1457 lines
307 KiB
Plaintext
1457 lines
307 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"<p style=\"text-align:center\">\n",
|
||
" <a href=\"https://skills.network\" target=\"_blank\">\n",
|
||
" <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",
|
||
" </a>\n",
|
||
"</p>\n",
|
||
"\n",
|
||
"\n",
|
||
"# Simple Linear Regression\n",
|
||
"\n",
|
||
"\n",
|
||
"Estimated time needed: **15** minutes\n",
|
||
" \n",
|
||
"\n",
|
||
"## Objectives\n",
|
||
"\n",
|
||
"After completing this lab you will be able to:\n",
|
||
"\n",
|
||
"* Use scikit-learn to implement simple Linear Regression\n",
|
||
"* Create a model, train it, test it and use the model\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Importing Needed packages\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 82,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import pandas as pd\n",
|
||
"import pylab as pl\n",
|
||
"import numpy as np\n",
|
||
"%matplotlib inline"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Downloading Data\n",
|
||
"To download the data, we will use !wget to download it from IBM Object Storage.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 83,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"--2025-10-20 06:27:34-- https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\n",
|
||
"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",
|
||
"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",
|
||
"HTTP request sent, awaiting response... 200 OK\n",
|
||
"Length: 72629 (71K) [text/csv]\n",
|
||
"Saving to: ‘FuelConsumption.csv’\n",
|
||
"\n",
|
||
"FuelConsumption.csv 100%[===================>] 70.93K --.-KB/s in 0.004s \n",
|
||
"\n",
|
||
"2025-10-20 06:27:35 (17.3 MB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!wget -O FuelConsumption.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"In case you're working **locally** uncomment the below line. \n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 84,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#!curl https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv -o FuelConsumptionCo2.csv"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"\n",
|
||
"## Understanding the Data\n",
|
||
"\n",
|
||
"### `FuelConsumption.csv`:\n",
|
||
"We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64)\n",
|
||
"\n",
|
||
"- **MODELYEAR** e.g. 2014\n",
|
||
"- **MAKE** e.g. Acura\n",
|
||
"- **MODEL** e.g. ILX\n",
|
||
"- **VEHICLE CLASS** e.g. SUV\n",
|
||
"- **ENGINE SIZE** e.g. 4.7\n",
|
||
"- **CYLINDERS** e.g 6\n",
|
||
"- **TRANSMISSION** e.g. A6\n",
|
||
"- **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
|
||
"- **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
