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

Created June 7, 2020 06:33
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ML0101EN-Reg-Simple-Linear-Regression-Co2-py-v1.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
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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",
"# <center>Simple Linear Regression</center>\n",
"\n",
"\n",
"#### About this Notebook\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": {
"button": false,
"deletable": true,
"new_sheet": false,
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},
"source": [
"### Importing Needed packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
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"button": false,
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"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": {
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}
},
"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,
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2020-06-07 06:17:21-- 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)... 67.228.254.196\n",
"Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|: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 0.06s \n",
"\n",
"2020-06-07 06:17:21 (1.13 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": {
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"deletable": true,
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},
"source": [
"\n",
"## Understanding the Data\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": [
"## Reading the data in"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"button": false,
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{
"data": {
"text/html": [
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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": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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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": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('FuelConsumption.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Data Exploration\n",
"Lets first have a descriptive exploration on our data."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"button": false,
"deletable": true,
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"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" }\n",
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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": 6,
"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": 8,
"metadata": {
"button": false,
"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",
" 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": 8,
"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:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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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",
"cdf.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": 11,
"metadata": {
"button": false,
"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": 13,
"metadata": {
"button": false,
"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('ENGINESIZE')\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": 16,
"metadata": {
"button": false,
"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('CYLINDER')\n",
"plt.ylabel('CO2EMISSION')\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": 17,
"metadata": {
"button": false,
"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": [
"### Simple Regression Model\n",
"Linear Regression fits a linear model with coefficients B = (B1, ..., Bn) 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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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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": 19,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coefficients: [[39.34288451]]\n",
"Intercept: [124.71571611]\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,
"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",
" - 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"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean absolute error: 25.13\n",
"Residual sum of squares (MSE): 1082.58\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_ = 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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Want to learn more?\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: [SPSS Modeler](http://cocl.us/ML0101EN-SPSSModeler).\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 [Watson Studio](https://cocl.us/ML0101EN_DSX)\n",
"\n",
"### Thanks for completing this lesson!\n",
"\n",
"Notebook created by: <a href = \"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>\n",
"\n",
"<hr>\n",
"Copyright &copy; 2018 [Cognitive Class](https://cocl.us/DX0108EN_CC). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/).​"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python",
"language": "python",
"name": "conda-env-python-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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