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Akash671/fuelconsmptionprjct.ipynb

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fuelconsmptionprjct.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# importing required packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt #for graph repersentation or data visuliasation\n",
"import pandas as pd # use for data analysis and data manupluation\n",
"import pylab as pl # \n",
"import numpy as np #use for mathematicals and numaric data\n",
"%matplotlib inline "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2020-05-23 10:32:47-- 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 0.04s \n",
"\n",
"2020-05-23 10:32:48 (1.64 MB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n",
"\n"
]
}
],
"source": [
"#downloading data from IBM cloud \n",
"!wget -O FuelConsumption.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/FuelConsumptionCo2.csv"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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": [
"# reading the data \n",
"df = pd.read_csv(\"FuelConsumption.csv\")\n",
"\n",
"# take a look at the dataset\n",
"df.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MODELYEAR</th>\n",
" <th>ENGINESIZE</th>\n",
" <th>CYLINDERS</th>\n",
" <th>FUELCONSUMPTION_CITY</th>\n",
" <th>FUELCONSUMPTION_HWY</th>\n",
" <th>FUELCONSUMPTION_COMB</th>\n",
" <th>FUELCONSUMPTION_COMB_MPG</th>\n",
" <th>CO2EMISSIONS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1067.0</td>\n",
" <td>1067.000000</td>\n",
" <td>1067.000000</td>\n",
" <td>1067.000000</td>\n",
" <td>1067.000000</td>\n",
" <td>1067.000000</td>\n",
" <td>1067.000000</td>\n",
" <td>1067.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2014.0</td>\n",
" <td>3.346298</td>\n",
" <td>5.794752</td>\n",
" <td>13.296532</td>\n",
" <td>9.474602</td>\n",
" <td>11.580881</td>\n",
" <td>26.441425</td>\n",
" <td>256.228679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.0</td>\n",
" <td>1.415895</td>\n",
" <td>1.797447</td>\n",
" <td>4.101253</td>\n",
" <td>2.794510</td>\n",
" <td>3.485595</td>\n",
" <td>7.468702</td>\n",
" <td>63.372304</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2014.0</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>4.600000</td>\n",
" <td>4.900000</td>\n",
" <td>4.700000</td>\n",
" <td>11.000000</td>\n",
" <td>108.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2014.0</td>\n",
" <td>2.000000</td>\n",
" <td>4.000000</td>\n",
" <td>10.250000</td>\n",
" <td>7.500000</td>\n",
" <td>9.000000</td>\n",
" <td>21.000000</td>\n",
" <td>207.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2014.0</td>\n",
" <td>3.400000</td>\n",
" <td>6.000000</td>\n",
" <td>12.600000</td>\n",
" <td>8.800000</td>\n",
" <td>10.900000</td>\n",
" <td>26.000000</td>\n",
" <td>251.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2014.0</td>\n",
" <td>4.300000</td>\n",
" <td>8.000000</td>\n",
" <td>15.550000</td>\n",
" <td>10.850000</td>\n",
" <td>13.350000</td>\n",
" <td>31.000000</td>\n",
" <td>294.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2014.0</td>\n",
" <td>8.400000</td>\n",
" <td>12.000000</td>\n",
" <td>30.200000</td>\n",
" <td>20.500000</td>\n",
" <td>25.800000</td>\n",
" <td>60.000000</td>\n",
" <td>488.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MODELYEAR ENGINESIZE CYLINDERS FUELCONSUMPTION_CITY \\\n",
"count 1067.0 1067.000000 1067.000000 1067.000000 \n",
"mean 2014.0 3.346298 5.794752 13.296532 \n",
"std 0.0 1.415895 1.797447 4.101253 \n",
"min 2014.0 1.000000 3.000000 4.600000 \n",
"25% 2014.0 2.000000 4.000000 10.250000 \n",
"50% 2014.0 3.400000 6.000000 12.600000 \n",
"75% 2014.0 4.300000 8.000000 15.550000 \n",
"max 2014.0 8.400000 12.000000 30.200000 \n",
"\n",
" FUELCONSUMPTION_HWY FUELCONSUMPTION_COMB FUELCONSUMPTION_COMB_MPG \\\n",
"count 1067.000000 1067.000000 1067.000000 \n",
"mean 9.474602 11.580881 26.441425 \n",
"std 2.794510 3.485595 7.468702 \n",
"min 4.900000 4.700000 11.000000 \n",
"25% 7.500000 9.000000 21.000000 \n",
"50% 8.800000 10.900000 26.000000 \n",
"75% 10.850000 13.350000 31.000000 \n",
"max 20.500000 25.800000 60.000000 \n",
"\n",
" CO2EMISSIONS \n",
"count 1067.000000 \n",
"mean 256.228679 \n",
"std 63.372304 \n",
"min 108.000000 \n",
"25% 207.000000 \n",
"50% 251.000000 \n",
"75% 294.000000 \n",
"max 488.000000 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Data Exploration\n",
"#Lets first have a descriptive exploration on our data.\n",
"# summarize the data\n",
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"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",
"</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": [
"#Lets select some features to explore more.\n",
"cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
"cdf.head(9)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>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": "code",
"execution_count": 7,
"metadata": {},
"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": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
"cdf.head(9)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#we can plot each of these fearues:\n",
"viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\n",