|
||
"- **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
|
||
"- **CO2 EMISSIONS (g/km)** e.g. 182 --> low --> 0\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Reading the data in\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 85,
|
||
"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>MODELYEAR</th>\n",
|
||
" <th>MAKE</th>\n",
|
||
" <th>MODEL</th>\n",
|
||
" <th>VEHICLECLASS</th>\n",
|
||
" <th>ENGINESIZE</th>\n",
|
||
" <th>CYLINDERS</th>\n",
|
||
" <th>TRANSMISSION</th>\n",
|
||
" <th>FUELTYPE</th>\n",
|
||
" <th>FUELCONSUMPTION_CITY</th>\n",
|
||
" <th>FUELCONSUMPTION_HWY</th>\n",
|
||
" <th>FUELCONSUMPTION_COMB</th>\n",
|
||
" <th>FUELCONSUMPTION_COMB_MPG</th>\n",
|
||
" <th>CO2EMISSIONS</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>2014</td>\n",
|
||
" <td>ACURA</td>\n",
|
||
" <td>ILX</td>\n",
|
||
" <td>COMPACT</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>AS5</td>\n",
|
||
" <td>Z</td>\n",
|
||
" <td>9.9</td>\n",
|
||
" <td>6.7</td>\n",
|
||
" <td>8.5</td>\n",
|
||
" <td>33</td>\n",
|
||
" <td>196</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2014</td>\n",
|
||
" <td>ACURA</td>\n",
|
||
" <td>ILX</td>\n",
|
||
" <td>COMPACT</td>\n",
|
||
" <td>2.4</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>M6</td>\n",
|
||
" <td>Z</td>\n",
|
||
" <td>11.2</td>\n",
|
||
" <td>7.7</td>\n",
|
||
" <td>9.6</td>\n",
|
||
" <td>29</td>\n",
|
||
" <td>221</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>2014</td>\n",
|
||
" <td>ACURA</td>\n",
|
||
" <td>ILX HYBRID</td>\n",
|
||
" <td>COMPACT</td>\n",
|
||
" <td>1.5</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>AV7</td>\n",
|
||
" <td>Z</td>\n",
|
||
" <td>6.0</td>\n",
|
||
" <td>5.8</td>\n",
|
||
" <td>5.9</td>\n",
|
||
" <td>48</td>\n",
|
||
" <td>136</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>2014</td>\n",
|
||
" <td>ACURA</td>\n",
|
||
" <td>MDX 4WD</td>\n",
|
||
" <td>SUV - SMALL</td>\n",
|
||
" <td>3.5</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>AS6</td>\n",
|
||
" <td>Z</td>\n",
|
||
" <td>12.7</td>\n",
|
||
" <td>9.1</td>\n",
|
||
" <td>11.1</td>\n",
|
||
" <td>25</td>\n",
|
||
" <td>255</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>2014</td>\n",
|
||
" <td>ACURA</td>\n",
|
||
" <td>RDX AWD</td>\n",
|
||
" <td>SUV - SMALL</td>\n",
|
||
" <td>3.5</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>AS6</td>\n",
|
||
" <td>Z</td>\n",
|
||
" <td>12.1</td>\n",
|
||
" <td>8.7</td>\n",
|
||
" <td>10.6</td>\n",
|
||
" <td>27</td>\n",
|
||
" <td>244</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" MODELYEAR MAKE MODEL VEHICLECLASS ENGINESIZE CYLINDERS \\\n",
|
||
"0 2014 ACURA ILX COMPACT 2.0 4 \n",
|
||
"1 2014 ACURA ILX COMPACT 2.4 4 \n",
