Delete Week 1
Browse files- Week 1/Week 1.ipynb +0 -1320
- Week 1/car_fuel_efficiency.csv +0 -0
- Week 1/readme.md +0 -83
Week 1/Week 1.ipynb
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"cells": [
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import warnings\n",
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"warnings.filterwarnings('ignore')\n",
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"import numpy as np"
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{
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"text/plain": [
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"'2.3.2'"
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"pd.__version__"
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"execution_count": 55,
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"id": "40099d81-2fd2-41cd-ae18-093e7174f8fb",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv(\"car_fuel_efficiency.csv\")"
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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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"<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>engine_displacement</th>\n",
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" <th>num_cylinders</th>\n",
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" <th>horsepower</th>\n",
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" <th>vehicle_weight</th>\n",
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" <th>acceleration</th>\n",
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" <th>model_year</th>\n",
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" <th>origin</th>\n",
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" <th>fuel_type</th>\n",
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" <th>drivetrain</th>\n",
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" <th>num_doors</th>\n",
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" <th>fuel_efficiency_mpg</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>170</td>\n",
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" <td>3.0</td>\n",
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" <td>159.0</td>\n",
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" <td>3413.433759</td>\n",
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" <td>17.7</td>\n",
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" <td>2003</td>\n",
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" <td>Europe</td>\n",
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" <td>Gasoline</td>\n",
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" <td>All-wheel drive</td>\n",
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" <td>0.0</td>\n",
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" <td>13.231729</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>130</td>\n",
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" <td>5.0</td>\n",
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" <td>97.0</td>\n",
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" <td>3149.664934</td>\n",
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" <td>17.8</td>\n",
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" <td>2007</td>\n",
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" <td>USA</td>\n",
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" <td>Gasoline</td>\n",
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" <td>Front-wheel drive</td>\n",
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" <td>0.0</td>\n",
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" <td>13.688217</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>170</td>\n",
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" <td>NaN</td>\n",
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" <td>78.0</td>\n",
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" <td>3079.038997</td>\n",
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" <td>15.1</td>\n",
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" <td>2018</td>\n",
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" <td>Europe</td>\n",
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" <td>Gasoline</td>\n",
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| 126 |
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" <td>Front-wheel drive</td>\n",
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| 127 |
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" <td>0.0</td>\n",
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| 128 |
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" <td>14.246341</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>220</td>\n",
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" <td>4.0</td>\n",
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" <td>NaN</td>\n",
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| 135 |
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" <td>2542.392402</td>\n",
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| 136 |
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" <td>20.2</td>\n",
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" <td>2009</td>\n",
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" <td>USA</td>\n",
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" <td>Diesel</td>\n",
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" <td>All-wheel drive</td>\n",
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" <td>2.0</td>\n",
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" <td>16.912736</td>\n",
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" </tr>\n",
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" <tr>\n",
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| 145 |
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" <th>4</th>\n",
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" <td>210</td>\n",
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| 147 |
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" <td>1.0</td>\n",
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| 148 |
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" <td>140.0</td>\n",
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| 149 |
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" <td>3460.870990</td>\n",
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| 150 |
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" <td>14.4</td>\n",
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| 151 |
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" <td>2009</td>\n",
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| 152 |
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" <td>Europe</td>\n",
