-squared: 0.981
Model: OLS Adj. R-squared: 0.896Model: OLS Adj. R-squared: 0.981
Method: Least Squares F-statistic: 859.6Method: Least Squares F-statistic: 5206.
Date: Thu, 11 Jan 2018 Prob (F-statistic): 4.83e-98Date: Thu, 11 Jan 2018 Prob (F-statistic): 6.73e-172
Time: 08:24:20 Log-Likelihood: -386.20Time: 08:59:58 Log-Likelihood: -426.71
No. Observations: 200 AIC: 778.4No. Observations: 200 AIC: 857.4
Df Residuals: 197 BIC: 788.3Df Residuals: 198 BIC: 864.0
Df Model: 2 Df Model: 2
Covariance Type: nonrobust Covariance Type: nonrobust
\n", "\n", @@ -308,27 +303,24 @@ " \n", "\n", "\n", - " \n", - "\n", - "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
TV 0.0458 0.001 32.909 0.000 0.043 0.048
radio 0.1880 0.008 23.382 0.000 0.172 0.204TV 0.0548 0.001 42.962 0.000 0.052 0.057
Const 2.9211 0.294 9.919 0.000 2.340 3.502radio 0.2356 0.008 29.909 0.000 0.220 0.251
\n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
Omnibus: 60.022 Durbin-Watson: 2.081Omnibus: 6.047 Durbin-Watson: 2.080
Prob(Omnibus): 0.000 Jarque-Bera (JB): 148.679Prob(Omnibus): 0.049 Jarque-Bera (JB): 8.829
Skew: -1.323 Prob(JB): 5.19e-33Skew: -0.112 Prob(JB): 0.0121
Kurtosis: 6.292 Cond. No. 425.Kurtosis: 4.005 Cond. No. 9.37
" ], @@ -337,26 +329,25 @@ "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", - "Dep. Variable: sales R-squared: 0.897\n", - "Model: OLS Adj. R-squared: 0.896\n", - "Method: Least Squares F-statistic: 859.6\n", - "Date: Thu, 11 Jan 2018 Prob (F-statistic): 4.83e-98\n", - "Time: 08:24:20 Log-Likelihood: -386.20\n", - "No. Observations: 200 AIC: 778.4\n", - "Df Residuals: 197 BIC: 788.3\n", + "Dep. Variable: sales R-squared: 0.981\n", + "Model: OLS Adj. R-squared: 0.981\n", + "Method: Least Squares F-statistic: 5206.\n", + "Date: Thu, 11 Jan 2018 Prob (F-statistic): 6.73e-172\n", + "Time: 08:59:58 Log-Likelihood: -426.71\n", + "No. Observations: 200 AIC: 857.4\n", + "Df Residuals: 198 BIC: 864.0\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", - "TV 0.0458 0.001 32.909 0.000 0.043 0.048\n", - "radio 0.1880 0.008 23.382 0.000 0.172 0.204\n", - "Const 2.9211 0.294 9.919 0.000 2.340 3.502\n", + "TV 0.0548 0.001 42.962 0.000 0.052 0.057\n", + "radio 0.2356 0.008 29.909 0.000 0.220 0.251\n", "==============================================================================\n", - "Omnibus: 60.022 Durbin-Watson: 2.081\n", - "Prob(Omnibus): 0.000 Jarque-Bera (JB): 148.679\n", - "Skew: -1.323 Prob(JB): 5.19e-33\n", - "Kurtosis: 6.292 Cond. No. 425.\n", + "Omnibus: 6.047 Durbin-Watson: 2.080\n", + "Prob(Omnibus): 0.049 Jarque-Bera (JB): 8.829\n", + "Skew: -0.112 Prob(JB): 0.0121\n", + "Kurtosis: 4.005 Cond. No. 9.37\n", "==============================================================================\n", "\n", "Warnings:\n", @@ -364,7 +355,7 @@ "\"\"\"" ] }, - "execution_count": 7, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -375,23 +366,88 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 22, "metadata": {}, "outputs": [ { - "ename": "ValueError", - "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.33\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m42\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlinear_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLinearRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfit_intercept\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 480\u001b[0m \u001b[0mn_jobs_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 481\u001b[0m X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],\n\u001b[1;32m--> 482\u001b[1;33m y_numeric=True, multi_output=True)\n\u001b[0m\u001b[0;32m 483\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 484\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m 