|
||
"2 2014 ACURA ILX HYBRID COMPACT 1.5 4 \n",
|
||
"3 2014 ACURA MDX 4WD SUV - SMALL 3.5 6 \n",
|
||
"4 2014 ACURA RDX AWD SUV - SMALL 3.5 6 \n",
|
||
"\n",
|
||
" TRANSMISSION FUELTYPE FUELCONSUMPTION_CITY FUELCONSUMPTION_HWY \\\n",
|
||
"0 AS5 Z 9.9 6.7 \n",
|
||
"1 M6 Z 11.2 7.7 \n",
|
||
"2 AV7 Z 6.0 5.8 \n",
|
||
"3 AS6 Z 12.7 9.1 \n",
|
||
"4 AS6 Z 12.1 8.7 \n",
|
||
"\n",
|
||
" FUELCONSUMPTION_COMB FUELCONSUMPTION_COMB_MPG CO2EMISSIONS \n",
|
||
"0 8.5 33 196 \n",
|
||
"1 9.6 29 221 \n",
|
||
"2 5.9 48 136 \n",
|
||
"3 11.1 25 255 \n",
|
||
"4 10.6 27 244 "
|
||
]
|
||
},
|
||
"execution_count": 85,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df = pd.read_csv(\"FuelConsumption.csv\")\n",
|
||
"\n",
|
||
"# take a look at the dataset\n",
|
||
"df.head()\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Data Exploration\n",
|
||
"Let's first have a descriptive exploration on our data.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 86,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>MODELYEAR</th>\n",
|
||
" <th>ENGINESIZE</th>\n",
|
||
" <th>CYLINDERS</th>\n",
|
||
" <th>FUELCONSUMPTION_CITY</th>\n",
|
||
" <th>FUELCONSUMPTION_HWY</th>\n",
|
||
" <th>FUELCONSUMPTION_COMB</th>\n",
|
||
" <th>FUELCONSUMPTION_COMB_MPG</th>\n",
|
||
" <th>CO2EMISSIONS</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>1067.0</td>\n",
|
||
" <td>1067.000000</td>\n",
|
||
" <td>1067.000000</td>\n",
|
||
" <td>1067.000000</td>\n",
|
||
" <td>1067.000000</td>\n",
|
||
" <td>1067.000000</td>\n",
|
||
" <td>1067.000000</td>\n",
|
||
" <td>1067.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>2014.0</td>\n",
|
||
" <td>3.346298</td>\n",
|
||
" <td>5.794752</td>\n",
|
||
" <td>13.296532</td>\n",
|
||
" <td>9.474602</td>\n",
|
||
" <td>11.580881</td>\n",
|
||
" <td>26.441425</td>\n",
|
||
" <td>256.228679</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.415895</td>\n",
|
||
" <td>1.797447</td>\n",
|
||
" <td>4.101253</td>\n",
|
||
" <td>2.794510</td>\n",
|
||
" <td>3.485595</td>\n",
|
||
" <td>7.468702</td>\n",
|
||
" <td>63.372304</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>2014.0</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>3.000000</td>\n",
|
||
" <td>4.600000</td>\n",
|
||
" <td>4.900000</td>\n",
|
||
" <td>4.700000</td>\n",
|
||
" <td>11.000000</td>\n",
|
||
" <td>108.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>2014.0</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>4.000000</td>\n",
|
||
" <td>10.250000</td>\n",
|
||
" <td>7.500000</td>\n",
|
||
" <td>9.000000</td>\n",
|
||
" <td>21.000000</td>\n",
|
||
" <td>207.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>2014.0</td>\n",
|
||
" <td>3.400000</td>\n",
