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

Created February 19, 2021 08:51
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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": {
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}
},
"source": [
"<center>\n",
" <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\" />\n",
"</center>\n",
"\n",
"# Simple Linear Regression\n",
"\n",
"Estimated time needed: **15** minutes\n",
"\n",
"## Objectives\n",
"\n",
"After completing this lab you will be able to:\n",
"\n",
"- Use scikit-learn to implement simple Linear Regression\n",
"- Create a model, train,test and use the model\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": [
"--2021-02-19 07:58:50-- https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/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 0.04s \n",
"\n",
"2021-02-19 07:58:50 (1.86 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-SkillsNetwork/labs/Module%202/data/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": [
"## Understanding the Data\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-SkillsNetwork-20718538&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-SkillsNetwork-20718538&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-SkillsNetwork-20718538&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-SkillsNetwork-20718538&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": [
"## Reading the data in\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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",
" <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": [
"### Data Exploration\n",
"\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",
"<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",
" <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",
" .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": 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 fearues:\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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": 7,
"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": 8,
"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": 10,
"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 = 'blue')\n",
"plt.xlabel(\"Cylinders\")\n",
"plt.ylabel(\"Emission\")\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<details><summary>Click here for the solution</summary>\n",
"\n",
"```python\n",
"plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
"plt.xlabel(\"Cylinders\")\n",
"plt.ylabel(\"Emission\")\n",
"plt.show()\n",
"\n",
"```\n",
"\n",
"</details>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"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": null,
"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": [
"### Simple Regression Model\n",
"\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": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Train data distribution\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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": 13,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coefficients: [[38.85727679]]\n",
"Intercept: [125.77516535]\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": 14,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Emission')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 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",
"```\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",
"- 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",
"- Root Mean Squared Error (RMSE).\n",
"- 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).\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean absolute error: 23.81\n",
"Residual sum of squares (MSE): 932.54\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": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<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=\"https://www.ibm.com/analytics/spss-statistics-software\">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://www.ibm.com/cloud/watson-studio\">Watson Studio</a>\n"
]
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"### Thank you for completing this lab!\n",
"\n",
"## Author\n",
"\n",
"Saeed Aghabozorgi\n",
"\n",
"### Other Contributors\n",
"\n",
"<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
"\n",
"## Change Log\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",
"## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
]
}
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