major upload of (python) course material & solutions
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "03c68072-8fd9-4c26-9f8b-e6f6e24fd583",
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"metadata": {},
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"source": [
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"\\vspace{-4cm}\n",
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"\\begin{center}\n",
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" \\LARGE{Machine Learning for Economics and Finance}\\\\[0.5cm]\n",
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" \\Large{\\textbf{01\\_Auto\\_data\\_1}}\\\\[1.0cm]\n",
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" \\large{Ole Wilms}\\\\[0.5cm]\n",
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" \\large{July 29, 2024}\\\\\n",
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"\\end{center}"
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]
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},
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{
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"cell_type": "raw",
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"id": "4e117807-3711-444b-838d-775303383d93",
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"metadata": {},
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"source": [
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"\\setcounter{secnumdepth}{0}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e0c43ee7-0ede-4d7e-9966-f00493b33f0a",
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"metadata": {
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"tags": [],
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"user_expressions": []
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},
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"source": [
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"**Get and Set working directory**:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "57ab9acc-8d99-4165-8930-db6ae2be39a9",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"/mnt/ds/home/UHH_MLSJ_2024/Code/Python/01_SupLearn_Regression\n"
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]
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}
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],
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"source": [
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"import os # Package to access system related information \n",
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"print(os.getcwd()) # Prints the current working directory\n",
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"path = os.getcwd()\n",
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"os.chdir(path) # Set the working directory"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "4424246b-bee1-4b9e-a5ac-79c20e4b4c26",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>mpg</th>\n",
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" <th>cylinders</th>\n",
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" <th>displacement</th>\n",
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" <th>horsepower</th>\n",
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" <th>weight</th>\n",
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" <th>acceleration</th>\n",
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" <th>year</th>\n",
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" <th>origin</th>\n",
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" <th>name</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>18.0</td>\n",
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" <td>8</td>\n",
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" <td>307.0</td>\n",
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" <td>130</td>\n",
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" <td>3504</td>\n",
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" <td>12.0</td>\n",
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" <td>70</td>\n",
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" <td>1</td>\n",
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" <td>chevrolet chevelle malibu</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>15.0</td>\n",
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" <td>8</td>\n",
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" <td>350.0</td>\n",
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" <td>165</td>\n",
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" <td>3693</td>\n",
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" <td>11.5</td>\n",
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" <td>70</td>\n",
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" <td>1</td>\n",
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" <td>buick skylark 320</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>18.0</td>\n",
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" <td>8</td>\n",
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" <td>318.0</td>\n",
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" <td>150</td>\n",
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" <td>3436</td>\n",
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" <td>11.0</td>\n",
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" <td>70</td>\n",
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" <td>1</td>\n",
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" <td>plymouth satellite</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>16.0</td>\n",
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" <td>8</td>\n",
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" <td>304.0</td>\n",
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" <td>150</td>\n",
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" <td>3433</td>\n",
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" <td>12.0</td>\n",
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" <td>70</td>\n",
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" <td>1</td>\n",
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" <td>amc rebel sst</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>17.0</td>\n",
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" <td>8</td>\n",
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" <td>302.0</td>\n",
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" <td>140</td>\n",
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" <td>3449</td>\n",
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" <td>10.5</td>\n",
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" <td>70</td>\n",
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" <td>1</td>\n",
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" <td>ford torino</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" mpg cylinders displacement horsepower weight acceleration year \\\n",
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"0 18.0 8 307.0 130 3504 12.0 70 \n",
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"1 15.0 8 350.0 165 3693 11.5 70 \n",
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"2 18.0 8 318.0 150 3436 11.0 70 \n",
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"3 16.0 8 304.0 150 3433 12.0 70 \n",
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"4 17.0 8 302.0 140 3449 10.5 70 \n",
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"\n",
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" origin name \n",
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"0 1 chevrolet chevelle malibu \n",
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"1 1 buick skylark 320 \n",
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"2 1 plymouth satellite \n",
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"3 1 amc rebel sst \n",
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"4 1 ford torino "
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from ISLP import load_data # Package which contains the data\n",
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"Auto = load_data('Auto') # Loading the data\n",
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"Auto.head() # Showing the first 5 Lines of Data."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "141f0257-39a4-4e21-9be0-78dfd645445a",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import statsmodels.formula.api as smf\n",
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"\n",
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"# fit model on training data and calculate training MSE\n",
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"fit_lm = smf.ols(formula='mpg ~ horsepower', data = Auto).fit()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "4ae9bf59-3b73-4020-b039-885948f6cbbd",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"==============================================================================\n",
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" coef std err t P>|t| [0.025 0.975]\n",
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"------------------------------------------------------------------------------\n",
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"Intercept 39.9359 0.717 55.660 0.000 38.525 41.347\n",
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"horsepower -0.1578 0.006 -24.489 0.000 -0.171 -0.145\n",
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"==============================================================================\n"
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]
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}
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],
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"source": [
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"print(fit_lm.summary().tables[1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "77170717-6eb1-41fa-a2b6-fdbf5e9193cf",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"date": " ",
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.7"
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},
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"title": " ",
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"toc-autonumbering": false,
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"toc-showcode": false,
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"toc-showmarkdowntxt": false,
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"toc-showtags": false
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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