1233 lines
242 KiB
Plaintext
1233 lines
242 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<p style=\"text-align:center\">\n",
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" <a href=\"https://skills.network\" target=\"_blank\">\n",
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" <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n",
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" </a>\n",
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"</p>\n",
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"\n",
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"\n",
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"# Simple Linear Regression\n",
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"\n",
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"\n",
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"Estimated time needed: **15** minutes\n",
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" \n",
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"\n",
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"## Objectives\n",
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"\n",
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"After completing this lab you will be able to:\n",
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"\n",
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"* Use scikit-learn to implement simple Linear Regression\n",
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"* Create a model, train it, test it and use the model\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Importing Needed packages\n"
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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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"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 matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"import pylab as pl\n",
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"import numpy as np\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Downloading Data\n",
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"To download the data, we will use !wget to download it from IBM Object Storage.\n"
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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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"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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"--2025-10-20 08:41:28-- https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\n",
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"Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104, 169.63.118.104\n",
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"Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
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"HTTP request sent, awaiting response... 200 OK\n",
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"Length: 72629 (71K) [text/csv]\n",
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"Saving to: ‘FuelConsumption.csv’\n",
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"\n",
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"FuelConsumption.csv 100%[===================>] 70.93K --.-KB/s in 0.002s \n",
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"\n",
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"2025-10-20 08:41:28 (33.9 MB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\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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"!wget -O FuelConsumption.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"In case you're working **locally** uncomment the below line. \n"
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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": 3,
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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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"#!curl https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv -o FuelConsumptionCo2.csv"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"## Understanding the Data\n",
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"\n",
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"### `FuelConsumption.csv`:\n",
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"We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64)\n",
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"\n",
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"- **MODELYEAR** e.g. 2014\n",
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"- **MAKE** e.g. Acura\n",
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"- **MODEL** e.g. ILX\n",
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"- **VEHICLE CLASS** e.g. SUV\n",
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||
"- **ENGINE SIZE** e.g. 4.7\n",
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"- **CYLINDERS** e.g 6\n",
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"- **TRANSMISSION** e.g. A6\n",
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"- **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
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"- **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
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"- **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