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| 153 |
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" <td>Gasoline</td>\n",
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| 154 |
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" <td>All-wheel drive</td>\n",
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| 155 |
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" <td>2.0</td>\n",
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| 156 |
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" <td>12.488369</td>\n",
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| 157 |
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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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" engine_displacement num_cylinders horsepower vehicle_weight \\\n",
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"0 170 3.0 159.0 3413.433759 \n",
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"1 130 5.0 97.0 3149.664934 \n",
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"2 170 NaN 78.0 3079.038997 \n",
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| 167 |
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"3 220 4.0 NaN 2542.392402 \n",
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| 168 |
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"4 210 1.0 140.0 3460.870990 \n",
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"\n",
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" acceleration model_year origin fuel_type drivetrain num_doors \\\n",
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"0 17.7 2003 Europe Gasoline All-wheel drive 0.0 \n",
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"1 17.8 2007 USA Gasoline Front-wheel drive 0.0 \n",
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"2 15.1 2018 Europe Gasoline Front-wheel drive 0.0 \n",
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"3 20.2 2009 USA Diesel All-wheel drive 2.0 \n",
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"4 14.4 2009 Europe Gasoline All-wheel drive 2.0 \n",
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"\n",
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" fuel_efficiency_mpg \n",
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"0 13.231729 \n",
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| 179 |
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"1 13.688217 \n",
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"2 14.246341 \n",
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| 181 |
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"3 16.912736 \n",
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"4 12.488369 "
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]
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},
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"execution_count": 56,
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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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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2b12116a-060a-48a5-afe9-fdde99e53fce",
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"metadata": {},
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"source": [
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"## 1. No. of Records"
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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": 57,
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"id": "515593f3-b510-48c2-9067-d9f8ffec3062",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(9704, 11)"
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]
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},
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"execution_count": 57,
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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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"df.shape"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2a91bf5f-4be4-49b1-bf26-299b71da934a",
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"metadata": {},
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"source": [
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"## 2. Distinct fuel types"
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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": 58,
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"id": "28148b46-460f-4dc0-895e-9b445b2c5cca",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0 Gasoline\n",
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"1 Gasoline\n",
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"2 Gasoline\n",
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"3 Diesel\n",
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"4 Gasoline\n",
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"Name: fuel_type, dtype: object"
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]
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},
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"execution_count": 58,
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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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"fuels = df['fuel_type']\n",
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"fuels.head()"
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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": 59,
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"id": "a94560b0-3240-45c0-b075-28873370b87f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2"
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]
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},
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"execution_count": 59,
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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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"fuels.nunique()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "48bb4b87-2a21-408e-866c-8d4bacc57caa",
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"metadata": {},
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"source": [
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"## 3. Null Values"
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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": 60,
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"id": "7f2d85e3-672f-4e09-a48c-1f26a5627c2d",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"engine_displacement 0\n",
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"num_cylinders 482\n",
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"horsepower 708\n",
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"vehicle_weight 0\n",
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"acceleration 930\n",
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"model_year 0\n",
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"origin 0\n",
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"fuel_type 0\n",
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"drivetrain 0\n",
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"num_doors 502\n",
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"fuel_efficiency_mpg 0\n",
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"dtype: int64"
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]
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},
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"execution_count": 60,
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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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"df.isnull().sum()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "98f06e86-38d3-4441-bafe-22f9089f6ee2",
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"metadata": {},
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"source": [
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"Clearly, in fuel types there is no missing values"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f052a64b-db12-4342-96d0-3f5ca215cca0",
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"metadata": {},
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"source": [
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"## 4. Max fuel efficiency "
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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": 61,
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"id": "de01f839-411a-4ed1-bc49-f479174cd8b3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index(['engine_displacement', 'num_cylinders', 'horsepower', 'vehicle_weight',\n",
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| 345 |
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" 'acceleration', 'model_year', 'origin', 'fuel_type', 'drivetrain',\n",
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" 'num_doors', 'fuel_efficiency_mpg'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 61,
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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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"df.columns"
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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": 62,
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"id": "98a48cd5-38df-4736-b873-1d78c1546bde",
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"metadata": {
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"scrolled": true
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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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"<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>engine_displacement</th>\n",
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" <th>num_cylinders</th>\n",
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" <th>horsepower</th>\n",
|
| 391 |
-
" <th>vehicle_weight</th>\n",
|
| 392 |
-
" <th>acceleration</th>\n",
|
| 393 |
-
" <th>model_year</th>\n",
|
| 394 |
-
" <th>origin</th>\n",
|
| 395 |
-
" <th>fuel_type</th>\n",
|
| 396 |
-
" <th>drivetrain</th>\n",
|
| 397 |
-
" <th>num_doors</th>\n",
|
| 398 |
-
" <th>fuel_efficiency_mpg</th>\n",
|
| 399 |
-
" </tr>\n",
|
| 400 |
-
" </thead>\n",
|
| 401 |
-
" <tbody>\n",
|
| 402 |
-
" <tr>\n",
|
| 403 |
-
" <th>0</th>\n",
|
| 404 |
-
" <td>170</td>\n",
|
| 405 |
-
" <td>3.0</td>\n",
|
| 406 |
-
" <td>159.0</td>\n",
|
| 407 |
-
" <td>3413.433759</td>\n",
|
| 408 |
-
" <td>17.7</td>\n",
|
| 409 |
-
" <td>2003</td>\n",
|
| 410 |
-
" <td>Europe</td>\n",
|
| 411 |
-
" <td>Gasoline</td>\n",
|
| 412 |
-
" <td>All-wheel drive</td>\n",
|
| 413 |
-
" <td>0.0</td>\n",
|
| 414 |
-
" <td>13.231729</td>\n",
|
| 415 |
-
" </tr>\n",
|
| 416 |
-
" <tr>\n",
|
| 417 |
-
" <th>1</th>\n",
|
| 418 |
-
" <td>130</td>\n",
|
| 419 |
-
" <td>5.0</td>\n",
|
| 420 |
-
" <td>97.0</td>\n",
|
| 421 |
-
" <td>3149.664934</td>\n",
|
| 422 |
-
" <td>17.8</td>\n",
|
| 423 |
-
" <td>2007</td>\n",
|
| 424 |
-
" <td>USA</td>\n",
|
| 425 |
-
" <td>Gasoline</td>\n",
|
| 426 |
-
" <td>Front-wheel drive</td>\n",
|
| 427 |
-
" <td>0.0</td>\n",
|
| 428 |
-
" <td>13.688217</td>\n",
|
| 429 |
-
" </tr>\n",
|
| 430 |
-
" <tr>\n",
|
| 431 |
-
" <th>2</th>\n",
|
| 432 |
-
" <td>170</td>\n",
|
| 433 |
-
" <td>NaN</td>\n",
|
| 434 |
-
" <td>78.0</td>\n",
|
| 435 |
-
" <td>3079.038997</td>\n",
|
| 436 |
-
" <td>15.1</td>\n",
|
| 437 |
-
" <td>2018</td>\n",
|
| 438 |
-
" <td>Europe</td>\n",
|
| 439 |
-
" <td>Gasoline</td>\n",
|
| 440 |
-
" <td>Front-wheel drive</td>\n",
|
| 441 |
-
" <td>0.0</td>\n",
|
| 442 |
-
" <td>14.246341</td>\n",
|
| 443 |
-
" </tr>\n",
|
| 444 |
-
" <tr>\n",
|
| 445 |
-
" <th>3</th>\n",
|
| 446 |
-
" <td>220</td>\n",
|
| 447 |
-
" <td>4.0</td>\n",
|
| 448 |
-
" <td>NaN</td>\n",
|
| 449 |
-
" <td>2542.392402</td>\n",
|
| 450 |
-
" <td>20.2</td>\n",
|
| 451 |
-
" <td>2009</td>\n",
|
| 452 |
-
" <td>USA</td>\n",
|
| 453 |
-
" <td>Diesel</td>\n",
|
| 454 |
-
" <td>All-wheel drive</td>\n",
|
| 455 |
-
" <td>2.0</td>\n",
|
| 456 |
-
" <td>16.912736</td>\n",
|
| 457 |
-
" </tr>\n",
|
| 458 |
-
" <tr>\n",
|
| 459 |
-
" <th>4</th>\n",
|
| 460 |
-
" <td>210</td>\n",
|
| 461 |
-
" <td>1.0</td>\n",
|
| 462 |
-
" <td>140.0</td>\n",
|
| 463 |
-
" <td>3460.870990</td>\n",
|
| 464 |
-
" <td>14.4</td>\n",
|
| 465 |
-
" <td>2009</td>\n",
|
| 466 |
-
" <td>Europe</td>\n",
|
| 467 |
-
" <td>Gasoline</td>\n",
|
| 468 |
-
" <td>All-wheel drive</td>\n",
|
| 469 |
-
" <td>2.0</td>\n",
|
| 470 |
-
" <td>12.488369</td>\n",
|
| 471 |
-
" </tr>\n",
|
| 472 |
-
" </tbody>\n",
|
| 473 |
-
"</table>\n",
|
| 474 |
-
"</div>"
|
| 475 |
-
],
|
| 476 |
-
"text/plain": [
|
| 477 |
-
" engine_displacement num_cylinders horsepower vehicle_weight \\\n",
|
| 478 |
-
"0 170 3.0 159.0 3413.433759 \n",
|
| 479 |
-
"1 130 5.0 97.0 3149.664934 \n",
|
| 480 |
-
"2 170 NaN 78.0 3079.038997 \n",
|
| 481 |
-
"3 220 4.0 NaN 2542.392402 \n",
|
| 482 |
-
"4 210 1.0 140.0 3460.870990 \n",
|
| 483 |
-
"\n",
|
| 484 |
-
" acceleration model_year origin fuel_type drivetrain num_doors \\\n",
|
| 485 |
-
"0 17.7 2003 Europe Gasoline All-wheel drive 0.0 \n",
|
| 486 |
-
"1 17.8 2007 USA Gasoline Front-wheel drive 0.0 \n",
|
| 487 |
-
"2 15.1 2018 Europe Gasoline Front-wheel drive 0.0 \n",
|
| 488 |
-
"3 20.2 2009 USA Diesel All-wheel drive 2.0 \n",
|
| 489 |
-