|
||
" <td>6.000000</td>\n",
|
||
" <td>12.600000</td>\n",
|
||
" <td>8.800000</td>\n",
|
||
" <td>10.900000</td>\n",
|
||
" <td>26.000000</td>\n",
|
||
" <td>251.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>2014.0</td>\n",
|
||
" <td>4.300000</td>\n",
|
||
" <td>8.000000</td>\n",
|
||
" <td>15.550000</td>\n",
|
||
" <td>10.850000</td>\n",
|
||
" <td>13.350000</td>\n",
|
||
" <td>31.000000</td>\n",
|
||
" <td>294.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>2014.0</td>\n",
|
||
" <td>8.400000</td>\n",
|
||
" <td>12.000000</td>\n",
|
||
" <td>30.200000</td>\n",
|
||
" <td>20.500000</td>\n",
|
||
" <td>25.800000</td>\n",
|
||
" <td>60.000000</td>\n",
|
||
" <td>488.000000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" MODELYEAR ENGINESIZE CYLINDERS FUELCONSUMPTION_CITY \\\n",
|
||
"count 1067.0 1067.000000 1067.000000 1067.000000 \n",
|
||
"mean 2014.0 3.346298 5.794752 13.296532 \n",
|
||
"std 0.0 1.415895 1.797447 4.101253 \n",
|
||
"min 2014.0 1.000000 3.000000 4.600000 \n",
|
||
"25% 2014.0 2.000000 4.000000 10.250000 \n",
|
||
"50% 2014.0 3.400000 6.000000 12.600000 \n",
|
||
"75% 2014.0 4.300000 8.000000 15.550000 \n",
|
||
"max 2014.0 8.400000 12.000000 30.200000 \n",
|
||
"\n",
|
||
" FUELCONSUMPTION_HWY FUELCONSUMPTION_COMB FUELCONSUMPTION_COMB_MPG \\\n",
|
||
"count 1067.000000 1067.000000 1067.000000 \n",
|
||
"mean 9.474602 11.580881 26.441425 \n",
|
||
"std 2.794510 3.485595 7.468702 \n",
|
||
"min 4.900000 4.700000 11.000000 \n",
|
||
"25% 7.500000 9.000000 21.000000 \n",
|
||
"50% 8.800000 10.900000 26.000000 \n",
|
||
"75% 10.850000 13.350000 31.000000 \n",
|
||
"max 20.500000 25.800000 60.000000 \n",
|
||
"\n",
|
||
" CO2EMISSIONS \n",
|
||
"count 1067.000000 \n",
|
||
"mean 256.228679 \n",
|
||
"std 63.372304 \n",
|
||
"min 108.000000 \n",
|
||
"25% 207.000000 \n",
|
||
"50% 251.000000 \n",
|
||
"75% 294.000000 \n",
|
||
"max 488.000000 "
|
||
]
|
||
},
|
||
"execution_count": 86,
|
||
"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": 87,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>ENGINESIZE</th>\n",
|
||
" <th>CYLINDERS</th>\n",
|
||
" <th>FUELCONSUMPTION_COMB</th>\n",
|
||
" <th>CO2EMISSIONS</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>8.5</td>\n",
|
||
" <td>196</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2.4</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>9.6</td>\n",
|
||
" <td>221</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>1.5</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>5.9</td>\n",
|
||
" <td>136</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>3.5</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>11.1</td>\n",
|
||
