1234 lines
242 KiB
Plaintext
1234 lines
242 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<p style=\"text-align:center\">\n",
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" <a href=\"https://skills.network\" target=\"_blank\">\n",
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" <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n",
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" </a>\n",
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"</p>\n",
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"\n",
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"\n",
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"# Simple Linear Regression\n",
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"\n",
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"\n",
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"Estimated time needed: **15** minutes\n",
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" \n",
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"\n",
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"## Objectives\n",
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"\n",
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"After completing this lab you will be able to:\n",
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"\n",
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"* Use scikit-learn to implement simple Linear Regression\n",
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"* Create a model, train it, test it and use the model\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Importing Needed packages\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"import pylab as pl\n",
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"import numpy as np\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Downloading Data\n",
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"To download the data, we will use !wget to download it from IBM Object Storage.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--2025-10-17 09:48:31-- https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\n",
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"Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104, 169.63.118.104\n",
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"Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
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"HTTP request sent, awaiting response... 200 OK\n",
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"Length: 72629 (71K) [text/csv]\n",
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"Saving to: ‘FuelConsumption.csv’\n",
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"\n",
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"FuelConsumption.csv 100%[===================>] 70.93K --.-KB/s in 0.002s \n",
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"\n",
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"2025-10-17 09:48:31 (31.2 MB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n",
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"\n"
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]
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}
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],
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"source": [
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"!wget -O FuelConsumption.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In case you're working **locally** uncomment the below line. \n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"#!curl https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv -o FuelConsumptionCo2.csv"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"## Understanding the Data\n",
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"\n",
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"### `FuelConsumption.csv`:\n",
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"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",
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"\n",
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"- **MODELYEAR** e.g. 2014\n",
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"- **MAKE** e.g. Acura\n",
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"- **MODEL** e.g. ILX\n",
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"- **VEHICLE CLASS** e.g. SUV\n",
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"- **ENGINE SIZE** e.g. 4.7\n",
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"- **CYLINDERS** e.g 6\n",
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"- **TRANSMISSION** e.g. A6\n",
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"- **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
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"- **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
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"- **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
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"- **CO2 EMISSIONS (g/km)** e.g. 182 --> low --> 0\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Reading the data in\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>MODELYEAR</th>\n",
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" <th>MAKE</th>\n",
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" <th>MODEL</th>\n",
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" <th>VEHICLECLASS</th>\n",
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" <th>ENGINESIZE</th>\n",
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" <th>CYLINDERS</th>\n",
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" <th>TRANSMISSION</th>\n",
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" <th>FUELTYPE</th>\n",
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" <th>FUELCONSUMPTION_CITY</th>\n",
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" <th>FUELCONSUMPTION_HWY</th>\n",
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" <th>FUELCONSUMPTION_COMB</th>\n",
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" <th>FUELCONSUMPTION_COMB_MPG</th>\n",
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" <th>CO2EMISSIONS</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>2014</td>\n",
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" <td>ACURA</td>\n",
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" <td>ILX</td>\n",
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" <td>COMPACT</td>\n",
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" <td>2.0</td>\n",
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" <td>4</td>\n",
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" <td>AS5</td>\n",
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" <td>Z</td>\n",
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" <td>9.9</td>\n",
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" <td>6.7</td>\n",
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" <td>8.5</td>\n",
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" <td>33</td>\n",
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" <td>196</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2014</td>\n",
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" <td>ACURA</td>\n",
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" <td>ILX</td>\n",
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" <td>COMPACT</td>\n",
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" <td>2.4</td>\n",
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" <td>4</td>\n",
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" <td>M6</td>\n",
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" <td>Z</td>\n",
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" <td>11.2</td>\n",
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" <td>7.7</td>\n",
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" <td>9.6</td>\n",
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" <td>29</td>\n",
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" <td>221</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2014</td>\n",
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" <td>ACURA</td>\n",
