1426 lines
305 KiB
Plaintext
1426 lines
305 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": 5,
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"metadata": {},
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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": 6,
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"metadata": {},
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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 10:20:05-- 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 10:20:05 (43.9 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": 7,
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"metadata": {},
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
|
||
" .dataframe thead th {\n",
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" text-align: right;\n",
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||
" }\n",
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||
"</style>\n",
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||
"<table border=\"1\" class=\"dataframe\">\n",
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||
" <thead>\n",
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||
" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>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",
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||
" <th>FUELCONSUMPTION_HWY</th>\n",
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||
" <th>FUELCONSUMPTION_COMB</th>\n",
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||
" <th>FUELCONSUMPTION_COMB_MPG</th>\n",
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||
" <th>CO2EMISSIONS</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>2014</td>\n",
|
||
" <td>ACURA</td>\n",
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||
" <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",
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||
" </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",
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||
"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": 8,
|
||
"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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||
]
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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": 9,
|
||
"metadata": {},
|
||
"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>MODELYEAR</th>\n",
|
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" <th>ENGINESIZE</th>\n",
|
||
" <th>CYLINDERS</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>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": 9,
|
||
"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": 10,
|
||
"metadata": {},
|
||
"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": 10,
|
||
"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": 11,
|
||
"metadata": {},
|
||
"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": 12,
|
||
"metadata": {},
|
||
"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": 13,
|
||
"metadata": {},
|
||
"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": 14,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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3KRs50rv98ne33AKEhQG7dgFlZeLXsDCx3dtkK3bGjBmDsrIylJaW2m7Dhg3DrFmzUFpaiuuuuw46nQ4HDhyw/YzFYkFhYSGSfh4bHjp0KAIDA+0yVVVVOHHihC1DRES+Ta0G1q0Tv29Z8Fy9v3ZtxwsPknRuuQVwMEMFgNju7YJHtjk7YWFhSEhIsGsLDQ1FVFSUrd1oNCI7Oxvx8fGIj49HdnY2QkJCMHPmTACAVqvF3LlzsWTJEkRFRSEyMhKZmZlITExsNeGZiEgqycnACy84lyPvSEsD3nsPWLQIOH/+l3aDAXjlFfE4eUd9fduFzlXFxWKuRw/v9EnWCcodycrKQkNDA+bPn4+amhqMGDEC+/fvR1hYmC2zZs0aBAQEID09HQ0NDRgzZgy2bt0KNUt4IvKQ5GQgKgq4cKHtTFQUix1v+/xz4N//tm8zmcR2Fjve8+CDzud27vRsX65SCYKjlQn8S11dHbRaLcxmM+fvEJFTCgqA++5r+/j77/MD1puysoBVq9o+vnQpV1H2lsGDxTk6HUlMBL74wr3ncvbzW/Z1doiIuqK0NLGgMRjs26+9loWOt1ksQF5e+5m8PDFHnqfEXehZ7BARuaFbi39FO1gZgzxgw4aOV0e2WsUced62bdLmpMBih4ioEwoKgGnTWq/ce/as2M69mLzn9Glpc+SeHj2A4cPbzwwf7r3JyQCLHSIil7W3