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tejas-2232/1_ML-Reg-Simple-Linear-Regression-Co2-py-v1.ipynb

Created February 11, 2020 06:47
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1_ML-Reg-Simple-Linear-Regression-Co2-py-v1.ipynb
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"source": [
"# <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. \n",
"Then, we split our data into \n",
"* training and test sets, \n",
"* create a model using training set,\n",
"* Evaluate your model using test set, and\n",
"* finally use model to predict unknown value\n"
]
},
{
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"metadata": {
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"source": [
"### Importing Needed packages"
]
},
{
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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": {
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"deletable": true,
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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": 3,
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"text": [
"--2020-02-11 06:12:54-- 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",
"2020-02-11 06:12:54 (1.63 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",
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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
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},
"source": [
"## Reading the data in"
]
},
{
"cell_type": "code",
"execution_count": 4,
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"data": {
"text/html": [
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"<style scoped>\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": 4,
"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,
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}
},
"source": [
"### Data Exploration\n",
"Lets first have a descriptive exploration on our data."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
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"outputs": [
{
"data": {
"text/html": [
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" 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>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": 5,
"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": 6,
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"button": false,
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"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\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": 6,
"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": 7,
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"outputs": [
{
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V5OBvZlZBHs/f1tPIcwDmTF+7zkPv/QwAs+7ikr+ZWQU5+JuZVZCDv5lZBTn4m5lVkIO/mVkFOfibmVWQg7+ZWQV1fD//Rvqcm5nZyHR88Lfu0MyXtG8MM2sfV/uYmVWQS/5mZmOk2WrsVvxKblnJX9L+kh6UtEzS3FZtx6xT+Jy3btKSkr+kCcB5wL7ACuAnkhZFxP2t2J5Zu3XrOV8sidYO1lcljZbIi8eo29usWlXtsyewLCJ+ASDpMmAm0NEXgtkGGLVz3j3cbCwoIkZ/pdKHgP0j4rj8+Sjg7RFxYmGe44Hj88fdgAdHPSOjowd4st2Z6ECdeFx2iYjXtmPDjZzzOb2Tz/tO+592Wn6g8/LU9DnfqpK/StLW+ZaJiHnAvBZtf9RIuisiZrQ7H53Gx2U9w57z0Nnnfaf9TzstP9CZeWpWqxp8VwBTC593Ah5v0bbMOoHPeesqrQr+PwGmSdpV0qbALGBRi7Zl1gl8zltXaUm1T0SslXQicB0wAbgwIpa0YltjoCN/oncAH5eCcXLOd9r/tNPyA52Zp6a0pMHXzMw6m4d3MDOrIAd/M7MKqnTwlzRV0s2SlkpaIumknD5J0g2SHsp/ty0sc2q+ff9BSfu1L/etJ2mCpP+SdG3+7OPS5SQtl3SvpHsk3VUyXZK+nv+XP5P01hbnZ7ecl8HXGkkn18zTJ+nZwjyntyAfF0paLem+Qlrd871m2e4c1iMiKvsCpgBvze+3AX4O7A58GZib0+cCX8rvdwd+CmwG7Ao8DExo93608Ph8CvgOcG3+7OPS5S9gOdAzxPQDgR+R7lvYC7hjDPM2AXiCdONSMb1v8Bxs4bbfBbwVuK+QVnq+l+T5YeB1wKb5Oti93f/nRl6VLvlHxMqIuDu/fw5YCuxIui1/fp5tPnBIfj8TuCwiXoyIR4BlpNv6xx1JOwEHAf9SSK78camAmcDFkdwOTJQ0ZYy2/T7g4Yj45Rht7xURcQvwdE1yvfO96JVhPSLiv4HBYT06XqWDf5GkXuAtwB3A5IhYCekLAtguz7Yj8FhhsRU5bTz6KvBZ4OVCmo9L9wvgekmL81ATtdr5v5wFXFpn2jsk/VTSjyTtMUb5qXe+F3Xtue/gD0jaGrgcODki1gw1a0nauOsrK+lgYHVELG50kZK0cXdcxom9I+KtwAHACZLeVTO9Lf/LfGPcB4DvlUy+m1QV9MfAN4CrWp2fEejac7/ywV/SJqTAvyAirsjJqwZ/6ua/q3N6VW7h3xv4gKTlpJ+x75V0CT4uXS8iHs9/VwNXsn71XLv+lwcAd0fEqtoJEbEmIgby+x8Cm0jqGYM81Tvfi7r23K908Jck4AJgaUScW5i0CDgmB79HgV0lDQCfB+ZIOk5SADOAOwvrWyGpr/B5mqTLJP0692J4SNI3cn36YC+GFYX5+yX9TtLUQto+OR+Dn5dLekHSQOH1zTxtU0nn5HwMSHpE0ldqlt0nv19Ss44BSS9KejkiTgU+AuwMvJrUkHUIqTH383l1xwBXF47XLEmbSdoVmFY8LtYZJG0laZvB98BfAPfVzLYIODr3+tkLeHaw6qPFjqBOlY+k7fO1iqQ9SXHrqTHI0yLSeQ7rnu9F3TusR7tbnNv5At5J+on2M+Ce/DoQeA1wE/B70k/OSYVl/iepBPAS8CzwqsK0FUBffv8GUgPSucBOOW074GRgVv7cB6woLN9POqnnFdL2AZYXPi8H9qmzP2cA/wHsQPo52gsc3eCyW5MavM8s5o1CT4vCcXko/609Lg+Thig+oN3/20555WP+AjBQeH24+H+v+f8fl99/IZ9/xeWeKcwbwBvqbHMKqVCzEngOeAA4E9iD1Bvlp6ReNU/mvD0N/Bupt5ZID6VZk7dxTGG9b0gh45XPewDXA78BngEWAwfmabOBW+scj33y+4vyNj6Uz/tX5/Sv5vT5+fO38+eXgLWkXnkHA0cWjs0LpPapV45X2TlPKpkvyNt7nlRIOZj0xbMyH/PI535P4Xx/BPhOXscOwA8L6zww5+lh4PT8v3sor385cCHQW5j/4Lzd53M+FpBjROHYBXBuzbE7JKdflD/35s+D+7wKOB/YpKFzs90XRye/ak+cmn/OrcA1wBmF9GLwvwS4Zpj197F+8D+DdMG+IaeNJPhfS2q3GNH+5GmXkS7kjcry5tfonUP1ji3rB/9LhlhvafAHJuVtfmcw4JCqJb4GvDl//kYOTu8gje+1Rw5GVxfWc1EOTNcX0mqD/y+Az5B+GW5Kqi58Z542m8aC/4PA5YXpGwO/IvUYm127LlKp/+PAb1m38FHvmBa3N3hs/hXYHtiC9ItjDfChmmP7FPDhQtoXyUF3mP/3IlKB8W15X14NnAAcm6d/KG/vyLz97UlfDsuBbQv7uywfh40L674iH6+L8ufenNeN8+ftgP9iiBhQfFW62mcUfB74pKRJJdP2IbUljNSvgG+RLv6Ruh34lKS/kzR98KfycCR9gnThfjgiXh5ufutonyIVHj4SEcsBIuKxiDgpIn4maRrwd8CREfHjiFgbaQC6Q4H9Jb23sK75wJslvbt2I7nOfVfgWxHx3/l1W0TcOsL8XgPsXbiBan/SL/EnymbO5+eFpMD5uhFu65OkEvKxEfFERLwQEZcCZwHn1FwvXwbOlNTw4Je5SnVfYGZE/CQf22cj4ryIuCCv/xzgixGxIG//CeC4nK9PFlb3BHAvsF9e9yTgzxiiSilSO84NpPtuhuXgP7yrJD1TeP3N4ISIuIdUWj6lZLkeCiewpBPz8gOSvjXMNv8ReP8QXdrq5ekfgS+RShV3Ab+SdEyddQzmay/gH4DDIqL2CUU71GznmVxXbJ1rH+CKIb7E30cqIa/TJhMRj5EKD/sWkn9LOjfOKlnPU6TS6SWSDpE0ucn8/o7cZpQ/Hw1cXG/mHIwHg+VDI9zWvqRfGbXHZiGpfeuPCmlXkEros0ew/n2AO/OxLLNb3s46PZpyfi5n3WMP6Tgcnd/PIrU5vFhv45J2IH1Z3N5IZh38h3dIREwsvGoD9+nAxyRtX5P+FKnuFYCI+GZETCTVZ24y1AYj4tfAN4G/H0meIuKlXMrYG5hIumgvlPTGspXk0tv3gFMj3dBT6/Ga7UyMiOeHyruVKn5Zj6Sb4uE1X7w3N7DMa0h11/X0DDF9ZZ5e9H+BnSUdUEyMVM/wHlJ1xTnASkm35F8WI3UxqZH51cC7Ke/KuZekZ0gFqiOAD0bEsyPcTr19X1mYPihIv+xPl7RZg+tv5NhTZ56yY38l0JePy1Bfik/mY/MrUjvC9xvJrIP/BoqIB0ilhM/VTLoJ+MsNWPU/kS6uP20yXy9ExHmkxrj1fgZK2ohUL3xbRHxjA/Jpwyt+WR9CarQsKwBsQmpwHLSw5ov3PQ1sa51CR4knh5g+hZrn00bEi8D/yi/VTFsRESdGxOuBXUiBZzBANbqP5Kqi1wKnkToXvFCy3O35GPRExF4RceMQ+1hPvX2fUphezNcPSb39ym6GK9PIsafOPGXH/gXgB6Tj0hMRt9VZb08uWG4J3EZqvB+Wg//oOBP4KKm0PegLwJ9LOlfSjvBKSbu0FF4rIp4hlag+22gmJJ2cu49uIWnjXOWzDakRqNYXSA2BxzW6fhs1jwI9SjcXAq90O94F2NChDW4EPpi/3Mv8OzA1d5l8Re5evBep0FLrX0kNlx+st9Fc1XEe8Kac9CjpF8MrXxiStiQ1Spbt4yXAHIao8hkFNwKHlhybw0l36f68ZJnTSD3Ztmxw/XsOduUu8SCpU8hhxcScn0MpP/YXk47Lt4fbeP6yuIh0N/Sw90E4+A/vmpq+8FfWzhBpPJtvA1sV0n5Ouph2An4q6TnSt/Lj/KGv/HC+Rure1mieXiB9YQx24zsBODQiflGyjtNIDWZPlPT33znPs0PJtEMbzLvVERGPkoYR+ZKkrXO1wmdIpeWG6muzTSVtXnhNIHUtfhUwX9IuAJJ2zIWQN+fz8p+BBZL2Uhq5dQ9SnfONZSXqiFhLKiy80rYlaVtJZ0p6g6SNcrD560L+7yDV58/NedsKOJvUFlUW/L9OqvO+ZQT7P1JfIR2bC5TuHdhc0hGk4P6ZXJW1jojoJzW8Dtl2lue9kdTgeqWkP80FsG0k/a2kv87r/zRwmqQP50La9qTxs16V81frP0jHZdhf5/k8Oop0/Q9/H0QjXYL88suv5l7U7y48ldTeMvhFfR2F0SAp7+c/AGyXp0fJa7Cb6A6kHjFP8Id+/mcAW+bpG5EC+TJSgeExUu+WzQvbv4jUK4XCMvfxSnU/W5F6Ay3P+XqC1Fd+x8Iyu+f9epLUB/37wNR626g5PrdS0tVziOPcxzBdPfPnnXM+nyZVU/2E1DunuMw63WiBt1PoXz9MPjYl1QQsy+v/JSm471yYZ2be7vM5H5fWHJe6+0uhyynr9/N/hvRl8bZGzk0/xtHMrIJc7WNmVkEO/mZmDZJ0ZEk72ICkJe3O20i52sfMrIIavnW5lXp6eqK3t7fd2eD5559nq6265wbWbssvtDbPixcvfjIiXtuSlbdAp5z3Zbrx3Bot3bTvG3LOd0Tw7+3t5a671nuW9Jjr7++nr6+v3dloWLflF1qbZ0mj8vg/SZuTuhxuRrpGvh8RZ+TxVb5L6mWxHDg8In6TlzkVOJbUNfcTEXHdcNvplPO+TDeeW6Olm/Z9Q8551/mbre9F4L2Rnhz1J6QBz/YiPcT7poiYRrohZy6ApN1JY6/sQRqY7Pzc596sYzn4m9WIZCB/3CS/Aj/A3saRjqj2Mes0ueS+mDSG/XkRcYekdR7oLan4APvinbl1H+Kt9ND04wEmT55Mf39/i/ZgwwwMDHRs3lqtKvs+LoN/79wfjHiZ5Wcf1IKcWLeKiJeAP5E0kXS7/puGmL3hh3hHxDxgHsCMGTOi3XXL9a6VOdNf4pxbywdwHe/XSjfV+W8IV/uYDSHSAHv9pLp8P8Dexg0Hf7Makl6bS/xI2oL0kI4HqP9Abz/A3rrOuKz2MdtAU0ijYk4gFZAWRsS1kn4MLJR0LGnI4sMAImKJpIXA/aSROU/I1UZmHcvB36xGRPwMeEtJ+lOkxyCWLXMW5Y87NOtIDv4byI3LZtaNXOdvZlZBDv5mZhXk4G9mVkEO/mZmFeTgb2ZWQQ7+ZmYV5OBvZlZBDv5mZhXk4G9mVkEO/mZmFeTgb2ZWQcMGf0lTJd0saamkJZJOyumTJN0g6aH8d9vCMqdKWibpQUn7tXIHzMxs5BoZ2G0tMCci7pa0DbBY0g3AbNLDrM+WNJf0MOtTah5mvQNwo6Q/8hC3ZuODBzMcH4Yt+UfEyoi4O79/DlhKej6pH2ZtZtalRjSks6Re0jjndwAb9DDrVj7Ies70tSNepr+/v6kHNze7rdHQjQ+a7sY8m41HDQd/SVsDlwMnR8QaqeyZ1WnWkrT1HmbdygdZz27mZ+mRfU09uLnZbY2GbnzQdDfmuRs0UxVj1dZQbx9Jm5AC/4KIuCIn+2HWZmZdqpHePgIuAJZGxLmFSX6YtZlZl2qk2mdv4CjgXkn35LTPAWfjh1mbmXWlYYN/RNxKeT0++GHWZmZdyXf4mtXwjY1WBQ7+ZusbvLHxjcBewAn55sW5pBsbpwE35c/U3Ni4P3C+pAltyblZgxz8zWr4xkarghHd5GVWNaN5Y2NeX0tubmzmZsOhTN5idNfZTTf2VeVGRAd/szpG+8ZGaN3Njc3cbDiUOdPXcs69oxceRuvGxrFQlRsRXe1jVsI3Ntp45+BvVsM3NloVuNrHbH2+sdHGPQd/sxq+sdGqwNU+ZmYV5OBvZlZBrvbJeuf+gDnT1456lzkzs07kkr+ZWQU5+JuZVZCDv5lZBTn4m5lVkIO/mVkFOfibmVWQg7+ZWQW5n3+X6C25/2C4+xKWn31QK7NkZl3MJX8zswpy8DczqyAHfzOzCnLwNzOroI5v8C1r6DQzsw3jkr+ZWQUNG/wlXShptaT7CmmTJN0g6aH8d9vCtFMlLZP0oKT9WpVxMzNrXiMl/4uA/WvS5gI3RcQ04Kb8GUm7A7OAPfIy50uaMGq5NTOzUTFs8I+IW4Cna5JnAvPz+/nAIYX0yyLixYh4BFgG7DlKeTUzs1HSbB1q60YAAAZVSURBVIPv5IhYCRARKyVtl9N3BG4vzLcip61H0vHA8QCTJ0+mv7+/dENzpq9tMosjN3mLsdlevX0dSlm+hstvM9tptYGBgY7Ml7VWMx03fId6a412bx+VpEXZjBExD5gHMGPGjOjr6ytd4Vg+VnHO9LWcc2/rO0AtP7JvxMuUHYfh8tvMdlqtv7+fev/rTiLpQuBgYHVEvCmnTQK+C/QCy4HDI+I3edqpwLHAS8AnIuK6NmTbrGHN9vZZJWkKQP67OqevAKYW5tsJeLz57Jm1zUW4rcvGsWaD/yLgmPz+GODqQvosSZtJ2hWYBty5YVk0G3tu67Lxbtg6DkmXAn1Aj6QVwBnA2cBCSccCjwKHAUTEEkkLgfuBtcAJEfFSi/JuNtbGrK1rpEa7rWqs2r+G0q62oaq0Sw0b/CPiiDqT3ldn/rOAszYkU2ZdZtTbukZqtNvGxqr9ayjtarPqlnapDdXxwzuYdZBVkqbkUr/bulqs2aFd3EuoMR7ewaxxbuuyccMlf7MSbuuy8c7B36yE27psvHO1j5lZBbnk3wZ+RoGZtZtL/mZmFeTgb2ZWQQ7+ZmYV5OBvZlZBbvAdx3yHpJnV45K/mVkFueRv6/FTl8zGP5f8zcwqyMHfzKyCHPzNzCrIwd/MrIIc/M3MKsi9fcxsXHFvtca45G9mVkEu+Zt1GA/5bWPBJX8zswpy8DczqyBX+5hZ5RWr2uZMX8vsBqreur2R2CV/M7MKcsnfzKwJ3d6ltGUlf0n7S3pQ0jJJc1u1HbNO4XPeuklLSv6SJgDnAfsCK4CfSFoUEfe3Yntm7eZz3hrRSQ9YalW1z57Asoj4BYCky4CZgC8Ee0W3/2yu4XPeukqrgv+OwGOFzyuAtxdnkHQ8cHz+OCDpwRblpWGfgB7gyXbno1GdlF99qeFZNyjPw2xnl2bXOwqGPeehM8/7Mp10bo21Ttz3Ic77ps/5VgV/laTFOh8i5gHzWrT9pki6KyJmtDsfjeq2/EJ35rlBw57z0JnnfZlx/H8aVlX2vVUNviuAqYXPOwGPt2hbZp3A57x1lVYF/58A0yTtKmlTYBawqEXbMusEPuetq7Sk2ici1ko6EbgOmABcGBFLWrGtUdbxP8drdFt+oTvzPKwuPufrGZf/pwZVYt8VsV61pJmZjXMe3sHMrIIc/M3MKqhywV/SVEk3S1oqaYmkk0rm6ZP0rKR78uv0duS1kJ/lku7NebmrZLokfT0PK/AzSW9tRz4L+dmtcOzukbRG0sk183TUMa4ySRdKWi3pvkLaJEk3SHoo/922nXlslXrxoAr7X7k6f0lTgCkRcbekbYDFwCHF2/Al9QGfjoiD25TNdUhaDsyIiNIbTyQdCHwcOJB0Y9HXImK9G4zaIQ978Cvg7RHxy0J6Hx10jKtM0ruAAeDiiHhTTvsy8HREnJ3HKdo2Ik5pZz5boV48AGYzzve/ciX/iFgZEXfn988BS0l3Z3azmaQLNyLidmBiPqk7wfuAh4uB3zpLRNwCPF2TPBOYn9/PJwXEcWeIeDDu979ywb9IUi/wFuCOksnvkPRTST+StMeYZmx9AVwvaXEeHqBW2dACnfKFNgu4tM60TjrGtq7JEbESUoAEtmtzflquJh6M+/2v7Hj+krYGLgdOjog1NZPvBnaJiIFcpXIVMG2s81iwd0Q8Lmk74AZJD+TS2qCGhhYYa/lmpw8Ap5ZM7rRjbBVWGw+ksktqfKlkyV/SJqR/9IKIuKJ2ekSsiYiB/P6HwCaSesY4m8X8PJ7/rgauJI0gWdSpQwscANwdEatqJ3TaMbb1rBqsOsx/V7c5Py1TJx6M+/2vXPBX+kq/AFgaEefWmWf7PB+S9iQdp6fGLpfr5GWr3BCFpK2AvwDuq5ltEXB07vWzF/Ds4E/WNjuCOlU+nXSMrdQi4Jj8/hjg6jbmpWWGiAfjfv+r2NvnncB/AvcCL+fkzwE7A0TEP+fb9D8GrAVeAD4VEf+vDdlF0utIpX1I1XTfiYizJP1tIb8CvgnsD/wW+GhErNcldCxJ2pLUDvG6iHg2pxXz3DHHuOokXQr0kYYyXgWcQaqGW0i6Lh4FDouI2kbhrjdEPLiDcb7/lQv+ZmZWwWofMzNz8DczqyQHfzOzCnLwNzOrIAd/M7MKcvA3M6sgB38zswr6/2Vb6j24kZMKAAAAAElFTkSuQmCC\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": 8,
"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": 10,
"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(\"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": [
"*write code to plot scatter plot of cylinder vs CO2Emission*\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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.CYLINDERS,cdf.CO2EMISSIONS,color='green')\n",
"plt.xlabel('CYLINDERS')\n",
"plt.ylabel('CO2EMISSON')\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"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 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",
"1062 2014 VOLVO XC60 AWD SUV - SMALL 3.0 6 \n",
"1063 2014 VOLVO XC60 AWD SUV - SMALL 3.2 6 \n",
"1064 2014 VOLVO XC70 AWD SUV - SMALL 3.0 6 \n",
"1065 2014 VOLVO XC70 AWD SUV - SMALL 3.2 6 \n",
"1066 2014 VOLVO XC90 AWD SUV - STANDARD 3.2 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",
"1062 AS6 X 13.4 9.8 \n",
"1063 AS6 X 13.2 9.5 \n",
"1064 AS6 X 13.4 9.8 \n",
"1065 AS6 X 12.9 9.3 \n",
"1066 AS6 X 14.9 10.2 \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 \n",
"... ... ... ... \n",
"1062 11.8 24 271 \n",
"1063 11.5 25 264 \n",
"1064 11.8 24 271 \n",
"1065 11.3 25 260 \n",
"1066 12.8 22 294 \n",
"\n",
"[1067 rows x 13 columns]\n"
]
}
],
"source": [
