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aish2997/ML0101EN-Reg-Simple-Linear-Regression-Co2-py-v1.ipynb

Created March 27, 2019 21:20
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ML0101EN-Reg-Simple-Linear-Regression-Co2-py-v1.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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},
"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",
"\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>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Importing Needed packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
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"collapsed": true,
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},
"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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Downloading Data\n",
"To download the data, we will use !wget to download it from IBM Object Storage."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"button": false,
"collapsed": true,
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2019-03-27 19:42:24-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/FuelConsumptionCo2.csv\n",
"Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193\n",
"Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|: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",
"2019-03-27 19:42:24 (1.64 MB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n",
"\n"
]
}
],
"source": [
"!wget -O FuelConsumption.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/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)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"\n",
"<h2 id=\"understanding_data\">Understanding the Data</h2>\n",
"\n",
"### `FuelConsumption.csv`:\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)\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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<h2 id=\"reading_data\">Reading the data in</h2>"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"button": false,
"collapsed": true,
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"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe thead th {\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>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,
"deletable": true,
"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."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
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},
"outputs": [
{
"data": {
"text/html": [
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" }\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."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"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",
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"\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" 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>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": 11,
"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:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"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:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"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": 23,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"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",
"plot __CYLINDER__ vs the Emission, to see how linear is their relation:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fe717eea828>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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jlzSP3EbhO5LS1yS9KOnHkj6c1NqBA2Me1sepfzGYZU7nkna+/Jk5x5esbpL48mfm0LnE/xSs9ooOeknnAj8F/iYi/gW4F5gPLAYOAn832jTPw09a/kXSKkk9knoGBwdL7rhZPeve1c9Pd/YzErm/+iMR/HRnP927+lPumTWiooJeUjO5kN8YEZsAIuLNiBiJiGPAfZwYnukDxi7IPhsYmPicEbE+IjoioqOtra2cn8Gs7qzbupfho+M/lho+OsK6rXtT6pE1smJm3Qi4H9gTEd8bU585ptkXgZeS25uB6yWdKelCYAHwy8p12az+DQwNl1Q3q6ZiZt0sA/4S6JW0O6n9V+AGSYvJDcu8Afw1QES8LOkx4FfkZuzc5hk31mhmtbbQnyfUZ7W2pNAba3QFgz4ifkH+cfctp3jM3cDdZfTLbEpbvXwhXZt6xw3ftDQ3sXr5whR7ZY2qmDN6MyvR6OyadVv3MjA0zKzWFlYvX+hZN5YKB31GrOnu5eEdBxiJoEnihkvnsLZzUdrdamidS9od7FYXHPQZsKa7l4fG7FM7EnH82GFvZl7ULAMe3nGgpLqZNRYHfQaMXpRTbN3MGouDPgNGL7Mvtm5mjcVBnwF/3HZ2SXUzaywO+gx45dDbJdXNrLE46M3MMs5Bb2aWcQ56M7OMc9CbmWWcg97MLOMc9GZmGeegz4APn91cUt3MGouDPgPu+sJFNDeNvwq2uUnc9YWLUuqRmdUTr16ZAV773MxOpWDQS5oDPAj8K+AYsD4ifiBpBvAoMI/cVoJ/ERFvJXvM/gC4BngH+KuIeKE63bdR9bD2efM0OHosf93M0lPMP8EPgG9ExJ8CS4HbJH0SuBN4KiIWAE8lxwBXk9sQfAGwCri34r22uvTlS+aWVDez2igY9BFxcPSMPCJ+D+wB2oEVwIak2QagM7m9AngwcrYDrZJmVrznVnee+fVgSXUzq42S/lMtaR6wBNgBfDQiDkLulwFwQdKsHRi740VfUrOMGxgaLqluZrVRdNBLOhf4KfA3EfEvp2qap3bSDhiSVknqkdQzOOgzviyY1dpSUt3MaqOooJfUTC7kN0bEpqT85uiQTPL9UFLvA+aMefhsYGDic0bE+ojoiIiOtra20+2/1ZHVyxfS0tw0rtbS3MTq5QtT6pGZQRFBn8yiuR/YExHfG3PXZmBlcnsl8MSY+s3KWQocGR3isWzrXNLOxXPPG1e7eO55qc8GMmt0xZzRLwP+ErhC0u7k6xrg28DnJL0CfC45BtgCvAbsA+4D/kPlu231aE13L8+9enhc7blXD7OmuzelHpkZFDGPPiJ+Qf5xd4Ar87QP4LYy+2VT0EPb909aX9u5qMa9MbNRvpTFzCzjHPRmZhnnoDczyzgHvVXMsvkzSqqbWW046K1iNt562Umhvmz+DDbeellKPTIzcNBbhV3Ydi5Nyk3SapK4sO3clHtkZl6P3ipmTXfvuCmWIxHHjz290iw9PqO3inl4x4GS6mZWGw56q5iROGntulPWzaw2HPRmZhnnoDczyzgHvVVM+yTrzk9WN7PacNBbxXg9erP65OmVVjGj686v27qXgaFhZrW2sHr5Qq9Hb5YyB71VVOeSdge7WZ3x0I2ZWcY56M3MMq6YPWN/LOmQpJfG1L4lqX/C1oKj93VJ2idpr6Tl1eq4mZkVp5gz+geAq/LU74mIxcnXFgBJnwSuBy5KHvPfJTXleayZmdVIwaCPiH8CDhdql1gBPBIR70XE6+Q2CL+kjP6ZmVmZypl18zVJNwM9wDci4i2gHdg+pk1fUquKG+97nudePfE7yGufm5md7HQ/jL0XmA8sBg4Cf5fUladt3hWtJK2S1COpZ3BwsOQOTAx5gOdePcyN9z1f8nOdrtF114utm5ml4bSCPiLejIiRiDgG3MeJ4Zk+YM6YprOBgUmeY31EdERER1tbW8l9mBjyherVcP65zSXVzczScFpBL2nmmMMvAqMzcjYD10s6U9KFwALgl+V1sX69+fv3S6qbmaWh4Bi9pIeBy4HzJfUBdwGXS1pMbljmDeCvASLiZUmPAb8CPgBui4iR6nTdzMyKUTDoI+KGPOX7T9H+buDucjpVjAUXnMMrh97OWzczsxOm7JWx77x/rKS6mVmjmrJBPzA0XFLdzKxRTdmgnzXJZhaT1c3MGtWUDfp62ORisjdvyr6pZpZJU3Y9+nrY5KLljCbefv/kSUUtZ3h5HzOrH1M26CH9TS7yhfyp6mZmafAog5lZxjnozcwyzkFvZpZxDnozs4xz0JfhnElm10xWNzNLg4O+DHd/cRFN08avPd80Tdz9xUUp9cjM7GRTenpl2uphLr+ZWSE+oy9Tz28O89sj7xLAb4+8S89varfxiZlZMXxGX4Y13b08tH3/8eORiOPHazs9fGNm9cFn9GXYOCbki6mbmaXBQV+GvLuen6JuZpaGgkEv6ceSDkl6aUxthqRtkl5Jvn84qUvSDyXtk/SipIur2XkzMyusmDP6B4CrJtTuBJ6KiAXAU8kxwNXkNgRfAKwC7q1MN+uT59Gb2VRQMOgj4p+AiVNJVgAbktsbgM4x9QcjZzvQKmlmpTpbbzyP3symgtOddfPRiDgIEBEHJV2Q1NuBA2Pa9SW1g6ffxfrlefRmNhVUenql8tTyfjYpaRW54R3mzp1b4W7UTtpr4puZFXK6s27eHB2SSb4fSup9wJwx7WYDA/meICLWR0RHRHS0tbWdZjfMzKyQ0w36zcDK5PZK4Ikx9ZuT2TdLgSOjQzxmZpaOgkM3kh4GLgfOl9QH3AV8G3hM0i3AfuBLSfMtwDXAPuAd4CtV6LOZmZWgYNBHxA2T3HVlnrYB3FZup8zMrHJ8ZayZWcYpdxKeciekQeA3afejTOcDv0u7E3XE78cJfi/G8/txQrnvxcciouBslroI+iyQ1BMRHWn3o174/TjB78V4fj9OqNV74aEbM7OMc9CbmWWcg75y1qfdgTrj9+MEvxfj+f04oSbvhcfozcwyzmf0ZmYZ56CvEElNknZJejLtvqRNUqukxyX9WtIeSZel3ae0SPrPkl6W9JKkhyWdlXafaqmUjYuybpL3Yl3y7+RFST+T1FqN13bQV87XgT1pd6JO/AD4x4j4BPBpGvR9kdQO/CegIyI+BTQB16fbq5p7gOI3Lsq6Bzj5vdgGfCoi/jXwf4Guarywg74CJM0GPg/8KO2+pE3Sh4B/C9wPEBHvR8RQur1K1XSgRdJ04GwmWc01q0rcuCjT8r0XEfHziPggOdxObsXfinPQV8b3gW8Cx9LuSB34Y2AQ+EkylPUjSeek3ak0REQ/8F1yC/8dJLea68/T7VVdGLdxEXBBgfaN4t8D/1CNJ3bQl0nStcChiNiZdl/qxHTgYuDeiFgCvE3j/Nd8nGTseQVwITALOEfSTen2yuqRpP8GfABsrMbzO+jLtwy4TtIbwCPAFZIeSrdLqeoD+iJiR3L8OLngb0SfBV6PiMGIOApsAv5Nyn2qB5NtXNSQJK0ErgVujCrNd3fQlykiuiJidkTMI/dB29MR0bBnbRHxW+CApIVJ6UrgVyl2KU37gaWSzpYkcu9FQ34wPcFkGxc1HElXAXcA10XEO9V6nUrvGWsG8B+BjZLOAF6jQTegiYgdkh4HXiD33/JdNNhVoSVuXJRpk7wXXcCZwLbcuQDbI+KrFX9tXxlrZpZtHroxM8s4B72ZWcY56M3MMs5Bb2aWcQ56M7OMc9CbmWWcg97MLOMc9GZmGff/ATS7MbxbaanaAAAAAElFTkSuQmCC\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)"
]
},
{
"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",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Creating train and test dataset\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: "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"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,
"deletable": true,
"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. "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Train data distribution"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Modeling\n",
"Using sklearn package to model data."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coefficients: [[39.2837783]]\n",
"Intercept: [125.23862352]\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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Plot outputs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"we can plot the fit line over the data:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Emission')"
]
},
"execution_count": 20,
"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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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",
"<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>"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean absolute error: 23.74\n",
"Residual sum of squares (MSE): 956.54\n",
"R2-score: 0.66\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) )"
]
},
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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",
"\n",
"<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>"
]
}
],
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