FxMgtnMvJu9R4mkTf3f0aNsFz/Dh4nFvYrFDROSijvZiArgXkzfNn9/xGjpqtZgj7zl6FLh4EZg6VZyMPHWqeN/bhQ7AYoeIyGXnzkmbI/cEBQEZGe1nMjLEHHmXWi1O2tfrxa9yrQrDYoeIyEXffy9tjtyXmyteXt7yw1St5mXncpk6FQgJAV59Fdi/X/waEiK2exuLHSIiF4WGSpsjaeTmApcvA2vWAAsXil8vX2ahI4epU4Hdux0f273b+wUPFxUEFxUkItcMGODclT39+wNff+35/hApSUODOILTkcuXgeBg956LiwoSEXkIL3UmatvSpdLmpMBih4iIiCRTXi5tTgosdoiIXOTsFSXcj5j8UXy8tDkpsNghInLRk09KmyPyJe1tyNqZnBRY7BARuejKFWlzRL4kOBiYMqX9zJQp7k9OdgWLHSIiF3F7AqL27drVdsEzZYp43Jt46Tl46TkRuaa+HggL6zh38aJ3NzskUpqGBvGqq/JycY7OqlXSjujw0nMiIg954w1pc0S+Sq0W16UaOFD8Ktek/QB5npaIqOtS4qW1REqTlQXk5QFW6y9tmZniPmXeXtWaxQ4RkYtUKmlzRL4mK8vx1VZW6y/t3ix4eBqLiMhFQ4ZImyPyJRaLOKLTnrw8MectLHaIiFz0t79JmyPyJRs22J+6csRqFXPewmKHiMhFZrO0OSJfosS94zhnh4jIRd9/L22OpGO1AocPA1VVgF4PjBzJbTu8TYnrUHFkh4jIRddcI22OpFFQAPTtC4waBcycKX7t21dsJ++ZP7/jAlOtFnPewmKHiMhFly9LmyP3FRQA06YBZ8/at587J7az4PGeoCBg0qT2M5MmiTlvYbFDROSi8+elzZF7rFZg8WLA0X4AV9uMxo4nzZI0rFagpKT9zPHj3n0/WOwQEbmopkbaHLnn8OHWIzq/JghAZaWYI8/r6P0AvP9+sNghInJRSIi0OXJPVZW0OXJPRYW0OSmw2CEicpHBIG2O3KPXS5sj97z5prQ5KbDYISJyUe/e0ubIPSNHArGx7Wfi4sQceZ4S16FisUNE5KKkJGlz5B61Ghg6tP3MzTdzvR1v6ddP2pwUWOwQEbmIl54ri8XS8dYcf/ubd/di8mdK/GOAxQ4RkYvWrJE2R+5R4l5M/uzcOWlzUpC12Nm4cSMGDx6M8PBwhIeH47bbbsOHH35oOz5nzhyoVCq726233mr3GI2NjVi0aBGio6MRGhqK1NRUnO3omjciIjdcuSJtjtyjxL2Y/Bm3i2ghNjYWL730Eo4dO4Zjx45h9OjRmDJlCk6ePGnL3H333aiqqrLd9u7da/cYRqMRO3fuRH5+PoqKilBfX49JkybBytWjiMhDeOm5sijxw9WfcbuIFiZPnox77rkHAwcOxMCBA/Hiiy+iR48e+Pzzz20ZjUYDnU5nu0VGRtqOmc1mvPnmm1i9ejVSUlIwZMgQbN++HWVlZTh48KAcL4nI4ywWYO1aYNEi8SvnIXjf1KnS5sg9Svxw9WdBQUBGRvuZjAw/3S7CarUiPz8fly5dwm233WZrP3ToEHr16oWBAwfiscceQ3V1te1YSUkJmpqaMG7cOFubwWBAQkICjhw50uZzNTY2oq6uzu5G1BVkZYmjBU8+CaxfL34NCRHbyXuUeGmtP1Pih6u/y80Fli5tXYSq1WJ7bq53+xPg3adrraysDLfddhuuXLmCHj16YOfOnbjhhhsAABMmTMD999+PPn36oKKiAs888wxGjx6NkpISaDQamEwmBAUFISIiwu4xY2JiYDKZ2nzOnJwcPPfccx59XURSy8oCVq1q3W61/tLu7X9A/BUXsVOeq//v5+XZT1ZWq8VCh78b3pebC7zwgjgx/PRp8TTi/PnyFJ0qQXC0dZr3WCwWfPfdd6itrcX777+PN954A4WFhbaC59eqqqrQp08f5OfnIy0tDTt27MDDDz+MxsZGu9zYsWPRv39/bNq0yeFzNjY22v1MXV0d4uLiYDabER4eLu0LJJKAxSKO4LQ3FU2tFi915l+vnrdyJeDM30vPPitmyXssFmV8uJJ31NXVQavVdvj5LfvITlBQEAYMGAAAGDZsGIqLi7Fu3Tq89tprrbJ6vR59+vRBeXk5AECn08FisaCmpsZudKe6uhpJ7VzAr9FooNFoJH4lRJ7jyqW1RqNXuuTX3nrL+RyLHe8KCuLvALWmmDk7VwmC0Gqk5qoLFy6gsrIS+p/HhocOHYrAwEAcOHDAlqmqqsKJEyfaLXaIuhpeWqss7Zwl71SOiDxL1pGdFStWYMKECYiLi8PFixeRn5+PQ4cOYd++faivr8fKlStx3333Qa/X48yZM1ixYgWio6Nx7733AgC0Wi3mzp2LJUuWICoqCpGRkcjMzERiYiJSUlLkfGlEkuKltcrS1CRtjog8S9Zi59///jcefPBBVFVVQavVYvDgwdi3bx/Gjh2LhoYGlJWV4Z133kFtbS30ej1GjRqFd999F2FhYbbHWLNmDQICApCeno6GhgaMGTMGW7duhZqboJAPmT8fyMzseM4OL60lImpN9gnKSuDsBCciObV1NdZVclzO6a+0WsCZFSvCw3n5OZEnOfv5rbg5O0TkmNLWrfBnt9wibY6IPIsjO+DIDnUtvLRWfpGRQE1Nx7mICODHHz3fHyJ/1WUuPSci16jVwH/8BxATIy5ax+lp3tfQIG2OiDyLxQ5RF1JQACxeDJw9+0tbbCywbh2QliZfv/wNdz0n6lo4Z4eoiygoAKZNsy90AODcObG9oECefhERKR2LHaIuwGoVR3QczbC72mY0drzKMhGRP2KxQ9QFHD7cekTn1wQBqKwUc+R50dHS5ojIs1jsEHUBVVXS5sg9vXpJmyMiz2KxQ9QF/LwdnGQ5cs9110mbIyLPYrFD1AWMHCleddWeuDgxR57n7D7D3I+YSBlY7BB1AWo1MGNG+5kHHuCaO97y7bfS5ojIs1jsEHUBVivwpz+1n8nP59VY3lJWJm2OiDyLxQ5RF9DR1VgAr8bypu+/lzZHRJ7V6RWUa2trcfToUVRXV6O5udnu2EMPPeR2x4joF7waS1k0GmlzRORZnSp2/vrXv2LWrFm4dOkSwsLCoFKpbMdUKhWLHSKJ8VJnZUlPB06ccC5HRPLr1GmsJUuW4JFHHsHFixdRW1uLmpoa2+1HbvFLRD6uuFjaHBF5VqeKnXPnzuGJJ55ASEiI1P0hIgfOn5c2R+755htpc0TkWZ0qdsaPH49jx45J3RciasNnn0mbI/fU1UmbIyLP6tScnYkTJ2Lp0qX45z//icTERAQGBtodT01NlaRzRCTiyI6y/PCDtDki8qxOFTuPPfYYAOD5559vdUylUsHKxT6IJBUWJm2O3NPiAlS3c0TkWZ06jdXc3NzmjYUOkfRmzZI2R+7p2VPaHBF5FhcVJOoCWpwpdjtH7vn3v6XNEZFndbrYKSwsxOTJkzFgwADEx8cjNTUVh7l8K5FHcM4OEVHndarY2b59O1JSUhASEoInnngCCxcuRHBwMMaMGYMdO3ZI3Uciv/e//yttjojIn3RqgvKLL76I3NxcPPnkk7a2xYsXIy8vD7///e8xc+ZMyTpIRM5v8Mkpc95xzTXO7Xt1zTWe7wsRdaxTIzvffPMNJk+e3Ko9NTUVFRUVbneKiOyp1dLmyD0xMdLmiMizOlXsxMXF4aOPPmrV/tFHHyEuLs7tThGRveHDpc2Re+bNkzZHRJ7VqdNYS5YswRNPPIHS0lIkJSVBpVKhqKgIW7duxbp166TuI5Hfq6mRNkfu+dOfnM8tWODZvhBRxzpV7Pznf/4ndDodVq9ejT//+c8AgN/85jd49913MWXKFEk7SERAcLC0OXKPs/sdc19kImVQCYIgyN0JudXV1UGr1cJsNiM8PFzu7hC10q8fcOZMx7m+fQFOm/M8nc65NXRiYgCTyfP9IfJXzn5+c1FBoi6gtlbaHLnH2b+J+LcTkTI4fRorMjISp06dQnR0NCIiIqBSqdrM/sixWyJJ9ezpXCHD7Qm8o7JS2hwReZbTxc6aNWsQ9vMug2vWrGm32CEiaT31lHNX9jz1lOf7QkTU1Thd7MyePdv2/Zw5czzRFyJqQ0ODtDkiIn/SqTk7x48fR1lZme3+7t27MXXqVKxYsQIWi8Xpx9m4cSMGDx6M8PBwhIeH47bbbsOHH35oOy4IAlauXAmDwYDg4GAkJyfj5MmTdo/R2NiIRYsWITo6GqGhoUhNTcXZs2c787KIFOvnQVXJcuQeZ5cT47JjRMrQqWLn8ccfx6lTpwCIqylPnz4dISEh+Mtf/oKsrCynHyc2NhYvvfQSjh07hmPHjmH06NGYMmWKraDJzc1FXl4e1q9fj+LiYuh0OowdOxYXL160PYbRaMTOnTuRn5+PoqIi1NfXY9KkSbBy3XzyIVu2SJsj99TVSZsjIg8TOiE8PFz4+uuvBUEQhJdeekkYN26cIAiCUFRUJMTGxnbmIW0iIiKEN954Q2hubhZ0Op3w0ksv2Y5duXJF0Gq1wqZNmwRBEITa2lohMDBQyM/Pt2XOnTsndOvWTdi3b1+bz3HlyhXBbDbbbpWVlQIAwWw2u9V3Ik9JSBAEoONbQoLcPfUP11/v3Ptx/fVy95TIt5nNZqc+vzs1siMIApqbmwEABw8exD333ANA3Ebihx9+6FTRZbVakZ+fj0uXLuG2225DRUUFTCYTxo0bZ8toNBrcddddOHLkCACgpKQETU1NdhmDwYCEhARbxpGcnBxotVrbjVtckNJptdLmyD1XrkibIyLP6lSxM2zYMLzwwgvYtm0bCgsLMXHiRABARUUFYlzc+a6srAw9evSARqPBvHnzsHPnTtxwww0w/bwSV8vHi4mJsR0zmUwICgpCREREmxlHli9fDrPZbLtV8vpQUrgZM6TNkXv0emlzRORZndouYu3atZg1axZ27dqFp59+GgMGDAAAvPfee0hKSnLpsQYNGoTS0lLU1tbi/fffx+zZs1FYWGg73vISd0EQOrzsvaOMRqOBRqNxqZ9Ectq0yfkc92LyvF9dnyFJjog8q1PFzuDBg+2uxrpq1apVUKvVLj1WUFCQrVgaNmwYiouLsW7dOixbtgyAOHqj/9WfR9XV1bbRHp1OB4vFgpqaGrvRnerqapeLLiIlc/bscCfPIpOLmpqkzRGRZ3XqNFZlZaXd5d1Hjx6F0WjEO++8g8DAQLc6JAgCGhsb0a9fP+h0Ohw4cMB2zGKxoLCw0FbIDB06FIGBgXaZqqoqnDhxgsUO+RRn9mFyJUfuaWyUNkdEntWpkZ2ZM2fid7/7HR588EGYTCaMHTsWN954I7Zv3w6TyYT//u//dupxVqxYgQkTJiAuLg4XL15Efn4+Dh06hH379kGlUsFoNCI7Oxvx8fGIj49HdnY2QkJCMHPmTACAVqvF3LlzsWTJEkRFRSEyMhKZmZlITExESkpKZ14akSI5u10vt/UlImqtU8XOiRMncMsttwAA/vznPyMhIQH/8z//g/3792PevHlOFzv//ve/8eCDD6KqqgparRaDBw/Gvn37MHbsWABAVlYWGhoaMH/+fNTU1GDEiBHYv3+/bdsKQNy6IiAgAOnp6WhoaMCYMWOwdetWl0+nUdsaGoClS4HyciA+Hli1CggOlrtX/kWlcq6Q4S4uREStqQTB9b8Fe/TogRMnTqBv375ITU3F7bffjmXLluG7777DoEGD0NDF1qx3dot4fzR1KrB7d+v2KVOAXbu83Rv/dc01zs3HiY4Gvv/e8/3xd64UlRxtI/IcZz+/OzVn58Ybb8SmTZtw+PBhHDhwAHfffTcA4Pz584iKiupcj0lx2ip0ALF96lRv9sa//fijtDkiIn/SqWLn5ZdfxmuvvYbk5GTMmDEDN910EwBgz549ttNb1LU1NLRd6Fy1ezc3nvQWztlRlp8vIJUsR0Se1ak5O8nJyfjhhx9QV1dnd8n37373O4SEhEjWOZLP0qXO59av92xfCAgIcO4y5oBO/UaTq2JigK+/di5HRPLr1MgOAKjV6lYrF/ft2xe9evVyu1Mkv/JyaXPknsREaXPknrlzpc0RkWc5/XfgzTffjI8++ggREREYMmRIuysUHz9+XJLOkXzi44H9+53Lkef93/9JmyP39OsnbY6IPMvpYmfKlCm2LRamcmaqz3vxReDVV53Lkedxzo6yjBghbY6IPMvpYufZZ591+D35pi1bnM8ZjR7tCpHivPaa8zn+fhDJz+3pjPX19WhubrZr41o1Xd/p09LmiHzJl19KmyMiz+rUBOWKigpMnDgRoaGh0Gq1iIiIQEREBHr27Nlq0jJ1Tf37S5sj8iVFRdLmiMizOjWyM2vWLADAW2+9hZiYmHYnK1PXNH8+kJkJWK1tZ9RqMUeeFxzs3JpG3MbDO7gLPVHX0qli54svvkBJSQkGDRokdX9IIYKCgIwMcR+stmRkiDnyvMhI4Nw553Lkec4WlSw+iZShU6exhg8fjsrKSqn7QgqTmwsMH+742PDh4nHyDmf3u+K+WN7h7GKBXFSQSBk6NbLzxhtvYN68eTh37hwSEhIQGBhod3zw4MGSdI7klZUFFBc7PlZcLB5nweMdFou0OXJPTY20OSLyrE4VO99//z1Onz6Nhx9+2NamUqkgCAJUKhWs7U30oC7BYgHy8trP5OUBL7zAU1nkf35eckyyHBF5VqdOYz3yyCMYMmQIPvvsM3zzzTeoqKiw+0pd34YN7U9OBsTjGzZ4pz9ESvLb30qbIyLP6tTIzrfffos9e/ZgALf09VlcZ0dZNBqgsdG5HHnek08CTz3lXI6I5NepkZ3Ro0fjH//4h9R9IQXh3j/K0mJanNs5ck9QUNuT968aPpyneImUolMjO5MnT8aTTz6JsrIyJCYmtpqgnJqaKknnSD7cZVtZ6uulzZF7LBbg2LH2M8eOiTkWPETy61SxM2/ePADA888/3+oYJyj7Bi6aRtS2V17peNNVQRBzmZne6RMRta1Tp7Gam5vbvLHQ8Q1RUdLmiHzJrl3S5ojIs1wqdu655x6YzWbb/RdffBG1tbW2+xcuXMANN9wgWedIPmVl0uaIfMmv/hmUJEdEnuVSsfP3v/8djb+6JOTll1/Gjz/+aLv/008/4auvvpKudySbM2ekzRH5EmfXTeX6qkTK4FKxI7Q4Sd3yPvmOvn2lzRH5kpkzpc0RkWd1as4O+T5ejUXUNmcHsDnQTaQMLhU7KpUKKpWqVRv5Hm48SdS28nJpc0TkWS5dei4IAubMmQPNz8u0XrlyBfPmzUNoaCgA2M3noa6NxQ5R20wmaXNE5FkuFTuzZ8+2u/9bBxu/PPTQQ+71iBQhIkLaHJEv0emkzRGRZ7lU7GzZssVT/SCF+ewz53MtamAiIiJF4QRlcujECWlzRL6kZ09pc0TkWSx2yCFn551zfjr5owAnx8SdzRGRZ7HYIYfuvlvaHJEvSU6WNkdEnsVihxw6elTaHLmHIwnKkpzc8b5wUVEsdoiUgsUOOfTNN9LmyD0//SRtjtyjVgObN7ef2bxZzBGR/GQtdnJycjB8+HCEhYWhV69emDp1aqu9tebMmWNbzPDq7dZbb7XLNDY2YtGiRYiOjkZoaChSU1Nx9uxZb74UnxMWJm2OyNekpQHvvw/Extq3x8aK7Wlp8vSLiFqTtdgpLCzEggUL8Pnnn+PAgQP46aefMG7cOFy6dMkud/fdd6Oqqsp227t3r91xo9GInTt3Ij8/H0VFRaivr8ekSZNgtVq9+XJ8Su/e0uaIfFFamrgZ7iefADt2iF/PnGGhQ6Q0sp7h37dvn939LVu2oFevXigpKcGdd95pa9doNNC1sTqX2WzGm2++iW3btiElJQUAsH37dsTFxeHgwYMYP358q59pbGy0W+