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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": 16,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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YunRuP8HChclt9mn9AkX3L9KnELcND5f7HRb9GaR5NN2nIP2pFxKYdVKHuNnEe/fCunVBW/tzz8Uf1z7hLDpqptVOH/Xii9Utz5k1h6B9VbRW/fKsvJa3f0T6XFK0mA+b7hSa0wsJzDqtQ9LIodY35jLfgJPyAEWTv0UV/Yxdu+L3LfJ7z/q5i9KdwvxDyp1CrvUUzGwZ8BvAGDA9qd7d31tXsMpD6yk0p4llKKuuQ9q3ZvegDyHu2/PwcDAaqIo6ZdWh0/PHaV86s6XsEppFfwZpXhXrKXwFeAWwB/iryCYDqhcSmOWpQ1rzUtZs4TwTx9pt3RpMBItauLC6juYqfu8HDxYrh/Tf46pV8ccklUuPS7qFiG7AHXn26/am5qPm9EKq46w6ZDUvbdoUf3y0kzYtrXWcrLTU7Yo2vVTxey/axJX1e6y6OUrqR6frKQD/DViXZ99ubgoKzdm1KxhVE70IpI2yqasOaRerPBfQohf9LEUv2kVnA1fRl1O0jln7q09h/kkLCnmbjy4DbjSzH5vZ0+H2VC23LjJvtLclp7Ut12FiIsjpE00et2HDzOzcPE0t0dnCR4/OHrVTRtHmnauuCuYxRC1aNDsPUVQViePWrStWnvUzJaUML5NKXHpAUrSYD5vuFJrTC81HVdwp5PmMpGUr4/ZJakopu/JaHaq+U1Dz0fxDFctxAm8D/jjc3pr3uDo3BYXmFG2XrkPWxSppreG1a/OdP09TTZ6JYWnNO000w1Xdp6Dmo/mn46AAXAnsBd4bbjcDV+Y5ts5NQaE5ddwp5PlWHpV1cev0G2yenzFpn+HhfD9HmQyjcUGkiDL/dmn/NnmCQnuAzhuYpR5VBIW7gKHI62HgrjzH1rkpKDSn6slrZc5XdwdonuPzfOvu9IIa1R4QygSGqv/tstJ/d3rHJtWrKigsibxeoqAgRb/Zpyn77TXt4tZpE1eeC3anw2KLBoVOA11Llf0YWUN7q6qzVKeKoPAu4EHgc8BO4H5gfZ5j69wUFPpH2Qt4ld/C2+U5vtPO7rqCQtpFv44UJWmfp6DQe6rqaF4edjZfDPyrvMfVuSko9I+ki+fSpbMv+mvX5v+G242g4J4emLKCXR1BIeube7dHjiko9J60oJA6ktjMfip8fH0YFA4RrKN8algmUonXvCa+/MknZ6+ZvHfv7KymnawDANWkb0hbW7jqBWjaU2jElV9zTfw+rfKkdaU7XW86SVJm16yMr9KQpGgRBBO2h4+3xGxfSzu2G5vuFJpVZZ9C3rWO47ak0URZ31DzNKOsWjX7/VWriv1eli6dm/ai0+GcWaOPss6Z9n4V/5ZxNPqot1BF81EvbgoKzam6XbpsQEi7gGYNSa1rWG3772XhwiA41NHvEaeToFDFv6X0vrSgkGsiupm908xeFj7/z2b2ZTN7XcYxx5vZbWZ2p5ntN7OPh+VLzOxmM7svfDw5cswWMztoZvea2YWFb3uka+IWg3/22fKLySRlLO3k2KRFY1rldWR6jfu9vPjizOI77c1LdTjxxPTyPL/rTv4tZX7Lm53kv7j702b288CFBCOQPpVxzPPAm9z9Z4CzgYvM7A3A5cBedz+TYELc5QBmtgpYD6wGLgK2mVkHlwqpU9UX1LR01GWPXbo0vbyOBeeTfv4HH6xuhbrNm4O1HpLWjf61X4s/rlV+3nn5PqebadCld+QNCq3vXG8Brnb3rwCLUvZv3fw+E75cGG5OMHppZ1i+E3h7+Pxi4Dp3f97d7wcOAufmrJ90WdUX1DVriidQGx4OOit37y53wd26FUZGZpeNjBRf+yB6kXZP3s/DzvJf//WZeiYlEUwqj1tCtL2zfffu+GNb5WnrJkR1EhxlHktqV4puwI3ANcA/AycBxwF35jhuGLgDeAb472HZD9r2eTJ8/HPg3ZHya4FfSTu/+hSaU3WfQlL7ficdxXna6zvtLE8a/pm1tdJYFF1eM0/qjqyfO2mYrPoUBgcVTF4bAX4JODN8vRx4c55jw/1PIhix9NqUoPAXMUHhl2POtRGYAqZWrlxZ729OUmXNii1ywc1zoYpueTKS1tGJ2y5t1FTWz5T39xiV53xZdUoLLGWDY5Uj0aR+VQSFVwPHhc/PA34bOCnPsZFzfBT4MHAvsNxngsu94fMtwJbI/l8F3ph2Tt0pNCfrG27RO4n2YZudbGUnhsU56aTZx5100uz3sz4j6/06Vmor+3srO0y0FxZckmKqCAp3AAuA14RNSJ8Admccs6wVOIATgL8D3gr8EXB5WH458Ifh89XAnWHT1BnAvwDDaZ+hoNCcpIv4okXB+0WHe1YVEKKfUSYoRL+1J23RwJDVnLN4cfz7ixcH7xfNkprnZ0o6Z9ZWdv2DMplepVlpQSFv195L7n40bEL6pLt/KPyWn2Y5cIuZ3QV8G7jZ3W8kSMN9gZndB1wQvsbd9wPXAweAm4BL3T1hUGH/S5tp2wvne+GF9PI6hnvmEe0oThp6mVTe3omb5Ac/mHmeNPKpVX7ccfHvt8qfeCL+/aTyOmX93El66WeQCiRFi+gGfIsgKd7dwBlh2d15jq1z69c7hV5IS50l6xtrN+8UhoaqmRhWZFZ1VFqfQBO5j4r2z3R6p1D0Z5DmUcGdwq8DbwS2uvv9ZnYGsKv6ECVQ/cSwqs+XR9Hhnp1MXjv55GomhpX9prxmDaxYEQwjXbEieN1Sx1yILGXPXXauSNZ8EJlnkqLFfNj69U6h6m9edXyTy3POIiNSOrlT6ORbdZk6RPsUsu7Cst7PWqCmXVYG1KTPXLRodqqNItlmsxTtLJfmUbajGbg+fPwOwUI7re07aJGd2lS9EHodC6t3M3CVDQpZneFl6xC9gOZpJksLjnku8u3yDGHt9hBRDUmdX9KCggXvxzOz5e5+2MxGE+4yakq2m8/4+LhPTU01WYVaJM1mheCS0fT56jjnKad01jEZ95nDw0GzUruhofimogUL8jUhDQ/D0aMz50r6ec2CppytW5ObtVqd2+02bYJt27LrIlKGme1z9/G491L7FNz9cPj4YBgAngSejmxSg9HYEJxc3u3z1eHss6s/Z1xASCvP26YeDRxp7ffuQVqLjRuTR3tt316sHKofSVaFXqyTlJR0CxHdgPcDjwIPECzFeT/wL3mOrXPr1+ajqtto844+qqoPoIxO1lOIbtH1Dso0m+WZpxA9Pu53m9Wc1MnvsY6RZJ3qxTpJOiqYvHYfcEqefbu59XNQqHqGaNYFv+gfdtVBoYqA0B4YyrTXR+U9Pvq7TapT0lrTRQNXt5fSzKMX6yTpqggKNwEjefbt5tavQaGJP7Iq5xUkSQtMVQaFaB2K5BWKq2PRUTpFf49FA1fWvIcm9GKdJF0VQeF1BKkurgH+rLXlObbOrV+DQhN/ZEUv8kX37ySjaSdBoYhdu+YOER0aKnaHVqYppUjgykqb0QTdKcw/aUEhdfRRi5ndBnyTYCjqdDedu+9MPKgL+nX00dhY/CLqo6PBBK06JI28iY60aS8vMrIn62dKG81URo7/1nMsXgw/+tHc8hNPhGeemVueZHIymBj40EPZo4+KShrtZJbcgV63ycmgMz06QXJkJOgsr3uVOSmn9OijiKPu/jvu/ll339naKqyjRKxbV6y8CllLV7bLM7InOiIlLiBAcnknVq0qd1xcQEgrr0qRkTtJwa5MEKzKxEQQAEZHg+A0OqqAMK8l3UJEN2ArwToGy4ElrS3PsXVu/dp81ETWyar7FPKOyml1qFbVbBQdfVRUFc1RRQcJFN2/jomIMnioIPfRfyRY7+AfgH3h1n/tNj2ijqyTWd9Gq1qasiUu31KcsvmG2rUuj/v3zy4v8i08aTnQIsuEXnYZvPji7LIXXwzKq9g/KyurSMeSosV82Pr1TqGODtRuz1PI+82+qjuFTn7ulk6HsOb5vXS6f6ueVeUtksFEB7mPPhJ5/s629/4g7dhubAoK+dQxOiSrjkXTUHcaFOICWZmfu9MLbjeCgkin0oJC1o3x+sjzLW3vXVTRzYq0qSIV8erVQaefWXc7eVvyNgtVlV7