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"- **CO2 EMISSIONS (g/km)** e.g. 182 --> low --> 0\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"## Reading the data in\n"
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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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||
"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",
|
||
" .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",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
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||
" }\n",
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||
"</style>\n",
|
||
"<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>MODELYEAR</th>\n",
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" <th>MAKE</th>\n",
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||
" <th>MODEL</th>\n",
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" <th>VEHICLECLASS</th>\n",
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" <th>ENGINESIZE</th>\n",
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" <th>CYLINDERS</th>\n",
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||
" <th>TRANSMISSION</th>\n",
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||
" <th>FUELTYPE</th>\n",
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||
" <th>FUELCONSUMPTION_CITY</th>\n",
|
||
" <th>FUELCONSUMPTION_HWY</th>\n",
|
||
" <th>FUELCONSUMPTION_COMB</th>\n",
|
||
" <th>FUELCONSUMPTION_COMB_MPG</th>\n",
|
||
" <th>CO2EMISSIONS</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>2014</td>\n",
|
||
" <td>ACURA</td>\n",
|
||
" <td>ILX</td>\n",
|
||
" <td>COMPACT</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>AS5</td>\n",
|
||
" <td>Z</td>\n",
|
||
" <td>9.9</td>\n",
|
||
" <td>6.7</td>\n",
|
||
" <td>8.5</td>\n",
|
||
" <td>33</td>\n",
|
||
" <td>196</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2014</td>\n",
|
||
" <td>ACURA</td>\n",
|
||
" <td>ILX</td>\n",
|
||
" <td>COMPACT</td>\n",
|
||
" <td>2.4</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>M6</td>\n",
|
||
" <td>Z</td>\n",
|
||
" <td>11.2</td>\n",
|
||
" <td>7.7</td>\n",
|
||
" <td>9.6</td>\n",
|
||
" <td>29</td>\n",
|
||
" <td>221</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>2014</td>\n",
|
||
" <td>ACURA</td>\n",
|
||
" <td>ILX HYBRID</td>\n",
|
||
" <td>COMPACT</td>\n",
|
||
" <td>1.5</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>AV7</td>\n",
|
||
" <td>Z</td>\n",
|
||
" <td>6.0</td>\n",
|
||
" <td>5.8</td>\n",
|
||
" <td>5.9</td>\n",
|
||
" <td>48</td>\n",
|
||
" <td>136</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>2014</td>\n",
|
||
" <td>ACURA</td>\n",
|
||
" <td>MDX 4WD</td>\n",
|
||
" <td>SUV - SMALL</td>\n",
|
||
" <td>3.5</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>AS6</td>\n",
|
||
" <td>Z</td>\n",
|
||
" <td>12.7</td>\n",
|
||
" <td>9.1</td>\n",
|
||
" <td>11.1</td>\n",
|
||
" <td>25</td>\n",
|
||
" <td>255</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>2014</td>\n",
|
||
" <td>ACURA</td>\n",
|
||
" <td>RDX AWD</td>\n",
|
||
" <td>SUV - SMALL</td>\n",
|
||
" <td>3.5</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>AS6</td>\n",
|
||
" <td>Z</td>\n",
|
||
" <td>12.1</td>\n",
|
||
" <td>8.7</td>\n",
|
||
" <td>10.6</td>\n",
|
||
" <td>27</td>\n",
|
||
" <td>244</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" MODELYEAR MAKE MODEL VEHICLECLASS ENGINESIZE CYLINDERS \\\n",
|
||
"0 2014 ACURA ILX COMPACT 2.0 4 \n",
|
||
"1 2014 ACURA ILX COMPACT 2.4 4 \n",
|
||
"2 2014 ACURA ILX HYBRID COMPACT 1.5 4 \n",
|
||
"3 2014 ACURA MDX 4WD SUV - SMALL 3.5 6 \n",
|
||
"4 2014 ACURA RDX AWD SUV - SMALL 3.5 6 \n",
|
||
"\n",
|
||
" TRANSMISSION FUELTYPE FUELCONSUMPTION_CITY FUELCONSUMPTION_HWY \\\n",
|
||
"0 AS5 Z 9.9 6.7 \n",
|
||
"1 M6 Z 11.2 7.7 \n",
|
||
"2 AV7 Z 6.0 5.8 \n",
|
||
"3 AS6 Z 12.7 9.1 \n",
|
||
"4 AS6 Z 12.1 8.7 \n",
|
||
"\n",
|
||
" FUELCONSUMPTION_COMB FUELCONSUMPTION_COMB_MPG CO2EMISSIONS \n",
|
||
"0 8.5 33 196 \n",
|
||
"1 9.6 29 221 \n",
|
||
"2 5.9 48 136 \n",
|
||
"3 11.1 25 255 \n",
|
||
"4 10.6 27 244 "
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df = pd.read_csv(\"FuelConsumption.csv\")\n",
|
||
"\n",
|
||
"# take a look at the dataset\n",
|
||
"df.head()\n",
|
||
"\n"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Data Exploration\n",
|
||
"Let's first have a descriptive exploration on our data.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
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||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
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||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>MODELYEAR</th>\n",
|
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" <th>ENGINESIZE</th>\n",
|
||
" <th>CYLINDERS</th>\n",
|
||
" <th>FUELCONSUMPTION_CITY</th>\n",
|
||
" <th>FUELCONSUMPTION_HWY</th>\n",
|
||
" <th>FUELCONSUMPTION_COMB</th>\n",
|
||
" <th>FUELCONSUMPTION_COMB_MPG</th>\n",
|
||
" <th>CO2EMISSIONS</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>1067.0</td>\n",
|
||
" <td>1067.000000</td>\n",
|
||
" <td>1067.000000</td>\n",
|
||
" <td>1067.000000</td>\n",
|
||
" <td>1067.000000</td>\n",
|
||
" <td>1067.000000</td>\n",
|
||
" <td>1067.000000</td>\n",
|
||
" <td>1067.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>2014.0</td>\n",
|
||
" <td>3.346298</td>\n",
|
||
" <td>5.794752</td>\n",
|
||
" <td>13.296532</td>\n",
|
||
" <td>9.474602</td>\n",
|
||
" <td>11.580881</td>\n",
|
||
" <td>26.441425</td>\n",