25rq5OipfjU5xdDJuLZhMRkdIpas6O2WwGAERGRtq1Hzp0CL169cLAgQPx2GOPobq62naspKQETU1NGDdunK3NYDAgISEBR44ccfg8OTk50Gq1tltcXJwHXk3XlpQkbY7IFxUUAH37AqNGiTucjxol3i8okLtnRPRriil2BEFARkYG7rjjDiQkJNjaJ0yYgD/+8Y/4+OOPsXr1ahQXF2P06NG2kRmTyYSgoCBEtNi3ICYmBqY2NqZZvnw5zGaz7VZZWem5F9ZF3XSTtDlyT/fu0ubIfQUFwLRpQMvpgefOie0seIiUQzEXqi5cuBBffPEFioqK7NqnT59u+z4hIQHDhg1Dnz598MEHHyCtnRPjgiC0uSO7RqOxbWZKjjlb/7FOJH9ktQKLFwOC0PqYIIiLbRqNwJQpvCKLSAkUMbKzaNEi7NmzB5988gliW17a0IJer0efPn1QXl4OANDpdLBYLKipqbHLVVdXIyYmxmN99nVr10qbI/dwDpWyHD7cekTn1wRB/EPg8GHv9YmI2iZrsSMIAhYuXIiCggJ8/PHH6NevX4c/c+HCBVRWVkKv1wMAhg4disDAQBw4cMCWqaqqwokTJ5DECSWdVlsrbY7cw+07lKWqStocEXmWrKexFixYgB07dmD37t0ICwuzzbHRarUIDg5GfX09Vq5cifvuuw96vR5nzpzBihUrEB0djXvvvdeWnTt3LpYsWYKoqChERkYiMzMTiYmJtquzyHXdnCyDnc2Re5qbpc2Re37+W0uyHBF5lqzFzsaNGwEAyS3WVN+yZQvmzJkDtVqNsrIyvPPOO6itrYVer8eoUaPw7rvvIuxXq9mtWbMGAQEBSE9PR0NDA8aMGYOtW7dCzZPlnRYcLG2OyJeMHCkuHnjunON5OyqVeHzkSO/3jYhaUwmCo19V/1JXVwetVguz2Yzw8HC5u6MIgwYBp051nBs4EGix6DV5gCunp/gb7R1Xr8YC7P+bX32v3nuPiwsSeZqzn988CUEO/WpNR0ly5B5nByk5mOk9aWliQXPttfbtsbEsdIiURjGXnpOyDBggbY7c07MncOGCcznynrQ08fLyw4fFych6vXjqikUnkbKw2CGHfr6yX7IcucfZHU2484n3qdVAi2mHRKQwLHbIoY8+kjanBBYLsGEDcPo00L8/MH8+EBQkd6+c09QkbY6IyJ+w2CGHLl+WNie3rCwgL09c+faqzEwgIwPIzZWvX0RE5Hksdsih77+XNienrCxg1arW7VbrL+0seIiIfBcvPQcvPXekWzfnLmFWqZS9kJ3FAoSE2I/otKRWiyNUSj6l1b27c1tBaDTAlSue7w8RkRLw0nNyi7MlsNJL5Q0b2i90APH4hg3e6U9n/fSTtDkiIn/CYoccCnDyBKezObmcPi1tTi5c0ZqIqPNY7JBDvjKS0L+/tDm5+NqEcSIib+KcHXDOjiO+sj2Br8zZ8ZX3g4hISpyzQwSxgLn55vYzN9+s7EIHcL7YcaUoIiLyFyx2yKdZLMDx4+1njh8Xc0rWo4e0OSIif8Jih3yar1yNFRgobY6IyJ+w2CGf9tVX0uaIiKjrYbFDPs1kkjYnF1+5Oo6ISA4sdsihPn2kzclFp5M2JxdfWfeIiEgOLHbIobNnpc3JRa2WNicXrrNDRNR5LHbIp40YIW1OLrz0nIio81jskEMhIdLm5GIwSJuTS79+0uaIiPwJix1yyNlF9pS+GF9Hl527mpPL4cPS5oiI/AmLHXLowgVpc3LxlSIhMrLjUbSQEDFHRET2WOwQdQEWC3DlSvuZK1eUvxI0EZEcWOyQT0tOljYnlz/8AWhubj/T3CzmiIjIHosdcqibk/9nOJuTS3IyEBXVfiYqSvnFTmGhtDkiIn+i8I8qkosgSJuTi1oNbN7cfmbzZuWvs/Ovf0mbIyLyJyx2yCFfWrE3LQ14/30gNta+PTZWbE9Lk6dfrggLkzZHRORPusBHFckhNBSorXUu1xWkpQFTpohXXVVVAXo9MHKk8kd0rho+HDh+3LkcERHZY7FDDjlT6LiSUwK1Wvlzc9oyZQrw2mvO5YiIyB5PYxF1Ab5YfBIReQuLHaIuQK+XNkdE5E9Y7BB1ASNHtp5g3VJcnJgjIiJ7LHbIoeBgaXPkHrUamDGj/cwDD3SdCddERN4ka7GTk5OD4cOHIywsDL169cLUqVPx1Vdf2WUEQcDKlSthMBgQHByM5ORknDx50i7T2NiIRYsWITo6GqGhoUhNTcXZs2e9+VJ8TlyctDlyj9UK/OlP7Wfy85W/oSkRkRxkLXYKCwuxYMECfP755zhw4AB++uknjBs3DpcuXbJlcnNzkZeXh/Xr16O4uBg6nQ5jx47FxYsXbRmj0YidO3ciPz8fRUVFqK+vx6RJk2Dlv/ydVlkpbY7cc/gw0FH9Xlmp/A1NiYjkoBIE5ayB+/3336NXr14oLCzEnXfeCUEQYDAYYDQasWzZMgDiKE5MTAxefvllPP744zCbzbjmmmuwbds2TJ8+HQBw/vx5xMXFYe/evRg/fnyHz1tXVwetVguz2Yzw8HCPvsauQqVyPquc/4N815/+BMyc2XFux46OT3cREfkKZz+/FTVnx2w2AwAiIyMBABUVFTCZTBg3bpwto9FocNddd+HIkSMAgJKSEjQ1NdllDAYDEhISbJmWGhsbUVdXZ3cjUjJejUVE1HmKKXYEQUBGRgbuuOMOJCQkAABMJhMAICYmxi4bExNjO2YymRAUFISIiIg2My3l5ORAq9XabnGceEIKd/VqrLZG3FQqXo1FRNQWxRQ7CxcuxBdffIE/OZiFqWrxL7wgCK3aWmovs3z5cpjNZtutkhNPSOHUamDdOvH7lv9bX72/di2vxiIickQRxc6iRYuwZ88efPLJJ4j91WIiOp0OAFqN0FRXV9tGe3Q6HSwWC2pqatrMtKTRaBAeHm53I1K6tDTgvfeAa6+1b4+NFdu7woamRERykLXYEQQBCxcuREFBAT7++GP069fP7ni/fv2g0+lw4MABW5vFYkFhYSGSkpIAAEOHDkVgYKBdpqqqCidOnLBliHxFWhpw6hSwYAEwbpz49auvWOgQEbVH1o1AFyxYgB07dmD37t0ICwuzjeBotVoEBwdDpVLBaDQiOzsb8fHxiI+PR3Z2NkJCQjDz50tTtFot5s6diyVLliAqKgqRkZHIzMxEYmIiUlJS5Hx5RJLLygL+3//75Qq4/fuBDRuAzEwgN1fevhERKZWsxc7GjRsBAMkttqLesmUL5syZAwDIyspCQ0MD5s+fj5qaGowYMQL79+9HWFiYLb9mzRoEBAQgPT0dDQ0NGDNmDLZu3Qo1JzCQD8nKAlatat0uCL+0s+AhImpNUevsyIXr7LTGdXaUxWIBNJqOc42NQFCQ5/tDRKQEXXKdHSJybM0aaXNERP6ExQ5RF7B9u7Q5IiJ/wmKHqAuwWKTNERH5ExY75FD37tLmyD2jRkmbIyLyJyx2yCFnJyi7MpGZOo9zdoiIOo/FDjnU0CBtjtwTHAxMmdJ+ZsoUMUdERPZY7BB1Ebt2tV3wTJkiHiciotZkXVSQiFyza5c4mrZ0KVBeDsTHiwsKckSHiKhtLHaIupjgYGD9erl7QUTUdfA0FhEREfk0juwQdTEWi7j55+nTQP/+wPz53CKCiKg9LHaIupCsLCAvD7Baf2nLzAQyMrgJKBFRW1jsEHURbe16brVy13MiovZw13Nw13NHuOu5slgsQEiI/YhOS2o1cPkyT2kRkf/grudEPmTDhvYLHUA8vmGDd/pDRNSVsNgh6gLKy6XNERH5ExY75JBGI22O3MO9yoiIOo/FDjl0xx3S5sg9I0ZImyMi8icsdsihqippc+SeuDhpc0RE/oTFDjn000/S5sg9I0cCsbHtZ+LixBwREdljsUMOVVRImyP3qNXAunVtz8lRqYC1a8UcERHZY7FD1EWkpQHvvdd6hCcuTmxPS5OnX0RESscVlMmh5mZpcySNtDRgyhTg8GFxvpReL5664ogOEVHbWOyQQwEBHS9idzVH3qVWA8nJcveCiKjr4GkscogjO0RE5CtY7JBDwcHS5oiIiOTCYocc4qXnRETkK1jskEPO7pzNHbaJiEjpWOyQQxzZISIiX8Fihxzq0UPaHBERkVxY7JBD3btLmyMiIpILix1yKCVF2hwREZFcWOyQQzfeKG2OiIhILrIWO59++ikmT54Mg8EAlUqFXbt22R2fM2cOVCqV3e3WW2+1yzQ2NmLRokWIjo5GaGgoUlNTcfbsWS++Ct/06KPS5oiIiOQia7Fz6dIl3HTTTVi/fn2bmbvvvhtVVVW22969e+2OG41G7Ny5E/n5+SgqKkJ9fT0mTZoEqzN7HXhQQwOwcCEwfrz4taFB1u647I03pM0RERHJRdadjSZMmIAJEya0m9FoNNDpdA6Pmc1mvPnmm9i2bRtSfp48sn37dsTFxeHgwYMYP3685H12xtSpwO7dv9zfvx949VVxA8cWg1eKdfq0tDkiIiK5KH7OzqFDh9CrVy8MHDgQjz32GKqrq23HSkpK0NTUhHHjxtnaDAYDEhIScOTIkTYfs7GxEXV1dXY3qbQsdH5t927xeFfQv7+0OSIiIrkoutiZMGEC/vjHP+Ljjz/G6tWrUVxcjNGjR6OxsREAYDKZEBQUhIiICLufi4mJgclkavNxc3JyoNVqbbe4uDhJ+tvQ0Hahc9Xu3V3jlNb8+eLu2u1Rq8UcERGRkim62Jk+fTomTpyIhIQETJ48GR9++CFOnTqFDz74oN2fEwQBKpWqzePLly+H2Wy23SorKyXp79Kl0ubkFBQE9O3bfqZvX24XQUREyqfoYqclvV6PPn36oLy8HACg0+lgsVhQU1Njl6uurkZMTEybj6PRaBAeHm53k8LP3ZIsJ6eGho7n45w+3TVGqYiIyL91qWLnwoULqKyshF6vBwAMHToUgYGBOHDggC1TVVWFEydOICkpyev9i4+XNicnXxqlIiIi/ybr1Vj19fX4+uuvbfcrKipQWlqKyMhIREZGYuXKlbjvvvug1+tx5swZrFixAtHR0bj33nsBAFqtFnPnzsWSJUsQFRWFyMhIZGZmIjEx0XZ1ljetWiVedeVMTum++kraHBERkVxkLXaOHTuGUaNG2e5nZGQAAGbPno2NGzeirKwM77zzDmpra6HX6zFq1Ci8++67CAsLs/3MmjVrEBAQgPT0dDQ0NGDMmDHYunUr1B3NrvWA4GDx8vL2JilPmSLmlM7Z01M8jUVEREqnEgRBkLsTcqurq4NWq4XZbJZk/s4ttwDFxa3bhw8Hjh51++G94j//E9i0qePcvHnAxo2e7w8REVFLzn5+d6k5O11BQQFw7JjjY8eOice7gkGDpM0RERHJhSM7kG5kx2oVL8dua2sulQqIjQUqKjpew0ZuZjPQs2fHudpaQKv1dG+IiIha48iODA4fbrvQAQBBACorxZzSPf20tDkiIiK5sNiRUFWVtDk5nTolbY6IiEguLHYk9PPyP5Ll5BQaKm2OiIhILix2JDRypDgnp62dKlQqIC5OzCmdsxuWdpWNTYmIyH+x2JGQWg2sWyd+37LguXp/7VrlT04GgD59pM0RERHJhcWOxNLSgPfeA6691r49NlZsT0uTp1+uGjkSiIpqPxMV1TVGqYiIyL/JuoKyr0pLE1dKPnxYnIys14tFQVcY0fm1xsb2j1ss3ukHERGRO1jseIhaDSQny92Lzjt0CKivbz9z8aKYGzPGGz0iIiLqHJ7GIocOHZI2R0REJBcWO0REROTTWOyQQ0lJ0uaIiIjkwmKHHPrnP6XNERERyYXFDjn0zTfS5oiIiOTCYoccamsV6M7miIiI5MJihxwaMULaHBERkVxY7JBDcXHS5oiIiOTCYoccurqpaXu6yqamRETk31jskENXNzVVqRxvaqpSdZ1NTYmIyL+x2KE2+cqmpkRE5N+4Nxa1y1c2NSUiIv/FYoc61NU3NSUiIv/GYoc6ZLEAGzYAp08D/fsD8+cDQUFy94qIiMg5LHaoXVlZQF4eYLX+0paZCWRkALm58vWLiIjIWSx2qE1ZWcCqVa3brdZf2lnwEBGR0qkEQRDk7oTc6urqoNVqYTabER4eLnd3FMFiAUJC7Ed0WlKrgcuXeUqLiIjk4eznNy89J4c2bGi/0AHE4xs2eKc/REREncVihxwqL5c2R0REJBcWO+QQdz0nIiJfwWKHHOKu50RE5CtY7JBD3PWciIh8BYsdcoi7nhMRka+Qtdj59NNPMXnyZBgMBqhUKuzatcvuuCAIWLlyJQwGA4KDg5GcnIyTJ0/aZRobG7Fo0SJER0cjNDQUqampOHv2rBdfhW/irudEROQrZC12Ll26hJtuugnr1693eDw3Nxd5eXlYv349iouLodPpMHbsWFy8eNGWMRqN2LlzJ/Lz81FUVIT6+npMmjQJ1o6um6YOcddzIiLyBYpZVFClUmHnzp2YOnUqAHFUx2AwwGg0YtmyZQDEUZyYmBi8/PLLePzxx2E2m3HNNddg27ZtmD59OgDg/PnziIuLw969ezF+/HinnpuLCrbPauWu50REpDxdflHBiooKmEwmjBs3ztam0Whw11134ciRIwCAkpISNDU12WUMBgMSEhJsGUcaGxtRV1dnd6O2Xd31fMYM8SsLHSIi6koUW+yYTCYAQExMjF17TEyM7ZjJZEJQUBAiIiLazDiSk5MDrVZru8XxkiIiIiKfpdhi5ypVi9mxgiC0amupo8zy5cthNpttt8rKSkn6SkRERMqj2GJHp9MBQKsRmurqattoj06ng8ViQU1NTZsZRzQaDcLDw+1uRERE5JsUW+z069cPOp0OBw4csLVZLBYUFhYiKSkJADB06FAEBgbaZaqqqnDixAlbhoiIiPxbgJxPXl9fj6+//tp2v6KiAqWlpYiMjETv3r1hNBqRnZ2N+Ph4xMfHIzs7GyEhIZg5cyYAQKvVYu7cuViyZAmioqIQGRmJzMxMJCYmIiUlRa6XRURERAoia7Fz7NgxjBo1ynY/IyMDADB79mxs3boVWVlZaGhowPz581FTU4MRI0Zg//79CAsLs/3MmjVrEBAQgPT0dDQ0NGDMmDHYunUr1LxkiIiIiKCgdXbkxHV2iIiIup4uv84OERERkRRY7BAREZFPk3XOjlJcPZPHlZSJiIi6jquf2x3NyGGxA9g2FuVKykRERF3PxYsXodVq2zzOCcoAmpubcf78eYSFhXW4OrO/qqurQ1xcHCorKzmJWwH4figL3w9l4fuhLJ58PwRBwMWLF2EwGNCtW9szcziyA6Bbt26IjY2VuxtdAlecVha+H8rC90NZ+H4oi6fej/ZGdK7iBGUiIiLyaSx2iIiIyKex2CGnaDQaPPvss9BoNHJ3hcD3Q2n4figL3w9lUcL7wQnKRERE5NM4skNEREQ+jcUOERER+TQWO0REROTTWOwQERGRT2OxQ07LycmBSqWC0WiUuyt+69y5c/jtb3+LqKgohISE4D/+4z9QUlIid7f81k8//YT/+q//Qr9+/RAcHIzrrrsOzz//PJqbm+Xuml/49NNPMXnyZBgMBqhUKuzatcvuuCAIWLlyJQwGA4KDg5GcnIyTJ0/K01k/0N770dTUhGXLliExMRGhoaEwGAx46KGHcP78ea/0jcUOOaW4uBibN2/G4MGD5e6K36qpqcHtt9+OwMBAfPjhh/jnP/+J1atXo2fPnnJ3zW+9/PLL2LRpE9avX48vv/wSubm5WLVqFf7whz/I3TW/cOnSJdx0001Yv369w+O5ubnIy8vD+vXrUVxcDJ1Oh7Fjx9r2QyRptfd+XL58GcePH8czzzyD48ePo6CgAKdOnUJqaqp3OicQdeDixYtCfHy8cODAAeGuu+4SFi9eLHeX/NKyZcuEO+64Q+5u0K9MnDhReOSRR+za0tLShN/+9rcy9ch/ARB27txpu9/c3CzodDrhpZdesrVduXJF0Gq1wqZNm2TooX9p+X44cvToUQGA8O2333q8PxzZoQ4tWLAAEydOREpKitxd8Wt79uzBsGHDcP/996NXr14YMmQIXn/9dbm75dfuuOMOfPTRRzh16hQA4B//+AeKiopwzz33yNwzqqiogMlkwrhx42xtGo0Gd911F44cOSJjz+gqs9kMlUrlldFpbgRK7crPz8fx48dRXFwsd1f83jfffIONGzciIyMDK1aswNGjR/HEE09Ao9HgoYcekrt7fmnZsmUwm824/vrroVarYbVa8eKLL2LGjBlyd83vmUwmAEBMTIxde0xMDL799ls5ukS/cuXKFTz11FOYOXOmVzZrZbFDbaqsrMTixYuxf/9+dO/eXe7u+L3m5mYMGzYM2dnZAIAhQ4bg5MmT2LhxI4sdmbz77rvYvn07duzYgRtvvBGlpaUwGo0wGAyYPXu23N0jACqVyu6+IAit2si7mpqa8MADD6C5uRkbNmzwynOy2KE2lZSUoLq6GkOHDrW1Wa1WfPrpp1i/fj0aGxuhVqtl7KF/0ev1uOGGG+zafvOb3+D999+XqUe0dOlSPPXUU3jggQcAAImJifj222+Rk5PDYkdmOp0OgDjCo9frbe3V1dWtRnvIe5qampCeno6Kigp8/PHHXhnVAXg1FrVjzJgxKCsrQ2lpqe02bNgwzJo1C6WlpSx0vOz222/HV199Zdd26tQp9OnTR6Ye0eXLl9Gtm/0/o2q1mpeeK0C/fv2g0+lw4MABW5vFYkFhYSGSkpJk7Jn/ulrolJeX4+DBg4iKivLac3Nkh9oUFhaGhIQEu7bQ0FBERUW1aifPe/LJJ5GUlITs7Gykp6fj6NGj2Lx5MzZv3ix31/zW5MmT8eKLL6J379648cYb8X//93/Iy8vDI488InfX/EJ9fT2+/vpr2/2KigqUlpYiMjISvXv3htFoRHZ2NuLj4xEfH4/s7GyEhIRg5syZMvbad7X3fhgMBkybNg3Hjx/H3/72N1itVtu8qsjISAQFBXm2cx6/3ot8Ci89l9df//pXISEhQdBoNML1118vbN68We4u+bW6ujph8eLFQu/evYXu3bsL1113nfD0008LjY2NcnfNL3zyyScCgFa32bNnC4IgXn7+7LPPCjqdTtBoNMKdd94plJWVydtpH9be+1FRUeHwGADhk08+8XjfVIIgCJ4tp4iIiIjkwzk7RERE5NNY7BAREZFPY7FDREREPo3FDhEREfk0FjtERETk01jsEBERkU9jsUNEREQ+jcUOERER+TQWO0TUZSQnJ8NoNNru9+3bF2vXrnXrMQ8dOgSVSoXa2lq3HoeIlIvFDhF5jclkwqJFi3DddddBo9EgLi4OkydPxkcffdSpxysuLsbvfvc7iXtJRL6GG4ESkVecOXMGt99+O3r27Inc3FwMHjwYTU1N+Pvf/44FCxbgX//6l8uPec0113igp66zWCye38iQiDqNIztE5BXz58+HSqXC0aNHMW3aNAwcOBA33ngjMjIy8Pnnn+ORRx7BpEmT7H7mp59+gk6nw1tvveXwMVuexlKpVHjjjTdw7733IiQkBPHx8dizZ4/dz+zduxcDBw5EcHAwRo0ahTNnzrR63CNHjuDOO+9EcHAw4uLi8MQTT+DSpUt2z/vCCy9gzpw50Gq1eOyxx2CxWLBw4ULo9Xp0794dffv2RU5OTuf/gxGRZFjsEJHH/fjjj9i3bx8WLFiA0NDQVsd79uyJRx99FPv27UNVVZWtfe/evaivr0d6errTz/Xcc88hPT0dX3zxBe655x7MmjULP/74IwCgsrISaWlpuOeee1BaWopHH30UTz31lN3Pl5WVYfz48UhLS8MXX3yBd999F0VFRVi4cKFdbtWqVUhISEBJSQmeeeYZvPLKK9izZw/+/Oc/46uvvsL27dvRt29fF/4rEZGnsNghIo/7+uuvIQgCrr/++jYzSUlJGDRoELZt22Zr27JlC+6//3706NHD6eeaM2cOZsyYgQEDBiA7OxuXLl3C0aNHAQAbN27EddddhzVr1mDQoEGYNWsW5syZY/fzq1atwsyZM2E0GhEfH4+kpCS88soreOedd3DlyhVbbvTo0cjMzMSAAQMwYMAAfPfdd4iPj8cdd9yBPn364I477sCMGTOc7jcReQ6LHSLyOEEQAIinmdrz6KOPYsuWLQCA6upqfPDBB3jkkUdceq7Bgwfbvg8NDUVYWBiqq6sBAF9++SVuvfVWu37cdtttdj9fUlKCrVu3okePHrbb+PHj0dzcjIqKCltu2LBhdj83Z84clJaWYtCgQXjiiSewf/9+l/pNRJ7DYoeIPC4+Ph4qlQpffvllu7mHHnoI33zzDT777DPbaaCRI0e69FyBgYF291UqFZqbmwH8UnS1p7m5GY8//jhKS0ttt3/84x8oLy9H//79bbmWp+NuvvlmVFRU4Pe//z0aGhqQnp6OadOmudR3IvIMXo1FRB4XGRmJ8ePH49VXX8UTTzzRqlCora1Fz549ERUVhalTp2LLli347LPP8PDDD0vajxtuuAG7du2ya/v888/t7t988804efIkBgwY4PLjh4eHY/r06Zg+fTqmTZuGu+++Gz/++CMiIyPd6TYRuYkjO0TkFRs2bIDVasUtt9yC999/H+Xl5fjyyy/xyiuv2J1KevTRR/H222/jyy+/xOzZsyXtw7x583D69GlkZGTgq6++wo4dO7B161a7zLJly/DZZ59hwYIFKC0tRXl5Ofbs2YNFixa1+9hr1qxBfn4+/vWvf+HUqVP4y1/+Ap1Oh549e0r6GojIdSx2iMgr+vXrh+PHj2PUqFFYsmQJEhISMHbsWHz00UfYuHGjLZeSkgK9Xo/x48fDYDBI2ofevXvj/fffx1//+lfcdNNN2LRpE7Kzs+0ygwcPRmFhIcrLyzFy5EgMGTIEzzzzDPR6fbuP3aNHD7z88ssYNmwYhg8fjjNnzmDv3r3o1o3/zBLJTSU4cxKbiMhLLl++DIPBgLfeegtpaWlyd4eIfADn7BCRIjQ3N8NkMmH16tXQarVITU2Vu0tE5CNY7BCRInz33Xfo168fYmNjsXXrVgQE8J8nIpIGT2MRERGRT+PMOSIiIvJpLHaIiIjIp7HYISIiIp/GYoeIiIh8GosdIiIi8mksdoiIiMinsdghIiIin8Zih4iIiHza/wdaGeaHhYFbrwAAAABJRU5ErkJggg==\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.describe()\n",
|
||
"plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
|
||
"plt.xlabel(\"Cylinders\")\n",
|
||
"plt.ylabel(\"Emission\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"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": 15,
|
||
"metadata": {},
|
||
"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": 16,
|
||
"metadata": {},
|
||
"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": 24,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Coefficients: [[39.58478793]]\n",
|
||
"Intercept: [123.87889336]\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": 18,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Emission')"