ZPfh53vvemSaiMgv9bNsWjLZyDx6L5h8q+m+ntNPSa7KCgiU8j3stFbnqKli0aHbZokVBeR6rV8OBA9XXq4i86yM8+WSxNvssL7ww0x7fxFoGRf/tOv23Fqla1p+jJzyPez2LmZ1uZreY2T1mtt/MLgvLP2ZmD5vZHeG2LnLMFjM7aGb3mtmFhX6SPjIxATt2zB7it2NH/iF+ZQNClUnN8t4pvPRS8K28Sq0O+ao7z/Mo+m/X6b+1SOWS2pWCZieOAU8RZEQ9Gj5vvX4x49jlwOvD5y8D/i+wCvgY8OGY/VcBdwLHAWcA/wwMp31Gv/YpdKpoJ6979bmPqkpwV3aL/lzK8y8yG2X7FNx92N1f7u4vc/cF4fPW64UZxx5299vD508D9wCnpRxyMXCduz/v7vcDB4Fz0z6jn3UjFXF0GGPVS3ZWNdS0UxMTwYzpKpbrFBkEFbbmJjOzMYL8Sd8Ki37TzO4ysx1mdnJYdhrwvchhh0gPIn1rchIuuSToOG11oF5ySXWBYXh47iIuZTpl0yR1lA4NBc0knazJLNm0voGUVXtQMLPFwJeAD7r7U8DVwKuBs4HDwJ+0do05fE5rs5ltNLMpM5s6cuRITbVu1nvfO7ed3T0oz6O94zJanjSqpludsiefHHxrP+usas8rM1q5iKJfKtIW+hGJqjUomNlCgoAw6e5fBnD3R939mLu/BHyamSaiQ8DpkcNXAI+0n9Pdt7v7uLuPL1u2rM7qN+aFF4qVV3F81Z2y3/9+ennTo6P6WdVNgTJYagsKZmbAtcA97v6nkfLlkd3eAdwdPr8BWG9mx5nZGcCZwG111W9QJTUnVJ3UbMmSYuVSnaqbAmWwLKjx3GuAS4DvmNkdYdnvAe8ys7MJmoYeIFjqE3ffb2bXAwcIRjpd6u490l3ZP6LNCTD7oj8xoY7YfrByZfzExDrnZ0j/yLWeQq/q1/UU0tYWyPPPlXdtgk7WZzjhBPjxj+eWH388PPdcdt7/qtdPiFq6FB5/vL7z9zqtbyBZqlhPQeaRtWvz7dfenLB5c7DYjlnwuHlz8rE/8RPp5U3MJgZYuFCzgbW+gXRCQaEmTQ4JvOOO7H1g9gV682a4+uqZ+QXHjgWvkwJDUkdxqzyr47rK1BaLF89c/D77WV38QPMzpDwFhRrUOSQwT5DJu+7Ca14z83z79vh9ksqzZH1brXLpyOee08VPpCrqU6hBp2ss52lvT2sjztteH11/uWg/Rrf6PfKax/+NRbpOfQpd1o0hgVWMO++VVBSd0uxokeooKNSgW52s83nceZUXci1FKVIdBYUarFtXrLysKoPM6Gix8k5VkeYiLoeTiHRGQaEG119frLyMqtcF6PbaA/feW/7YU08tvzKaiKRTUKhB0uifvKOC0tQ17rzbY9s76c949NHq6iEisykoNKTIRLGoOodednNseyd9Cv3SQS7SixQUanDiienlRSeK9aNOOoc12kikPgoKNTj++PTyqieKNSHpwpz3gr1tW9BJXOYCr9FGIvVRUKhBVp9CUvPHfGoWOe+8YuVxtm0LOovdk/M1nXrqTODQaCOR+iko1CDrW3Sn37J7QVJ+pbx5l9rt2TM3MKxdCw8/PBM4NNpIpH4KCiVkJbvLuhNIav7odrPIpk3lj61jhNWePcHFv7Xt2VP+XCJSjoJCQVUku9u2Lf5bcbe/BX/jG939PBHpfQoKBVWx/u3kJNx66+yyW2/t/sLqnayTvHRpsXIRmR/qXKP5dDO7xczuMbP9ZnZZWL7EzG42s/vCx5Mjx2wxs4Nmdq+ZXVhX3TpRRbK7flhY/aqrYNGi2WWLFmmBG5H5rs47haPA77r7vwbeAFxqZquAy4G97n4msDd8TfjeemA1cBGwzcx6ruu1imR3cWm108qLWrWqmvOkmZiAHTtmz4DesUPrGYjMd7UFBXc/7O63h8+fBu4BTgMuBnaGu+0E3h4+vxi4zt2fd/f7gYPAuXXVr6wqkt0lrSVQ1RoDVQWXLFrdS6T/dKVPwczGgNcB3wJe5e6HIQgcwCvD3U4Dvhc57FBY1lN2744v3759Zuln5hoAAA7DSURBVDRSlqQFYapaKOZHP6rmPCIyeGoPCma2GPgS8EF3fypt15iyOZdJM9toZlNmNnXkyJGqqplb0rfwY8dmRiPVqYk1n0VkcNQaFMxsIUFAmHT3L4fFj5rZ8vD95cBjYfkh4PTI4SuAR9rP6e7b3X3c3ceXLVtWX+UTND3BrOo1n0VEouocfWTAtcA97v6nkbduADaEzzcAX4mUrzez48zsDOBM4La66ldWr6SiSButlJSQr2pZk/iaPp+IFLegxnOvAS4BvmNmreQHvwdcCVxvZu8DHgLeCeDu+83seuAAwcilS929Ry7B1TKL7z8o2tGcNAz2mmvg3e/OPr6Tu57WJL7W0NrW3QuU63CenIT3vCdIZdE633veU/58IlKOeVW9mw0YHx/3qamprn5mpyOE3NPPkfV+1OhoMOqnXSs1d5Zdu8pfcMfG4vtPkuqU5WUvg2eemVu+eDE8/XTx84lIMjPb5+7jce/VeacgNUpbKvNTn0o/dng4+FbfyTfwKibxRcUFhLRyEamH0lzMM3mWysy6+Tt2DHbu7KzNvopJfCLSexQU5pmqJop1mlZj69bgbiUq7e4lS90T+kQkHwWFPpT3Qlq2qQeCoLR9++w0F2l3L1k+8IFi5SJSD/Up9KG8Ywc6beqZmKhuZFArbfj27UHzVqvfQ4vqiHSX7hT60Ohovv2K5GvqhjVrYMWK4M5jxYrgtYh0l4JCA44/vlh5UXHt/XE+//lqPq8KVSxeJCKdU1BowI9/XKy8qPb2/iS9lDivH9aYEOkHCgp9KprWej6oet6DiJSjoNCApNxErfL29ZtbksqzzIfhnpr3INIbFBS66KSTgsesPoWDB+PfTyrvB1XPexCRchQUuugVrwgen3gi/v1WedXLdda9qE8Vqp73ICLlaJ5CF7Xax4eH41NwN71WQ9OqnPcgIuXoTiHG5s2wYEHwjXXBguB1FZYsCR6T1mTolbUaRGRwKSi0aaWdbl2gjx0LXrcCw+LFzdWtrKo7rkWkfykotNm+Pb38U58K7h7K+P73yx3XqT175gaAtWuDchGRKPUptMlq2mm1eV9xRdBHUKSztjW8sok+BQUAEcmjzjWad5jZY2Z2d6TsY2b2sJndEW7rIu9tMbODZnavmV1YV70gfS3gpAtztDw6MSxvnqGFC2eGV7aWrWzXKs+axyAiUpc6m48+B1wUU/4Jdz873HYDmNkqYD2wOjxmm5nV8r05K8dO1gW7Xdz4+oUL5zYxRSeKbdsGmzbNBJrh4eB1KyPoNdcEAStqaCgoFxGplbvXtgFjwN2R1x8DPhyz3xZgS+T1V4E3Zp3/nHPO8aJGR92DcDB7Gx2d2WfTJvfh4aB8eDh4nWbXruB4s+Bx6dLsz8jSfs5duwr+oCIiCYApT7iumtc4g8nMxoAb3f214euPAe8BngKmgN919yfN7M+Bf3T3XeF+1wJ/7e5fTDv/+Pi4T01NFarT0FB8P4BZdXmCuvEZIiJlmdk+dx+Pe6/bo4+uBl4NnA0cBv4kLI/LwhMbrcxso5lNmdnUkSNHClcgKaV0nlTTSdr7KFrzEdoVyeOT1u/RjeNFZDB1NSi4+6PufszdXwI+DZwbvnUIOD2y6wrgkYRzbHf3cXcfX7ZsWeE6PPdcsXJIv8DG9VE89RQsWjT7HEXy+HS6toDWJhCR0pLalarYmNunsDzy/EPAdeHz1cCdwHHAGcC/AMNZ5y/TpxDX1t/a4uza5T4yMnu/kZGZNv6kPoqlS8v3CeTp96jzeBHpbzTRp2BmXwDOA04BHgU+Gr4+m6Bp6AHg/e5+ONz/CuC9wFHgg+7+11mfUaZPYcGC5DkCR48GzycnZ+YhDA3F7z86GgxLraP/oNNzJh3fovWPRQZbWp9CbZPX3P1dMcXXpuy/Fag9UfLGjUHairhymGl6aa0CljSZrZXcbuXK+OylnawDsGRJfCbVpL6KuM9Oy6jaSt0BCgwiMtvApbnImiMQtyxknNZFf926+PeTyrsh7xrNSSk9RGRwDVxQgCAAHD0aNLEcPTr723Ke5R+jnca7d8fvk1SeR1KOpLy5k/Ku0aysrCLSbiCDQpqkZp