"4 14.4 2009 Europe Gasoline All-wheel drive 2.0 \n",
|
| 490 |
-
"\n",
|
| 491 |
-
" fuel_efficiency_mpg \n",
|
| 492 |
-
"0 13.231729 \n",
|
| 493 |
-
"1 13.688217 \n",
|
| 494 |
-
"2 14.246341 \n",
|
| 495 |
-
"3 16.912736 \n",
|
| 496 |
-
"4 12.488369 "
|
| 497 |
-
]
|
| 498 |
-
},
|
| 499 |
-
"execution_count": 62,
|
| 500 |
-
"metadata": {},
|
| 501 |
-
"output_type": "execute_result"
|
| 502 |
-
}
|
| 503 |
-
],
|
| 504 |
-
"source": [
|
| 505 |
-
"df.head()"
|
| 506 |
-
]
|
| 507 |
-
},
|
| 508 |
-
{
|
| 509 |
-
"cell_type": "code",
|
| 510 |
-
"execution_count": 63,
|
| 511 |
-
"id": "25faf234-3f06-4f9b-bd07-0f25de03ee1c",
|
| 512 |
-
"metadata": {},
|
| 513 |
-
"outputs": [
|
| 514 |
-
{
|
| 515 |
-
"data": {
|
| 516 |
-
"text/html": [
|
| 517 |
-
"<div>\n",
|
| 518 |
-
"<style scoped>\n",
|
| 519 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
| 520 |
-
" vertical-align: middle;\n",
|
| 521 |
-
" }\n",
|
| 522 |
-
"\n",
|
| 523 |
-
" .dataframe tbody tr th {\n",
|
| 524 |
-
" vertical-align: top;\n",
|
| 525 |
-
" }\n",
|
| 526 |
-
"\n",
|
| 527 |
-
" .dataframe thead th {\n",
|
| 528 |
-
" text-align: right;\n",
|
| 529 |
-
" }\n",
|
| 530 |
-
"</style>\n",
|
| 531 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
| 532 |
-
" <thead>\n",
|
| 533 |
-
" <tr style=\"text-align: right;\">\n",
|
| 534 |
-
" <th></th>\n",
|
| 535 |
-
" <th>engine_displacement</th>\n",
|
| 536 |
-
" <th>num_cylinders</th>\n",
|
| 537 |
-
" <th>horsepower</th>\n",
|
| 538 |
-
" <th>vehicle_weight</th>\n",
|
| 539 |
-
" <th>acceleration</th>\n",
|
| 540 |
-
" <th>model_year</th>\n",
|
| 541 |
-
" <th>origin</th>\n",
|
| 542 |
-
" <th>fuel_type</th>\n",
|
| 543 |
-
" <th>drivetrain</th>\n",
|
| 544 |
-
" <th>num_doors</th>\n",
|
| 545 |
-
" <th>fuel_efficiency_mpg</th>\n",
|
| 546 |
-
" </tr>\n",
|
| 547 |
-
" </thead>\n",
|
| 548 |
-
" <tbody>\n",
|
| 549 |
-
" <tr>\n",
|
| 550 |
-
" <th>8</th>\n",
|
| 551 |
-
" <td>250</td>\n",
|
| 552 |
-
" <td>1.0</td>\n",
|
| 553 |
-
" <td>174.0</td>\n",
|
| 554 |
-
" <td>2714.219310</td>\n",
|
| 555 |
-
" <td>10.3</td>\n",
|
| 556 |
-
" <td>2016</td>\n",
|
| 557 |
-
" <td>Asia</td>\n",
|
| 558 |
-
" <td>Diesel</td>\n",
|
| 559 |
-
" <td>Front-wheel drive</td>\n",
|
| 560 |
-
" <td>-1.0</td>\n",
|
| 561 |
-
" <td>16.823554</td>\n",
|
| 562 |
-
" </tr>\n",
|
| 563 |
-
" <tr>\n",
|
| 564 |
-
" <th>12</th>\n",
|
| 565 |
-
" <td>320</td>\n",
|
| 566 |
-
" <td>5.0</td>\n",
|
| 567 |
-
" <td>145.0</td>\n",
|
| 568 |
-
" <td>2783.868974</td>\n",
|
| 569 |
-
" <td>15.1</td>\n",
|
| 570 |
-
" <td>2010</td>\n",
|
| 571 |
-
" <td>Asia</td>\n",
|
| 572 |
-
" <td>Diesel</td>\n",
|
| 573 |
-
" <td>All-wheel drive</td>\n",
|
| 574 |
-
" <td>1.0</td>\n",
|
| 575 |
-
" <td>16.175820</td>\n",
|
| 576 |
-
" </tr>\n",
|
| 577 |
-
" <tr>\n",
|
| 578 |
-
" <th>14</th>\n",
|
| 579 |
-
" <td>200</td>\n",
|
| 580 |
-
" <td>6.0</td>\n",
|
| 581 |
-
" <td>160.0</td>\n",
|
| 582 |
-
" <td>3582.687368</td>\n",
|
| 583 |
-
" <td>14.9</td>\n",
|
| 584 |
-
" <td>2007</td>\n",
|
| 585 |
-
" <td>Asia</td>\n",
|
| 586 |
-
" <td>Diesel</td>\n",
|
| 587 |
-
" <td>All-wheel drive</td>\n",
|
| 588 |
-
" <td>0.0</td>\n",
|
| 589 |
-
" <td>11.871091</td>\n",
|
| 590 |
-
" </tr>\n",
|
| 591 |
-
" <tr>\n",
|
| 592 |
-
" <th>20</th>\n",
|
| 593 |
-
" <td>150</td>\n",
|
| 594 |
-
" <td>3.0</td>\n",
|
| 595 |
-
" <td>197.0</td>\n",
|
| 596 |
-
" <td>2231.808142</td>\n",
|
| 597 |
-
" <td>18.7</td>\n",
|
| 598 |
-
" <td>2011</td>\n",
|
| 599 |
-
" <td>Asia</td>\n",
|
| 600 |
-
" <td>Gasoline</td>\n",
|
| 601 |
-
" <td>Front-wheel drive</td>\n",
|
| 602 |
-
" <td>1.0</td>\n",
|
| 603 |
-
" <td>18.889083</td>\n",
|
| 604 |
-
" </tr>\n",
|
| 605 |
-
" <tr>\n",
|
| 606 |
-
" <th>21</th>\n",
|
| 607 |
-
" <td>160</td>\n",
|
| 608 |
-
" <td>4.0</td>\n",
|
| 609 |
-
" <td>133.0</td>\n",
|
| 610 |
-
" <td>2659.431451</td>\n",
|
| 611 |
-
" <td>NaN</td>\n",
|
| 612 |
-
" <td>2016</td>\n",
|
| 613 |
-
" <td>Asia</td>\n",
|
| 614 |
-
" <td>Gasoline</td>\n",
|
| 615 |
-
" <td>Front-wheel drive</td>\n",
|
| 616 |
-
" <td>-1.0</td>\n",
|
| 617 |
-
" <td>16.077730</td>\n",
|
| 618 |
-
" </tr>\n",
|
| 619 |
-
" </tbody>\n",
|
| 620 |
-
"</table>\n",
|
| 621 |
-
"</div>"
|
| 622 |
-
],
|
| 623 |
-
"text/plain": [
|
| 624 |
-
" engine_displacement num_cylinders horsepower vehicle_weight \\\n",
|
| 625 |
-
"8 250 1.0 174.0 2714.219310 \n",
|
| 626 |
-
"12 320 5.0 145.0 2783.868974 \n",
|
| 627 |
-
"14 200 6.0 160.0 3582.687368 \n",
|
| 628 |
-
"20 150 3.0 197.0 2231.808142 \n",
|
| 629 |
-
"21 160 4.0 133.0 2659.431451 \n",
|
| 630 |
-
"\n",
|
| 631 |
-
" acceleration model_year origin fuel_type drivetrain num_doors \\\n",
|
| 632 |
-
"8 10.3 2016 Asia Diesel Front-wheel drive -1.0 \n",
|
| 633 |
-
"12 15.1 2010 Asia Diesel All-wheel drive 1.0 \n",
|
| 634 |
-
"14 14.9 2007 Asia Diesel All-wheel drive 0.0 \n",
|
| 635 |
-
"20 18.7 2011 Asia Gasoline Front-wheel drive 1.0 \n",
|
| 636 |
-
"21 NaN 2016 Asia Gasoline Front-wheel drive -1.0 \n",
|
| 637 |
-
"\n",
|
| 638 |
-
" fuel_efficiency_mpg \n",
|
| 639 |
-
"8 16.823554 \n",
|
| 640 |
-
"12 16.175820 \n",
|
| 641 |
-
"14 11.871091 \n",
|
| 642 |
-
"20 18.889083 \n",
|
| 643 |
-
"21 16.077730 "
|
| 644 |
-
]
|
| 645 |
-
},
|
| 646 |
-
"execution_count": 63,
|
| 647 |
-
"metadata": {},
|
| 648 |
-
"output_type": "execute_result"
|
| 649 |
-
}
|
| 650 |
-
],
|
| 651 |
-
"source": [
|
| 652 |
-
"mask_asia = df['origin'] == 'Asia'\n",
|
| 653 |
-
"eff = df[mask_asia]\n",
|
| 654 |
-
"eff.head()"
|
| 655 |
-
]
|
| 656 |
-
},
|
| 657 |
-
{
|
| 658 |
-
"cell_type": "code",
|
| 659 |
-
"execution_count": 64,
|
| 660 |
-
"id": "5d1f7efd-77f9-4568-a012-5cd1ba753fd6",
|
| 661 |
-
"metadata": {},
|
| 662 |
-
"outputs": [
|
| 663 |
-
{
|
| 664 |
-
"data": {
|
| 665 |
-
"text/plain": [
|
| 666 |
-
"23.759122836520497"
|
| 667 |
-
]
|
| 668 |
-
},
|
| 669 |
-
"execution_count": 64,
|
| 670 |
-
"metadata": {},
|
| 671 |
-
"output_type": "execute_result"
|
| 672 |
-
}
|
| 673 |
-
],
|
| 674 |
-
"source": [
|
| 675 |
-
"max_eff = max(eff['fuel_efficiency_mpg'])\n",
|
| 676 |
-
"max_eff"
|
| 677 |
-
]
|
| 678 |
-
},
|
| 679 |
-
{
|
| 680 |
-
"cell_type": "markdown",
|
| 681 |
-
"id": "cda34c7c-f2c0-497d-b419-dae670db022b",
|
| 682 |
-
"metadata": {},
|
| 683 |
-
"source": [
|
| 684 |
-
"## 5. Median value of horsepower"
|
| 685 |
-
]
|
| 686 |
-
},
|
| 687 |
-
{
|
| 688 |
-
"cell_type": "code",
|
| 689 |
-
"execution_count": 65,
|
| 690 |
-
"id": "e8328da7-f04f-41bd-94f5-b534aa00f2c1",
|
| 691 |
-
"metadata": {},
|
| 692 |
-
"outputs": [
|
| 693 |
-
{
|
| 694 |
-
"data": {
|
| 695 |
-
"text/plain": [
|
| 696 |
-
"engine_displacement 0\n",
|
| 697 |
-
"num_cylinders 482\n",
|
| 698 |
-
"horsepower 708\n",
|
| 699 |
-