|
||
]
|
||
},
|
||
"execution_count": 18,
|
||
"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": 19,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Mean absolute error: 25.12\n",
|
||
"Residual sum of squares (MSE): 1069.33\n",
|
||
"R2-score: 0.76\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": 20,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Coefficients: [[15.96526128]]\n",
|
||
"Intercept: [70.90496725]\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"msk = np.random.rand(len(df)) < 0.8\n",
|
||
"train = cdf[msk]\n",
|
||
"test = cdf[~msk]\n",
|
||
"\n",
|
||
"plt.scatter(train.FUELCONSUMPTION_COMB, train.CO2EMISSIONS, color='blue')\n",
|
||
"plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
|
||
"plt.ylabel(\"Emission\")\n",
|
||
"plt.show() \n",
|
||
"\n",
|
||
"from sklearn import linear_model\n",
|
||
"regr = linear_model.LinearRegression()\n",
|
||
"train_x = np.asanyarray(train[['FUELCONSUMPTION_COMB']])\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_)\n",
|
||
"\n",
|
||
"plt.scatter(train.FUELCONSUMPTION_COMB, train.CO2EMISSIONS, color='blue')\n",
|
||
"plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
|
||
"plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
|
||
"plt.ylabel(\"Emission\")\n",
|
||
"\n",
|
||
"train_x = train[[\"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": 21,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Coefficients: [[15.96526128]]\n",
|
||
"Intercept: [70.90496725]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"regr = linear_model.LinearRegression()\n",
|
||
"train_x = np.asanyarray(train[['FUELCONSUMPTION_COMB']])\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": [
|
||
"<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": 22,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[[165.10000882]\n",
|
||
" [145.94169528]\n",
|
||
" [145.94169528]\n",
|
||
" [102.83548981]\n",
|
||
" [102.83548981]\n",
|
||
" [118.8007511 ]\n",
|
||
" [134.76601238]\n",
|
||
" [102.83548981]\n",
|
||
" [118.8007511 ]\n",
|
||
" [118.8007511 ]\n",
|
||
" [153.92432592]\n",
|
||
" [118.8007511 ]\n",
|
||
" [118.8007511 ]\n",
|
||
" [118.8007511 ]\n",
|
||
" [102.83548981]\n",
|
||
" [134.76601238]\n",
|
||
" [102.83548981]\n",
|
||
" [118.8007511 ]\n",
|
||
" [118.8007511 ]\n",
|
||
" [102.83548981]\n",
|
||
" [118.8007511 ]\n",
|
||
" [118.8007511 ]\n",
|
||
" [118.8007511 ]\n",
|
||
" [141.1521169 ]\n",
|
||
" [141.1521169 ]\n",
|
||
" [166.69653495]\n",
|
||
" [118.8007511 ]\n",
|
||
" [118.8007511 ]\n",
|
||
" [141.1521169 ]\n",
|
||
" [141.1521169 ]\n",
|
||
" [141.1521169 ]\n",
|
||
" [141.1521169 ]\n",
|
||
" [102.83548981]\n",
|
||
" [109.22159433]\n",
|
||
" [118.8007511 ]\n",
|
||
" [128.37990787]\n",
|
||
" [169.88958721]\n",
|
||
" [169.88958721]\n",
|
||
" [169.88958721]\n",
|
||
" [169.88958721]\n",
|
||
" [128.37990787]\n",
|
||
" [128.37990787]\n",
|
||
" [169.88958721]\n",
|
||
" [ 93.25633304]\n",
|
||
" [155.52085205]\n",
|
||
" [155.52085205]\n",
|
||
" [155.52085205]\n",
|
||
" [155.52085205]\n",
|
||
" [166.69653495]\n",
|
||
" [166.69653495]\n",
|
||
" [147.53822141]\n",
|
||
" [128.37990787]\n",
|
||
" [109.22159433]\n",
|
||
" [155.52085205]\n",
|
||
" [155.52085205]\n",
|
||
" [139.55559077]\n",
|
||
" [169.88958721]\n",
|
||
" [ 99.64243756]\n",
|
||
" [155.52085205]\n",
|
||
" [155.52085205]\n",
|
||
" [155.52085205]\n",
|
||
" [128.37990787]\n",
|
||
" [128.37990787]\n",
|
||
" [128.37990787]\n",
|
||
" [173.08263946]\n",
|
||
" [128.37990787]\n",
|
||
" [161.90695656]\n",
|
||
" [173.08263946]\n",
|
||
" [161.90695656]\n",
|
||
" [109.22159433]\n",
|
||
" [ 93.25633304]\n",
|
||
" [ 93.25633304]\n",
|
||
" [161.90695656]\n",
|
||
" [128.37990787]\n",
|
||
" [ 93.25633304]\n",
|
||
" [ 93.25633304]\n",
|
||
" [ 93.25633304]\n",
|
||
" [157.11737818]\n",
|
||
" [129.976434 ]\n",
|
||