571\u001b[0m X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,\n\u001b[0;32m 572\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 573\u001b[1;33m ensure_min_features, warn_on_dtype, estimator)\n\u001b[0m\u001b[0;32m 574\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 575\u001b[0m y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m 451\u001b[0m % (array.ndim, estimator_name))\n\u001b[0;32m 452\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 453\u001b[1;33m \u001b[0m_assert_all_finite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 454\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 455\u001b[0m \u001b[0mshape_repr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X)\u001b[0m\n\u001b[0;32m 42\u001b[0m and not np.isfinite(X).all()):\n\u001b[0;32m 43\u001b[0m raise ValueError(\"Input contains NaN, infinity\"\n\u001b[1;32m---> 44\u001b[1;33m \" or a value too large for %r.\" % X.dtype)\n\u001b[0m\u001b[0;32m 45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')." - ] + "data": { + "text/html": [ + "
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CoefColumnsIntScore
0[0.0482245128152]TV7.0665830.623689
1[0.223774515044]radio9.2904170.081757
2[0.0658344742479]newspaper12.602571-0.111407
3[0.0447396196487, 0.199355464099]TV, radio2.8673550.859348
4[0.0472036046549, 0.0507723040181]TV, newspaper5.6733100.643582
\n", + "
" + ], + "text/plain": [ + " Coef Columns Int Score\n", + "0 [0.0482245128152] TV 7.066583 0.623689\n", + "1 [0.223774515044] radio 9.290417 0.081757\n", + "2 [0.0658344742479] newspaper 12.602571 -0.111407\n", + "3 [0.0447396196487, 0.199355464099] TV, radio 2.867355 0.859348\n", + "4 [0.0472036046549, 0.0507723040181] TV, newspaper 5.673310 0.643582" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -399,7 +455,7 @@ "\n", "for i in range(1,11):\n", "\n", - " combos = list(combinations(['TV', 'radio', 'newspaper', 'Const'],i))\n", + " combos = list(combinations(['TV', 'radio', 'newspaper'],i))\n", "\n", " #combos = [[*c] for c in combos]\n", "\n", @@ -424,27 +480,6 @@ " #print('score:', score, 'columns:', com)" ] }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "list indices must be integers or slices, not str", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrows\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Score'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0midxmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m: list indices must be integers or slices, not str" - ] - } - ], - "source": [ - "df1.loc[rows['Score'].idxmax()]" - ] - }, { "cell_type": "code", "execution_count": 15, @@ -556,7 +591,9 @@ "output_type": "execute_result" } ], - "source": [] + "source": [ + "df1" + ] }, { "cell_type": "code", diff --git a/Advertising Assessment.ipynb b/Advertising Assessment.ipynb index a218422..5018765 100644 --- a/Advertising Assessment.ipynb +++ b/Advertising Assessment.ipynb @@ -5,13 +5,14 @@ "metadata": {}, "source": [ "ANSWERS\n", - "Best R2 Value: 0.897\n", + "Best R2 Value: 0.859348\n", + "\n", + "sales = 2.867355 + 0.0447396196487*tv + 0.199355464099*radio\n", "\n", - "sales = 2.9211 + 0.0458*tv + 9.2*radio\n", "\n", "Predicted Sales at TV=199, Radio=32, Newspaper=88\n", - "sales = 2.9211 + 0.0458*199 + 9.2*32\n", - "sales = $306.44\n" + "sales = 2.867355 + 0.0447396196487*199 + 0.199355464099*32\n", + "sales = $18.15\n" ] }, { @@ -179,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -205,7 +206,6 @@ " \n", " TV\n", " radio\n", - " Const\n", " \n", " \n", " \n", @@ -213,46 +213,41 @@ " 1\n", " 230.1\n", " 37.8\n", - " 1\n", " \n", " \n", " 2\n", " 44.5\n", " 39.3\n", - " 1\n", " \n", " \n", " 3\n", " 17.2\n", " 45.9\n", - " 1\n", " \n", " \n", " 