"vehicle_weight 0\n",
|
| 700 |
-
"acceleration 930\n",
|
| 701 |
-
"model_year 0\n",
|
| 702 |
-
"origin 0\n",
|
| 703 |
-
"fuel_type 0\n",
|
| 704 |
-
"drivetrain 0\n",
|
| 705 |
-
"num_doors 502\n",
|
| 706 |
-
"fuel_efficiency_mpg 0\n",
|
| 707 |
-
"dtype: int64"
|
| 708 |
-
]
|
| 709 |
-
},
|
| 710 |
-
"execution_count": 65,
|
| 711 |
-
"metadata": {},
|
| 712 |
-
"output_type": "execute_result"
|
| 713 |
-
}
|
| 714 |
-
],
|
| 715 |
-
"source": [
|
| 716 |
-
"df.isnull().sum()"
|
| 717 |
-
]
|
| 718 |
-
},
|
| 719 |
-
{
|
| 720 |
-
"cell_type": "code",
|
| 721 |
-
"execution_count": 66,
|
| 722 |
-
"id": "6eaafaf8-6674-443d-b26c-6d8212d91754",
|
| 723 |
-
"metadata": {},
|
| 724 |
-
"outputs": [
|
| 725 |
-
{
|
| 726 |
-
"data": {
|
| 727 |
-
"text/plain": [
|
| 728 |
-
"149.0"
|
| 729 |
-
]
|
| 730 |
-
},
|
| 731 |
-
"execution_count": 66,
|
| 732 |
-
"metadata": {},
|
| 733 |
-
"output_type": "execute_result"
|
| 734 |
-
}
|
| 735 |
-
],
|
| 736 |
-
"source": [
|
| 737 |
-
"# median of the horsepower col\n",
|
| 738 |
-
"df['horsepower'].median()"
|
| 739 |
-
]
|
| 740 |
-
},
|
| 741 |
-
{
|
| 742 |
-
"cell_type": "code",
|
| 743 |
-
"execution_count": 67,
|
| 744 |
-
"id": "9b785320-6b9a-41c0-bb27-c0f126145177",
|
| 745 |
-
"metadata": {},
|
| 746 |
-
"outputs": [
|
| 747 |
-
{
|
| 748 |
-
"data": {
|
| 749 |
-
"text/plain": [
|
| 750 |
-
"horsepower\n",
|
| 751 |
-
"152.0 142\n",
|
| 752 |
-
"145.0 141\n",
|
| 753 |
-
"151.0 134\n",
|
| 754 |
-
"148.0 130\n",
|
| 755 |
-
"141.0 130\n",
|
| 756 |
-
" ... \n",
|
| 757 |
-
"40.0 1\n",
|
| 758 |
-
"57.0 1\n",
|
| 759 |
-
"245.0 1\n",
|
| 760 |
-
"252.0 1\n",
|
| 761 |
-
"61.0 1\n",
|
| 762 |
-
"Name: count, Length: 192, dtype: int64"
|
| 763 |
-
]
|
| 764 |
-
},
|
| 765 |
-
"execution_count": 67,
|
| 766 |
-
"metadata": {},
|
| 767 |
-
"output_type": "execute_result"
|
| 768 |
-
}
|
| 769 |
-
],
|
| 770 |
-
"source": [
|
| 771 |
-
"# most frequent value here\n",
|
| 772 |
-
"df['horsepower'].value_counts()"
|
| 773 |
-
]
|
| 774 |
-
},
|
| 775 |
-
{
|
| 776 |
-
"cell_type": "code",
|
| 777 |
-
"execution_count": 74,
|
| 778 |
-
"id": "126b6df6-515f-463e-83f3-10abbf2c25e2",
|
| 779 |
-
"metadata": {},
|
| 780 |
-
"outputs": [
|
| 781 |
-
{
|
| 782 |
-
"data": {
|
| 783 |
-
"text/plain": [
|
| 784 |
-
"np.float64(152.0)"
|
| 785 |
-
]
|
| 786 |
-
},
|
| 787 |
-
"execution_count": 74,
|
| 788 |
-
"metadata": {},
|
| 789 |
-
"output_type": "execute_result"
|
| 790 |
-
}
|
| 791 |
-
],
|
| 792 |
-
"source": [
|
| 793 |
-
"# alternatively mode gives the max freq count\n",
|
| 794 |
-
"mode_horsepower = df['horsepower'].mode()[0]\n",
|
| 795 |
-
"mode_horsepower"
|
| 796 |
-
]
|
| 797 |
-
},
|
| 798 |
-
{
|
| 799 |
-
"cell_type": "code",
|
| 800 |
-
"execution_count": 80,
|
| 801 |
-
"id": "bd17e63f-c5c1-4d8a-8ba5-1b8e106175fc",
|
| 802 |
-
"metadata": {},
|
| 803 |
-
"outputs": [],
|
| 804 |
-
"source": [
|
| 805 |
-
"# fill the missing values in the col with mode\n",
|
| 806 |
-
"df['horsepower'].fillna(mode_horsepower, inplace=True)"
|
| 807 |
-
]
|
| 808 |
-
},
|
| 809 |
-
{
|
| 810 |
-
"cell_type": "code",
|
| 811 |
-
"execution_count": 81,
|
| 812 |
-
"id": "e7dc6b1a-323a-4f88-b475-f76059759e66",
|
| 813 |
-
"metadata": {},
|
| 814 |
-
"outputs": [
|
| 815 |
-
{
|
| 816 |
-
"data": {
|
| 817 |
-
"text/plain": [
|
| 818 |
-
"engine_displacement 0\n",
|
| 819 |
-
"num_cylinders 482\n",
|
| 820 |
-
"horsepower 0\n",
|
| 821 |
-
"vehicle_weight 0\n",
|
| 822 |
-
"acceleration 930\n",
|
| 823 |
-
"model_year 0\n",
|
| 824 |
-
"origin 0\n",
|
| 825 |
-
"fuel_type 0\n",
|
| 826 |
-
"drivetrain 0\n",
|
| 827 |
-
"num_doors 502\n",
|
| 828 |
-
"fuel_efficiency_mpg 0\n",
|
| 829 |
-
"dtype: int64"
|
| 830 |
-
]
|
| 831 |
-
},
|
| 832 |
-
"execution_count": 81,
|
| 833 |
-
"metadata": {},
|
| 834 |
-
"output_type": "execute_result"
|
| 835 |
-
}
|
| 836 |
-
],
|
| 837 |
-
"source": [
|
| 838 |
-
"# check if null values are removed or not\n",
|
| 839 |
-
"df.isnull().sum()"
|
| 840 |
-
]
|
| 841 |
-
},
|
| 842 |
-
{
|
| 843 |
-
"cell_type": "markdown",
|
| 844 |
-
"id": "3eaf5439-3f99-4506-bdd8-ca35e03c18bf",
|
| 845 |
-
"metadata": {},
|
| 846 |
-
"source": [
|
| 847 |
-
"Clearly the null values have been imputed"
|
| 848 |
-
]
|
| 849 |
-
},
|
| 850 |
-
{
|
| 851 |
-
"cell_type": "code",
|
| 852 |
-
"execution_count": 82,
|
| 853 |
-
"id": "94dc61c5-bbcb-47f4-9370-f3238c26e2a2",
|
| 854 |
-
"metadata": {},
|
| 855 |
-
"outputs": [
|
| 856 |
-
{
|
| 857 |
-
"data": {
|
| 858 |
-
"text/plain": [
|
| 859 |
-
"152.0"
|
| 860 |
-
]
|
| 861 |
-
},
|
| 862 |
-
"execution_count": 82,
|
| 863 |
-
"metadata": {},
|
| 864 |
-
"output_type": "execute_result"
|
| 865 |
-
}
|
| 866 |
-
],
|
| 867 |
-
"source": [
|
| 868 |
-
"# now recalculate the median\n",
|
| 869 |
-
"df['horsepower'].median()"
|
| 870 |
-
]
|
| 871 |
-
},
|
| 872 |
-
{
|
| 873 |
-
"cell_type": "markdown",
|
| 874 |
-
"id": "32337e0a-dbfc-4e96-9fab-15723b3a5166",
|
| 875 |
-
"metadata": {},
|
| 876 |
-
"source": [
|
| 877 |
-
"## 6. Model building"
|
| 878 |
-
]
|
| 879 |
-
},
|
| 880 |
-
{
|
| 881 |
-
"cell_type": "code",
|
| 882 |
-
"execution_count": 84,
|
| 883 |
-
"id": "a28d7bfb-3f4d-4018-8881-b39bf43d4089",
|
| 884 |
-
"metadata": {},
|
| 885 |
-
"outputs": [
|
| 886 |
-
{
|
| 887 |
-
"data": {
|
| 888 |
-
"text/html": [
|
| 889 |
-
"<div>\n",
|
| 890 |
-
"<style scoped>\n",
|
| 891 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
| 892 |
-
" vertical-align: middle;\n",
|
| 893 |
-
" }\n",
|
| 894 |
-
"\n",
|
| 895 |
-
" .dataframe tbody tr th {\n",
|
| 896 |
-
" vertical-align: top;\n",
|
| 897 |
-
" }\n",
|
| 898 |
-
"\n",
|
| 899 |
-
" .dataframe thead th {\n",
|
| 900 |
-
" text-align: right;\n",
|
| 901 |
-
" }\n",
|
| 902 |
-
"</style>\n",
|
| 903 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
| 904 |
-
" <thead>\n",
|
| 905 |
-
" <tr style=\"text-align: right;\">\n",
|
| 906 |
-
" <th></th>\n",
|
| 907 |
-
" <th>engine_displacement</th>\n",
|
| 908 |
-
" <th>num_cylinders</th>\n",
|
| 909 |
-
" <th>horsepower</th>\n",
|
| 910 |
-
" <th>vehicle_weight</th>\n",
|
| 911 |
-
" <th>acceleration</th>\n",
|
| 912 |
-
" <th>model_year</th>\n",
|
| 913 |
-
" <th>origin</th>\n",
|
| 914 |
-
" <th>fuel_type</th>\n",
|
| 915 |
-
" <th>drivetrain</th>\n",
|
| 916 |
-
" <th>num_doors</th>\n",
|
| 917 |
-
" <th>fuel_efficiency_mpg</th>\n",
|
| 918 |
-
" </tr>\n",
|
| 919 |
-
" </thead>\n",
|
| 920 |
-
" <tbody>\n",
|
| 921 |
-
" <tr>\n",
|
| 922 |
-
" <th>0</th>\n",
|
| 923 |
-
" <td>170</td>\n",
|
| 924 |
-
" <td>3.0</td>\n",
|
| 925 |
-
" <td>159.0</td>\n",
|
| 926 |
-
" <td>3413.433759</td>\n",
|
| 927 |
-
" <td>17.7</td>\n",
|
| 928 |
-
" <td>2003</td>\n",
|
| 929 |
-
" <td>Europe</td>\n",
|
| 930 |