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" <td>ILX HYBRID</td>\n",
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" <td>COMPACT</td>\n",
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" <td>1.5</td>\n",
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" <td>4</td>\n",
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" <td>AV7</td>\n",
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" <td>Z</td>\n",
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" <td>6.0</td>\n",
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" <td>5.8</td>\n",
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" <td>5.9</td>\n",
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" <td>48</td>\n",
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" <td>136</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>2014</td>\n",
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" <td>ACURA</td>\n",
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" <td>MDX 4WD</td>\n",
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" <td>SUV - SMALL</td>\n",
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" <td>3.5</td>\n",
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" <td>6</td>\n",
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" <td>AS6</td>\n",
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" <td>Z</td>\n",
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" <td>12.7</td>\n",
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" <td>9.1</td>\n",
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" <td>11.1</td>\n",
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" <td>25</td>\n",
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" <td>255</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>2014</td>\n",
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" <td>ACURA</td>\n",
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" <td>RDX AWD</td>\n",
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" <td>SUV - SMALL</td>\n",
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" <td>3.5</td>\n",
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" <td>6</td>\n",
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" <td>AS6</td>\n",
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" <td>Z</td>\n",
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" <td>12.1</td>\n",
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" <td>8.7</td>\n",
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" <td>10.6</td>\n",
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" <td>27</td>\n",
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" <td>244</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" MODELYEAR MAKE MODEL VEHICLECLASS ENGINESIZE CYLINDERS \\\n",
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"0 2014 ACURA ILX COMPACT 2.0 4 \n",
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"1 2014 ACURA ILX COMPACT 2.4 4 \n",
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"2 2014 ACURA ILX HYBRID COMPACT 1.5 4 \n",
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"3 2014 ACURA MDX 4WD SUV - SMALL 3.5 6 \n",
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"4 2014 ACURA RDX AWD SUV - SMALL 3.5 6 \n",
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"\n",
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" TRANSMISSION FUELTYPE FUELCONSUMPTION_CITY FUELCONSUMPTION_HWY \\\n",
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"0 AS5 Z 9.9 6.7 \n",
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"1 M6 Z 11.2 7.7 \n",
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"2 AV7 Z 6.0 5.8 \n",
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"3 AS6 Z 12.7 9.1 \n",
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"4 AS6 Z 12.1 8.7 \n",
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"\n",
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" FUELCONSUMPTION_COMB FUELCONSUMPTION_COMB_MPG CO2EMISSIONS \n",
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"0 8.5 33 196 \n",
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"1 9.6 29 221 \n",
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"2 5.9 48 136 \n",
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"3 11.1 25 255 \n",
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"4 10.6 27 244 "
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = pd.read_csv(\"FuelConsumption.csv\")\n",
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"\n",
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"# take a look at the dataset\n",
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"df.head()\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Data Exploration\n",
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"Let's first have a descriptive exploration on our data.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/html": [
|
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"<div>\n",
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||
"<style scoped>\n",
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||
" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>MODELYEAR</th>\n",
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" <th>ENGINESIZE</th>\n",
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" <th>CYLINDERS</th>\n",
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" <th>FUELCONSUMPTION_CITY</th>\n",
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" <th>FUELCONSUMPTION_HWY</th>\n",
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" <th>FUELCONSUMPTION_COMB</th>\n",
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" <th>FUELCONSUMPTION_COMB_MPG</th>\n",
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" <th>CO2EMISSIONS</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>count</th>\n",
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" <td>1067.0</td>\n",
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" <td>1067.000000</td>\n",
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" <td>1067.000000</td>\n",
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" <td>1067.000000</td>\n",
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" <td>1067.000000</td>\n",
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" <td>1067.000000</td>\n",
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" <td>1067.000000</td>\n",
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" <td>1067.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>mean</th>\n",
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" <td>2014.0</td>\n",
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" <td>3.346298</td>\n",
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" <td>5.794752</td>\n",
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" <td>13.296532</td>\n",
|
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" <td>9.474602</td>\n",
|
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" <td>11.580881</td>\n",
|
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" <td>26.441425</td>\n",
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" <td>256.228679</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>std</th>\n",
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" <td>0.0</td>\n",
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" <td>1.415895</td>\n",
|
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" <td>1.797447</td>\n",
|
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" <td>4.101253</td>\n",
|
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" <td>2.794510</td>\n",
|
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" <td>3.485595</td>\n",
|
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" <td>7.468702</td>\n",
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" <td>63.372304</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>min</th>\n",
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" <td>2014.0</td>\n",
|
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" <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",
|
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" <td>108.000000</td>\n",
