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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\eky6j6p\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
+ " from pandas.core import datetools\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import statsmodels.api as sm\n",
+ "from sklearn import linear_model\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from pandas.plotting import scatter_matrix\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "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": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "advertisingdf = pd.read_csv('advertising.csv', index_col=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " TV | \n",
+ " radio | \n",
+ " newspaper | \n",
+ " sales | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 1 | \n",
+ " 230.1 | \n",
+ " 37.8 | \n",
+ " 69.2 | \n",
+ " 22.1 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 44.5 | \n",
+ " 39.3 | \n",
+ " 45.1 | \n",
+ " 10.4 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 17.2 | \n",
+ " 45.9 | \n",
+ " 69.3 | \n",
+ " 9.3 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "OLS Regression Results\n",
+ "\n",
+ " | Dep. Variable: | sales | R-squared: | 0.981 | \n",
+ "
\n",
+ "\n",
+ " | Model: | OLS | Adj. R-squared: | 0.981 | \n",
+ "
\n",
+ "\n",
+ " | Method: | Least Squares | F-statistic: | 5206. | \n",
+ "
\n",
+ "\n",
+ " | Date: | Thu, 11 Jan 2018 | Prob (F-statistic): | 6.73e-172 | \n",
+ "
\n",
+ "\n",
+ " | Time: | 09:30:03 | Log-Likelihood: | -426.71 | \n",
+ "
\n",
+ "\n",
+ " | No. Observations: | 200 | AIC: | 857.4 | \n",
+ "
\n",
+ "\n",
+ " | Df Residuals: | 198 | BIC: | 864.0 | \n",
+ "
\n",
+ "\n",
+ " | Df Model: | 2 | | | \n",
+ "
\n",
+ "\n",
+ " | Covariance Type: | nonrobust | | | \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "\n",
+ " | coef | std err | t | P>|t| | [0.025 | 0.975] | \n",
+ "
\n",
+ "\n",
+ " | TV | 0.0548 | 0.001 | 42.962 | 0.000 | 0.052 | 0.057 | \n",
+ "
\n",
+ "\n",
+ " | radio | 0.2356 | 0.008 | 29.909 | 0.000 | 0.220 | 0.251 | \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "\n",
+ " | Omnibus: | 6.047 | Durbin-Watson: | 2.080 | \n",
+ "
\n",
+ "\n",
+ " | Prob(Omnibus): | 0.049 | Jarque-Bera (JB): | 8.829 | \n",
+ "
\n",
+ "\n",
+ " | Skew: | -0.112 | Prob(JB): | 0.0121 | \n",
+ "
\n",
+ "\n",
+ " | Kurtosis: | 4.005 | Cond. No. | 9.37 | \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "\n",
+ "\"\"\"\n",
+ " OLS Regression Results \n",
+ "==============================================================================\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: 09:30:03 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 0.0548 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: 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",
+ "[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 = sm.OLS(y, X).fit()\n",
+ "model.params\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from itertools import combinations\n",
+ "results=[]\n",
+ "for i in range(1,4):\n",
+ " combos = list(combinations(['TV', 'radio', 'newspaper'],i))\n",
+ " #combos = [[*c] for c in combos]\n",
+ " for com in combos:\n",
+ " y = advertisingdf.sales\n",
+ " X = pd.DataFrame(advertisingdf, columns=com)\n",
+ " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
+ " model = linear_model.LinearRegression(fit_intercept=True).fit(X_train, y_train)\n",
+ " score = model.score(X_test, y_test)\n",
+ " results.append((score, com))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "answer = pd.DataFrame(results, columns=('score', 'com'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
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+ " \n",
+ " \n",
+ " | \n",
+ " score | \n",
+ " com | \n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 0.623689 | \n",
+ " (TV,) | \n",
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+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " TV | \n",
+ " radio | \n",
+ " newspaper | \n",
+ " sales | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 1 | \n",
+ " 230.1 | \n",
+ " 37.8 | \n",
+ " 69.2 | \n",
+ " 22.1 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 44.5 | \n",
+ " 39.3 | \n",
+ " 45.1 | \n",
+ " 10.4 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 17.2 | \n",
+ " 45.9 | \n",
+ " 69.3 | \n",
+ " 9.3 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "OLS Regression Results\n",
+ "\n",
+ " | Dep. Variable: | sales | R-squared: | 0.981 | \n",
+ "
\n",
+ "\n",
+ " | Model: | OLS | Adj. R-squared: | 0.981 | \n",
+ "
\n",
+ "\n",
+ " | Method: | Least Squares | F-statistic: | 5206. | \n",
+ "
\n",
+ "\n",
+ " | Date: | Thu, 11 Jan 2018 | Prob (F-statistic): | 6.73e-172 | \n",
+ "
\n",
+ "\n",
+ " | Time: | 09:30:03 | Log-Likelihood: | -426.71 | \n",
+ "
\n",
+ "\n",
+ " | No. Observations: | 200 | AIC: | 857.4 | \n",
+ "
\n",
+ "\n",
+ " | Df Residuals: | 198 | BIC: | 864.0 | \n",
+ "
\n",
+ "\n",
+ " | Df Model: | 2 | | | \n",
+ "
\n",
+ "\n",
+ " | Covariance Type: | nonrobust | | | \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "\n",
+ " | coef | std err | t | P>|t| | [0.025 | 0.975] | \n",
+ "
\n",
+ "\n",
+ " | TV | 0.0548 | 0.001 | 42.962 | 0.000 | 0.052 | 0.057 | \n",
+ "
\n",
+ "\n",
+ " | radio | 0.2356 | 0.008 | 29.909 | 0.000 | 0.220 | 0.251 | \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "\n",
+ " | Omnibus: | 6.047 | Durbin-Watson: | 2.080 | \n",
+ "
\n",
+ "\n",
+ " | Prob(Omnibus): | 0.049 | Jarque-Bera (JB): | 8.829 | \n",
+ "
\n",
+ "\n",
+ " | Skew: | -0.112 | Prob(JB): | 0.0121 | \n",
+ "
\n",
+ "\n",
+ " | Kurtosis: | 4.005 | Cond. No. | 9.37 | \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "\n",
+ "\"\"\"\n",
+ " OLS Regression Results \n",
+ "==============================================================================\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: 09:30:03 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 0.0548 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: 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",
+ "[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 = sm.OLS(y, X).fit()\n",
+ "model.params\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from itertools import combinations\n",
+ "results=[]\n",
+ "for i in range(1,4):\n",
+ " combos = list(combinations(['TV', 'radio', 'newspaper'],i))\n",
+ " #combos = [[*c] for c in combos]\n",
+ " for com in combos:\n",
+ " y = advertisingdf.sales\n",
+ " X = pd.DataFrame(advertisingdf, columns=com)\n",
+ " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
+ " model = linear_model.LinearRegression(fit_intercept=True).fit(X_train, y_train)\n",
+ " score = model.score(X_test, y_test)\n",
+ " results.append((score, com))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "answer = pd.DataFrame(results, columns=('score', 'com'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
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