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acheampomaa/ML0101EN-Reg-Simple-Linear-Regression-Co2-py-v1.ipynb

Created March 23, 2021 19:08
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ML0101EN-Reg-Simple-Linear-Regression-Co2-py-v1.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width=\"400\" align=\"center\"></a>\n",
"\n",
"<h1><center>Simple Linear Regression</center></h1>\n",
"\n",
"<h4>About this Notebook</h4>\n",
"In this notebook, we learn how to use scikit-learn to implement simple linear regression. We download a dataset that is related to fuel consumption and Carbon dioxide emission of cars. Then, we split our data into training and test sets, create a model using training set, evaluate your model using test set, and finally use model to predict unknown value.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1>Table of contents</h1>\n",
"\n",
"<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
" <ol>\n",
" <li><a href=\"#understanding_data\">Understanding the Data</a></li>\n",
" <li><a href=\"#reading_data\">Reading the data in</a></li>\n",
" <li><a href=\"#data_exploration\">Data Exploration</a></li>\n",
" <li><a href=\"#simple_regression\">Simple Regression Model</a></li>\n",
" </ol>\n",
"</div>\n",
"<br>\n",
"<hr>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Importing Needed packages\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import pylab as pl\n",
"import numpy as np\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Downloading Data\n",
"\n",
"To download the data, we will use !wget to download it from IBM Object Storage.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2021-03-23 18:01:29-- https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-Coursera/labs/Data_files/FuelConsumptionCo2.csv\n",
"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\n",
"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",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 72629 (71K) [text/csv]\n",
"Saving to: ‘FuelConsumption.csv’\n",
"\n",
"FuelConsumption.csv 100%[===================>] 70.93K --.-KB/s in 0.07s \n",
"\n",
"2021-03-23 18:01:29 (1.04 MB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n",
"\n"
]
}
],
"source": [
"!wget -O FuelConsumption.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-Coursera/labs/Data_files/FuelConsumptionCo2.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Did you know?** When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<h2 id=\"understanding_data\">Understanding the Data</h2>\n",
"\n",
"### `FuelConsumption.csv`:\n",
"\n",
"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?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-ML0101EN-Coursera-20231514&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-ML0101EN-Coursera-20231514&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-ML0101EN-Coursera-20231514&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-ML0101EN-Coursera-20231514&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n",
"\n",
"- **MODELYEAR** e.g. 2014\n",
"- **MAKE** e.g. Acura\n",
"- **MODEL** e.g. ILX\n",
"- **VEHICLE CLASS** e.g. SUV\n",
"- **ENGINE SIZE** e.g. 4.7\n",
"- **CYLINDERS** e.g 6\n",
"- **TRANSMISSION** e.g. A6\n",
"- **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
"- **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
"- **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
"- **CO2 EMISSIONS (g/km)** e.g. 182 --> low --> 0\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<h2 id=\"reading_data\">Reading the data in</h2>\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
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"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MODELYEAR</th>\n",
" <th>MAKE</th>\n",
" <th>MODEL</th>\n",
" <th>VEHICLECLASS</th>\n",
" <th>ENGINESIZE</th>\n",
" <th>CYLINDERS</th>\n",
" <th>TRANSMISSION</th>\n",
" <th>FUELTYPE</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>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": 3,
"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"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<h2 id=\"data_exploration\">Data Exploration</h2>\n",
"Lets first have a descriptive exploration on our data.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"\n",
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" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MODELYEAR</th>\n",
" <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": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# summarize the data\n",
"df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets select some features to explore more.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</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": 5,
"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": 8,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\n",
"viz.hist()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, lets plot each of these features vs the Emission, to see how linear is their relation:\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"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": 10,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"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",
"\n",
"plot **CYLINDER** vs the Emission, to see how linear is their relation:\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# write your code here\n",
"plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color= 'red')\n",
"plt.xlabel('CYLINDERS')\n",
"plt.ylabel('EMISSION')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click **here** for the solution.\n",
"\n",
"<!-- Your answer is below:\n",
" \n",
"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": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Creating train and test dataset\n",
"\n",
"Train/Test Split involves splitting the dataset into training and testing sets respectively, which 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 data. It is more realistic for real world problems.\n",
"\n",
"This means that we know the outcome of each data point in this dataset, making it great to test with! And 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",
"Lets split our dataset into train and test sets, 80% of the entire data for training, and the 20% for testing. We create a mask to select random rows using **np.random.rand()** function: \n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"msk = np.random.rand(len(df)) < 0.8\n",
"train = cdf[msk]\n",
"test = cdf[~msk]"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<h2 id=\"simple_regression\">Simple Regression Model</h2>\n",
"Linear Regression fits a linear model with coefficients $\\theta = (\\theta_1, ..., \\theta_n)$ to minimize the 'residual sum of squares' between the independent x in the dataset, and the dependent y by the linear approximation. \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Train data distribution\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"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": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Modeling\n",
"\n",
"Using sklearn package to model data.\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coefficients: [[38.82517169]]\n",
"Intercept: [125.76815446]\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": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Plot outputs\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"we can plot the fit line over the data:\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Emission')"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"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": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Evaluation\n",
"\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",
"\n",
"<ul>\n",
" <li> 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.</li>\n",
" <li> 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.</li>\n",
" <li> Root Mean Squared Error (RMSE): This is the square root of the Mean Square Error. </li>\n",
" <li> R-squared is not error, but is a popular metric for accuracy of your model. It represents how close the data are to the fitted regression line. The higher the R-squared, the better the model fits your data. Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse).</li>\n",
"</ul>\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean absolute error: 24.22\n",
"Residual sum of squares (MSE): 1048.52\n",
"R2-score: 0.64\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_hat = regr.predict(test_x)\n",
"\n",
"print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_hat - test_y)))\n",
"print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_hat - test_y) ** 2))\n",
"print(\"R2-score: %.2f\" % r2_score(test_y_hat , test_y) )"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
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"<h2>Want to learn more?</h2>\n",
"\n",
"IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler\">SPSS Modeler</a>\n",
"\n",
"Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX\">Watson Studio</a>\n",
"\n",
"<h3>Thanks for completing this lesson!</h3>\n",
"\n",
"<h4>Author: <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a></h4>\n",
"<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n"
]
},
{
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"| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n",
"| ----------------- | ------- | ---------- | --------------------- |\n",
"| 2020-08-4 | 0 | Nayef | Upload file to Gitlab |\n",
"| | | | |\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<hr>\n",
"\n",
"<p>Copyright &copy; 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p>\n"
]
}
],
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