" <td>255</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>3.5</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>10.6</td>\n",
|
||
" <td>244</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>3.5</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>10.0</td>\n",
|
||
" <td>230</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>3.5</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>10.1</td>\n",
|
||
" <td>232</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>3.7</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>11.1</td>\n",
|
||
" <td>255</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>3.7</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>11.6</td>\n",
|
||
" <td>267</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" ENGINESIZE CYLINDERS FUELCONSUMPTION_COMB CO2EMISSIONS\n",
|
||
"0 2.0 4 8.5 196\n",
|
||
"1 2.4 4 9.6 221\n",
|
||
"2 1.5 4 5.9 136\n",
|
||
"3 3.5 6 11.1 255\n",
|
||
"4 3.5 6 10.6 244\n",
|
||
"5 3.5 6 10.0 230\n",
|
||
"6 3.5 6 10.1 232\n",
|
||
"7 3.7 6 11.1 255\n",
|
||
"8 3.7 6 11.6 267"
|
||
]
|
||
},
|
||
"execution_count": 87,
|
||
"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": 88,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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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": 89,
|
||
"metadata": {},
|
||
"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": 90,
|
||
"metadata": {},
|
||
"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": 91,
|
||
"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": [
|
||
"df.describe()\n",
|
||
"plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
|
||
"plt.xlabel(\"Cylinders\")\n",
|
||
"plt.ylabel(\"Emission\")\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"\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": 92,
|
||
"metadata": {},
|
||
"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": 93,
|
||
"metadata": {},
|
||
"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": 94,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Coefficients: [[38.74434612]]\n",
|
||
"Intercept: [125.60720169]\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": 95,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Emission')"
|
||
]
|
||
},
|
||
"execution_count": 95,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAGwCAYAAABPSaTdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB/L0lEQVR4nO3deXxTVfo/8E8a2lKgFFrsZsuighvgKCCKCyCLOgLFqig6Iyozg8MiZXXEGcUZFUVtdURQR78sIuBPKeCKgAKCqFNAZHFUxKJYqEWWlkJpIT2/P443zZ5zk5vcJP28X6+80t6c3HuSFvL0nOecxyKEECAiIiKKUXFmd4CIiIgolBjsEBERUUxjsENEREQxjcEOERERxTQGO0RERBTTGOwQERFRTGOwQ0RERDGtidkdiAT19fXYv38/kpOTYbFYzO4OERERKRBC4NixY8jOzkZcnPfxGwY7APbv34/c3Fyzu0FEREQB2LdvH3Jycrw+zmAHQHJyMgD5ZrVs2dLk3hAREZGKqqoq5Obm2j/HvWGwA9inrlq2bMlgh4iIKMr4S0FhgjIRERHFNAY7REREFNMY7BAREVFMY7BDREREMY3BDhEREcU0BjtEREQU0xjsEBERUUxjsENEREQxjcEOERERxTQGO0RERBTTGOwQERFRTIuYYGfGjBmwWCwoKCiwH7vrrrtgsVicbpdddpnT82prazFu3Di0adMGzZs3x5AhQ/Dzzz+HufdEREQUqSIi2CkpKcHLL7+Mrl27uj123XXX4cCBA/bb+++/7/R4QUEBli1bhiVLlmDjxo2orq7GoEGDYLPZwtV9IiIi8qWmxtTLmx7sVFdX44477sB//vMftG7d2u3xxMREZGZm2m+pqan2xyorK/Hqq6/imWeeQf/+/XHxxRdj4cKF2LFjB9asWRPOl0FERESuliwBLBagWTOgpMS0bpge7IwZMwY33HAD+vfv7/HxdevWIT09HZ06dcKf//xnVFRU2B/bsmULTp06hYEDB9qPZWdno3Pnzti0aZPXa9bW1qKqqsrpRkRERAY5dEgGOcOHNxxr2dK07pga7CxZsgRbt27FjBkzPD5+/fXX4/XXX8fHH3+MZ555BiUlJbjmmmtQW1sLACgvL0dCQoLbiFBGRgbKy8u9XnfGjBlISUmx33Jzc417UURERI3Z+PFAmzbOxz77DDj3XHP6A6CJWRfet28fxo8fj1WrVqFp06Ye29x66632rzt37ozu3bujXbt2eO+995Cfn+/13EIIWCwWr48/8MADmDhxov37qqoqBjxERETB2LoV6NbN+dif/wy8/LI5/XFgWrCzZcsWVFRUoJvDG2Oz2fDJJ59g1qxZqK2thdVqdXpOVlYW2rVrh927dwMAMjMzUVdXhyNHjjiN7lRUVKBXr15er52YmIjExESDXxEREVEjdOoUcMklwM6dzsfLy4GMDHP65MK0aax+/fphx44d2LZtm/3WvXt33HHHHdi2bZtboAMAhw4dwr59+5CVlQUA6NatG+Lj47F69Wp7mwMHDmDnzp0+gx0iIiIywPz5QEKCc6Azdy4gRMQEOoCJIzvJycno3Lmz07HmzZsjLS0NnTt3RnV1NaZPn46bbroJWVlZ2Lt3L6ZNm4Y2bdrgxhtvBACkpKRg5MiRmDRpEtLS0pCamorJkyejS5cuXhOeiYiIKEi//AJkZjofu/BC4Msvgfh4c/rkg2nBjj9WqxU7duzAggULcPToUWRlZaFv37544403kJycbG9XVFSEJk2aYNiwYaipqUG/fv0wb948jyNDREREFKRRo9zzcDZvds/XiSAWIYQwuxNmq6qqQkpKCiorK9HSxKVxREREEeuLLwCXKgYYNw7497/N6Q/UP78jdmSHiIiIIkBdHXDBBcCePc7HDx50X2IeoUzfVJCIiCgUbDZg3Tpg8WJ5zypCAfjPf4DEROdAZ9EimYAcJYEOwJEdIiKKQcXFcm87x7rQOTnAc88BPrZpI83+/cCZZzof695dbg7YJPpCB47sEBGRm7o64NlnZUrGs8/K76NFcTFw883OgQ4AlJXJ48XF5vQrKggB3Hmne6Dz1VeytlUUBjoAE5QBMEGZiMjR1KlAYaHztI/VCkycCMycaV6/VNhsQPv27oGOxmKRIzylpfI1kYONG4GrrnI+NnUq8OST5vRHAROUiYhIt6lTgaeecj9uszUcj+SAZ8MG74EOIAcu9u2T7fr0CVu3ItvJk8BZZwEHDjgfP3wYcKk9Ga04jUVERADkVFVhoe82hYWRPaXl+nkdbLuY9/zzQFKS8xtSXCyjwhgJdAAGO0RE9JvZs/2vWLLZZLtI9Vs1IcPaxayffpJzevfd13Ds6qvlD/i3KgWxhMEOEREBcN9GJdh2ZrjqKpmTY7F4ftxiAXJz3VNTGg0hZJZ2u3bOx7/+Gli/HoiLzbAgNl8VERHp1r69se3MYLXK5eWAe8Cjff/ss400Ofnjj2Uws3Rpw7GHHpIB0Pnnm9evMGCCMhERAQC6dDG2nVny84G33vK8z86zzzbCfXZOnJBLyY8ebThmtcoE5EayApkjO0REBAA4dMjYdmbKzwf27gXWrpUb/q5dK5ebN7pA5+mngebNnQOdd98FTp9uNIEOwJEdIiL6Tawl91qtjXh5+Q8/AGef7Xxs4EDggw9iNi/Hl8b3iomIyCMm98YAIYBBg9wDnd27gQ8/bJSBDsBgh4iIfsPk3ii3cqUMZt57r+HY44/LAOicc8zrVwTgNBYREdkxuTcKHTsGnHEGUFvbcKxFC7lRYIsW5vUrgjDYISKKADabLGFw4IDMibnqKvNGUPLzgbw88/oTSe9FxPvXv+TycUcffijzc8iOwQ4RkcmKiz2PpDz3nHkjKWYl90biexGRvvsOOPdc52NDh8o30FvSVSPGnB0iIhMVF8sNbV2LV5aVyePFxeb0ywx8LxTU1wPXXOMe6JSWAsuWMdDxgsEOEZFJbDY5iiGE+2PasYIC//WqYgHfCwUrVsght7VrG44VFck3KJK3tY4AnMYiIjLJhg3uoxiOhAD27ZPtYn2/GL4XPlRWAq1aOR9LT5e7JiYlmdGjqMORHSIikxw4YGy7aMb3wosHH3QPdNatA375hYGODhzZISIySaztWBwMvhcudu0COnd2PjZ8OPD668zLCQCDHSJqdOrqgNmzgT175Eazo0cDCQnh74e2