-
" <td>Gasoline</td>\n",
|
| 931 |
-
" <td>All-wheel drive</td>\n",
|
| 932 |
-
" <td>0.0</td>\n",
|
| 933 |
-
" <td>13.231729</td>\n",
|
| 934 |
-
" </tr>\n",
|
| 935 |
-
" <tr>\n",
|
| 936 |
-
" <th>1</th>\n",
|
| 937 |
-
" <td>130</td>\n",
|
| 938 |
-
" <td>5.0</td>\n",
|
| 939 |
-
" <td>97.0</td>\n",
|
| 940 |
-
" <td>3149.664934</td>\n",
|
| 941 |
-
" <td>17.8</td>\n",
|
| 942 |
-
" <td>2007</td>\n",
|
| 943 |
-
" <td>USA</td>\n",
|
| 944 |
-
" <td>Gasoline</td>\n",
|
| 945 |
-
" <td>Front-wheel drive</td>\n",
|
| 946 |
-
" <td>0.0</td>\n",
|
| 947 |
-
" <td>13.688217</td>\n",
|
| 948 |
-
" </tr>\n",
|
| 949 |
-
" <tr>\n",
|
| 950 |
-
" <th>2</th>\n",
|
| 951 |
-
" <td>170</td>\n",
|
| 952 |
-
" <td>NaN</td>\n",
|
| 953 |
-
" <td>78.0</td>\n",
|
| 954 |
-
" <td>3079.038997</td>\n",
|
| 955 |
-
" <td>15.1</td>\n",
|
| 956 |
-
" <td>2018</td>\n",
|
| 957 |
-
" <td>Europe</td>\n",
|
| 958 |
-
" <td>Gasoline</td>\n",
|
| 959 |
-
" <td>Front-wheel drive</td>\n",
|
| 960 |
-
" <td>0.0</td>\n",
|
| 961 |
-
" <td>14.246341</td>\n",
|
| 962 |
-
" </tr>\n",
|
| 963 |
-
" <tr>\n",
|
| 964 |
-
" <th>3</th>\n",
|
| 965 |
-
" <td>220</td>\n",
|
| 966 |
-
" <td>4.0</td>\n",
|
| 967 |
-
" <td>152.0</td>\n",
|
| 968 |
-
" <td>2542.392402</td>\n",
|
| 969 |
-
" <td>20.2</td>\n",
|
| 970 |
-
" <td>2009</td>\n",
|
| 971 |
-
" <td>USA</td>\n",
|
| 972 |
-
" <td>Diesel</td>\n",
|
| 973 |
-
" <td>All-wheel drive</td>\n",
|
| 974 |
-
" <td>2.0</td>\n",
|
| 975 |
-
" <td>16.912736</td>\n",
|
| 976 |
-
" </tr>\n",
|
| 977 |
-
" <tr>\n",
|
| 978 |
-
" <th>4</th>\n",
|
| 979 |
-
" <td>210</td>\n",
|
| 980 |
-
" <td>1.0</td>\n",
|
| 981 |
-
" <td>140.0</td>\n",
|
| 982 |
-
" <td>3460.870990</td>\n",
|
| 983 |
-
" <td>14.4</td>\n",
|
| 984 |
-
" <td>2009</td>\n",
|
| 985 |
-
" <td>Europe</td>\n",
|
| 986 |
-
" <td>Gasoline</td>\n",
|
| 987 |
-
" <td>All-wheel drive</td>\n",
|
| 988 |
-
" <td>2.0</td>\n",
|
| 989 |
-
" <td>12.488369</td>\n",
|
| 990 |
-
" </tr>\n",
|
| 991 |
-
" </tbody>\n",
|
| 992 |
-
"</table>\n",
|
| 993 |
-
"</div>"
|
| 994 |
-
],
|
| 995 |
-
"text/plain": [
|
| 996 |
-
" engine_displacement num_cylinders horsepower vehicle_weight \\\n",
|
| 997 |
-
"0 170 3.0 159.0 3413.433759 \n",
|
| 998 |
-
"1 130 5.0 97.0 3149.664934 \n",
|
| 999 |
-
"2 170 NaN 78.0 3079.038997 \n",
|
| 1000 |
-
"3 220 4.0 152.0 2542.392402 \n",
|
| 1001 |
-
"4 210 1.0 140.0 3460.870990 \n",
|
| 1002 |
-
"\n",
|
| 1003 |
-
" acceleration model_year origin fuel_type drivetrain num_doors \\\n",
|
| 1004 |
-
"0 17.7 2003 Europe Gasoline All-wheel drive 0.0 \n",
|
| 1005 |
-
"1 17.8 2007 USA Gasoline Front-wheel drive 0.0 \n",
|
| 1006 |
-
"2 15.1 2018 Europe Gasoline Front-wheel drive 0.0 \n",
|
| 1007 |
-
"3 20.2 2009 USA Diesel All-wheel drive 2.0 \n",
|
| 1008 |
-
"4 14.4 2009 Europe Gasoline All-wheel drive 2.0 \n",
|
| 1009 |
-
"\n",
|
| 1010 |
-
" fuel_efficiency_mpg \n",
|
| 1011 |
-
"0 13.231729 \n",
|
| 1012 |
-
"1 13.688217 \n",
|
| 1013 |
-
"2 14.246341 \n",
|
| 1014 |
-
"3 16.912736 \n",
|
| 1015 |
-
"4 12.488369 "
|
| 1016 |
-
]
|
| 1017 |
-
},
|
| 1018 |
-
"execution_count": 84,
|
| 1019 |
-
"metadata": {},
|
| 1020 |
-
"output_type": "execute_result"
|
| 1021 |
-
}
|
| 1022 |
-
],
|
| 1023 |
-
"source": [
|
| 1024 |
-
"df.head()"
|
| 1025 |
-
]
|
| 1026 |
-
},
|
| 1027 |
-
{
|
| 1028 |
-
"cell_type": "code",
|
| 1029 |
-
"execution_count": 83,
|
| 1030 |
-
"id": "30057fab-fad4-44ae-9b9b-2aae11614f84",
|
| 1031 |
-
"metadata": {},
|
| 1032 |
-
"outputs": [
|
| 1033 |
-
{
|
| 1034 |
-
"data": {
|
| 1035 |
-
"text/plain": [
|
| 1036 |
-
"0 False\n",
|
| 1037 |
-
"1 False\n",
|
| 1038 |
-
"2 False\n",
|
| 1039 |
-
"3 False\n",
|
| 1040 |
-
"4 False\n",
|
| 1041 |
-
"Name: origin, dtype: bool"
|
| 1042 |
-
]
|
| 1043 |
-
},
|
| 1044 |
-
"execution_count": 83,
|
| 1045 |
-
"metadata": {},
|
| 1046 |
-
"output_type": "execute_result"
|
| 1047 |
-
}
|
| 1048 |
-
],
|
| 1049 |
-
"source": [
|
| 1050 |
-
"mask_asia.head()"
|
| 1051 |
-
]
|
| 1052 |
-
},
|
| 1053 |
-
{
|
| 1054 |
-
"cell_type": "code",
|
| 1055 |
-
"execution_count": 88,
|
| 1056 |
-
"id": "dbaa1132-9a2f-411a-9668-b5110109e3aa",
|
| 1057 |
-
"metadata": {},
|
| 1058 |
-
"outputs": [],
|
| 1059 |
-
"source": [
|
| 1060 |
-
"columns_to_keep = ['vehicle_weight', 'model_year']"
|
| 1061 |
-
]
|
| 1062 |
-
},
|
| 1063 |
-
{
|
| 1064 |
-
"cell_type": "code",
|
| 1065 |
-
"execution_count": 94,
|
| 1066 |
-
"id": "c37eb7f0-4e38-4a8d-b5a0-f54ba43ef6c7",
|
| 1067 |
-
"metadata": {},
|
| 1068 |
-
"outputs": [
|
| 1069 |
-
{
|
| 1070 |
-
"data": {
|
| 1071 |
-
"text/html": [
|
| 1072 |
-
"<div>\n",
|
| 1073 |
-
"<style scoped>\n",
|
| 1074 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
| 1075 |
-
" vertical-align: middle;\n",
|
| 1076 |
-
" }\n",
|
| 1077 |
-
"\n",
|
| 1078 |
-
" .dataframe tbody tr th {\n",
|
| 1079 |
-
" vertical-align: top;\n",
|
| 1080 |
-
" }\n",
|
| 1081 |
-
"\n",
|
| 1082 |
-
" .dataframe thead th {\n",
|
| 1083 |
-
" text-align: right;\n",
|
| 1084 |
-
" }\n",
|
| 1085 |
-
"</style>\n",
|
| 1086 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
| 1087 |
-
" <thead>\n",
|
| 1088 |
-
" <tr style=\"text-align: right;\">\n",
|
| 1089 |
-
" <th></th>\n",
|
| 1090 |
-
" <th>vehicle_weight</th>\n",
|
| 1091 |
-
" <th>model_year</th>\n",
|
| 1092 |
-
" </tr>\n",
|
| 1093 |
-
" </thead>\n",
|
| 1094 |
-
" <tbody>\n",
|
| 1095 |
-
" <tr>\n",
|
| 1096 |
-
" <th>8</th>\n",
|
| 1097 |
-
" <td>2714.219310</td>\n",
|
| 1098 |
-
" <td>2016</td>\n",
|
| 1099 |
-
" </tr>\n",
|
| 1100 |
-
" <tr>\n",
|
| 1101 |
-
" <th>12</th>\n",
|
| 1102 |
-
" <td>2783.868974</td>\n",
|
| 1103 |
-
" <td>2010</td>\n",
|
| 1104 |
-
" </tr>\n",
|
| 1105 |
-
" <tr>\n",
|
| 1106 |
-
" <th>14</th>\n",
|
| 1107 |
-
" <td>3582.687368</td>\n",
|
| 1108 |
-
" <td>2007</td>\n",
|
| 1109 |
-
" </tr>\n",
|
| 1110 |
-
" <tr>\n",
|
| 1111 |
-
" <th>20</th>\n",
|
| 1112 |
-
" <td>2231.808142</td>\n",
|
| 1113 |
-
" <td>2011</td>\n",
|
| 1114 |
-
" </tr>\n",
|
| 1115 |
-
" <tr>\n",
|
| 1116 |
-
" <th>21</th>\n",
|
| 1117 |
-
" <td>2659.431451</td>\n",
|
| 1118 |
-
" <td>2016</td>\n",
|
| 1119 |
-
" </tr>\n",
|
| 1120 |
-
" <tr>\n",
|
| 1121 |
-
" <th>34</th>\n",
|
| 1122 |
-
" <td>2844.227534</td>\n",
|
| 1123 |
-
" <td>2014</td>\n",
|
| 1124 |
-
" </tr>\n",
|
| 1125 |
-
" <tr>\n",
|
| 1126 |
-
" <th>38</th>\n",
|
| 1127 |
-
" <td>3761.994038</td>\n",
|
| 1128 |
-
" <td>2019</td>\n",
|
| 1129 |
-
" </tr>\n",
|
| 1130 |
-
" </tbody>\n",
|
| 1131 |
-
"</table>\n",
|
| 1132 |
-
"</div>"
|
| 1133 |
-
],
|
| 1134 |
-
"text/plain": [
|
| 1135 |
-
" vehicle_weight model_year\n",
|
| 1136 |
-
"8 2714.219310 2016\n",
|
| 1137 |
-
"12 2783.868974 2010\n",
|
| 1138 |
-
"14 3582.687368 2007\n",
|
| 1139 |
-