|
||
" <td>256.228679</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.415895</td>\n",
|
||
" <td>1.797447</td>\n",
|
||
" <td>4.101253</td>\n",
|
||
" <td>2.794510</td>\n",
|
||
" <td>3.485595</td>\n",
|
||
" <td>7.468702</td>\n",
|
||
" <td>63.372304</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>2014.0</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>3.000000</td>\n",
|
||
" <td>4.600000</td>\n",
|
||
" <td>4.900000</td>\n",
|
||
" <td>4.700000</td>\n",
|
||
" <td>11.000000</td>\n",
|
||
" <td>108.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>2014.0</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>4.000000</td>\n",
|
||
" <td>10.250000</td>\n",
|
||
" <td>7.500000</td>\n",
|
||
" <td>9.000000</td>\n",
|
||
" <td>21.000000</td>\n",
|
||
" <td>207.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>2014.0</td>\n",
|
||
" <td>3.400000</td>\n",
|
||
" <td>6.000000</td>\n",
|
||
" <td>12.600000</td>\n",
|
||
" <td>8.800000</td>\n",
|
||
" <td>10.900000</td>\n",
|
||
" <td>26.000000</td>\n",
|
||
" <td>251.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>2014.0</td>\n",
|
||
" <td>4.300000</td>\n",
|
||
" <td>8.000000</td>\n",
|
||
" <td>15.550000</td>\n",
|
||
" <td>10.850000</td>\n",
|
||
" <td>13.350000</td>\n",
|
||
" <td>31.000000</td>\n",
|
||
" <td>294.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>2014.0</td>\n",
|
||
" <td>8.400000</td>\n",
|
||
" <td>12.000000</td>\n",
|
||
" <td>30.200000</td>\n",
|
||
" <td>20.500000</td>\n",
|
||
" <td>25.800000</td>\n",
|
||
" <td>60.000000</td>\n",
|
||
" <td>488.000000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" MODELYEAR ENGINESIZE CYLINDERS FUELCONSUMPTION_CITY \\\n",
|
||
"count 1067.0 1067.000000 1067.000000 1067.000000 \n",
|
||
"mean 2014.0 3.346298 5.794752 13.296532 \n",
|
||
"std 0.0 1.415895 1.797447 4.101253 \n",
|
||
"min 2014.0 1.000000 3.000000 4.600000 \n",
|
||
"25% 2014.0 2.000000 4.000000 10.250000 \n",
|
||
"50% 2014.0 3.400000 6.000000 12.600000 \n",
|
||
"75% 2014.0 4.300000 8.000000 15.550000 \n",
|
||
"max 2014.0 8.400000 12.000000 30.200000 \n",
|
||
"\n",
|
||
" FUELCONSUMPTION_HWY FUELCONSUMPTION_COMB FUELCONSUMPTION_COMB_MPG \\\n",
|
||
"count 1067.000000 1067.000000 1067.000000 \n",
|
||
"mean 9.474602 11.580881 26.441425 \n",
|
||
"std 2.794510 3.485595 7.468702 \n",
|
||
"min 4.900000 4.700000 11.000000 \n",
|
||
"25% 7.500000 9.000000 21.000000 \n",
|
||
"50% 8.800000 10.900000 26.000000 \n",
|
||
"75% 10.850000 13.350000 31.000000 \n",
|
||
"max 20.500000 25.800000 60.000000 \n",
|
||
"\n",
|
||
" CO2EMISSIONS \n",
|
||
"count 1067.000000 \n",
|
||
"mean 256.228679 \n",
|
||
"std 63.372304 \n",
|
||
"min 108.000000 \n",
|
||
"25% 207.000000 \n",
|
||
"50% 251.000000 \n",
|
||
"75% 294.000000 \n",
|
||
"max 488.000000 "
|
||
]
|
||
},
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# summarize the data\n",
|
||
"df.describe()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Let's select some features to explore more.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>ENGINESIZE</th>\n",
|
||
" <th>CYLINDERS</th>\n",
|
||
" <th>FUELCONSUMPTION_COMB</th>\n",
|
||
" <th>CO2EMISSIONS</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>8.5</td>\n",
|
||
" <td>196</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2.4</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>9.6</td>\n",
|
||
" <td>221</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>1.5</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>5.9</td>\n",
|
||
" <td>136</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>3.5</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>11.1</td>\n",
|
||
" <td>255</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>3.5</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>10.6</td>\n",
|
||
" <td>244</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>3.5</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>10.0</td>\n",
|
||
" <td>230</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>3.5</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>10.1</td>\n",
|
||
" <td>232</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>3.7</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>11.1</td>\n",
|
||
" <td>255</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>3.7</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>11.6</td>\n",
|
||
" <td>267</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" ENGINESIZE CYLINDERS FUELCONSUMPTION_COMB CO2EMISSIONS\n",
|
||
"0 2.0 4 8.5 196\n",
|
||
"1 2.4 4 9.6 221\n",
|
||
"2 1.5 4 5.9 136\n",
|
||
"3 3.5 6 11.1 255\n",
|
||
"4 3.5 6 10.6 244\n",
|
||
"5 3.5 6 10.0 230\n",
|
||
"6 3.5 6 10.1 232\n",
|
||
"7 3.7 6 11.1 255\n",
|
||
"8 3.7 6 11.6 267"
|
||
]
|
||
},
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
|
||
"cdf.head(9)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"We can plot each of these features:\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\n",
|
||
"viz.hist()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Now, let's plot each of these features against the Emission, to see how linear their relationship is:\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS, color='blue')\n",
|
||
"plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
|
||
"plt.ylabel(\"Emission\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS, color='blue')\n",
|
||
"plt.xlabel(\"Engine size\")\n",
|
||