|
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" </tr>\n",
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" <tr>\n",
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" <th>25%</th>\n",
|
||
" <td>2014.0</td>\n",
|
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" <td>2.000000</td>\n",
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||
" <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",
|
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" <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",
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" </tr>\n",
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" <tr>\n",
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" <th>75%</th>\n",
|
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" <td>2014.0</td>\n",
|
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" <td>4.300000</td>\n",
|
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" <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",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>max</th>\n",
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" <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",
|
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
|
||
" MODELYEAR ENGINESIZE CYLINDERS FUELCONSUMPTION_CITY \\\n",
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"count 1067.0 1067.000000 1067.000000 1067.000000 \n",
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"mean 2014.0 3.346298 5.794752 13.296532 \n",
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"std 0.0 1.415895 1.797447 4.101253 \n",
|
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"min 2014.0 1.000000 3.000000 4.600000 \n",
|
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"25% 2014.0 2.000000 4.000000 10.250000 \n",
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"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": [
|
||
"Let's select some features to explore more.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"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": 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 features:\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\n",
|
||
"viz.hist()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Now, let's plot each of these features against the Emission, to see how linear their relationship is:\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"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": 9,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS, color='blue')\n",
|
||
"plt.xlabel(\"Engine size\")\n",
|
||
"plt.ylabel(\"Emission\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Practice\n",
|
||
"Plot __CYLINDER__ vs the Emission, to see how linear is their relationship is:\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# write your code here\n",
|
||
"\n",
|
||
"plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='green')\n",
|
||
"plt.xlabel(\"Cylinders\")\n",
|
||
"plt.ylabel(\"Emission\")\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"<details><summary>Click here for the solution</summary>\n",
|
||
"\n",
|
||
"```python \n",
|
||
"plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
|
||
"plt.xlabel(\"Cylinders\")\n",
|
||
"plt.ylabel(\"Emission\")\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"```\n",
|
||
"\n",
|
||
"</details>\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### Creating train and test dataset\n",
|
||
"Train/Test Split involves splitting the dataset into training and testing sets that 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 model. Therefore, it gives us a better understanding of how well our model generalizes on new data.\n",
|
||
"\n",
|
||
"This means that we know the outcome of each data point in the testing dataset, making it great to test with! 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",
|
||
"Let's split our dataset into train and test sets. 80% of the entire dataset will be used for training and 20% for testing. We create a mask to select random rows using __np.random.rand()__ function: \n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"msk = np.random.rand(len(df)) < 0.8\n",
|
||
"train = cdf[msk]\n",
|
||
"test = cdf[~msk]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"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 actual value y in the dataset, and the predicted value yhat using linear approximation. \n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### Train data distribution\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"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": {},
|
||
"source": [
|
||
"#### Modeling\n",
|
||
"Using sklearn package to model data.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/utils/validation.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
|
||
" LARGE_SPARSE_SUPPORTED = LooseVersion(scipy_version) >= '0.14.0'\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Coefficients: [[38.87822959]]\n",
|
||
"Intercept: [125.66696188]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:35: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" eps=np.finfo(np.float).eps,\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:597: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:836: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:862: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" eps=np.finfo(np.float).eps, positive=False):\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1097: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1344: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1480: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" eps=np.finfo(np.float).eps, copy_X=True, positive=False):\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:152: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" precompute=False, eps=np.finfo(np.float).eps,\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:320: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" eps=np.finfo(np.float).eps, random_state=None,\n",
|
||
"/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:580: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
|
||
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
|
||
" eps=4 * np.finfo(np.float).eps, n_jobs=None,\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": {},
|
||
"source": [
|
||
"#### Plot outputs\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"We can plot the fit line over the data:\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Emission')"
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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Lcwx07r9fLj5moBNwzLNDRAEVKqMcwVigPGMG8MwzrsctlobjagtpBnNKR2kQU1gI9OsXmD5EHHfzghdfDOzYIYMfCgqO7BBRwITSKEegtzbX1spK4d7Mm+da1sibUCptQSrt2yd/qZwDnR9/BPbuZaATZAx2iCggrKMczluWraMcwQ54Ar21+aWXfG/PtlhkOyWMKG2hdK2Sv2uaItrp00DbtsBFFzke/+ADOWXVoYMx/WrkGOwQke4CMcqhh0BubT5wQL92RhXF7NNH2fZ8BjtuCAGMGQM0by5TcVv99a/y3I03Gtc3YrBDRPrTe5RDT3l5wMGDctfVsmXyubjY/+3UHTvq186o0hZmM7Bwofc2CxdyJ5aLJUuAJk1ktmOrnBwZzf/978b1i2wY7BCR7vQc5QgEs1mOTgwfLp/1+PAeN873dcxm2c4XI0tb5OXJXDruRr+CkWMnrOzeLedAR41yPH74MPD550B0tCHdIlcMdohId3qOcoSLmBi5u9ibKVOU7UQzurRFXp7Me2c/+nXwIAMdm5MngcRE13pVhYVy2M25vhUZjkkFwaSCRHqrrZW7rrxNZZnNwJkz4ZFsT41hw4B333U9npsLrF6t7BoWi9x1dfiw+3U7JpMcaSku5pRSUNXXy5w4zqvDn3kGmDbNmD41cmGXVHDOnDkwmUzIz8+3HRNCYObMmcjIyEBcXBz69OmDvXv3OryupqYGEydOREpKCuLj4zF06FAc8jbZTUQBp+coRzgpKADWrHE9bjLJ40p3UCnZOfbss3LNTrAKdTZ6L74ofzD2P8SBA4G6OgY64UCEgG3bton27duLLl26iEmTJtmOP/XUUyIhIUGsXLlSFBUViTvuuEOkp6eLyspKW5v77rtPnHfeeWLdunVi165dom/fvuLSSy8VdXV1iu9fUVEhAIiKigo93xZRozd9uhBmsxByfEI+zGZ5PNLU1QmRmen4Xu0fJpMQWVmynVIrV7peMytLfv+cj2dmyvaksy++cP1hRkUJcfSo0T0jofzz2/BprKqqKlx++eV46aWXMGvWLFx22WV47rnnIIRARkYG8vPz8cADDwCQozipqal4+umnMXbsWFRUVKB169Z48803cccddwAAjhw5gqysLHz44Ye44YYb3N6zpqYGNTU1tq8rKyuRlZXFaSyiAAiVDMqB5m/tLU+Zkp2P//orcPvtrtNb1hGfiKwOboTyclkozNm2bbK+FYWEsJnGGj9+PG666Sb079/f4XhxcTHKysowYMAA27HY2Fj07t0bW7duBQDs3LkT586dc2iTkZGB7OxsWxt35syZg8TERNsjKytL53dFRFYxMTInzAsvyOdIDHQA/3ZQecuUbL9zrFcvmYU62Pl3GpW6OqB/f9dA55VX5DeZgU5YMjTYWbFiBXbt2oU5c+a4nCsrKwMApDr9wqWmptrOlZWVISYmBq1atfLYxp2HHnoIFRUVtkeJfQIoIiINtO6gUpMp2aj8O6GuuhqYMAG44Qb5XF2t8UJPPSW3i2/Y0HDszjvlwuSxY3XpKxnDsEKgJSUlmDRpEtauXYumTZt6bGdyWp0nhHA55sxXm9jYWMTGxqrrMBGRF9baW752UNnX3vKVKdlkkiM1ublyhMfI/Duhynn329q1ci2xmt1v+PRT18qmycly7jUxUaeekpEMG9nZuXMnysvL0a1bN0RFRSEqKgqbNm3CP//5T0RFRdlGdJxHaMrLy23n0tLSUFtbixMnTnhsQ0QUDFpqb6kdqTE6/06o8bTNH5DHhw3zcYFDh+QPxznQ+fZbuTiKgU7EMCzY6devH4qKirB7927bo3v37hgxYgR2796N888/H2lpaVi3bp3tNbW1tdi0aRNycnIAAN26dUN0dLRDm9LSUuzZs8fWhogoWNTW3lI7UhPoyu3hpLrac6Bj9e67Hqa0amvl2hvn9ZpvvSUjzM6ddesnhQbDprESEhKQnZ3tcCw+Ph7Jycm24/n5+Zg9ezY6deqETp06Yfbs2WjWrBnuuusuAEBiYiLGjBmDqVOnIjk5GUlJSZg2bRo6d+7ssuCZiCgY8vLkFIq7nVXO1I7UWEePbr1VBjb20196VG4PJ9OnK2+3YIHdgYcekmtz7N13n9wy6GOJBKnnaZdhsBkW7CgxY8YMVFdXY9y4cThx4gR69uyJtWvXIiEhwdZm/vz5iIqKwu23347q6mr069cPixcvhrkx/NdORCHJuoPKFy3rfKyjR5MmOU6BZWbKQKexbDvfv19lu/feA4YOdTx5/vlAUZFM9026Kyhw/3v6/PPB/z01PM9OKGC5CCIyinU3FuB+pMZT3pxQ+YvZKBMmyIXIvjw28gBmLr3A9cT+/cAFbo6TLqy/14HOB6X085vBDhjsEIW7cP/gd/cXcFZW4xqpUau62vuATBzO4Btcik74wfHE6tVynpECxlrbzdPiez1ru4VNUkEiIn94S8gXLvLyZFVx+yrjxcXaAh2LRWZzjvSaWXFxnmIWgRcxDmcQ7xjoTJ8uhxkY6ARcKOaDCuk1O0RE3ngaKrcm5Aun0glK1/l4E0prJIJh9WrH7ed3YAVWYLhjo8svB7ZuBZhbLWhCMR8Ugx0iCktqE/KFOn+n4owO/IyqgbZ6NXB251407Z7tevLnn123l1PAhWI+KK7ZAdfsEIUjfwtvhhJ/R2SCuUbCnRkzgHnzHKfMzGZgyhRg7lz972dTWSkjq19/dTy+bp2sb0WGsP4++tplyDU7REQ+hOJQuS9HjwIdOgDNm8vno0fV1cbyxMg1EjNmAM8847o2yGKRx2fM0P+eEAIYMUJmOLYPdGbNkucY6BhKSzbxQGOwQ0RhKRSHyr1p2RJo00YuRD59Wj63aeN+6glQV8XcqMCvtlaO6Hgzb55sp5vXXgOaNJErua369gXOnQMeeUTHG5E/1GYTDzSu2SGisKQlIZ9RWrYEKircn/O2kMB+RMbbVJxRgd9LL/kOxCwW2S4/38+b7dghSzw4KysDWAsxJKnJJh5oDHaIKCyFS+mEo0c9BzpK+RqRycmR79Nb4GE2y3Z6Up3FWItjx4CMDNfhoc8/1/8Nke702GWoB05jEVHYCrWhcneuuML/a/gakdm6VdkIy9at/vfFntJSUppKTlkswE03ASkpjoHO88/LyJaBDqnAYIeIwpqeCfkC4ehR7a9VWsXcqDU7PXvq285m/nwgKgr48MOGY8OGyQDo/vtVXoyI01hERAHVurVckKyWmqk4o9bspKXp2w6bNwPXXut4rHlzmS+nVStVfSOyx5EdIgproV4uYts2Ze2cAwI1U3HWxdqepouUjhCpVVSkU7vSUtlJ50Bn1y7g1CkGOuQ3BjtEFLb0yFETaK1by3Qw3iQmyvegdSrOqLwmxcV+tjt3DrjmGrkA2d6//y3X5XTt6lf/iKwY7BBRyFFSzNJXuQhAWY6aYDh50nPAk5goz1t3rQwfLp/VBiZGLNbu2NGPdo8/LutJfP55w7HRo4H6euDuu/XoHpENy0WA5SKIQonS0gnhWC7i6FG5O+voUTnis22bfNaTvzW21KitBZo1873l/cwZuzpZH38MDBrk2Oi884D//U+uzyFSQennNxcoE1HIUFPMMhzLRbRu7XlKR69CmsHMaxITI+tfPfOM5zZTpvz2Pn76SS6mcvbf/wIXXaTofkYVG6Xwx2ksIgoJaqelwq1cBOB5em7GDDlCMnkysGCBfG7WLEB1pXQ2dy4wfbqs4GDPbJbH5z5xFujc2TXQ+c9/5A9WYaATzt8jMh5HdogoJKgpZtmnT3iViwA8T8916wa8+65re2shTSDAlcN1cOWVMqg8fLjhWFoa8Of/TgbinnNsPGmSXC2tgrXYqLNw+h6RsTiyQ0QhQe20VChWVvbE066xQ4fcBzr2dC+kqTPre7MPdG5GAQ4dNqHT+881HLz4YqC6WnWgY0ixUYo4DHaIKCRomZYKh3IR3qbnlL7+pZf07ZNenN/bhdgHARMKcItju/0/Anv3Ak2bOrzW1447QF2xUSJPGOwQUUjQmhgv1MtF+JqeU+LAAX36ojfre4tHFX5GFvbBcf3NIHwIEwQ2H+rgcFxNIkil7z1Uv0cUGhjsEFFI8Gdayt8cNXqzH7XYsMH/6ynNZxNspUcE/oUxqEICstAQ0T2Bv8EEgY8ht5jbT1GqTQTpVy4fot8wzw6YZ4colLhbyJuVJQOdUBmt8cZd//3hkqcmVLzxhkwCaOdz5KAPClGHaIfj1lxHFoscwfH0vbEuKi8ubghYNeXyoUaDeXaIKCzl5QG5ufokxgtmgj3Ac54gf9jy1ASI6tw1u3e7LeOQgcMoRYbL8ebNG6Ye1e64A1Tm8iHygMEOEYUcPRLjKc3ErBd/FyJ7cuWV+l7P3owZcieT/ajJtGkyeHDZyn3iBNCunSzMaaevqRCForfHe5w5I69vNmtPBDl3LvD99+53ruXmcts5+cZgh4gijppMzHrRYyGyM5NJBlCJiUB5ubLRKaWjWb5y19TVAW3bAj/+UI+Jm29Dp2+dFtM88wyei5qGwsne30N9vRw5ys/XngiyoABYs8a1nckkjxcUhMcUJxlIkKioqBAAREVFhdFdISI/1dUJkZkphAx1XB8mkxBZWbKdnpYt83xPPR+ZmUKsXOm+DytXur53d+1raoQwm33faxwWuB4cNMj2zZswQVmfJ0xw/NmYTMp/Nkb9PCk8KP385m4sIoooataFKFVbKxdIT5won90lsAtWWQpPu5bU7HLylbvmSnwBARNexATbsXOIQgqOYkb2h7ahIrU7pbTsuAvEz5MaHwY7RBRR9C4QqrQmkzVPUKC5qxPmq66YEI7tPeWkaY1yCJjwBXIcjvfANsTgHI4hxSFb8bhxvhd8m82ynZXaRJDhWPCVQg+DHSKKKHoWCLWua3EeBbGua7EPeMxmmedHrehoz4kUPXEezVCyXsi+vfOIjBl1WI9+KEeqw/GxeAUmCOxAD9sx+2zFMTHA4MHe7zt4sOtOKTWJIMOx4CuFHgY7RBRRcnKUjTbk5Hhvo7Ymk8Uikwh6k54ODBkii4APGyY3Nq1YIc+pDXiAhtEM+7pU3ljb2Y/IPIg5qEM0+uFTW7vluBMm1GMhxrq9jnVkyGIBdu70fs9du9xPmSlNBKk1szaRPQY7RBRRtm5VVktp61bvbdTWZFIyulJaKrd1f/stsGqVzEHjaVpHCetoxtGjytpb28XEAC/e8ikETJiDhxvOIwWJOIm7sByA5+jLOjKkdkRJi3Aq+Eqhy9Bg5+WXX0aXLl3QokULtGjRAldddRU++ugj2/nRo0fDZDI5PK50SjpRU1ODiRMnIiUlBfHx8Rg6dCgO6b3/k4jChtpRDk/U1mTyZ22J87TO+vXqRjNat1Z279atIaMTkwlj/6+fw7lsFKENjqISiV6vYb8GR6/vtS/hUPCVQpuheXYyMzPx1FNP4YILLgAAvPHGG8jNzcXXX3+NSy65BAAwcOBALFq0yPaaGKfJ3/z8fLz33ntYsWIFkpOTMXXqVAwePBg7d+6EmaE+UUhTnb1XAbWjHJ6o3Wnk79oS50SKzz8vd1GZTI4Lj92NZigZFYpGLYY+kQN87zjvVPfGW1hw/C70PQD8qaMcifE2fWefrViv77USembWpkYoSFvhFWvVqpX417/+JYQQYtSoUSI3N9dj25MnT4ro6GixYsUK27HDhw+LJk2aiI8//ljxPZlnhyj4pk93zfViNsvj/liyRFnulyVLvF9HSS4as1m2E0JbDhnn+82fL3PSzJ8vv3aXNycryzVvjq9cNLPxoOvB++4Tor7ebV+U/myWLlX2vV661OePjUgTpZ/fIZNB2WKx4D//+Q9Onz6Nq666yna8sLAQbdq0QcuWLdG7d288+eSTaNOmDQBg586dOHfuHAYMGGBrn5GRgezsbGzduhU33HCD23vV1NSgpqbG9nVlZWWA3hURueMrey+gvQTAsWP6tLPuNHJXosDKfqeRdW2J0tEYe97KNhw86H40wzlT8vz5wO23O953CNZgDXIdb3bBBcA338j98x7MnQvMmuV71E3pOiMt65GIdBWk4Mujb7/9VsTHxwuz2SwSExPFBx98YDu3YsUK8f7774uioiKxZs0acemll4pLLrlEnD17VgghxFtvvSViYmJcrnn99deLe++91+M9H3vsMQHA5cGRHaLAUztiopZeIzu+RkusoyzOIzXeRmOOHRMiO1uIpCT5fOyYHC3xdg93I12eMiVPny6fz8cP7i+2f7+2b6oHgf5ZEvmidGTH8GCnpqZG7N+/X2zfvl08+OCDIiUlRezdu9dt2yNHjojo6Gix8rcxXE/BTv/+/cXYsWM93vPs2bOioqLC9igpKWGwQxQk8+crC0bmzzf2+hs3KrvOxo2ur62rk8eXLZPPdXVCpKYqu56vYGHlSvdTZSaTEM1wWlSmXeB68t13tX0zffDne0Skh7ApFxETE4MLLrgA3bt3x5w5c3DppZfiees+Qyfp6elo164d9u/fDwBIS0tDbW0tTpw44dCuvLwcqamp7i4BAIiNjbXtALM+iCg41O5yUkvVziQv/Nlp5JxD5rzzgF9+UXY9Z/bb2z1nShZ4UfwFpxGPhLIfGg7PmCEbDx2q7eY+MLsxhQvDgx1nQgiH9TT2jh07hpKSEqT/tp2hW7duiI6Oxrp162xtSktLsWfPHuT4yhhGRIZQu8tJLb3Wkei10+j4ce2BjpU18HOX1+YOrIBAE/wFr9iO/dz6ckz+y1k8l/602zpeemF2YwoXhgY7Dz/8MDZv3oyDBw+iqKgIjzzyCAoLCzFixAhUVVVh2rRp+OKLL3Dw4EEUFhZiyJAhSElJwc033wwASExMxJgxYzB16lRs2LABX3/9NUaOHInOnTujf//+Rr41IvJASz0lNZTUqFKScTc5Wdn9fLXr3VvZdbyxBn72IyQXYy8ETFgBxxoVWfgZ7Y7uxHMvx3qs46UXvb7XRIFm6G6sX375BX/4wx9QWlqKxMREdOnSBR9//DGuv/56VFdXo6ioCEuWLMHJkyeRnp6Ovn374u2330ZCQoLtGvPnz0dUVBRuv/12VFdXo1+/fli8eDFz7BCFqJgYucvI3W4sq8mTZYZjLflU7HdFuU73yJ1RSjLulpUpu5+vdkeOKLuOJ/aBX3o6kIBKHEBHtMavDu36Yx02wPWPPD12uHnrW2ys9zYxMcyFQyEgOEuIQhvz7BAFn6dcLrm57ncaOeeW8SU31/1iWS+puxwMH65s8e3w4d6vk52tbWGyy26s+nphudO1Uw9jlqaFzno4dUrZezh1St/7ElmFzQJlIopstbVyJGXiRPlsXUMydy5w5ozMDzNhgnx+6y1gzRrXdSmHD8uRmoICZfecMcNzfpx331U2rVNSouxevtpt2qTsOk2c/m9sNgPTp/82GvPaa0CTJmiyoqHS6AZchyicw2w8ouj69gud9fKHP+jbjihQQiapIBFFHm/J8ubOlVMc+fnyuMUCtG/vfupJCDn9lJ8vSwZ4mxZRWq181izvpSmysrxfQ2m7pCQgNdX7IuXUVODnn90k8ft2B2Dq4dL+g3//gvsebQOLyjKAWne4efLbxljd2hEFCoMdIj84Z7ENx1o9gXoParMk+6qgLURDBW37GlLO1FQrtwZa7nTtCixf7vm8fTtfysqAtDT3AU9qasO6H1t/fv0ViE8H6uocG3/+OZCTg5sAHPxjw8/t88+BF1/03Q+tO9w88VSoVGs7okDhNBaRRgUFciSib1/grrvkc/v2yqdaQkGg3oPS0RX7bdF65WzRK4/Pb1VpfFLa7o9/VHDcYgFuukkmAbIPdP75Txnt2aXUsM/lM29eYHe4eaI0wwczgZDRGOwQaVBQINeQ+Lu2xEiBfA9qRles9MrZolcen+3blV1HSTtPo1yAPD5jBoBnnwWiooAPP2w4efPN8hs1caLX61t3uHljX61cL7//vb7tiALFJIS7GfLGpbKyEomJiaioqGA2ZfLJurbE05SLySRzjxQXh+6UVqDfw8SJwIIFvttNmAC88IJjnw4f9rxlXEmfamtlbhlvwZbZLBdHe/vwHz9e2YLeceO8TyH56k8vfIbP4JSMp3lzuYinVSvfHbAzbJj7hdm5ucDq1aoupYhe32sirZR+fnNkh0glNWtLQlWg34OW0RVrfhzAdY2Hr6rhFgtQWCjX2Gzd6n0tDqBslKNTJ+/nlbbzNMqVhlIImFwDna+/Bk6dUh3oFBTInWzurFkTmNFGo0aUiNRisEOkUiTUAwr0e9CaJTkvD3jnHddSDpmZ8nhenut13K07evtt97u2HLZz+zB2rO82Sto5rw2KwjlswdUoRYbD8aX9Fsko87LLlN3YjueaWQ3y831PLWoxdy7Qw3XDGAB5XO9EhkRaMNghUikS6gEF+j348xd/Xh5w8CCwcSOwbJl8Li72HOh4Wne0Zg3w5ptyOmrAAPl86pTyD9+vvtKnnf3o1WOYiXOIwdXYaju2CKNhQj1+HTxa2Q3dMHK0ccYMz+uWtm8PXKkKIlWCkuIwxDGDMqlRVycz+ppM7rPFmkxCZGXJdqEqWO/BU5ZkW1ZgHd6Dr6zBWjMxL1umLDvwsmXer1NTI8SNTT5yeeHPyBTxOKVLdmO9+qpWTY3r9zgYmZuJrJhBmShA/FlbEiqC9R7cZUk+c0afqQ1foxmA67SNmp1muox+HTyImFgTPqgf5HD4IvwXbVGC02gOwP91LUaNNmrZdUdkBAY7RBpoWVsSaoL1HqxZkl94QT4r+VC3X3BcWOj+A1XLeiLrmhYl61esFb09JcQzmbxU9D57FsjOBjp0cDh8e5P/wASBfbgIgLo1RAHrqx/0ymlEFGiaMyifPHkS27ZtQ3l5Oerr6x3O/dFT9iyiCJKXJxfBhnMG5bw8YPBgN2UKvAQkgc4aXVAgF9vaj9pkZsqRKPsATOsohdJMzPbV000mx8W/Xke/Jk+WJ+xNmgQ89xyW1gI5Kr7XSln7esst7s8LEZjRRr1yGhEFnJY5sjVr1oiEhATRpEkTkZiYKFq2bGl7tGrVStO8m5G4Zocaq5Ur1VUYV9teS3/crSMymeTD/j6+1h3ptX7F3XvOynLznt95x/Um2dlCVFfr881R0E9v71evn5G9mhohmjTxft8mTbhmhwJH6ee3pqSCF154IW688UbMnj0bzZo10z8CCzImFaTGyLqTyfn/ANZRC+epLLXt1dKS6NDaJ8D7tmt35s+XdamUjE7V1noZ/dq3D7joItcXFRfLNxQERiW6tFhk/sOzZz23adoUqKpyvW8k1JUj4yn+/NYSSTVr1kwcOHBAy0tDEkd2qLHxtZPJeTeWkp1P/u7e2rhR2YjMxo2Or3M38qJkh5C/o1mrl55y/0358EPt3wSNtH7v/LV+vbL7rl/v+LpAjxBS4xHQ3Vg33HADduzYoS0MIyLDqc3LomTnk795XLQmOnSXl2f5cjma4WnBrtJdWu7z+Ag8cehu5I5McDzx6KPyGzfIcedVMBiV6LKwUH27SKgrR+FH0wLlm266CdOnT8d3332Hzp07Izo62uH80KFDdekcEXnmzzSA2g/Hw4eVtVfazh1/qoxbK4A7H3Ne6Gw2u9+FJYQMjPLzGzIvu8tK/Ee