"20 2231.808142 2011\n",
|
| 1140 |
-
"21 2659.431451 2016\n",
|
| 1141 |
-
"34 2844.227534 2014\n",
|
| 1142 |
-
"38 3761.994038 2019"
|
| 1143 |
-
]
|
| 1144 |
-
},
|
| 1145 |
-
"execution_count": 94,
|
| 1146 |
-
"metadata": {},
|
| 1147 |
-
"output_type": "execute_result"
|
| 1148 |
-
}
|
| 1149 |
-
],
|
| 1150 |
-
"source": [
|
| 1151 |
-
"# subset the asian data\n",
|
| 1152 |
-
"df_asia = df[mask_asia]\n",
|
| 1153 |
-
"df_asia_final = df_asia[columns_to_keep].head(7)\n",
|
| 1154 |
-
"df_asia_final"
|
| 1155 |
-
]
|
| 1156 |
-
},
|
| 1157 |
-
{
|
| 1158 |
-
"cell_type": "code",
|
| 1159 |
-
"execution_count": 100,
|
| 1160 |
-
"id": "89abd22c-7cc2-49b9-8afd-4e824f4360c7",
|
| 1161 |
-
"metadata": {},
|
| 1162 |
-
"outputs": [
|
| 1163 |
-
{
|
| 1164 |
-
"data": {
|
| 1165 |
-
"text/plain": [
|
| 1166 |
-
"(7, 2)"
|
| 1167 |
-
]
|
| 1168 |
-
},
|
| 1169 |
-
"execution_count": 100,
|
| 1170 |
-
"metadata": {},
|
| 1171 |
-
"output_type": "execute_result"
|
| 1172 |
-
}
|
| 1173 |
-
],
|
| 1174 |
-
"source": [
|
| 1175 |
-
"# get the underlying numpy array\n",
|
| 1176 |
-
"X = np.array(df_asia_final)\n",
|
| 1177 |
-
"X.shape"
|
| 1178 |
-
]
|
| 1179 |
-
},
|
| 1180 |
-
{
|
| 1181 |
-
"cell_type": "code",
|
| 1182 |
-
"execution_count": 110,
|
| 1183 |
-
"id": "252a6e2f-c7f9-4c30-b74a-8b4e1ea876ab",
|
| 1184 |
-
"metadata": {},
|
| 1185 |
-
"outputs": [
|
| 1186 |
-
{
|
| 1187 |
-
"data": {
|
| 1188 |
-
"text/plain": [
|
| 1189 |
-
"(2, 2)"
|
| 1190 |
-
]
|
| 1191 |
-
},
|
| 1192 |
-
"execution_count": 110,
|
| 1193 |
-
"metadata": {},
|
| 1194 |
-
"output_type": "execute_result"
|
| 1195 |
-
}
|
| 1196 |
-
],
|
| 1197 |
-
"source": [
|
| 1198 |
-
"# take the dot product with the traspose (7,2).(2,7) -> (7,7)\n",
|
| 1199 |
-
"XTX = X.T @ X\n",
|
| 1200 |
-
"XTX.shape"
|
| 1201 |
-
]
|
| 1202 |
-
},
|
| 1203 |
-
{
|
| 1204 |
-
"cell_type": "code",
|
| 1205 |
-
"execution_count": 111,
|
| 1206 |
-
"id": "63342692-a307-48cd-a6cf-bdfc8e1985c1",
|
| 1207 |
-
"metadata": {},
|
| 1208 |
-
"outputs": [
|
| 1209 |
-
{
|
| 1210 |
-
"data": {
|
| 1211 |
-
"text/plain": [
|
| 1212 |
-
"(2, 2)"
|
| 1213 |
-
]
|
| 1214 |
-
},
|
| 1215 |
-
"execution_count": 111,
|
| 1216 |
-
"metadata": {},
|
| 1217 |
-
"output_type": "execute_result"
|
| 1218 |
-
}
|
| 1219 |
-
],
|
| 1220 |
-
"source": [
|
| 1221 |
-
"XTX_inv = np.linalg.inv(XTX)\n",
|
| 1222 |
-
"XTX_inv.shape"
|
| 1223 |
-
]
|
| 1224 |
-
},
|
| 1225 |
-
{
|
| 1226 |
-
"cell_type": "code",
|
| 1227 |
-
"execution_count": 112,
|
| 1228 |
-
"id": "e4b0a33e-ee66-48d3-82d4-7953e0a64461",
|
| 1229 |
-
"metadata": {},
|
| 1230 |
-
"outputs": [
|
| 1231 |
-
{
|
| 1232 |
-
"data": {
|
| 1233 |
-
"text/plain": [
|
| 1234 |
-
"array([1100, 1300, 800, 900, 1000, 1100, 1200])"
|
| 1235 |
-
]
|
| 1236 |
-
},
|
| 1237 |
-
"execution_count": 112,
|
| 1238 |
-
"metadata": {},
|
| 1239 |
-
"output_type": "execute_result"
|
| 1240 |
-
}
|
| 1241 |
-
],
|
| 1242 |
-
"source": [
|
| 1243 |
-
"# Create an array y with values \n",
|
| 1244 |
-
"y = np.array([1100, 1300, 800, 900, 1000, 1100, 1200])\n",
|
| 1245 |
-
"y "
|
| 1246 |
-
]
|
| 1247 |
-
},
|
| 1248 |
-
{
|
| 1249 |
-
"cell_type": "code",
|
| 1250 |
-
"execution_count": 114,
|
| 1251 |
-
"id": "e5b0bf7e-9e5d-46c8-9d47-4555f05bfc6f",
|
| 1252 |
-
"metadata": {},
|
| 1253 |
-
"outputs": [],
|
| 1254 |
-
"source": [
|
| 1255 |
-
"# Multiply the inverse of XTX with the transpose of X, and then multiply the result by y. Call the result w\n",
|
| 1256 |
-
"step = XTX_inv @ X.T\n",
|
| 1257 |
-
"w = step @ y"
|
| 1258 |
-
]
|
| 1259 |
-
},
|
| 1260 |
-
{
|
| 1261 |
-
"cell_type": "code",
|
| 1262 |
-
"execution_count": 115,
|
| 1263 |
-
"id": "1ddb3d1f-b877-4c66-9245-098cd63b850a",
|
| 1264 |
-
"metadata": {},
|
| 1265 |
-
"outputs": [
|
| 1266 |
-
{
|
| 1267 |
-
"data": {
|
| 1268 |
-
"text/plain": [
|
| 1269 |
-
"np.float64(0.5187709081074016)"
|
| 1270 |
-
]
|
| 1271 |
-
},
|
| 1272 |
-
"execution_count": 115,
|
| 1273 |
-
"metadata": {},
|
| 1274 |
-
"output_type": "execute_result"
|
| 1275 |
-
}
|
| 1276 |
-
],
|
| 1277 |
-
"source": [
|
| 1278 |
-
"# sum of all the elements of the result / weights\n",
|
| 1279 |
-
"np.sum(w)"
|
| 1280 |
-
]
|
| 1281 |
-
},
|
| 1282 |
-
{
|
| 1283 |
-
"cell_type": "markdown",
|
| 1284 |
-
"id": "5cad1468-2329-4fa3-9b91-0dc30dffafbc",
|
| 1285 |
-
"metadata": {},
|
| 1286 |
-
"source": [
|
| 1287 |
-
"## End of Week 1"
|
| 1288 |
-
]
|
| 1289 |
-
},
|
| 1290 |
-
{
|
| 1291 |
-
"cell_type": "code",
|
| 1292 |
-
"execution_count": null,
|
| 1293 |
-
"id": "2bbec182-f585-43fa-9960-ca979139c0e2",
|
| 1294 |
-
"metadata": {},
|
| 1295 |
-
"outputs": [],
|
| 1296 |
-
"source": []
|
| 1297 |
-
}
|
| 1298 |
-
],
|
| 1299 |
-
"metadata": {
|
| 1300 |
-
"kernelspec": {
|
| 1301 |
-
"display_name": "Python 3 (ipykernel)",
|
| 1302 |
-
"language": "python",
|
| 1303 |
-
"name": "python3"
|
| 1304 |
-
},
|
| 1305 |
-
"language_info": {
|
| 1306 |
-
"codemirror_mode": {
|
| 1307 |
-
"name": "ipython",
|
| 1308 |
-
"version": 3
|
| 1309 |
-
},
|
| 1310 |
-
"file_extension": ".py",
|
| 1311 |
-
"mimetype": "text/x-python",
|
| 1312 |
-
"name": "python",
|
| 1313 |
-
"nbconvert_exporter": "python",
|
| 1314 |
-
"pygments_lexer": "ipython3",
|
| 1315 |
-
"version": "3.11.13"
|
| 1316 |
-
}
|
| 1317 |
-
},
|
| 1318 |
-
"nbformat": 4,
|
| 1319 |
-
"nbformat_minor": 5
|
| 1320 |
-
}
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Week 1/car_fuel_efficiency.csv
DELETED
|
The diff for this file is too large to render.
See raw diff
|
|
|
Week 1/readme.md
DELETED
|
@@ -1,83 +0,0 @@
|
|
| 1 |
-
# Machine Learning Zoomcamp – Week 1: Linear Algebra Foundations
|
| 2 |
-
|
| 3 |
-
[](https://www.python.org/)
|
| 4 |
-
[](https://jupyter.org/)
|
| 5 |
-
[](https://numpy.org/)
|
| 6 |
-
|
| 7 |
-
This repository documents my journey through **Week 1** of the **Machine Learning Zoomcamp**, a comprehensive 4-month course offered by **DataTalksClub**. Week 1 focuses on building the **mathematical foundation** required for machine learning, including linear algebra and matrix operations.
|
| 8 |
-
|
| 9 |
-
---
|
| 10 |
-
|
| 11 |
-
## 📘 Week 1 Overview
|
| 12 |
-
|
| 13 |
-
The goal of this week was to understand the mathematical underpinnings of machine learning algorithms. Key topics included:
|
| 14 |
-
|
| 15 |
-
- **Matrix Operations**: Matrix multiplication, transposition, and inversion.
|
| 16 |
-
- **Linear Algebra Fundamentals**: Dot products, matrix shapes, and their relevance in ML.
|
| 17 |
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- **Practical Applications**: Implementing linear algebra concepts using Python and NumPy.
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| 18 |