"plt.ylabel(\"Emission\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Practice\n",
|
||
"Plot __CYLINDER__ vs the Emission, to see how linear is their relationship is:\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
|
||
"plt.xlabel(\"Cylinders\")\n",
|
||
"plt.ylabel(\"Emission\")\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"<details><summary>Click here for the solution</summary>\n",
|
||
"\n",
|
||
"```python \n",
|
||
"plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
|
||
"plt.xlabel(\"Cylinders\")\n",
|
||
"plt.ylabel(\"Emission\")\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"```\n",
|
||
"\n",
|
||
"</details>\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### Creating train and test dataset\n",
|
||
"Train/Test Split involves splitting the dataset into training and testing sets that are mutually exclusive. After which, you train with the training set and test with the testing set. \n",
|
||
"This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the model. Therefore, it gives us a better understanding of how well our model generalizes on new data.\n",
|
||
"\n",
|
||
"This means that we know the outcome of each data point in the testing dataset, making it great to test with! Since this data has not been used to train the model, the model has no knowledge of the outcome of these data points. So, in essence, it is truly an out-of-sample testing.\n",
|
||
"\n",
|
||
"Let's split our dataset into train and test sets. 80% of the entire dataset will be used for training and 20% for testing. We create a mask to select random rows using __np.random.rand()__ function: \n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"msk = np.random.rand(len(df)) < 0.8\n",
|
||
"train = cdf[msk]\n",
|
||
"test = cdf[~msk]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Simple Regression Model\n",
|
||
"Linear Regression fits a linear model with coefficients B = (B1, ..., Bn) to minimize the 'residual sum of squares' between the actual value y in the dataset, and the predicted value yhat using linear approximation. \n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### Train data distribution\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue')\n",
|
||
"plt.xlabel(\"Engine size\")\n",
|
||
"plt.ylabel(\"Emission\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### Modeling\n",
|
||
"Using sklearn package to model data.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/utils/validation.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
|
||
" LARGE_SPARSE_SUPPORTED = LooseVersion(scipy_version) >= '0.14.0'\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Coefficients: [[39.68932467]]\n",
|
||
"Intercept: [123.98428795]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:35: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" eps=np.finfo(np.float).eps,\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:597: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:836: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:862: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" eps=np.finfo(np.float).eps, positive=False):\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1097: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1344: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1480: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" eps=np.finfo(np.float).eps, copy_X=True, positive=False):\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:152: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" precompute=False, eps=np.finfo(np.float).eps,\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:320: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" eps=np.finfo(np.float).eps, random_state=None,\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:580: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" eps=4 * np.finfo(np.float).eps, n_jobs=None,\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn import linear_model\n",
|
||
"regr = linear_model.LinearRegression()\n",
|
||
"train_x = np.asanyarray(train[['ENGINESIZE']])\n",
|
||
"train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
|
||
"regr.fit(train_x, train_y)\n",
|
||
"# The coefficients\n",
|
||
"print ('Coefficients: ', regr.coef_)\n",
|
||
"print ('Intercept: ',regr.intercept_)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"As mentioned before, __Coefficient__ and __Intercept__ in the simple linear regression, are the parameters of the fit line. \n",
|
||
"Given that it is a simple linear regression, with only 2 parameters, and knowing that the parameters are the intercept and slope of the line, sklearn can estimate them directly from our data. \n",
|
||
"Notice that all of the data must be available to traverse and calculate the parameters.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### Plot outputs\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"We can plot the fit line over the data:\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Emission')"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue')\n",
|
||
"plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
|
||
"plt.xlabel(\"Engine size\")\n",
|
||
"plt.ylabel(\"Emission\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### Evaluation\n",
|
||
"We compare the actual values and predicted values to calculate the accuracy of a regression model. Evaluation metrics provide a key role in the development of a model, as it provides insight to areas that require improvement.\n",