8gTcw2vG1V+XAtKkQcPr/oDtHjwJXXCGfW7cGtm2Tz/4Kl0SX3jI9u/sZEOlFU7Dz5z//GQDwxBNPuJwzmUywBCInORHZKN2x5InaD8ejR5W1V9ouGJx3y/3yi9wo5YnzLi370axLsRu70dXlNRk4jGWzM9DHd5yDli2BioqGr0+floFbYiJw8qSad+bKuvXcVxFVvbee9+kDzJqlrB2gbkTR2045IrU0TWPV19d7fDDQIQosPaYB1OZlUTr64M8oRXm5vu2AhhGf4cPlYmQlrKNZpaVAS5xAJRJcAp1rsQkmCJQiQ9EomXOgY6+iQp73h1GJLvv0AZKTvbdJTm4IXCKhrhyFJyYVJAojvqYBAHl+wwbvCfnUfjg6Jx70RGk7d5KS9G3nTNVoVn09+r+chxNIQgKqbOem4h8wQWAzrlV83aNHPQc6VhUV/o+KGZHo0mwGFi703mbhwobfI1+BkZXSdkRKaQ52Nm3ahCFDhuCCCy5Ap06dMHToUGwORJU5IrJRMg1w6BDQv39DBfD27d2P9qj5cOzVS9lf8P5Mk7z7rr7tnCl9D9d+uwAwm9F68yrb8Q9wI8yowzxMdWjftKnv93zFFcr6p7SdN2qKqOolLw9YudL979HKlY73LipSdk2l7YiU0rRmZ+nSpbj77ruRl5eH+++/H0IIbN26Ff369cPixYtx11136d1PIoK24X3r9Ja7v+5DKQu00aUHrsQX+OJYDjCp4VgtopGOUhyH+yjp7FmZgycuzvN1g73eyd1i7UBT+nt08KCy6yltR6SUpmDnySefxNy5czHZbrXfpEmTMG/ePPz9739nsEMUIFp20/ja5aLkw3HzZuDYMe9tjh3zb2Fpp07A2rXK2mnh6T20wS/4BWkux+feug0PvNPD53WnTwcWLPB8vnVruRjZF0/rnbwmNAwhSn6PWF6CjKJpGuvHH3/EkCFDXI4PHToUxcXFfneKiNzztbDYE+e8OWoFY2HpM8/o286Zc9/MqMN69HMJdLaNeRUQAhsqfQc6ALB/v/fz27Yp65+7djNmAM2ayV1kCxbI52bN5PFwNG6c71FDs1m2I9KTpmAnKysLGzZscDm+YcMGZGVl+d0pInLPfmGxFv/+NzBxolx8XFur/HX+5HFRUsEckFNBubner5+b633KSGnfHsJs1CEa/fCp7dgyDIcJ9Tgz8l4AykeQfLVr3VpuL/cmMdF1ZGfGDBnYOX+/LBZ53FPAU1srf75afs6BFhMDTJnivc2UKaE5ckVhTkt65pdeeknExMSI++67TyxZskS8+eabYuzYsSI2Nla88sorWi5pKJaLoHCTm6ut+KVzyYTp05Xdr6ZGWQkG54KPWsoCeHpvubkav1m/qasT4o4U1/oG5UgRLXDSpUTGqVPKvo+nTim7f2Ki+9cnJrq21fr9nj7d9XVqfs7BEi79pNCn9PNbU7AjhBAFBQXi6quvFklJSSIpKUlcffXVYvXq1VovZygGOxROpk/3P9Cxfyj5gNFSe0lNBXNnZ84IMX68EAMGyOczZ/z8ppWUuO3wJSjy2KdA1JsqLxeifXsh4uPlc3m5+3bz5yu79/z5Da/x9XsRaoFETY3s/4QJ8pmV0UmLgFY9jzSsek5aBbtyc22tXLOhZ+5Osxk4c8b71MHy5XIruy/LlskEfr6qcAMyaaHeVbhd1NYCV10F7NrlcHhi0ltYcLzhDWVlySkf+91qat+znsaPl4uSfRk3DnjxRWW/F02aAB9/DPz6K6uMU+RQ+vnNpIJEGhUUyA/0vn1957TRy0sv6RvoAPJ6vj5Y1a7ZCUbhUJ8efBCIjXUMdO67D6ivx3Pld/nMRWNkvSmlC9Ct7ZT8XtTXAwMGBO93lSiUKN56npSUhO+//x4pKSlo1aoVTF7+azx+/LgunSMKVdaSDc7jot5y2ughUDlmfF03J8dzEU0rs1m2A4JTONSjNWtcVzpfcAHwzTdy+APKtkl36aLsdkrbqdGzpxyxUdIO0PZ7EejfVaJQojjYmT9/PhISEmz/9hbsEEUyIys3Byr/iK/rbt3qe+TAYpHt+vQxqHDoDz+43xq1f78MdlQaPFh5u61bVV/eq4wMde20/F4E+neVKJQoDnZGjRpl+/fo0aMD0ReisGBk5eZx44Bp0/Rfs+Mrr4naPDvBKBxqc+aMHF5xHt54911g6FDNl/3hB33bBZLW34tA/q4ShRJNa3Z27dqFIrviJe+++y6GDRuGhx9+GLUqkjq8/PLL6NKlC1q0aIEWLVrgqquuwkcffWQ7L4TAzJkzkZGRgbi4OPTp0wd79+51uEZNTQ0mTpyIlJQUxMfHY+jQoTjka7EAkR+MrNysJE+JWkrymqhdvxKMwqEQAvjLX4D4eMdA54EH5DkvgY6S3D9KK5GrqViuNAeO2grw/v5esMo4RTwtW726d+8u3nnnHSGEEAcOHBCxsbFi+PDh4oILLhCTJk1SfJ01a9aIDz74QOzbt0/s27dPPPzwwyI6Olrs2bNHCCHEU089JRISEsTKlStFUVGRuOOOO0R6erqorKy0XeO+++4T5513nli3bp3YtWuX6Nu3r7j00ktFnTVZhgLcek5qrHdN1eL2sX594PoQ7Dw7Z84ou6Z1i3hdnWt+HeeHfU4b1d56y/WC3boJcfasz5cqzf2zcqWy9+xtC709Nbll1q5Vdu+1a33fQ+/t80ShJKB5dlq0aCF++OEHIYQMSAYMGCCEEGLLli0iMzNTyyVtWrVqJf71r3+J+vp6kZaWJp566inbubNnz4rExERb4sKTJ0+K6OhosWLFClubw4cPiyZNmoiPP/5Y8T0Z7JAaRgc7nvLXKHmMH++a16SuTn7YLVsmn90FIFryvnjrp688Ox7t2eP2gnXFP/t8D2r7pOfPWW0OnGefVXbvZ591vZd9/ppnnxXivPO8v2e/gk4igwU02ElISBDff/+9EEKI/v37i+eee04IIcRPP/0kmjZtquWSoq6uTixfvlzExMSIvXv3igMHDggAYteuXQ7thg4dKv74xz8KIYTYsGGDACCOHz/u0KZLly7i0Ucf9Xivs2fPioqKCtujpKSEwQ4ptmyZsg+iZcv0v7eSERM1fVI6yjFhgrLrT5jg+/pZWRoCnYoKIVJS3EYaSt+D2tEmvX7OWrIha/1+u2MN8JwDHiXJHYlCndJgR9Oane7du2PWrFl48803sWnTJtx0000AgOLiYqSmpqq6VlFREZo3b47Y2Fjcd999WLVqFS6++GKUlZUBgMv1UlNTbefKysoQExODVq1aeWzjzpw5c5CYmGh7sJ4XqeFv/hWltaLcUZK/xptffmm47zvvyK3Hztezbkm2z8GitVp1Xh5w8CB85rTxSAiZsS8xUWbDs3rySUAIFFT0U/we1Ob+0SvPjpIcOM65jpR+v4Vw/T1yXhc0eLD8WTuvj8rM1HfbuT+/10QBpyWS+uabb0R2drZo0aKFmDlzpu34hAkTxPDhw1Vdq6amRuzfv19s375dPPjggyIlJUXs3btXfP755wKAOHLkiEP7P/3pT+KGG24QQgjx1ltviZiYGJdr9u/fX4wdO9bjPTmyQ/6wjhBomRrQUivKntLRBiUPb6MNzu+hvFzZNT2VP9Dk1Vddb3DddUKcOyeE8D1S4/weli5V9h6WLhWKru88EuSJllEapXW5nH+PcnM9rwtSMl2plb+/10RaBbw2ljvV1dWitrbWr2v069dP3HvvvQGdxnLGNTuklpapAX9qRVkprdek18O6cPXqq5W1v/pqHb6527e7v/gvv2j6Xljfg1H1prTcV+lr1DwCVRsrIGuziBQK6DRWSUmJw/bubdu2IT8/H0uWLEF0dLS/I02oqalBhw4dkJaWhnXr1tnO1dbWYtOmTcj5LU1rt27dEB0d7dCmtLQUe/bssbUhCoS8PKB7d/m/dHtCyOPOUwO+EhECMrmbr6F/a8bcYLFuSf75Z2XtlbZz69dfgehooEcPh8NXYStMEEjr0sZt33zRmvvHYpFTMt6sWOH7Z3bbbcrua98uEJmy583zvNVdK2+/14A8ruT3mijQNAU7d911FzZu3AhArpu5/vrrsW3bNjz88MN44oknFF/n4YcfxubNm3Hw4EEUFRXhkUceQWFhIUaMGAGTyYT8/HzMnj0bq1atwp49ezB69Gg0a9YMd/1WnS8xMRFjxozB1KlTsWHDBnz99dcYOXIkOnfujP79+2t5a0SKDBsGbN/u/tz27fK8PTWJCL159VU1vfSfdT1K27bK2ittZ1VbCzw/z4K97W+UUUZdne3cBLwAEwS+xFUA5HqjtDTXvvmiNfePXvW9rrlG2X3t2wUiU7aSGmhqhUQNNCIltAwbtWzZUvzvf/8TQgjx/PPPi5ycHCGEEJ988ono0KGD4uvcc889ol27diImJka0bt1a9OvXT6y1SxxRX18vHnvsMZGWliZiY2PFtddeK4qKihyuUV1dLSZMmCCSkpJEXFycGDx4sPj5559VvR9OY5EaanPOCKHfzp5x44IzfeW83uXYMWWvO3ZM+fdx+nQhppn+4XKRd5AnTLD4vIfatVNq1+CoXePjSXy8suvExze8RskOLi0PJbu31NDre0SkldLPb8XlIuydO3cOsbGxAID169dj6G+ZSi+66CKUqkjF+frrr3s9bzKZMHPmTMycOdNjm6ZNm+KFF17ACy+8oPi+FHlqa+VfrQcOyL+Kx43znRVYq+nTlbdbsED+W6+dPcEoSWe9x3PPNdRL+vZbZa/99ltlZQdeHv4Z5q7o7XCsEgloh59wEq08vErq3RsoKpJ9e/55uevKZHKcSnH3HuzbA77b61Xfq3lz4PRp39dp3rzh39aMyM88o6wPSuk9YmRIDTQiDTRNY11yySV45ZVXsHnzZqxbtw4DBw4EABw5cgTJycm6dpDIlxkzZDHryZNlcDF5svx6xozA3G//fvXtevWSW309BSsmE5CVJdt5E4w1O+62JOtWxfzIEcBkwl+cAp3L8DUSUekz0LFewiovT922ajXtW/nuiqJ2jz+u7DrO7ebOlQGzXgU6ldRAUyuoNdCI/KBpZOfpp5/GzTffjGeeeQajRo3CpZdeCgBYs2YNrrjiCl07SOTNjBnu//q1WBqOz52r7z07dQLWrlXWzkrtKIQngUoJNX8+kJoqR5Z69XLth99/wZ87J4d8nMqDj8JiLMEoVX11rgielyerdm/eLBcje3oPatt7WpPlbPt2YJSXt/C73ym7jrt2c+cCs2Y5jlqmp8vUQ4Dj75EvSmqgqRWUGmhEetA6T1ZXV+ey5bu4uFj84rQ9NBxwzU540pKZVg9a1uxY+ZtROFBrOXytFVqyRNl1lixx8+JHH3Vp+DruFkC9pr6qWRfkD6Xro8aN836dQNQJ8/R75C3PTiAEvAYakQ8BXbMDAGaz2SVzcfv27f2LvIhUUJOZNj9fv/vGxLiOzjgzmdz/Fa12FMLZ1q2B2cbra63QsWPKruPQ7qOPgBtvdGyQmYkXJ/wXEx5sDi1SU4GkJE0vVc1+ZM6fdvajeu5+Z0wmZaN69rz9HgVz/ZradVBERlEc7Fx++eXYsGEDWrVqha5du8LkZaXkrl27dOkckTdKc5HonbNk7Vrf0wdCyHaDBrmeM5uVLeJ1R8X6f8WUrBVStTbj4EGgQwfXk//7H/C73+GeamDCg2p7KQMdL1VgdDduHDBtmvfgUuk6GOtaoUmTHLdqZ2XJYEBLyQZPv0cxMfoG9754em+ZmdrfG5HeFAc7ubm5th1Yw5yTiBAZQGu9Jn89+qjydu6CHX8o3dWlRlqa77+8lay5iMVZDHukG/DTd44n3nkHuOUW25dffaWsX6mpQFWVDKC2bQv+IlclO6LUrIPxd1QvlEXye6PIYBJCzRK3yFRZWYnExERUVFSgRYsWRneHFKqulruufDlzBoiL0+++HTrIwQtf2reXRS/1ZLHI6x4+rG5xqi++vkfW+3pKIDcf+cjH844HJ02Sf9o7Wb4c+C0vqGKZmXK6xIhRghkzgGefBerrG46ZzTLQ0XvxOxGpo/TzW9PWc3tVVVWorKx0eBAFg9IRAqXtlHLeDeRvOzWsayQAfXPu+ModZDYD3bq5Hr8F70DA5BjoZGfLSNRNoANoG51yV8U8mJwDS/vAh4hCn6Zgp7i4GDfddBPi4+ORmJiIVq1aoVWrVmjZsqXLomWiQFFbG0kvo0fr204tT7li/PH9997P19YC77/f8PXv8D8ImPAOHAs/1e4rlhn/mjb1eC1fOYfcsQYbaussWSxAYaEcTSosVL+425rawDnYEUIeV5vLqbZWxoATJ8pnvWtVEZF7mnZjjRgxAgDw73//G6mpqV4XKxMFil5ZidX65ht922mRlyfXA02fLpMXHjkC7Nmj/Xrx8d7PW3e+xaMK/8NFyIRj9sCB+AifYCDmfwjkX+j9Wt5yDnkjREOdJSULvAsKgPvvd0x0eN55wD//qWw6rLZWFs/0Zt48mQdHybqdGTNke/uAa9q0yJkOs1i4ZodCmJZ97fHx8bbaWJGAeXbCk9raSHrRK/+KP6ZP1zffzuLF3u83YXy9+DdGu7xwJh51OKSm9pK7XDF65ASyXtvbNZTkNZo/X1l/5s/3fa3p071fI1B5cILF3c8yM1N5/igirZR+fmuaxurRowdKSkr0jbqIVPK2fsVXjg9/pjcCVQFcKevUip75dl57DejSBbj5ZrkDysHixXjhxSa4G4tth7bgakSjFjPhWONAzc63vDy50HvjRmDZMpnFWQlfI3UWC3Dvvd7b3Huv7++fXqkNlI4QheuUVkGBHKVzXrxu9DorIgdaIqkffvhB9O/fXyxevFjs2LFDfPPNNw6PcMORnfCmNiuxv3+FDh2q7C/+oUP1e49Wgcqg7Pzo0UMI8fXXbk+m4Yjb1/ibrVqvkbr165W9x/XrvV9Hr5EdPUeIQo2vDMqBGl0lsgroyM7Ro0dx4MAB3H333ejRowcuu+wydO3a1fZMFEzOIwQbN8ot3+7WZejxV+iPPyrrl9J2aijJGu2vljiBT7c3B5z+W375zk0wQaAM7odW/K295M9Inb3CQmX389Vu3Djf91KSVNCo5JfBsHmz53QEgAx5rOusiIykKdi555570LVrV3zxxRf48ccfUVxc7PBMFGzWbLLDh8tnT1NXkybJ/wE7sx5TstsnIUFZn5S2U0NpxXUtTKhHAW7GCSShOU43nPjHPwAh8Jfl17qtwm02y4XSWhbZOk8n5uaqq2IeSNakgt4oCfCMSn4ZDEbtiCRSTcuwUbNmzcT+/fs1DTmFIk5jNQ4bNyqbTti40ft1pk1Tdp1p0/R/D+PH+zc91bOnXOB79dWOxyfgny6N38eNIi/Xdf6hpkZOuUyYIJ+1Tl15m06sq5M/h2XL5LOaaRC9prGs3C0GV1Nc06iCtcGg139TRFoFtBDoddddh2+++QYXXHCBvpEXUQDp9VeolxQymtqp0bMn8OKL2l/fvr0sJmodgL0SX+AL5Di0qUEMMnAEx5GMzm4GavWovWSdTnQeZTt0SB73ZxSnTx8gOdl78dLkZOX1yebOldvLtRbX1LvsRCix5kzylNHbZJLnfdVeIwo0TcHOkCFDMHnyZBQVFaFz586Ijo52OD906FBdOkfhJZjVlrVo00afdkr/x+2pnad8JErylPiblfntt+VzG/wCgTSX892xHTvR3fZ1IKZWvE0nAvJ4fr6c0tKSp8VsBhYudCjH5WLhQnXXDnZxzXDhLWcSq55TSNEybGQymTw+mjRpomkoykicxvKfv0P9waDX9IY/1/E0dTN9urIdYkrv7XG6BOfEBvR1OfFnvOq2/alT+n3/rYIx9RFKeW0ieRrLSu2OSCK9BHQ3Vn19vceHJdBbRSjkeMr7YrFoS6kfKOXl+rTTeh1PO8EOHZLfJyU7xMrKlN3bnYcwG3WIxnXYaDv2Fu6CCfV4Da6JaXr0AJo3134/T5Sm6NKayqu2Vhbu9ObZZ4OX10bJDjqLRbYLV2p2RBIZQVWwc+ONN6KiosL29ZNPPomTJ0/avj527Bguvvhi3TpHoS+cEqbpVV4iJUXZdezb+Zq6ccfa1n6HmJZgpx/WQ8CE2XjEdqwcrdECFRiJtwC4lnvp0QPYtk39vZQIdAHXF17wXaizvl62U8qfJJSRvPXcnpIdkURGURXsfPLJJ6ipqbF9/fTTT+P48eO2r+vq6rBv3z79ekchL5z+avVVgNJkArKyfK/JKSpSdj/7dr7ykXgihGOeErv/3HzKRAkETFiP6x2OZ6MIqSjHKbSwHWvVCujcGRg2DDh1KnCBDqA84FMTGNrbskXfdgUFcmF3377AXXfJ5/btlWcGjuSt50ThQlWwI5z+7+P8NTU+4fRXq15J64qLld3Pvp2/eUasr2+i4L/YGNRgB7qhBI71KoZjGUwQ2Itsl9dcfDHw7bfAqlWBmbqy16mTvu2cKe2/knbeph6VJqHUKzkhEWmnac0OkVW4/dWal+d/0rqsLGX3sm+ndCeYJ9aptZwc7+2ewgOoQVN0wy7bsZfwF5hQjxUY7vF1F/qoVK6nQH/4/+EP+rRTumvM18imXskJiUg7VVvPTSYTTE5/Ejt/TY3LuHHAtGne/4ev51+t/mzbtsrLk9ualbZ3pmVkRyvnPCXffee+3RCswRrkOhzbjwtwKb5BNZr5vM9PPynvk5rvtTuBzjvTr5/McXT2rOc2TZvKdt4omXq0TjH6ytljzS49b57jfytms3yvWrJPE5FyqoIdIQRGjx6N2NhYAMDZs2dx3333IT4+HgAc1vNQ4xDMhGkFBfIvbfsPoMxMuSDyrbeAI0cajmdkyAWonkZqrIsptdAydad0B5c9d1NrBw86tumIH/ADXOd7OuIH/Ajlw2mHDsnFt76CF08/g+efV7fzJtAf/vHx3oOd3/6X5dXhw8rupbSdv8kJQ52/QTBRIKkKdkaNGuXw9ciRI13a/PGPf/SvRxR2gvFXq7eMu+4CrSNHZFK5lSv13/6q5IPSuZ3SnWD2mjSR3z/7/lunA+NwBkXojI5wTHE8FO/iPahP6vn993LxLeA5ePH0M7BukVeb9ThQH/6bN3vPngzI875GZI4eVXY/pe2AyE1OqFcQTBQwwUj6E+qYVFAfetVMclZX55qwTOmjeXN1dZWUWLhQ2b0XLmx4TU2NEE2aqO+/yeSYmO3kiXrxMsa6NJyDB/xKNuh8T+f7+voZmEwyiZze32stli1T9j6XLfN+naVLlV1n6dLgvK9QtXKl/Pkr+T0i0ltAkwoSuWP9q/WFF+SzXsPzWrdtA0BVFbBhgz79sHrvPfXtNm/2nfvFE9si2GXLkNiqCe7Dq7Zz29EdMajBQ3hK28XdcJffx9fPwHmLvJH0yqfkvIjd33aRyNsibne/R0RGYbBDIc/fbdtvvqlPP6yUppKyb1dYqO1eQgAtSvbAHGUCRoxwOJeJElyB7TgH/Rd9OAcvehVRDQa98ilZr+ONkutEsnAKgqlxY7BDIU/Lehd7VVX69MNKTbVrfySgEkeRgj3o7HC8H9bDBIHD8PFJrANr8KLXaEkw6JVPyXodk8n9dUwmFrkMpyCYGjcGOxTyunb17/XXXKNPP6ycBlgUtVO380tgGYajEolIQcNK24cwGyYIfAofe6Z1ZM0PpNdoSbDokU9Jz+tEqnAKgqlxU7Ubi8gIo0drf22TJsDEia7H/dkm262b+nZ9+gDJyb53Cf0ZC7EQYx2OrUc/DMTHsBj4n6t1lOPWW2VgY79GQ81oSTD5m09J7+tEImsQfPiw+3U7znmiiIzCYIdC3g8/aH/t1Kmu00n+bpO1z+ejtJ3ZDCxcKLfDu9Md27EdV7gcb4NfcBR+pl/2g33hUesoh7vv3XPPheYohz/5lAJxnUgTjkEwNU6GTmPNmTMHPXr0QEJCAtq0aYNhw4a5FBIdPXq0LXOz9XHllVc6tKmpqcHEiRORkpKC+Ph4DB06FIe0bt8hzfypDO1NYqL615jNwPTprjl+PNU6suaKUVLraPVqZX1wbpeXJ/P+2E+JJONXnEOUS6BzFbbCBGFooAO45pDJy5OJDTduBJYtk8/FxaEZ6FBwcKqPwoGhwc6mTZswfvx4fPnll1i3bh3q6uowYMAAnD592qHdwIEDUVpaant8+OGHDufz8/OxatUqrFixAlu2bEFVVRUGDx4MC/c7Bo2/laG9uftuZe1uvx2YMAGYPx84c8Y10NFrm+ypU8r6465dXp4szbBxvQWHLx2EX9EaUWi44f77X8DyZQL9/3qVspsEWHKy6zHrKMfw4fKZf7UTg2AKeUHK+6NIeXm5ACA2bdpkOzZq1CiRm5vr8TUnT54U0dHRYsWKFbZjhw8fFk2aNBEff/yx29ecPXtWVFRU2B4lJSVMKugHT0nF3CXF02L+fGXJ3ebP936djRuVXWfjRu/XufpqZde5+moPF8jLc22clyeExaK6r4F++PqeEhEZKSyTClZUVAAAkpKSHI4XFhaiTZs2uPDCC/HnP/8Z5XaFhnbu3Ilz585hwIABtmMZGRnIzs7G1q1b3d5nzpw5SExMtD2ylJaxJhd6VYb2pnVrfdrptU22c2fv5z22W7JELmSwH+5q0QI4flzObzVp+M9RSY6XYGjVyugeEBH5L2SCHSEEpkyZgmuuuQbZ2dm244MGDcJbb72FTz/9FM8++yy2b9+O6667zlZ0tKysDDExMWjl9H/l1NRUlNmvrrTz0EMPoaKiwvYoKSkJ3BuLcGoqQ2ulVyZbvbbJ/u53yq5ja7dvnwxynGrL4eOPgYoKtxGF2ax811cgbd9udA+IiPwXMruxJkyYgG+//RZbtmxxOH7HHXfY/p2dnY3u3bujXbt2+OCDD5DnZUJYCAGTh6QgsbGxtsrt5B+lcaI/8aR1lMNbUKUmI66/22T/9Cdg8mTf/f7TyLOAKc71xOTJsmqqF7W1wPvv+75HoHkasSMiCichMbIzceJErFmzBhs3bkSmj7H79PR0tGvXDvv37wcApKWloba2FidOnHBoV15ejtTU1ID1maSvvtK3nTtms1wM682ddyrPiAv4l1n3X//yfh4APkMvNG/tFOgkJwP19bA8M8/nrrWXXgqNekKdOhndAyIi/xka7AghMGHCBBQUFODTTz9Fhw4dfL7m2LFjKCkpQfpvcw3dunVDdHQ01q1bZ2tTWlqKPXv2ICcnJ2B9J0npX/7+jBBYLDIw8GbFCmXBgR7bZH+Ls92aiH9CwIRecByhxPHjwK+/omCVSdGutQMHfPcjGMaO9d2GiCjUGTqNNX78eCxbtgzvvvsuEhISbGtsEhMTERcXh6qqKsycORO33HIL0tPTcfDgQTz88MNISUnBzTffbGs7ZswYTJ06FcnJyUhKSsK0adPQuXNn9O/f38i31ygo/cvfnxECpeuCJk2SIzQdOwLjxnmuTeVvRlx3s6OX4Wt8jctdT3z+OfBb0G3N8eMc+Flz/NgHWx07KutLoH31FZPpEVEECMbWME8AuH0sWrRICCHEmTNnxIABA0Tr1q1FdHS0aNu2rRg1apT4+eefHa5TXV0tJkyYIJKSkkRcXJwYPHiwSxtvlG5dI1enTinbwnzqlPZ7LFumfsu02SzE9On6vU97S5Y03Cce7r8Bj+DvYsmShtfU1QmRmem5vyaTEFlZsp0QQtTUyPdg9NbzZcsC8z0kItKD0s9vQ0d2hI+5jbi4OHzyySc+r9O0aVO88MILeOGFF/TqGimkZP2KtV1+vrZ7aCkiaLEAzzwj/+2cXNBf1kwF/8Pv8Dt873DuO/wel+A7AMBGu4wGvkanhGjYtdanjxyVuvxy43dDsYAjEUWCkFigTOHL2/oVLe3c8SfnzLx5cmeTnq7d+DgETC6BTnOcsgU6zrvD1Ob4qa0Fdu3So7fahFoVcyIifzDYIb942N2vuZ07/uScsVjkziZ3x1XX8dqyBTCZ0OSJmQ6HL8VumCBwGs1txy6/3HENkNocP0buxmIBRyKKNAx2yC9KgxB/EuT5m3PGeWeT6jpex4/LCMBpmGM8FsAEgW9xqctL3n/fcURJyeiU/UiKPyNhavTo4dovFnAkokjDYIf8snOnvu3c8XeUw35nk6qq50LIkutO1TAPdewNEwRewniP93QeUVKbK8ifkTClTCZgxw451ccCjkQUyRjskF/OntW3nTv79ml/rdkst6EDKque5+fLWlWVlY4Nz57FnBsKFd3bfnRGba6gnj0V3cIv1vc8daocUWIVcyKKVAx2yC9KMyNv2KByfYydoiLV3bK5/PKGfDtKdkRdUvIRzFGmhlTLVt9/LxvExmpap6S2hliwatPa7wIjIopUDHbIL/X1ytodPKhwfYwb/pQx27WrYe2Mtx1R6TgCARM+wo2OJ958U0YEdlkRtaxTUrsbKxgjO+7uS0QUiRjskF+aaPgNcrs+xovfCtxrYr92xt2OqCawQMCEI3CqH3HbbTLIGTnS5TVa1imp3Y316qvK2uuF+XSIKJIx2CG/aNll5bI+xofzz1d/D3vW3Vg5OY7rUd7ESFjcVEypPX0O+L//83i9c+eU3de+nXU3lqcpMOe8NsGqjcV8OkTUGDDYIb9s2qTtdWrWiviz7Rxo2I21dasMrm7H2xAwYSTecmiXiRKYILB1m/fE4nv3KruvfTu1FdeDURuL+XSIqLFgsNOIaEqk50N1tX+vV7JWxJ+dXADwpz/J54qvf4SACW/jTofzQ7AGJggchkw4c/iw9+t5KjDqq52aiuvjxgU+AGE+HSJqLBjsNBKqE+kpdPq0f69XslakaVP/7vHvV2oBkwm5UxyHS17CX2CCwPsY4nD86FHv11NafsJdu7w8uVjbV16bmBhgyhRl91Hj8suZT4eIGh9DC4FScBQUALfc4nr80CF5fOVK7R96iYlAVZX615lMcmRByVqRvDzg9dfV3wMAPsJADJzuWEy2Gk0Rj9MQHmL91q29X7NzZ+Dzz33fu3Nn98fNZpnPxpcrr/TdRq1Zs4BBg/S/LhFRKOPIToSzWIB77/Xe5t57tU9paSnQqXatiN2ub8X+jIUQMGEgHAOd1ihHM1R7DHQA12kmZ7/7nbI+KG3njjUBop6aNgUGDND3mkRE4YDBToQrLASOHfPe5tgx2U4Ld9mIfVG7VmTrVuXXvgR7IGDCQox1PLFxIyx1Ak0zvQ/bKNmZpGQ9jX3mZi2UJCFUa+JELkQmosaJwU6EUxrEaA12iouVtUtM1L5WRMk94nAGAibsgePc0Sw8gqRWAujTx7Yjytv2byWjTTExcu2LN/aZm7UIRJI/+3IURESNCYOdCKc0w7HSds4SEpS1S07WXnupfXvv53ficpxBvMOxg2gHE+rxN8xy2M1l3RHlPP2WlaV8tKm2VmZm9sY+c7MWgUjyx7IQRNRYMdiJcElJ+rZzpnUbthqe1hxNx1wImHA5vnY43gIV6ICDAOQQTosWjq9TuiPKEyVV2J2rnqvlKwmhViwLQUSNEXdjRbi0NO3tamvlB/aBAzLJ3bhxrkGL0p1YWnZsWZ065fj1FfgKX8F1q1J3bMdOdHc5ftNNrtdUuiPKHaXZjf3Jgmydcrv1VhnwaFkb5Q7LQhBRY8SRnQinNdiZMQNo1gyYPBlYsEA+N2smj9tr107Z9ZW2c6dlS/ncAhUQMLkEOlPxD5gg3AY6AHDRRdrv7Y7S7Mb+ZkH2lIRQC5aFIKLGjMEOuZgxA3jmGdepGotFHrcPeB58UNk1lbZzZ827AoeRgQq0dDi+DT1ggsA8TNV+cQ2CsRvLynnK7aOP1F+DZSGIqLFjsBPGlJR/KCtTdi1ru9paYN48723nzWtYfOtrW7uV0nYuHnwQL7/aBBlwXGwShzPoiW2KLvHjj67Hamvlh//EifJZzWJiJdmNp0zxb52SPeuU2/DhwMCBQI8e3ttHRzt+zbIQRNTYMdgJU0rLP6gNdtQuvn37bWXXV9rOZsMGOSTx9NMOhy/GXpggcBZxii/l/D1QOkXnzdy5wPTpriMlZrM8Pneu8mupYbH4XmScmgqsX8+yEEREVlygHIYKCuTCVedFq4cPy+P2f8UfP67smtZ2ahffKimboKYdysvlp7WTe/A6FuEehRdxZL8o1zpF58w6RQcoD1TmzpXlF3wt4taTkmSDhw7JoGv48MD1g4gonDDYCTPWMgLuducIIQdD8vOB3Fz5gddE4didtZ3axbfnzilr77Ndfb2cf3FO+HPTTcg5/j6++ELZfdy58EL5rHSKbtYsdVvq8/O1900tpVvHucWciKgBp7HCjK+/7IVwTB6ndHu1tZ3axbdqgym3/vxneVHnQKe2Fnj/fQwerOwe7tj3NRj5cQJN6dZxbjEnImrAYCfMqP3Lvk8foHlz722bN28IdtQuvk1MVNYft+1Wr5ZDUf/6l+Px4mIZtf220tafUQr7vgYjP06g+Uo2yC3mRESuGOyEGS1/2cfGem/rfH7uXDkN5k5uruOaljZtlPXHod3PP8tP5Ztvdmz0n//IIMepPkRGhrJ7OOvY0bGvwcqPE0jWZIOAa8DDLeZERO4x2AkzvXrJOlPeJCc3/GW/ebOyquf2NZMKCoB333Xf9t13HXd8ffON7z7b2tXVyU9k5wyDo0bJIOfWW92+Vut6nQMHgGHDGr4OZn6cQPKUbJBbzImI3GOwE+F++kldO4vFcy0qq3vvbVj7orSMwTsizzUBjPWGixd7fe3Bg8ru4c677wLV1fLfwc6PE0j+1vciImpMGOyEGbUjNStXKruutV1hobLrFxYqu+4fsAQCJuRhleOJ0lIZKSlY4ezvtNKkSQ3/Nio/DhERGYfBTphRu0D5yBFl7a3tlAYx1natWrk/fyH2QcCEJRjleOKTT2SQo7RoF4A331Tc1C3nEgtz5wJnzgDz5wMTJsjnM2fCK9BRmlSSiIiYZyfsqF2g7CkYcaa0nbOUFODEiYavY3HWbXbjf7ecgntOPKvpHs2byzyDv/yirY/uBDs/jp7UJJUkIiKDR3bmzJmDHj16ICEhAW3atMGwYcOwb98+hzZCCMycORMZGRmIi4tDnz59sHfvXoc2NTU1mDhxIlJSUhAfH4+hQ4fikK80s2HKuvXYG/utx/ZTON5Y2yndsmxtN2tWw7En8DeXQOcoUmBCPZq/qi3QAWS6nV9/1fxyDByo/bWhxldSSUAGcb7yCRERNSaGBjubNm3C+PHj8eWXX2LdunWoq6vDgAEDcPr0aVubuXPnYt68eViwYAG2b9+OtLQ0XH/99Th16pStTX5+PlatWoUVK1Zgy5YtqKqqwuDBg2GJwP/jKykDcOedDWtS4hSWkLK2U7pl2dqusBAYhA8hYMLfMMuhTSscRxscBWBSPD3mjpJkgN7ccov214YatUkliYgIgAgh5eXlAoDYtGmTEEKI+vp6kZaWJp566ilbm7Nnz4rExETxyiuvCCGEOHnypIiOjhYrVqywtTl8+LBo0qSJ+PjjjxXdt6KiQgAQFRUVOr6bwKirEyIzUwj5seb+kZUl2wkhxOLF3ttaH4sXy/ZLlihrv2SJEOLHH92evAqfuxzu21f7e773XmV98vRYtszvb3vIWLas8b1nIiJPlH5+h9QC5YqKCgBAUlISAKC4uBhlZWUYMGCArU1sbCx69+6NrVu3AgB27tyJc+fOObTJyMhAdna2rY2zmpoaVFZWOjzChZJCkPZ/2a9erey61nZffeW7bVNU4/pJvwfOP9/h+M0ogAkCXyDH5TXFxcr64Y7TrKVqkVQ6geUiiIjUC5lgRwiBKVOm4JprrkF2djYAoKysDACQ6lQFOzU11XaurKwMMTExaOW0wta+jbM5c+YgMTHR9sjKytL77QSM2t1YdrN9Xlnb+SrY+RwmoRrNkHbif7Zj8zAZJgisxs0eX2exAMuXy2kvtVNSvjJA+9K9u3+vDyUsF0FEpF7IBDsTJkzAt99+i+XLl7ucMzn9n10I4XLMmbc2Dz30ECoqKmyPkpIS7R0PMrXlGZQGCtZ2noKpW/EfCJgwCf9sONilCzJaVWMqfJQShxxt0rpF+uxZ5W3dmT7dv9eHEpaLICJSLySCnYkTJ2LNmjXYuHEjMu22GqX9lovFeYSmvLzcNtqTlpaG2tpanLDf/+zUxllsbCxatGjh8IhUakeCnBMKXoT/QsCE/+B2h+N5lx8EvvkG5ZVNVffJukVaacDjqzyGLzt2+Pf6UMNyEURE6hga7AghMGHCBBQUFODTTz9Fhw4dHM536NABaWlpWLdune1YbW0tNm3ahJwcuS6kW7duiI6OdmhTWlqKPXv22NpEkvJyde1OnlTW3trOOjrQHKdwCOfhv7jYod0N+BgmCJTHyfpWWnZJqd0irbSyuictW/r3em8sFjk1p3WKTiuWiyAiUs7QYGf8+PFYunQpli1bhoSEBJSVlaGsrAzVvxUzMplMyM/Px+zZs7Fq1Srs2bMHo0ePRrNmzXDXXXcBABITEzFmzBhMnToVGzZswNdff42RI0eic+fO6N+/v5FvLyCUjnJY23Xpoqy9td2wXIHFGIVTaIHz0JB++THMhAkCa3GDbDdMHm/eXNn1nanZIv2HP2i7h5WvelhaGZ3F2GwG+vSRqQj69OHUFRGRR0HYGeYRALePRYsW2drU19eLxx57TKSlpYnY2Fhx7bXXiqKiIofrVFdXiwkTJoikpCQRFxcnBg8eLH7++WfF/Qinref/+Ieyrcf/+Idsf+qUsvanTgkh/v1vlxOb0EtEodalfU2NvH5sbOC3hdfVCdG8ubbrR0c3bMPX08qVQphMrvczmeRj5Ur97+nszBkhxo8XYsAA+XzmTODvSUQUSpR+fpuEcJeLtXGprKxEYmIiKioqQn79zvjxMsmeL+PGAS++CJSV+d6GfBm+xte43OV4Oo6gDO5ffOaMTERoNgP19Up67t7GjXJUwpeCAm3JAZOTZZkJPUc9LBY5guMpBYDJJNfPFBcHbrRl2DBZ0d1Zbq7ydANEROFO6ed3SCxQJuV8bEKzOXJEriO5+GLPbVriBKoQ7xLoXIPNMEF4DHSAhh1OWreFB2uLtH0FeL0YncXYU6ADyOPWKUYiIpIY7ISZnj2VtVu9Wq4jcdqkBgAwoR6rkYsTSEI8zjScmDcP/a4T+BzX+Lz+f/8rn+++W1l/HO6vcou0tR6UVocPa3+tO2p3uOmputpzoGP17ruyHRERSQx2woy/+Q/vx/Oohxm5WGM79h4GI6NNHTB5Mn78Udl1rO20TGGp3SKtJGu0N0ePan+tO0ZmMVaaMyiScgsREfkryugOkDrWDLpqP/yvwlZsxdUOx6rRFJk4hONIxk/b5bH4eGXXs7arrVXW/qabgBEjZADQq5e6tSz+jsz4m6fHmfVncPhwwzZ6e9Y1O4GYotu/X992RESNAUd2wozZDHTrprx9G/wCAZNLoNMd29EM1TgOGQlYt0t36qTsutZ227Ypa//TT9q3SPs7MuOcKNFfRmYxVvvzISIiBjthp7YWeP993+3MqMNG9MEvSHM4/ie8BhMEdsKxYNSePfJ58WJl/bC2U5pEz59ke61ba38toP/IDmBcFuNnntG3HRFRY8BgJ8y89JLvwOERzEIdotEHm2zH3sRImFCP1/Ent6+xjux8/bWyfljbHT+urL3Sdu44BxRq6T2yY2VEFuO4OLm93JvcXNmOiIgkrtkJM/v2eT7XH+uwDgMcjv2CNuiE/TgF7/mDamrk808/KeuHtZ01j40v/oyu5OTIKSGto0P+jgx5Y81iHEyrVzPPDhGRGhzZCTNONVEBAJkogYDJJdC5BHuQhl98BjpAQ5V0pR+U1nYdOyprr7SdO1u3+jcN5u/IUChavVomdhw/HhgwQD6fOcNAh4jIHY7shBn77cwxqMGXuBJdsduhzZ1Yjrdxp6rrWhcanz6trL21XU4O8N57vtv7U5PVn3w1wUhcaJS4OGDBAqN7QUQU+jiyE2aseXbmYjpq0NQh0FmA8TChXnWgAzTsHLrgAmXtre2Ki5W1V9rOHa35akymwO2KCgW1tfL9TZwon5WmASAiamwY7ISZ+oLVEDBhOv5hO7YPFyIOZzARCwAorCfh5OrfdqbfdJOy9tZ2RUXK2itt5441r423UhnOAU1WVmB3RRltxgygWTNg8mQ5ujN5svx6xgyje0ZEFHo4jRUu9u8HLrwQjzgd7ogf8CP8WBDzmwMH5POXXypr/+WXMuD59Vdl7ZW2c8ea1+bWW2XAY5/IzxoArVgBpKTIKS8tiQvDyYwZ7reWWywNx+fODW6fiIhCGUd2Qt3p00CHDsCFFzocHoz3YILQJdCxp7T8g7Wd0kKgWguGWvnKa3PrrXJXlNbEheGithaYN897m3nzOKVFRGSPwU6oEgIYOxZo3lwmc/nN/tseggkCH2Cwrrdr2lQ+JyUpa29tN3KksvZK23ljRF6bUKMkz5LFItsREZHEaaxQ9NZbrtHBFVcAmzejHWKA/+h/y4QE+ZyW5r2dlbXd5MnAgw/6bj95srZ+OfOU18ZikQVDI30ayzrdqFc7IqLGgCM7oaSoSC5CcQ50SkqAr74CYmKweXNgbt2hg3xWmpPG2i4mxneF7enTZbtAKSiQC5L79gXuuks+Z2U1ZIWOJO3b69uOiKgxYLATCioqZIrhLl0cj2/YIKezMjNthwoLA9OFDz6Qz716+c52nJzsmLvmyiu9t/d13h8FBcAtt7jm4iktlccjLeDp3FnfdkREjQGDHSMJAdxxB9CypWPxqKeekueuuy4o3UhNBRITG762lo7wxH7xq8UCTJrkua3JBOTn+5cB2ROLBRgxwnubESMCc2+jKK3zFah6YERE4YjBjlFeeQVo0gT4v/9rONa/P3DuHPDAAx5f5k8mYk9iYhoCgsJCoKrKe/tTpxpGmDZvBg4d8txWCDkLF4jpt7VrgbNnvbc5e1a2ixRKEyxqTcRIRBSJGOwE27ZtcrjjL39xPP7LL8C6dUCU9zXj332nf5fsgxGl02TWdkpLOfhT8sETX1uw1bYLB74SLJpMkV0ig4hICwY7wfLrr3J7UM+ejse//FIOf1grcfpgtwtdV9ZgRG2eHaXVzP2peu7JyZP6tgsH1gSLgGvAY/06kktkEBFpwWAn0CwW4IYbgNatHSOJBQtkkOMc/PjgT/Vwb6zTHmqDl927lbVX2k6N7t31bRcufCVYbEx5h4iIlDAJYZ98v3GqrKxEYmIiKioq0KJFC/0ufPBgw55uq1tvBd5+W67X0aC2Vla7VjoC44vJJD8ki4vlaIC7FD/uLF0qF//26gVs2eK7/TXX6L9up7pa1oPy5cwZ+T2LNI0ltxARkSdKP7+ZVDCQFi1q+HfLljKiaNnSY/PaWpn59sABOYIzbpxrfhqzWX7A+1pErJQQjtMeapMKKg2VAxFSx8UBubnAu+96bpObG5mBDuA5wSIRETniNFYgTZ4sI4ndu4ETJ7wGOkqrWG/erF+go4fsbH3bqbV6tQxo3MnNleeJiKhxY7ATSC1byiQ0l17qtZm1irVzPhhrFWv7gEfvXU3OeXCOHFH2Oms7pUuOVC5NUmX1ajlVNX48MGCAfD5zhoEOERFJDHYMpraKtd75U5zz4Hz1lbLXWdvt3KmsvdJ2WsXFyRGxTz6Rz5E6dUVEROox2DGY2irW1jwrerOOGKldg2Pkmh0iIiIlGOwYTG0Va7MZGD5c/35YR4w6dVLW3tpObXsiIqJgY7BjMKV5c6ztLBZg+XLvbZ23H3vbjuyccXfsWGX9sbYbN873dmezWbYjIiIyAoOdALFYZEmF5cvls6epKrXBgq9aVNZ7z58PLFsGbNwo++CpvADguPVc7ZqdmBhgyhTvbadMcd1CT0REFCwMdgKgoABo3x7o2xe46y753L69PO5MbbCgdDdWaqqc7urTB7jtNmDaNPcjPtOmOWbc1VLrau5cYPp099efPl2eJyIiMgqDHZ0VFMgkyc6jL4cPy+PuAh41wYKWqtcFBcA//uE6ulRfL4/b90lrVe25c+V27/nzgQkT5POZMwx0iIjIeIaWi/jss8/wzDPPYOfOnSgtLcWqVaswbNgw2/nRo0fjjTfecHhNz5498eWXX9q+rqmpwbRp07B8+XJUV1ejX79+eOmll5CpYsuSXuUiLBY5guNpmsm5NIMzJRmUrfc4fNj9Difne6jtk9rrExERGUXp57ehIzunT5/GpZdeigULFnhsM3DgQJSWltoeH374ocP5/Px8rFq1CitWrMCWLVtQVVWFwYMHw+JrP3cA+FpP45zTxpnZDFx2GZCTI5/dBRNqq16r7ROrahMRUaQxtDbWoEGDMGjQIK9tYmNjkeahYFNFRQVef/11vPnmm+jfvz8AYOnSpcjKysL69etxww03uH1dTU0NampqbF9XVlZqfAeOtKx3sSookMmW7QOTzEwZeDhXsbZWvXbX/rnn/F+Do+b6REREoS7kC4EWFhaiTZs2aNmyJXr37o0nn3wSbdq0AQDs3LkT586dw4ABA2ztMzIykJ2dja1bt3oMdubMmYPHH39c975qXe9iXefjPG1kXefzzjvuA57cXPdVr+2rYf/yi7Y+ebu+J6zCTUREocjQNTv2TCaTy5qdt99+G82bN0e7du1QXFyMv/3tb6irq8POnTsRGxuLZcuW4e6773YYpQGAAQMGoEOHDnj11Vfd3svdyE5WVpbfa3Zqa2XxTm8zaGazXLhrXYvj7zofZ+5GiKwBkB7XV3NfTyNTREREelC6ZiekR3buuOMO27+zs7PRvXt3tGvXDh988AHyvHyCCiFg8pJYJjY2FrGxsbr2FQC2blVW+mHrVrklHFC+puaFF+R2cm8jJp5GiLwFOoD/a3C0jExpxdEjIiJSK6y2nqenp6Ndu3bYv38/ACAtLQ21tbU4ceKEQ7vy8nKkpqYGvX9a1scofc3kyd5z9lgscmTF2zidc1CQmel/IOLtvtZj9lXV/aEmfxEREZFVWAU7x44dQ0lJCdJ/W2DSrVs3REdHY926dbY2paWl2LNnD3JycoLePy1rdrRUMXeXs0dLZuXiYv9HXPzdgaaUlvxFREREgMHBTlVVFXbv3o3du3cDAIqLi7F79278/PPPqKqqwrRp0/DFF1/g4MGDKCwsxJAhQ5CSkoKbb74ZAJCYmIgxY8Zg6tSp2LBhA77++muMHDkSnTt3tu3OCiZrRXJPM2jOdaiUvMYddyMmWjIr6zH9488ONKWCOXpERESRx9BgZ8eOHejatSu6du0KAJgyZQq6du2KRx99FGazGUVFRcjNzcWFF16IUaNG4cILL8QXX3yBhIQE2zXmz5+PYcOG4fbbb8fVV1+NZs2a4b333oPZgIUcWnLUeHuNN84jJlp3gvkrGPdVs67JVy0yIiJqfEJmN5aR9MqgbOVuZ1JWlvccNe5eo8SyZXKkxqjMx752kwHyvftz3+XL5RodNbgTjIgo8oVFBuVIlZcHHDwo18UoXR/j/Jr585XdyzpiYlTmY7NZBlve3Hmnf/fVa10TERE1ThzZgf4jO3rQOlKjZVRJj34GcmTH1/fCE9bxIiKKbBzZCXNaR2q0jCr5Q8kuMH93Y+m1romIiBonBjshzFqj6rzzHI/7yo9jNsvdVnruuvIkGLuxAM/fi2Dcm4iIwltIZ1AmbTWqgimYu8Ccvxe//CKTLQbj3kREFL64ZgehuWYnXBi1C8z+3oFcL0RERKGLa3YoKIzaBWa9d6B3ghERUfhjsEN+07q2yF8Wi8zB482KFUwwSETU2HHNDunCiLVFanaCWavMExFR48NghzyyWEJ3YTQQvJ1gREQU3hjshLFABiPukhN6K8Ggtr0ejKoHRkRE4YVrdsJUQYHcidS3r6wb1bev/FqP8ggFBbLUgvMUkacSDGrb60VLlXkiImp8GOyEoUAGFxaLHKFxt43ceiw/v2HRr9r2ejJyJxgREYUPBjthJtDBha9Fv84lGNS215tRO8GIiCh8cM1OmFETXGjZgaR20W8oLBIO9SzTRERkLAY7YSbQwYXaRb+hskjYWg+MiIjIGaexwkyggwu1i365SJiIiEIdg50wE+jgQu2iXy4SJiKiUMdgJ8wEI7hQu+iXi4SJiCiUseo5wrPqubskfllZMtDRK7hQm7Qw1DMuExFRZFH6+c1gB+EZ7AAMLoiIqHFT+vnN3VhhjDuQiIiIfOOaHSIiIopoHNkhj7hmh4iIIgGDHXIrHKqeExERKcFpLHIRLlXPiYiIlOBuLITvbqxAsFiA9u09198ymeSITXGxnKJS256IiEgvSj+/ObJDDsKt6jkREZEvDHbIQThWPSciIvKGwQ45CNeq50RERJ4w2CEHrHpORESRhsEOOWDVcyIiijQMdsgFq54TEVEkMTTY+eyzzzBkyBBkZGTAZDJh9erVDueFEJg5cyYyMjIQFxeHPn36YO/evQ5tampqMHHiRKSkpCA+Ph5Dhw7FIW/bg0iRvDzg4EFg40Zg2TL5XFzsOXBR256IiChYDA12Tp8+jUsvvRQLFixwe37u3LmYN28eFixYgO3btyMtLQ3XX389Tp06ZWuTn5+PVatWYcWKFdiyZQuqqqowePBgWCyWYL2NiGUtNDp8uHz2NRWltj0REVEwhExSQZPJhFWrVmHYsGEA5KhORkYG8vPz8cADDwCQozipqal4+umnMXbsWFRUVKB169Z48803cccddwAAjhw5gqysLHz44Ye44YYbFN2bSQWJiIjCT9gnFSwuLkZZWRkGDBhgOxYbG4vevXtj69atAICdO3fi3LlzDm0yMjKQnZ1ta+NOTU0NKisrHR5EREQUmUI22CkrKwMApKamOhxPTU21nSsrK0NMTAxatWrlsY07c+bMQWJiou2RlZWlc++JiIgoVIRssGNlctrPLIRwOebMV5uHHnoIFRUVtkdJSYkufSUiIqLQE7LBTlpaGgC4jNCUl5fbRnvS0tJQW1uLEydOeGzjTmxsLFq0aOHwICIiosgUssFOhw4dkJaWhnXr1tmO1dbWYtOmTcjJyQEAdOvWDdHR0Q5tSktLsWfPHlsbIiIiatyijLx5VVUVfvjhB9vXxcXF2L17N5KSktC2bVvk5+dj9uzZ6NSpEzp16oTZs2ejWbNmuOuuuwAAiYmJGDNmDKZOnYrk5GQkJSVh2rRp6Ny5M/r372/U2yIiIqIQYmiws2PHDvTt29f29ZQpUwAAo0aNwuLFizFjxgxUV1dj3LhxOHHiBHr27Im1a9ciISHB9pr58+cjKioKt99+O6qrq9GvXz8sXrwYZiZ5ISIiIoRQnh0jMc8OERFR+FH6+W3oyE6osMZ7zLdDREQUPqyf277GbRjsALbyE8y3Q0REFH5OnTqFxMREj+c5jQWgvr4eR44cQUJCgs8cPmpUVlYiKysLJSUljWZ6jO+Z7zlS8T3zPUeqcH7PQgicOnUKGRkZaNLE8wZzjuwAaNKkCTIzMwN2/caYy4fvuXHge24c+J4bh3B9z95GdKxCNs8OERERkR4Y7BAREVFEY7ATQLGxsXjssccQGxtrdFeChu+5ceB7bhz4nhuHxvCeuUCZiIiIIhpHdoiIiCiiMdghIiKiiMZgh4iIiCIagx0iIiKKaAx2AuSzzz7DkCFDkJGRAZPJhNWrVxvdpYCaM2cOevTogYSEBLRp0wbDhg3Dvn37jO5WQL388svo0qWLLRHXVVddhY8++sjobgXNnDlzYDKZkJ+fb3RXAmrmzJkwmUwOj7S0NKO7FXCHDx/GyJEjkZycjGbNmuGyyy7Dzp07je5WwLRv397l52wymTB+/HijuxYwdXV1+Otf/4oOHTogLi4O559/Pp544gnU19cb3TXdMYNygJw+fRqXXnop7r77btxyyy1GdyfgNm3ahPHjx6NHjx6oq6vDI488ggEDBuC7775DfHy80d0LiMzMTDz11FO44IILAABvvPEGcnNz8fXXX+OSSy4xuHeBtX37dixcuBBdunQxuitBcckll2D9+vW2r81ms4G9CbwTJ07g6quvRt++ffHRRx+hTZs2OHDgAFq2bGl01wJm+/btsFgstq/37NmD66+/HrfddpuBvQqsp59+Gq+88greeOMNXHLJJdixYwfuvvtuJCYmYtKkSUZ3T1cMdgJk0KBBGDRokNHdCJqPP/7Y4etFixahTZs22LlzJ6699lqDehVYQ4YMcfj6ySefxMsvv4wvv/wyooOdqqoqjBgxAq+99hpmzZpldHeCIioqqlGM5lg9/fTTyMrKwqJFi2zH2rdvb1yHgqB169YOXz/11FPo2LEjevfubVCPAu+LL75Abm4ubrrpJgDyZ7x8+XLs2LHD4J7pj9NYFBAVFRUAgKSkJIN7EhwWiwUrVqzA6dOncdVVVxndnYAaP348brrpJvTv39/orgTN/v37kZGRgQ4dOuDOO+/Ejz/+aHSXAmrNmjXo3r07brvtNrRp0wZdu3bFa6+9ZnS3gqa2thZLly7FPffco2tx6FBzzTXXYMOGDfj+++8BAN988w22bNmCG2+80eCe6Y8jO6Q7IQSmTJmCa665BtnZ2UZ3J6CKiopw1VVX4ezZs2jevDlWrVqFiy++2OhuBcyKFSuwa9cubN++3eiuBE3Pnj2xZMkSXHjhhfjll18wa9Ys5OTkYO/evUhOTja6ewHx448/4uWXX8aUKVPw8MMPY9u2bbj//vsRGxuLP/7xj0Z3L+BWr16NkydPYvTo0UZ3JaAeeOABVFRU4KKLLoLZbIbFYsGTTz6J4cOHG9013THYId1NmDAB3377LbZs2WJ0VwLud7/7HXbv3o2TJ09i5cqVGDVqFDZt2hSRAU9JSQkmTZqEtWvXomnTpkZ3J2jsp6M7d+6Mq666Ch07dsQbb7yBKVOmGNizwKmvr0f37t0xe/ZsAEDXrl2xd+9evPzyy40i2Hn99dcxaNAgZGRkGN2VgHr77bexdOlSLFu2DJdccgl2796N/Px8ZGRkYNSoUUZ3T1cMdkhXEydOxJo1a/DZZ58hMzPT6O4EXExMjG2Bcvfu3bF9+3Y8//zzePXVVw3umf527tyJ8vJydOvWzXbMYrHgs88+w4IFC1BTUxPxC3cBID4+Hp07d8b+/fuN7krApKenuwTsv//977Fy5UqDehQ8P/30E9avX4+CggKjuxJw06dPx4MPPog777wTgAzmf/rpJ8yZM4fBDpE7QghMnDgRq1atQmFhITp06GB0lwwhhEBNTY3R3QiIfv36oaioyOHY3XffjYsuuggPPPBAowh0AKCmpgb//e9/0atXL6O7EjBXX321S+qI77//Hu3atTOoR8Fj3VxhXbQbyc6cOYMmTRyX7prNZm49J+Wqqqrwww8/2L4uLi7G7t27kZSUhLZt2xrYs8AYP348li1bhnfffRcJCQkoKysDACQmJiIuLs7g3gXGww8/jEGDBiErKwunTp3CihUrUFhY6LIzLVIkJCS4rMGKj49HcnJyRK/NmjZtGoYMGYK2bduivLwcs2bNQmVlZcT95Wtv8uTJyMnJwezZs3H77bdj27ZtWLhwIRYuXGh01wKqvr4eixYtwqhRoxAVFfkfj0OGDMGTTz6Jtm3b4pJLLsHXX3+NefPm4Z577jG6a/oTFBAbN24UAFweo0aNMrprAeHuvQIQixYtMrprAXPPPfeIdu3aiZiYGNG6dWvRr18/sXbtWqO7FVS9e/cWkyZNMrobAXXHHXeI9PR0ER0dLTIyMkReXp7Yu3ev0d0KuPfee09kZ2eL2NhYcdFFF4mFCxca3aWA++STTwQAsW/fPqO7EhSVlZVi0qRJom3btqJp06bi/PPPF4888oioqakxumu6MwkhhDFhFhEREVHgMc8OERERRTQGO0RERBTRGOwQERFRRGOwQ0RERBGNwQ4RERFFNAY7REREFNEY7BAREVFEY7BDREREEY3BDhGFncWLF6Nly5aG9qFPnz7Iz883tA9EpAwzKBORbkaPHo033njD5fgNN9yga82w6upqnDp1Cm3atNHtmmodP34c0dHRSEhIMKwPRKRM5Fc6I6KgGjhwIBYtWuRwLDY2Vtd7xMXFGV5gNikpydD7E5FynMYiIl3FxsYiLS3N4dGqVSvbeZPJhH/961+4+eab0axZM3Tq1Alr1qxxuMaaNWvQqVMnxMXFoW/fvnjjjTdgMplw8uRJAK7TWDNnzsRll12GN998E+3bt0diYiLuvPNOnDp1ytZGCIG5c+fi/PPPR1xcHC699FK88847Xt/LSy+9hE6dOqFp06ZITU3FrbfeajtnP41VWFgIk8nk8hg9erSt/XvvvYdu3bqhadOmOP/88/H444+jrq5O5XeXiLRgsENEQff444/j9ttvx7fffosbb7wRI0aMwPHjxwEABw8exK233ophw4Zh9+7dGDt2LB555BGf1zxw4ABWr16N999/H++//z42bdqEp556ynb+r3/9KxYtWoSXX34Ze/fuxeTJkzFy5Ehs2rTJ7fV27NiB+++/H0888QT27duHjz/+GNdee63btjk5OSgtLbU9Pv30UzRt2tTW/pNPPsHIkSNx//3347vvvsOrr76KxYsX48knn1T7rSMiLQytuU5EEWXUqFHCbDaL+Ph4h8cTTzxhawNA/PWvf7V9XVVVJUwmk/joo4+EEEI88MADIjs72+G6jzzyiAAgTpw4IYQQYtGiRSIxMdF2/rHHHhPNmjUTlZWVtmPTp08XPXv2tN2jadOmYuvWrQ7XHTNmjBg+fLjb97Jy5UrRokULh2va6927t5g0aZLL8V9//VV07NhRjBs3znasV69eYvbs2Q7t3nzzTZGenu722kSkL67ZISJd9e3bFy+//LLDMef1LV26dLH9Oz4+HgkJCSgvLwcA7Nu3Dz169HBof8UVV/i8b/v27R0WC6enp9uu+d133+Hs2bO4/vrrHV5TW1uLrl27ur3e9ddfj3bt2uH888/HwIEDMXDgQNvUmyfnzp3DLbfcgrZt2+L555+3Hd+5cye2b9/uMJJjsVhw9uxZnDlzxus1ich/DHaISFfx8fG44IILvLaJjo52+NpkMqG+vh6AXFtjMpkczgsFm0a9XdP6/MEHH+C8885zaOdp8XRCQgJ27dqFwsJCrF27Fo8++ihmzpyJ7du3e9z2/pe//AU///wztm/fjqiohv+91tfX4/HHH0deXp7La5o2berzvRGRfxjsEFFIueiii/Dhhx86HNuxY4df17z44osRGxuLn3/+Gb1791b8uqioKPTv3x/9+/fHY489hpYtW+LTTz91G7TMmzcPb7/9Nr744gskJyc7nLv88suxb98+n0EgEQUGgx0i0lVNTQ3KysocjkVFRSElJUXR68eOHYt58+bhgQcewJgxY7B7924sXrwYAFxGfJRKSEjAtGnTMHnyZNTX1+Oaa65BZWUltm7diubNm2PUqFEur3n//ffx448/4tprr0WrVq3w4Ycfor6+Hr/73e9c2q5fvx4zZszAiy++iJSUFNv7j4uLQ2JiIh599FEMHjwYWVlZuO2229CkSRN8++23KCoqwqxZszS9JyJSjruxiEhXH3/8MdLT0x0e11xzjeLXd+jQAe+88w4KCgrQpUsXvPzyy7bdWP7k6/n73/+ORx99FHPmzMHvf/973HDDDXjvvffQoUMHt+1btmyJgoICXHfddfj973+PV155BcuXL8cll1zi0nbLli2wWCy47777HN73pEmTAMikiu+//z7WrVuHHj164Morr8S8efPQrl07ze+HiJRjBmUiCnlPPvkkXnnlFZSUlBjdFSIKQ5zGIqKQ89JLL6FHjx5ITk7G559/jmeeeQYTJkwwultEFKYY7BBRyNm/fz9mzZqF48ePo23btpg6dSoeeugho7tFRGGK01hEREQU0bhAmYiIiCIagx0iIiKKaAx2iIiIKKIx2CEiIqKIxmCHiIiIIhqDHSIiIopoDHaIiIgoojHYISIiooj2//aCkiiTIsslAAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"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": {},
|
||
"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",
|
||
"\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",
|
||
"\n",
|
||
"* Root Mean Squared Error (RMSE). \n",
|
||
"\n",
|
||
"* R-squared is not an error, but rather a popular metric to measure the performance of your regression model. It represents how close the data points are to the fitted regression line. The higher the R-squared value, the better the model fits your data. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse).\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Mean absolute error: 25.92\n",
|
||
"Residual sum of squares (MSE): 1131.33\n",
|
||
"R2-score: 0.74\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": {},
|
||
"source": [
|
||
"## Exercise\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Lets see what the evaluation metrics are if we trained a regression model using the `FUELCONSUMPTION_COMB` feature.\n",
|
||
"\n",
|
||
"Start by selecting `FUELCONSUMPTION_COMB` as the train_x data from the `train` dataframe, then select `FUELCONSUMPTION_COMB` as the test_x data from the `test` dataframe\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n",
|
||
"\n",
|
||
"test_x = test[[\"FUELCONSUMPTION_COMB\"]]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"<details><summary>Click here for the solution</summary>\n",
|
||
"\n",
|
||
"```python \n",
|
||
"train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n",
|
||
"\n",
|
||
"test_x = test[[\"FUELCONSUMPTION_COMB\"]]\n",
|
||
"\n",
|
||
"```\n",
|
||
"\n",
|
||
"</details>\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Now train a Linear Regression Model using the `train_x` you created and the `train_y` created previously\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
|
||
" normalize=False)"
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"regr = linear_model.LinearRegression()\n",
|
||
"\n",
|
||
"regr.fit(train_x, train_y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"<details><summary>Click here for the solution</summary>\n",
|
||
"\n",
|
||
"```python \n",
|
||
"regr = linear_model.LinearRegression()\n",
|
||
"\n",
|
||
"regr.fit(train_x, train_y)\n",
|
||
"\n",
|
||
"```\n",
|
||
"\n",
|
||
"</details>\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Find the predictions using the model's `predict` function and the `test_x` data\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"predictions = regr.predict(test_x)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"<details><summary>Click here for the solution</summary>\n",
|
||
"\n",
|
||
"```python \n",
|
||
"predictions = regr.predict(test_x)\n",
|
||
"\n",
|
||
"```\n",
|
||
"\n",
|
||
"</details>\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Finally use the `predictions` and the `test_y` data and find the Mean Absolute Error value using the `np.absolute` and `np.mean` function like done previously\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Mean Absolute Error: 21.60\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"#ADD CODE\n",
|
||
"print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"<details><summary>Click here for the solution</summary>\n",
|
||
"\n",
|
||
"```python \n",
|
||
"print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n",
|
||
"\n",
|
||
"```\n",
|
||
"\n",
|
||
"</details>\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"We can see that the MAE is much worse when we train using `ENGINESIZE` than `FUELCONSUMPTION_COMB`\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Thank you for completing this lab!\n",
|
||
"\n",
|
||
"\n",
|
||
"## Author\n",
|
||
"\n",
|
||
"Saeed Aghabozorgi\n",
|
||
"\n",
|
||
"\n",
|
||
"### Other Contributors\n",
|
||
"\n",
|
||
"<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
|
||
"\n",
|
||
"Azim Hirjani\n",
|
||
"\n",
|
||
"## <h3 align=\"center\"> © IBM Corporation. All rights reserved. <h3/>\n",
|
||
"\n",
|
||
"<!--\n",
|
||
"## Change Log\n",
|
||
"\n",
|
||
"\n",
|
||
"| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n",
|
||
"|---|---|---|---|\n",
|
||
"| 2020-11-03 | 2.1 | Lakshmi Holla | Changed URL of the csv |\n",
|
||
"| 2020-08-27 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n",
|
||
"| | | | |\n",
|
||
"| | | | |\n",
|
||
"\n",
|
||
"--!>\n",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"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": "3.7.12"
|
||
},
|
||
"prev_pub_hash": "20d6dc1d9e74df451be22381c972d7921c93657bea402a00c749dca52bb85996"
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|