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| 19 |
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---
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| 20 |
-
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| 21 |
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## 🔧 Exercises and Implementations
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| 22 |
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| 23 |
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The exercises involved:
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| 24 |
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| 25 |
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- Computing the transpose of a matrix `X` and performing `X.T @ X`.
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| 26 |
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- Inverting the resulting matrix `(X.T @ X)^(-1)`.
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| 27 |
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- Using the inverse to solve linear equations, a fundamental step in linear regression.
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| 28 |
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---
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| 30 |
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| 31 |
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## 🧪 Example Problem
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| 32 |
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| 33 |
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One of the exercises included:
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| 34 |
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| 35 |
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1. Creating a dataset:
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| 36 |
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| 37 |
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```python
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| 38 |
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y = [1100, 1300, 800, 900, 1000, 1100, 1200]
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| 39 |
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````
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| 40 |
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| 41 |
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2. Computing `X.T @ X`, inverting it, multiplying by `X.T`, and then multiplying by `y` to get the weight vector `w`.
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| 42 |
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| 43 |
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```python
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| 44 |
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import numpy as np
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| 45 |
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| 46 |
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# Example steps
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| 47 |
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XTX = X.T @ X
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| 48 |
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XTX_inv = np.linalg.inv(XTX)
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| 49 |
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w = XTX_inv @ X.T @ y
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| 50 |
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```
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| 51 |
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| 52 |
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3. Summing all elements of `w` to analyze the result:
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| 53 |
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|
| 54 |
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```python
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| 55 |
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total_weight = np.sum(w)
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| 56 |
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print("Sum of weights:", total_weight)
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| 57 |
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```
|
| 58 |
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| 59 |
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---
|
| 60 |
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| 61 |
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## 🛠️ Technologies Used
|
| 62 |
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|
| 63 |
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* **Python** – Programming language for implementation
|
| 64 |
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* **NumPy** – Efficient numerical computations and linear algebra
|
| 65 |
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* **Jupyter Notebooks** – Interactive environment for running exercises
|
| 66 |
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| 67 |
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---
|
| 68 |
-
|
| 69 |
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## 📌 Key Takeaways
|
| 70 |
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|
| 71 |
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* Mastering linear algebra is essential for understanding machine learning algorithms.
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| 72 |
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* Operations like matrix multiplication and inversion form the core of regression and many ML models.
|
| 73 |
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* Hands-on exercises help translate theoretical concepts into practical applications.
|
| 74 |
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|
| 75 |
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---
|
| 76 |
-
|
| 77 |
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## 🔗 Resources
|
| 78 |
-
|
| 79 |
-
* [Machine Learning Zoomcamp](https://github.com/DataTalksClub/mlzoomcamp) – Official course repository
|
| 80 |
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* [NumPy Documentation](https://numpy.org/doc/) – For matrix operations and linear algebra
|
| 81 |
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* [Jupyter Notebooks](https://jupyter.org/) – Interactive coding environment
|
| 82 |
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|
| 83 |
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```
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