/h4fjFX+pYW7iKpSmjqTjypO4QEQEFhTmSloXcuTO4wD7wwOyFYMpewNOGuVa9NOV55xUrF5HBpaDQpuiykGUu4FnzCKpemnIQ13wWkXIUFGJEm17a7wzi9i16AY/rzH722aC8DnU0cYlIf1JQKKG96QfyBxFIHi7aKq96RnIVfRQiMhgUFAqq4oKd1fFb9Z1E1X0UItK/FBQKquKCnbW6W9XNPVX3UYhI/9JynAVVccEeHY1vQmqt4lbHLOmJCQUBEcmmO4WCqmifz2rOUXOPiDRFQaGgKi7YWc05au4RkabUuvJa3cpkSa1CNIvqypVBQNAFW0Tmi15aea0vFJnHUJZWThORJqijuQe1p+9uDXsF3ZGISL10p9CDuj3jWUSkRUGhBykthYg0pbagYGY7zOwxM7s7UrbEzG42s/vCx5Mj720xs4Nmdq+ZXVhXveYDpaUQkabUeafwOeCitrLLgb3ufiawN3yNma0C1gOrw2O2mdnAZvvXPAURaUptQcHd/xZoXyvsYmBn+Hwn8PZI+XXu/ry73w8cBM6tq269TvMURKQp3R599Cp3Pwzg7ofN7JVh+WnAP0b2OxSWDSylpRCRJvRKR3PcSsKxs+rMbKOZTZnZ1JEjR2qulojIYOl2UHjUzJYDhI+PheWHgNMj+60AHok7gbtvd/dxdx9ftmxZrZUVERk03Q4KNwAbwucbgK9Eyteb2XFmdgZwJnBbl+smIjLwautTMLMvAOcBp5jZIeCjwJXA9Wb2PuAh4J0A7r7fzK4HDgBHgUvdPWHVARERqUttQcHd35Xw1tqE/bcCGnQpItKgeZ0l1cyOAAkrHudyCvB4RdWpi+pYDdWxGqpjNZqu46i7x3bKzuug0Ckzm0pKH9srVMdqqI7VUB2r0ct17JUhqSIi0gMUFEREZNqgB4XtTVcgB9WxGqpjNVTHavRsHQe6T0FERGYb9DsFERGJGMigELfWQ68xs9PN7BYzu8fM9pvZZU3XqZ2ZHW9mt5nZnWEdP950neKY2bCZ/ZOZ3dh0XZKY2QNm9h0zu8PMppquTxwzO8nMvmhm3w3/X76x6TpFmdlZ4e+vtT1lZh9sul7tzOxD4d/L3Wb2BTM7vuk6RQ1k85GZ/TvgGeDz7v7apusTJ8wNtdzdbzezlwH7gLe7+4GGqzbNzAw40d2fMbOFwDeBy9z9HzMO7Soz+x1gHHi5u7+16frEMbMHgHF379nx9Wa2E/g7d/+MmS0CRtz9B03XK064HsvDwM+5eydzmSplZqcR/J2scvfnwkwOu939c83WbMZA3ikkrPXQU9z9sLvfHj5/GriHHksn7oFnwpcLw62nvmWY2QrgLcBnmq7LfGZmLwf+HXAtgLu/0KsBIbQW+OdeCggRC4ATzGwBMEJC8s+mDGRQmG/MbAx4HfCtZmsyV9g0cwdBxtub3b3X6vhJ4CPAS01XJIMDf2Nm+8xsY9OVifETwBHgs2FT3GfM7MSmK5ViPfCFpivRzt0fBv6YIPfbYeCH7v43zdZqNgWFHmdmi4EvAR9096eark87dz/m7mcTpDs/18x6pjnOzN4KPObu+5quSw5r3P31wC8Cl4ZNnL1kAfB64Gp3fx3wI8LldHtN2LT1NuB/N12XduG69BcDZwCnAiea2bubrdVsCgo9LGyn/xIw6e5fbro+acKmhK8zd13uJq0B3ha2118HvMnMdjVbpXju/kj4+Bjwl/TecrSHgEORO8EvEgSJXvSLwO3u/mjTFYlxPnC/ux9x9xeBLwP/tuE6zaKg0KPCTtxrgXvc/U+brk8cM1tmZieFz08g+A//3WZrNcPdt7j7CncfI2hO+Jq799S3MgAzOzEcTEDYJPNmoKdGxrn7/wO+Z2ZnhUVrCVLd96J30YNNR6GHgDeY2Uj4N76WoL+wZwxkUAjXergVOMvMDoXrO/SaNcAlBN9uW0Ps1jVdqTbLgVvM7C7g2wR9Cj077LOHvQr4ppndSbC41F+5+00N1ynObwGT4b/32cAfNFyfOcxsBLiA4Bt4zwnvtL4I3A58h+Aa3FOzmwdySKqIiMQbyDsFERGJp6AgIiLTFBRERGSagoKIiExTUBARkWkKCjIwzOxYWxbN0jNyzewfqqxb27nHzezP6jq/SBoNSZWBYWbPuPvipush0st0pyADL1zL4ONmdnu4psFPheXLzOzmsPwaM3vQzE4J33smfDzPzL4eWWdgMpypipmdY2bfCJPcfTVMh97+2e8M8+rfaWZ/GznnjeHz3ZE7mx+a2YYwCeEfmdm3zewuM3t/t35X0v8UFGSQnNDWfPQfIu89Hiakuxr4cFj2UYLUGK8nyEe0MuG8rwM+CKwiyCa6Jsxb9T+AX3H3c4AdwNaYY38fuNDdf4Ygidss7r4uTDj4PuBB4P+Ez3/o7j8L/CzwG2Z2Rv5fg0iyBU1XQKSLngsvsHFaaRH2Ab8UPv954B0A7n6TmT2ZcOxt7n4IIEwjPgb8AHgtcHN44zBMkCq53d8DnwsXW4lNzRDenfxP4Ffd/Ydm9mbgp83sV8JdXgGcCdyfUD+R3BQURALPh4/HmPm7sILHRo83YL+7py5Z6e4fMLOfI1gI6A4zmxW0whXErgP+q7u3kuQZ8Fvu/tWc9RPJTc1HIsm+CfwqQPjt/OQCx94LLLNwHWMzW2hmq9t3MrNXu/u33P33gceB09t2uRK4y92vi5R9FdgUNlFhZj/Z4wveyDyiOwUZJCeEzTstN7l72rDUjwNfCPsevkHQ/PN0ng9y9xfC5p0/M7NXEPytfRLY37brH5nZmQTf/vcCdwL/PvL+h4H9kXr/PsHSomPA7WGn9hHg7XnqJZJFQ1JFEpjZccAxdz8afuO/OqVPQqQv6E5BJNlK4HozGwJeAH6j4fqI1E53CiIiMk0dzSIiMk1BQUREpikoiIjINAUFERGZpqAgIiLTFBRERGTa/wdtjdb5qFeZAgAAAABJRU5ErkJggg==\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": "code",
"execution_count": 17,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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0RV1NPG3IDeT+gNY4UZ/CX6SPdwH7era552aDW7myXPmgyiaim5hoPg9UnUuWtuGC7P6A1ugbFCLi9enjORHx0p7tnIh46XCqaCPvlFPKlQ8qLxHdSSdllx850vz60XUuVP/931+uvA4zM8n8kOnppMloerq+eTHWV9HU2RdJWpn+/GZJH5Dk+7phanp0SJ2efLJc+aCfxc6dsGnTsTuGiYnk+fPPFzu+qotxGXU28dxxR7nyugxzGLTlKjokdRfwdDqr+R3AQyR9CzYMdTYdtEGZ5ouqPouLLoK1a5NvpWvXJs/LNJfkrb1QlzqbePL6WLra92IDKRoUDqfDmK4AdkTEDuAF9VXLFqiz6aANyrQnV/FZ5AWWMs0lVSyQU4bb3G1IigaF70jaCrwZ+CtJE8CK+qplC7RhdEidyrQnV/FZ5AWWMs0lw/4W7TZ3G5KiQeHngGeBayLiH4Ezgd+urVa2UJ1NB13rq6jis8gLIGUu9NPTxfetSl1t7nnvpYn3aM3Lm9XWuwErgYn055cBbwBWFDm2zm1sZjTXNeO0DTNZ5+qxfPnCeixfnl2PKuq8cmW5Gc2Lt1FLx9yW/wejpOUzq6lgPYW7gEmSO4RHgI8Bs0WOrXMbm6AQUc9/srYsjHLaadn1OO207P0H/Szy0mr021r8B16Jll/EOqUDQbZfUCiaOvvuiHiVpF8CTo2I35J0T0ScX/29S3FOczGgtqQWGHaai6VkX52ebm6dAOuWNqQNOYEqUmdL0o8BM8BfpWVDHn5hlWvDTNYmLGXkUNYQ2K71x9hwdHxgSNGg8DZgK/CxiNgv6aXAZ+qrlg3FuA5zvPji7PKidxBPPw1btoz23BFbuo5/2SoUFCLisxHxhoj4zfT530fEL/c7RtJZkj4j6X5J+9PV25D0Xkn/IOmedLu855itkg5IekDS6wZ5Y1bAqA1zLPrN/cCB7PJVq4oHhn/6p9GeO2JL1/EvW337FCR9MCLeJul/