"viz.hist()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"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": [
"#Now, lets plot each of these features vs the Emission, to see how linear is their relation:\n",
"plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS, color='blue')\n",
"plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
"plt.ylabel(\"Emission\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"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": "code",
"execution_count": 12,
"metadata": {},
"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": [
"#plot CYLINDER vs the Emission, to see how linear is their relation:\n",
"# write your code here\n",
"plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
"plt.xlabel(\"Cylinder\")\n",
"plt.ylabel(\"Emission\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"Creating train and test dataset\n",
"Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive.\n",
"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.\n",
"It is more realistic for real world problems.\n",
"\n",
"This means that we know the outcome of each data point in this dataset,\n",
"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.\n",
"So, in essence, it is truly an out-of-sample testing.\"\"\"\n",
"\n",
"msk = np.random.rand(len(df)) < 0.8\n",
"train = cdf[msk]\n",
"test = cdf[~msk]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"#Simple Regression Model\n",
"#Linear Regression fits a linear model with coefficients B = (B1, ..., Bn)\n",
"#to minimize the 'residual sum of squares' between the independent x in the dataset, and the dependent y by the linear approximation.\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"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": [
"#Train data distribution\n",
"plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue')\n",
"plt.xlabel(\"Engine size\")\n",
"plt.ylabel(\"Emission\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coefficients: [[39.15686956]]\n",
"Intercept: [125.39539879]\n"
]
}
],
"source": [
"#Modeling\n",
"#Using sklearn package to model data.\n",
"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": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'As mentioned before, Coefficient and Intercept in the simple linear regression, \\nare the parameters of the fit line. Given that it is a simple linear regression, with only 2 parameters, \\nand knowing that the parameters are the intercept and slope of the line, sklearn can estimate them directly from our data.\\nNotice that all of the data must be available to traverse and calculate the parameters.'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''As mentioned before, Coefficient and Intercept in the simple linear regression, \n",
"are the parameters of the fit line. Given that it is a simple linear regression, with only 2 parameters, \n",
"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.'''"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"#Plot outputs\n",
"#we can plot the fit line over the data:\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Emission')"
]
},
"execution_count": 19,
"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": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Evaluation\\nwe compare the actual values and predicted values to calculate the accuracy\\nof a regression model. Evaluation metrics provide a key role in the development of a model, \\nas it provides insight to areas that require improvement.\\n\\nThere 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. \\nThis is the easiest of the metrics to understand since it’s just average error. - Mean Squared Error (MSE): Mean Squared Error (MSE) is the mean of the squared error.\\nIt’s more popular than Mean absolute error because the focus is geared more towards large errors. \\nThis is due to the squared term exponentially increasing larger errors in comparison to smaller ones. - 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. \\nThe 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)'"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''Evaluation\n",
"we compare the actual values and predicted values to calculate the accuracy\n",
"of a regression model. Evaluation metrics provide a key role in the development of a model, \n",
"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. \n",
"This is the easiest of the metrics to understand since it’s just average error. - Mean Squared Error (MSE): Mean Squared Error (MSE) is the mean of the squared error.\n",
"It’s more popular than Mean absolute error because the focus is geared more towards large errors. \n",
"This is due to the squared term exponentially increasing larger errors in comparison to smaller ones. - 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. \n",
"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)'''"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean absolute error: 24.69\n",
"Residual sum of squares (MSE): 974.98\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_ = 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": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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