" [129.976434 ]\n",
|
||
" [150.73127367]\n",
|
||
" [150.73127367]\n",
|
||
" [150.73127367]\n",
|
||
" [169.88958721]\n",
|
||
" [126.78338174]\n",
|
||
" [ 96.4493853 ]\n",
|
||
" [102.83548981]\n",
|
||
" [126.78338174]\n",
|
||
" [128.37990787]\n",
|
||
" [155.52085205]\n",
|
||
" [155.52085205]\n",
|
||
" [147.53822141]\n",
|
||
" [139.55559077]\n",
|
||
" [155.52085205]\n",
|
||
" [169.88958721]\n",
|
||
" [139.55559077]\n",
|
||
" [109.22159433]\n",
|
||
" [128.37990787]\n",
|
||
" [169.88958721]\n",
|
||
" [126.78338174]\n",
|
||
" [ 96.4493853 ]\n",
|
||
" [102.83548981]\n",
|
||
" [109.22159433]\n",
|
||
" [ 96.4493853 ]\n",
|
||
" [126.78338174]\n",
|
||
" [129.976434 ]\n",
|
||
" [126.78338174]\n",
|
||
" [110.81812046]\n",
|
||
" [118.8007511 ]\n",
|
||
" [150.73127367]\n",
|
||
" [150.73127367]\n",
|
||
" [150.73127367]\n",
|
||
" [150.73127367]\n",
|
||
" [109.22159433]\n",
|
||
" [121.99380336]\n",
|
||
" [102.83548981]\n",
|
||
" [109.22159433]\n",
|
||
" [109.22159433]\n",
|
||
" [128.37990787]\n",
|
||
" [128.37990787]\n",
|
||
" [ 96.4493853 ]\n",
|
||
" [ 96.4493853 ]\n",
|
||
" [102.83548981]\n",
|
||
" [123.59032948]\n",
|
||
" [109.22159433]\n",
|
||
" [109.22159433]\n",
|
||
" [118.8007511 ]\n",
|
||
" [150.73127367]\n",
|
||
" [150.73127367]\n",
|
||
" [118.8007511 ]\n",
|
||
" [150.73127367]\n",
|
||
" [150.73127367]\n",
|
||
" [118.8007511 ]\n",
|
||
" [150.73127367]\n",
|
||
" [126.78338174]\n",
|
||
" [126.78338174]\n",
|
||
" [126.78338174]\n",
|
||
" [150.73127367]\n",
|
||
" [144.34516915]\n",
|
||
" [126.78338174]\n",
|
||
" [129.976434 ]\n",
|
||
" [157.11737818]\n",
|
||
" [131.57296013]\n",
|
||
" [110.81812046]\n",
|
||
" [129.976434 ]\n",
|
||
" [110.81812046]\n",
|
||
" [102.83548981]\n",
|
||
" [110.81812046]\n",
|
||
" [102.83548981]\n",
|
||
" [110.81812046]\n",
|
||
" [102.83548981]\n",
|
||
" [126.78338174]\n",
|
||
" [169.88958721]\n",
|
||
" [158.71390431]\n",
|
||
" [126.78338174]\n",
|
||
" [126.78338174]\n",
|
||
" [145.94169528]\n",
|
||
" [145.94169528]\n",
|
||
" [158.71390431]\n",
|
||
" [158.71390431]\n",
|
||
" [126.78338174]\n",
|
||
" [126.78338174]\n",
|
||
" [145.94169528]\n",
|
||
" [145.94169528]\n",
|
||
" [ 96.4493853 ]\n",
|
||
" [ 96.4493853 ]\n",
|
||
" [ 96.4493853 ]\n",
|
||
" [ 96.4493853 ]\n",
|
||
" [ 96.4493853 ]\n",
|
||
" [ 96.4493853 ]\n",
|
||
" [ 96.4493853 ]\n",
|
||
" [ 90.06328079]\n",
|
||
" [129.976434 ]\n",
|
||
" [126.78338174]\n",
|
||
" [110.81812046]\n",
|
||
" [ 96.4493853 ]\n",
|
||
" [126.78338174]\n",
|
||
" [ 96.4493853 ]\n",
|
||
" [134.76601238]\n",
|
||
" [125.18685561]\n",
|
||
" [131.57296013]\n",
|
||
" [131.57296013]\n",
|
||
" [131.57296013]\n",
|
||
" [114.01117271]\n",
|
||
" [114.01117271]\n",
|
||
" [128.37990787]\n",
|
||
" [118.8007511 ]\n",
|
||
" [147.53822141]\n",
|
||
" [161.90695656]\n",
|
||
" [161.90695656]\n",
|
||
" [128.37990787]\n",
|
||
" [118.8007511 ]\n",
|
||
" [176.27569172]\n",
|
||
" [177.87221785]\n",
|
||
" [177.87221785]\n",
|
||
" [ 86.87022853]\n",
|
||
" [ 86.87022853]\n",
|
||
" [102.83548981]\n",
|
||
" [110.81812046]\n",
|
||
" [102.83548981]\n",
|
||
" [102.83548981]\n",
|
||
" [134.76601238]\n",
|
||
" [ 99.64243756]\n",
|
||
" [126.78338174]\n",
|
||
" [ 99.64243756]\n",
|
||
" [126.78338174]\n",
|
||
" [134.76601238]\n",
|
||
" [161.90695656]\n",
|
||
" [ 94.85285917]\n",
|
||
" [110.81812046]\n",
|
||
" [110.81812046]\n",
|
||
" [102.83548981]\n",
|
||
" [ 99.64243756]\n",
|
||
" [102.83548981]\n",
|
||
" [110.81812046]]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"predictions = regr.predict(test_x)\n",
|
||
"print(predictions)"
|
||
]
|
||
},
|
||
{
|
||
"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": 23,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Mean Absolute Error: 136.71\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))"
|
||
]
|
||
},
|
||
{
|
||
"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
|
||
}
|