AcftGBFv6HPsGmBNRNwt6QUkndVvBH4WeCoifmfR/uuBDwMXAt8LfBp4WUTkjhN0n8KIqKJPocy60nl9KZDkP3ruuWKvmcWpng2aX6/6BPr1KSw/wbFzqSx+p+9eGSLiMeCx9OfvSLqfZPRSniuAmyLiWeAbkg6QBIgvlH1t65ipqeSbd1Z5Uf1mOi/+Y1y1Kvv1li3LDgjSwiAyOQmnnpp9jo40EVjNZmZaFQTKOFGW1LvSx89mbUVfRNI64JXAF9OiX5R0r6TrJZ2els0Nd51zkIwgImmjpH2S9h06dKhoFazNduyAFYsmyK9YkZQXVUXnXt43/Ijjm9h27Oh0E4FZnqJZUl8v6UuSnpT0bUnfkfTtgseeBnyEZPnOb5Mk1/s+4HySO4nfnds14/CsJqvdEbEhIjasXr26SBWs7WZm4IYbFl54b7ih3DetMp17edlX88wNJeydSTxq/TFmqaKjjz4IXAVMRcQLI+IFEfHCEx0kaQVJQJiNiI8CRMQ3I+JIRBwF/oSkiQiSO4Ozeg5fCzxasH6jb9SHPw6awqFM597iu5I5ExPHr6lw0kn53/6d6tlGUNGg8Ajw1Sgy0y0lScB1wP0R8YGe8jU9u70J+Gr68y3AlZJOlnQOcC5wZ9HXG2mjnjq7CmW+ued1JB85cnwHdB2T58xarOiM5h8Bfh34LEliPAB6L/YZx/w48LfAV4C5xtp3Af+RpOkoSNZ9fmvaKY2kbcDVwGGS5qZP9qvX2Iw+amKGZMtHT5Sy+L2UXQuhRTNRzarQb/RR0aDw18BTLLzAExHvq6qSSzE2QaHOdBRZF38oPrwz7xxtCSBZQ1XL8jBTGzFVBIV9eSdo0tgEhbruFPLG9ucNt8x6vTLzA5qQ99mVMTUFTzxRSXXM2qCK3EeflvTaCutkZdQ1QzJvbH9WQIDs4Z1NrApXptO9I/lmzNqiaFC4FvikpGfKDkm1CtQ1/LHsN+is4Z3DTv5VttO9islkeUHSbAQVDQrfA/xn4DfSoajnAa+pq1LWAkXvTIad/KvsnUnWXVZZw16P2axBRYPCHwE/SjJyCOA7wB/WUiM7XhNDUovemVTVtFW0SajsnUnWXVZZw16P2axJeavv9G7A3enjl3rKvlzk2Dq3sVl5ra4V0vqtNFbGJZcsPPaSS8odv2dPxIoVC8+xYkX2SlVVfBZ558hbkW1qqtz7MWs5+qy8VvRO4XlJE6RpJyStpmdoqtWszYt2bN4Me/cuLNu7NykvassWeP75hWXPP5+UL1bFnUneOU49tfg5zEZU0aDw+yTrMr9Y0nbg88B/r61WtlATi3YUbc7ZvbtceZa8jtys8io63Wdm4KqrjvUVTEwkz/PmMpTNlWTWYYXmKQBI+gHgEpLEdXsj4v46K1bE2MxTuPTS47+NA1xyCXz600s/7wteAE89dXz5yScnk7V6v72vWJGdpK6KtRCGvUZzFfMzzDps4MlrbTU2QWH58uzOzokJOHx46ec944xywy2zJnFVUbe8etQ1aSxvQtvUFDzzTHsn4plVpIrJa1ZUHdlM80a/DDoqpmyzSNaFe+PG7H3zyrPs2JGdnbTMegpl5PXFPPmk02Gb5fVAd2Fr3eijPXsiTjpp4ciVk07KHkVTxsRE9qiYiYnBzps3CqfsqKRNm47VcWIieV7Wnj1JfaTkcdDPrJ+pKY8ysrFGn9FHbj6qUl3NIOedB/fdd3z5+vWwf//Sz5vXtv7MM9lt+cuWjcaY/WE3V5m1jJuPhqXMKJoyHnigXHlReaNw8r4ojEqm0LxmM48yMnNQ6IS6+hRmZ+HGG4+d58iR5PnKldn7T00N9nonqsuwVpZrYoivWUc4KJxImYtV3kVz0Itp3pDNfkM5i8jLI/TMM+XOs3lzMgpJSh7LTFyD4afxuPzycuVm4ySvs6ELW+0dzXv2RExOLuyMnJzM7wQtk66hjKrSUZQ5b9YmHX+OTZuy9y3T2VxXGo+2vJ5Zy+CO5iVayuI2daxCVtfkrrw5Bnmy3ncV8xTqXFmuDa9n1jLuaF6qunMODbMdPUu/gFA0v1AV/R3DbuNftapcudkYcVDop+zFqkzbeBPpsBfLWydgYqL4JK5+5yiqrpXlzKw0B4V+yl6syiwA08QylotV8S2/ihnNda0sl8dDUs3y5XU2dGEbyozmMjNtpeIdtGX2raujOa/DdWqqXAd7FTOah8kdzTbmqGA9hfE1M5N0rh49mjz2+/ZaprmpDWPlt2/PzjkE5e5idu5MOpUjksedO6uva5W2b0+yvvZascLNVWbU2Hwk6SxJn5F0v6T9krak5ask3S7p6+nj6T3HbJV0QNIDkl5XV91qU2b8e5kL06ZN2efNKy9j8Qihw4fzZ2C3YVGfqiwe0TXonA+zEVHnncJh4L9GxL8mWd/5WknrgXeSrMdwLrA3fU76uyuB84DLgJ3pam/dceut5cqLXpguuqhceVFbthw/BPPo0WQ0VJZRmfG7bRs899zCsueeG25/jllLDW2egqSPA3+YbhdHxGOS1gB3RMTLJW0FiIjfSPe/DXhvRHwh75ytS4hXZj5BmTkQdSVw61ffycnRXVfA8xRszDU+T0HSOuCVwBeBl0TEYwDp44vT3c4EHuk57GBatvhcGyXtk7Tv0KFDdVa7vDLDM7MCQl55XYn2+hnldQXa0J9j1lK1BwVJpwEfAd4WEd/ut2tG2XFf5yJid0RsiIgNq1evrqqa1SgzxLOK8f11KtPB3jWeF2GWq9agIGkFSUCYjYiPpsXfTJuNSB8fT8sPAmf1HL4WeLTO+lVuerp4eV2ZT6vS9GzrOg17XoRZh9Q5+kjAdcD9EfGBnl/dAlyV/nwV8PGe8islnSzpHOBc4M666leLvJTTWeVlAkhd8rK3rlzZ/Gzruo3ynZDZAOq8U7gI+Hng1ZLuSbfLgfcDr5H0deA16XMiYj9wM3Af8Cng2ohoydfmgrJWR8srb0MTxo4d2cNiTzml+dnWdRvlOyGzQeTNauvC1ro1msvOPC46W7quGc15dajz9dqgbEp0sxGDU2cPSV0prus6b54q0mG32VJSopuNkMaHpHZamVXF1q8vV95Wbe8EH1TdKdHNOsxBoZ/Nm2HXroVrGO/alR8YDhwoV160Xfu008qVD6rtw2UH5XkKZrkcFPr54z8uV744dUK/8jLrKXz3u9nnzSsf1KjfKbShk9+spRwU+slrr6+iHb/Megp11iNLG4bL1snzFMxyOSg0pUyai2E354zDN2nPUzDL5KDQT96onyrKy1zoq1jdrAx/kzYbWw4K/ZRttilTXqbdfufOZO2EuYAxMZE8r3Mxm1H/Ju3Ja2aZHBT6qbNtvey5u7a6WZvNzsLVVy/s5L/6agcGMxwU+ivbtl6m+Wgc2u3basuW7EV2tmxppj5mLeKg0E/ZtvVf+IXi5W63b04T61OYdYSDwonkta1ntUk30fZvZlah5U1XoJPmJp7NzTOYm3gGSQAoEgT6ncN3C/Wamspf3tRszPlOYSnKTDyr8xy2NHkpw3fsaKY+Zi3ioHAiWc1EVSRUK3sOD6GszswM3HDDwv6cG27wHZoZOHV2X4ubeCAZIXTqqdnND2VSL59xRn4TxhNPHF+Pq69eOGLmpJPg+ut9ITOz0pw6e6nymnhguMNJPYTSzIbEQaGfvKacJ58cfDjpk08WL/cQSjMbEo8+6ufss7MT1J19dhIABmm6WbUq+6K+atXSz2lmNiDfKfSzfXv2KJVhzzrOGyrpIZRmVjEHhRNZnKKi33rJZZRpEvIQSjMbEgeFfrZty+7grWIuQZnU2R5CaWZDUltQkHS9pMclfbWn7L2S/kHSPel2ec/vtko6IOkBSa+rq16l1LnAe9klL0c9lbWZtUKddwp/ClyWUf57EXF+ut0KIGk9cCVwXnrMTknNrxJf5wLvo77kpZl1Um1BISI+B+SMuzzOFcBNEfFsRHwDOABcWFfdCs8OrjO9tVNnm1kLNdGn8IuS7k2bl05Py84EHunZ52BadhxJGyXtk7Tv0KFD5V99bpZy7wIrGzdmB4Y601s7dbaZtVCtaS4krQM+ERGvSJ+/BHgCCODXgTURcbWkPwK+EBF70v2uA26NiI/0O/+S0lysW5c996BMigozsw5rTZqLiPhmRByJiKPAn3CsieggcFbPrmuBR2upRJ2dx13kRHtm1mOoQUHSmp6nbwLmRibdAlwp6WRJ5wDnAnfWUok6O4+7pkxTmpmNhTqHpH4Y+ALwckkHJV0D/Jakr0i6F/hJ4FcAImI/cDNwH/Ap4NqIyBmbOSB38B7jNR3MbJHxTJ09O5tc+B5+OLlD2L59PDt4ly1L7hAWk5L5EGY2kvr1KYxnQrxBk9mNin4J/8xsLDnNxThzU5qZLeKgMM48V8LMFhnP5iM7xk1pZtbDdwpmZjbPQcHMzOY5KJiZ2TwHBTMzm+egYGZm8xwUzMxsnoOCmZnNc1AwM7N5DgpmZjbPQcHMzOY5KJiZ2TwHBTMzm+egYGZm8xwUmjQ7C+vWJSugrVvntZHNrHEOCks16AV9dhY2bkxWPotIHjdudGAws0Y5KCxFFRf0bdvg6acXlj39dFJuZtYQB4WlqOKC/vDD5crNzIbAQWEpqrign312uXIzsyFwUFiKKi7o27fD5OTCssnJpNzMrCG1BQVJ10t6XNJXe8pWSbpd0tfTx9N7frdV0gFJD0h6XV31qkQVF/SZGdi9G6anQUoed+/2eslm1qg67xT+FLhsUdk7gb0RcS6wN32OpPXAlcB56TE7JU3UWLfBVHVBn5mBBx+Eo0eTRwcEM2tYbUEhIj4HPLmo+ArgxvTnG4E39pTfFBHPRsQ3gAPAhXXVrRK+oJvZCBp2n8JLIuIxgPTxxWn5mcAjPfsdTMuOI2mjpH2S9h06dKjWypqZjZu2dDQroyyydoyI3RGxISI2rF69uuZqmZmNl2EHhW9KWgOQPj6elh8EzurZby3w6JDrZmY29oYdFG4Brkp/vgr4eE/5lZJOlnQOcC5w55DrZmY29pbXdWJJHwYuBs6QdBB4D/B+4GZJ1wAPAz8DEBH7Jd0M3AccBq6NiCN11c3MzLIpIrPpvhMkHQIeWlR8BvBEA9UZFr+/bmWpzIwAAAV2SURBVBv19wej/x5H4f1NR0Rmp2yng0IWSfsiYkPT9aiL31+3jfr7g9F/j6P+/toy+sjMzFrAQcHMzOaNYlDY3XQFaub3122j/v5g9N/jSL+/ketTMDOzpRvFOwUzM1siBwUzM5s3MkEha/2GUSLpLEmfkXS/pP2StjRdpypJOkXSnZK+nL6/9zVdpzpImpD0JUmfaLouVZP0oKSvSLpH0r6m61M1SS+S9JeSvpb+Hf5Y03Wqw8j0KUj6CeAp4M8i4hVN16dqaa6oNRFxt6QXAHcBb4yI+xquWiUkCVgZEU9JWgF8HtgSEf+n4apVStKvAhuAF0bE65uuT5UkPQhsiIiuT+zKJOlG4G8j4kOSTgImI+JbTderaiNzp5CzfsPIiIjHIuLu9OfvAPeTk168iyLxVPp0RbqNxjeWlKS1wE8BH2q6LlaOpBcCPwFcBxARz41iQIARCgrjRNI64JXAF5utSbXSppV7SLLn3h4RI/X+gA8C7wCONl2RmgTw15LukrSx6cpU7KXAIeCGtPnvQ5JWNl2pOjgodIyk04CPAG+LiG83XZ8qRcSRiDifJHX6hZJGphlQ0uuBxyPirqbrUqOLIuJVwL8Drk2bdEfFcuBVwK6IeCXwXdLlhEeNg0KHpG3tHwFmI+KjTdenLult+R0cv8Z3l10EvCFtd78JeLWkPc1WqVoR8Wj6+DjwMdq+pG45B4GDPXevf0kSJEaOg0JHpB2x1wH3R8QHmq5P1SStlvSi9OdTgUuBrzVbq+pExNaIWBsR64Argb+JiDc3XK3KSFqZDoAgbVZ5LTAyIwEj4h+BRyS9PC26hCTV/8ipbT2FYctavyEirmu2VpW6CPh54CtpuzvAuyLi1gbrVKU1wI2SJki+rNwcESM3bHOEvQT4WPLdheXAX0TEp5qtUuV+CZhNRx79PfBfGq5PLUZmSKqZmQ3OzUdmZjbPQcHMzOY5KJiZ2TwHBTMzm+egYGZm8xwUbGxIOpJm8JzbljwjVdL/rqhOL5d0R1qf+yXtTss3SPr9Kl7DrAwPSbWxIempiDit6Xr0knQbsDMiPp4+/8GI+ErD1bIx5jsFG3vpOgDvk3R3uh7AD6TlqyXdnpb/D0kPSToj/d1T6ePF6Tf9uTz7s+nscyRdIOmzaYK429L054utIUmhAMBcQEjP+4n051t77m7+RdJVafLA35b0d5LulfTWej8lGxcOCjZOTl3UfPRzPb97Ik3mtgt4e1r2HpJ0FK8iyeVzds55Xwm8DVhPkk3zojRP1R8APx0RFwDXA9szjv094G8kfVLSr8yl+ugVEZeniQKvAR4C/mf6879ExI8APwK8RdI5JT4Ls0wjk+bCrIBn0otrlrkEg3cB/z79+ceBNwFExKck/XPOsXdGxEGANAXJOuBbwCuA29MbhwngscUHRsQNaRPSZcAVwFsl/fDi/dI7lD8HfjYi/kXSa4EfkvTT6S7fA5wLfCOnjmaFOCiYJZ5NH49w7O9CJY/tPV7A/og44ZKNaXbR64HrlSwnuyBleJoP6ibg1yJiLsmcgF+KiNsK1tGsEDcfmeX7PPCzAOk389NLHPsAsHpuHV9JKySdt3gnSZelTU1I+lfAFPAPi3Z7P3BvRNzUU3YbsKnn2JeN6qIvNly+U7BxcmpPhlmAT0VEv2Gp7wM+nPY9fJak+ec7RV4oIp5Lm3Z+X9L3kPytfRDYv2jX1wI7JP2/9Pl/i4h/nOvsTr0d2N9T93eTLOm5Drg77dg+BLyxSN3M+vGQVLMckk4GjkTE4fQb/64+fRJmI8F3Cmb5zgZulrQMeA54S8P1Maud7xTMzGyeO5rNzGyeg4KZmc1zUDAzs3kOCmZmNs9BwczM5v1/QVYE5LyZZC8AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plt.scatter(test.ENGINESIZE,test.CO2EMISSIONS,color='red')\n",
"# plt.xlabel('Engine Size')\n",
"# plt.ylabel('emission')\n",
"# plt.show()\n"
]
},
{
"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-- [[39.18809268]]\n",
"--Intercept-- [125.86614316]\n"
]
}
],
"source": [
"from sklearn import linear_model\n",
"regr = linear_model.LinearRegression()\n",
"\n",
"train_x = np.asanyarray(train[['ENGINESIZE']])\n",
"train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
"# asanyarray() function is used to convert input to array\n",
"regr.fit (train_x, train_y)\n",
"\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": 46,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Emission')"
]
},
"execution_count": 46,
"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='red')\n",
"plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '+')\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": 31,
"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: 23.20\n",
"Residual sum of squares (MSE): 918.71\n",
"R2-score: 0.67\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": []
},
{
"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": "3.6.7"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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