4\n", " 151.5\n", " 41.3\n", - " 1\n", " \n", " \n", " 5\n", " 180.8\n", " 10.8\n", - " 1\n", " \n", " \n", "\n", "" ], "text/plain": [ - " TV radio Const\n", - "1 230.1 37.8 1\n", - "2 44.5 39.3 1\n", - "3 17.2 45.9 1\n", - "4 151.5 41.3 1\n", - "5 180.8 10.8 1" + " TV radio\n", + "1 230.1 37.8\n", + "2 44.5 39.3\n", + "3 17.2 45.9\n", + "4 151.5 41.3\n", + "5 180.8 10.8" ] }, - "execution_count": 6, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -260,14 +255,14 @@ "source": [ "y = df.sales\n", "X = df[['TV', 'radio']].astype(float)\n", - "X['Const']= 1\n", + "#X['Const']= 1\n", "model = sm.OLS(endog=y, exog= X).fit() #exogenous and endogenous (what we predict) variables\n", "X.head()" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -276,31 +271,31 @@ "\n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
OLS Regression Results
Dep. Variable: sales R-squared: 0.897Dep. Variable: sales R-squared: 0.981
Model: OLS Adj. R-squared: 0.896Model: OLS Adj. R-squared: 0.981
Method: Least Squares F-statistic: 859.6Method: Least Squares F-statistic: 5206.
Date: Thu, 11 Jan 2018 Prob (F-statistic): 4.83e-98Date: Thu, 11 Jan 2018 Prob (F-statistic): 6.73e-172
Time: 08:24:20 Log-Likelihood: -386.20Time: 08:59:58 Log-Likelihood: -426.71
No. Observations: 200 AIC: 778.4No. Observations: 200 AIC: 857.4
Df Residuals: 197 BIC: 788.3Df Residuals: 198 BIC: 864.0
Df Model: 2 Df Model: 2
Covariance Type: nonrobust Covariance Type: nonrobust
\n", "\n", @@ -308,27 +303,24 @@ " \n", "\n", "\n", - " \n", - "\n", - "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
TV 0.0458 0.001 32.909 0.000 0.043 0.048
radio 0.1880 0.008 23.382 0.000 0.172 0.204TV 0.0548 0.001 42.962 0.000 0.052 0.057
Const 2.9211 0.294 9.919 0.000 2.340 3.502radio 0.2356 0.008 29.909 0.000 0.220 0.251
\n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
Omnibus: 60.022 Durbin-Watson: 2.081Omnibus: 6.047 Durbin-Watson: 2.080
Prob(Omnibus): 0.000 Jarque-Bera (JB): 148.679Prob(Omnibus): 0.049 Jarque-Bera (JB): 8.829
Skew: -1.323 Prob(JB): 5.19e-33Skew: -0.112 Prob(JB): 0.0121
Kurtosis: 6.292 Cond. No. 425.Kurtosis: 4.005 Cond. No. 9.37
" ], @@ -337,26 +329,25 @@ "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", - "Dep. Variable: sales R-squared: 0.897\n", - "Model: OLS Adj. R-squared: 0.896\n", - "Method: Least Squares F-statistic: 859.6\n", - "Date: Thu, 11 Jan 2018 Prob (F-statistic): 4.83e-98\n", - "Time: 08:24:20 Log-Likelihood: -386.20\n", - "No. Observations: 200 AIC: 778.4\n", - "Df Residuals: 197 BIC: 788.3\n", + "Dep. Variable: sales R-squared: 0.981\n", + "Model: OLS Adj. R-squared: 0.981\n", + "Method: Least Squares F-statistic: 5206.\n", + "Date: Thu, 11 Jan 2018 Prob (F-statistic): 6.73e-172\n", + "Time: 08:59:58 Log-Likelihood: -426.71\n", + "No. Observations: 200 AIC: 857.4\n", + "Df Residuals: 198 BIC: 864.0\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", - "TV 0.0458 0.001 32.909 0.000 0.043 0.048\n", - "radio 0.1880 0.008 23.382 0.000 0.172 0.204\n", - "Const 2.9211 0.294 9.919 0.000 2.340 3.502\n", + "TV 0.0548 0.001 42.962 0.000 0.052 0.057\n", + "radio 0.2356 0.008 29.909 0.000 0.220 0.251\n", "==============================================================================\n", - "Omnibus: 60.022 Durbin-Watson: 2.081\n", - "Prob(Omnibus): 0.000 Jarque-Bera (JB): 148.679\n", - "Skew: -1.323 Prob(JB): 5.19e-33\n", - "Kurtosis: 6.292 Cond. No. 425.