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

Created February 13, 2019 15:33
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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,
"deletable": true,
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2019-02-13 14:59:50-- 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",
"2019-02-13 14:59:50 (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,
"deletable": 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": [
"<div>\n",
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"\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": 5,
"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": 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:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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": 7,
"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": 8,
"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": 10,
"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": [
"# write your code here\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": [
"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": 11,
"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": 12,
"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": 13,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coefficients: [[38.21252746]]\n",
"Intercept: [128.29071118]\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": 14,
"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": 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,
"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": 15,
"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: 25.08\n",
"Residual sum of squares (MSE): 1053.13\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) )"
]
},
{
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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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