|
||
"\n",
|
||
"There are different model evaluation metrics, lets use MSE here to calculate the accuracy of our model based on the test set: \n",
|
||
"* Mean Absolute Error: It is the mean of the absolute value of the errors. This is the easiest of the metrics to understand since it’s just average error.\n",
|
||
"\n",
|
||
"* Mean Squared Error (MSE): Mean Squared Error (MSE) is the mean of the squared error. It’s more popular than Mean Absolute Error because the focus is geared more towards large errors. This is due to the squared term exponentially increasing larger errors in comparison to smaller ones.\n",
|
||
"\n",
|
||
"* Root Mean Squared Error (RMSE). \n",
|
||
"\n",
|
||
"* R-squared is not an error, but rather a popular metric to measure the performance of your regression model. It represents how close the data points are to the fitted regression line. The higher the R-squared value, the better the model fits your data. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse).\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Mean absolute error: 23.79\n",
|
||
"Residual sum of squares (MSE): 976.90\n",
|
||
"R2-score: 0.72\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.metrics import r2_score\n",
|
||
"\n",
|
||
"test_x = np.asanyarray(test[['ENGINESIZE']])\n",
|
||
"test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
|
||
"test_y_ = regr.predict(test_x)\n",
|
||
"\n",
|
||
"print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n",
|
||
"print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n",
|
||
"print(\"R2-score: %.2f\" % r2_score(test_y , test_y_) )"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Exercise\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Lets see what the evaluation metrics are if we trained a regression model using the `FUELCONSUMPTION_COMB` feature.\n",
|
||
"\n",
|
||
"Start by selecting `FUELCONSUMPTION_COMB` as the train_x data from the `train` dataframe, then select `FUELCONSUMPTION_COMB` as the test_x data from the `test` dataframe\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n",
|
||
"\n",
|
||
"test_x = test[[\"FUELCONSUMPTION_COMB\"]]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"<details><summary>Click here for the solution</summary>\n",
|
||
"\n",
|
||
"```python \n",
|
||
"train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n",
|
||
"\n",
|
||
"test_x = test[[\"FUELCONSUMPTION_COMB\"]]\n",
|
||
"\n",
|
||
"```\n",
|
||
"\n",
|
||
"</details>\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Now train a Linear Regression Model using the `train_x` you created and the `train_y` created previously\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
|
||
" normalize=False)"
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"regr = linear_model.LinearRegression()\n",
|
||
"\n",
|
||
"regr.fit(train_x, train_y)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"<details><summary>Click here for the solution</summary>\n",
|
||
"\n",
|
||
"```python \n",
|
||
"regr = linear_model.LinearRegression()\n",
|
||
"\n",
|
||
"regr.fit(train_x, train_y)\n",
|
||
"\n",
|
||
"```\n",
|
||
"\n",
|
||
"</details>\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Find the predictions using the model's `predict` function and the `test_x` data\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"predictions = regr.predict(test_x)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"<details><summary>Click here for the solution</summary>\n",
|
||
"\n",
|
||
"```python \n",
|
||
"predictions = regr.predict(test_x)\n",
|
||
"\n",
|
||
"```\n",
|
||
"\n",
|
||
"</details>\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Finally use the `predictions` and the `test_y` data and find the Mean Absolute Error value using the `np.absolute` and `np.mean` function like done previously\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Mean Absolute Error: 20.20\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"<details><summary>Click here for the solution</summary>\n",
|
||
"\n",
|
||
"```python \n",
|
||
"print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n",
|
||
"\n",
|
||
"```\n",
|
||
"\n",
|
||
"</details>\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"We can see that the MAE is much worse when we train using `ENGINESIZE` than `FUELCONSUMPTION_COMB`\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Thank you for completing this lab!\n",
|
||
"\n",
|
||
"\n",
|
||
"## Author\n",
|
||
"\n",
|
||
"Saeed Aghabozorgi\n",
|
||
"\n",
|
||
"\n",
|
||
"### Other Contributors\n",
|
||
"\n",
|
||
"<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
|
||
"\n",
|
||
"Azim Hirjani\n",
|
||
"\n",
|
||
"## <h3 align=\"center\"> © IBM Corporation. All rights reserved. <h3/>\n",
|
||
"\n",
|
||
"<!--\n",
|
||
"## Change Log\n",
|
||
"\n",
|
||
"\n",
|
||
"| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n",
|
||
"|---|---|---|---|\n",
|
||
"| 2020-11-03 | 2.1 | Lakshmi Holla | Changed URL of the csv |\n",
|
||
"| 2020-08-27 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n",
|
||
"| | | | |\n",
|
||
"| | | | |\n",
|
||
"\n",
|
||
"--!>\n",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python",
|
||
"language": "python",
|
||
"name": "conda-env-python-py"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.7.12"
|
||
},
|
||
"prev_pub_hash": "20d6dc1d9e74df451be22381c972d7921c93657bea402a00c749dca52bb85996"
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|