\n", + "Omnibus: 6.047 Durbin-Watson: 2.080\n", + "Prob(Omnibus): 0.049 Jarque-Bera (JB): 8.829\n", + "Skew: -0.112 Prob(JB): 0.0121\n", + "Kurtosis: 4.005 Cond. No. 9.37\n", "==============================================================================\n", "\n", "Warnings:\n", @@ -364,7 +355,7 @@ "\"\"\"" ] }, - "execution_count": 7, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -375,23 +366,88 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 22, "metadata": {}, "outputs": [ { - "ename": "ValueError", - "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.33\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m42\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlinear_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLinearRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfit_intercept\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 480\u001b[0m \u001b[0mn_jobs_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 481\u001b[0m X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],\n\u001b[1;32m--> 482\u001b[1;33m y_numeric=True, multi_output=True)\n\u001b[0m\u001b[0;32m 483\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 484\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m 571\u001b[0m X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,\n\u001b[0;32m 572\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 573\u001b[1;33m ensure_min_features, warn_on_dtype, estimator)\n\u001b[0m\u001b[0;32m 574\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 575\u001b[0m y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m 451\u001b[0m % (array.ndim, estimator_name))\n\u001b[0;32m 452\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 453\u001b[1;33m \u001b[0m_assert_all_finite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 454\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 455\u001b[0m \u001b[0mshape_repr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X)\u001b[0m\n\u001b[0;32m 42\u001b[0m and not np.isfinite(X).all()):\n\u001b[0;32m 43\u001b[0m raise ValueError(\"Input contains NaN, infinity\"\n\u001b[1;32m---> 44\u001b[1;33m \" or a value too large for %r.\" % X.dtype)\n\u001b[0m\u001b[0;32m 45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')." - ] + "data": { + "text/html": [ + "
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CoefColumnsIntScore
0[0.0482245128152]TV7.0665830.623689
1[0.223774515044]radio9.2904170.081757
2[0.0658344742479]newspaper12.602571-0.111407
3[0.0447396196487, 0.199355464099]TV, radio2.8673550.859348
4[0.0472036046549, 0.0507723040181]TV, newspaper5.6733100.643582
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" + ], + "text/plain": [ + " Coef Columns Int Score\n", + "0 [0.0482245128152] TV 7.066583 0.623689\n", + "1 [0.223774515044] radio 9.290417 0.081757\n", + "2 [0.0658344742479] newspaper 12.602571 -0.111407\n", + "3 [0.0447396196487, 0.199355464099] TV, radio 2.867355 0.859348\n", + "4 [0.0472036046549, 0.0507723040181] TV, newspaper 5.673310 0.643582" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -399,7 +455,7 @@ "\n", "for i in range(1,11):\n", "\n", - " combos = list(combinations(['TV', 'radio', 'newspaper', 'Const'],i))\n", + " combos = list(combinations(['TV', 'radio', 'newspaper'],i))\n", "\n", " #combos = [[*c] for c in combos]\n", "\n", @@ -424,27 +480,6 @@ " #print('score:', score, 'columns:', com)" ] }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "list indices must be integers or slices, not str", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrows\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Score'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0midxmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m: list indices must be integers or slices, not str" - ] - } - ], - "source": [ - "df1.loc[rows['Score'].idxmax()]" - ] - }, { "cell_type": "code", "execution_count": 15, @@ -556,7 +591,9 @@ "output_type": "execute_result" } ], - "source": [] + "source": [ + "df1" + ] }, { "cell_type": "code",