Y3FZmedcFYtFPh4NOxYHu1w8lt6LoNhswJVXAp9/7nx83z75BlBAOI1FRI3K1KlAs2bAhAnArFnyvlkzeTzcInnH4ro6ee1x4+R9XZ33tsXFMj+2b1/g9tvlffv2+lZPRfJ7ETZvvgk0aeIc6MyeLaM/BjrBESQqKysFAFFZWWl2V4gohKZMEUJ+cni+TZliTr+WLhUiJ8e5L7m58rgZpkwRwmp17o/V6vn9WbpUCIvF/b20WORN72uItPciLA4dcn8D27YV4uRJs3sW8VQ/vy1CeBowbFyqqqqQkpKCyspKtGxEJe+JGpO6OjmC42vpstUKnDhhzpRWpOwaPHUq8NRT3h+fMgWYOVN+bbPJERxvq6i0qafSUn2vJVLei7CYNAkoLHQ+9umnQK9e5vQnyqh+fjPYAYMdosbg2WfllJU/RUVyP5fGSG9AuG6dnLLyZ+3aRrhc3J9t24CLL3Y+dvfdwP/9nyndiVaqn99MUCaiRmHPHmPb+RIpCdB6zZ7tf9M+m022KyjgcvGAnD4NdO8OfPWV8/H9+xvRUrPwY4IyETUKZ59tbDtvIikBWq/du/W143JxnRYuBOLjnQOdV16RWTp8k0KKIztEFFKRkn8xejQwebL/KZrRowO/hrd8F5ut4biW76IqnO+f6vYtWjsuF1d08KDc8djRuecC27dHx5BfDODIDhGFjBFLko2SkABMnOi7zcSJgX/21NW555m6Kiz0vYTbVbjfv5499bXjcnEFo0e7Bzr//S/wzTcMdMKIwQ4RhUQkVrCeOVOuJnL98LVanVcZBUJPvosKM96/3Fz97fLzgbfeAs4807lNTo48np9vXP+iSkmJjPjmzGk4Nnq0HALr0cO8fjVSXI0FrsYiMlqoliQbJRQJxOPGyRwdf8aOBZ5/3ncbs94/f9cFZKDj6bqRMl1puro6WebBNQGqogI44wxz+hTDuBqLiEwT6RWsExKMX15uZAK0We+fNi11883ec3C8TUtZrVxejldfBf70J+djCxcCd9xhTn/IjtNYRGS4xrgkefRo/yMZqgnQZr5/2rSUa3WC3Fzf01I2m9x3Z/Fiee9vSi+mHDggI0HHQOfii4FTpxjoRAgGO0RkuMa4JNnIBGiz37/8fGDvXrkZ4KJF8r601HugE0mJ6GF3991AdrbzsW3bgK1bZZ0rigjM2QFzdoiMpuV++FuSbFbOTihNnQo88wxQX99wLC5OVgVQTYCOpvdPS6R27ae2Gitmk5Q3bQKuuML52KRJwNNPm9OfRkr18ztiRnZmzJgBi8WCAoeJdCEEpk+fjuzsbCQlJaFPnz7YtWuX0/Nqa2sxbtw4tGnTBs2bN8eQIUPws6/JbiIKuca8JPmyy9xHXLKy5HFV0fL+2WzA+PGeAzLtWEFBjE1pnTwp5/RcA51DhxjoRLCICHZKSkrw8ssvo2vXrk7HZ86cicLCQsyaNQslJSXIzMzEgAEDcOzYMXubgoICLFu2DEuWLMHGjRtRXV2NQYMGwRZT/7qIok9jXJKsjXKUlTkf379f/3Jxf+9fXp75OTJ6EqljwuzZQFKS84t+8035QlNTzesX+Rfa4uv+HTt2THTs2FGsXr1a9O7dW4wfP14IIUR9fb3IzMwUTzzxhL3tyZMnRUpKinjxxReFEEIcPXpUxMfHiyVLltjblJWVibi4OLFy5UrlPqiWiCci/U6fFmLtWiEWLZL3p0+b3aPQOH1aiJwcIeQnn/vNYhEiN1f/6/f0/i1d6n6tnBx5PJwWLfL+eh1vixaFt1+G++kn9xfVq1fs/jJHEdXPb9NHdsaMGYMbbrgB/fv3dzpeWlqK8vJyDBw40H4sMTERvXv3xqZNmwAAW7ZswalTp5zaZGdno3PnzvY2ntTW1qKqqsrpRkShoS1JHj5c3ps99RIqoRrlcH3/VqyInM0azU6kDjkhgFtvBdq2dT6+cyfw6aex+8scg0wNdpYsWYKtW7dixowZbo+Vl5cDADIyMpyOZ2Rk2B8rLy9HQkICWrdu7bWNJzNmzEBKSor9lqu6bSgRkRfhWC4eaTkyWm0sbzW1LBaZ3hKVtbHWrZOZ5f/v/zUce/BB+UZfeKFp3aLAmBbs7Nu3D+PHj8fChQvRtGlTr+0sLv+KhBBux1z5a/PAAw+gsrLSftu3b5++zhMRuQjHKEek5chESyK1LjU1QJs2cv28o6NHgUcfNaVLFDzTgp0tW7agoqIC3bp1Q5MmTdCkSROsX78e//73v9GkSRP7iI7rCE1FRYX9sczMTNTV1eHIkSNe23iSmJiIli1bOt2IiIIRjlGOSNysMRIS0evqZFA1bpy811Ns1UlhIdCsmVxZpVmxQkaRKSkG9JTMYlqw069fP+zYsQPbtm2z37p374477rgD27Ztw1lnnYXMzEysXr3a/py6ujqsX78evXr1AgB069YN8fHxTm0OHDiAnTt32tsQEYVDOEY5IjVHRu8mhEaaOlXGJxMmyNpkEybI76dO1XGS0lL5Q5o0qeFYv35yPnDIEMP7TCYIS7q0IsfVWEII8cQTT4iUlBRRXFwsduzYIYYPHy6ysrJEVVWVvc29994rcnJyxJo1a8TWrVvFNddcIy666CJxWkeWPFdjEZFRPK2Uys01ZqWUtuLLYjF2xVe0mjLF9yqwKVP8nKC+XojBg92f+O23Yek/BU/18zui97KeOnUqampqMHr0aBw5cgQ9e/bEqlWrkJycbG9TVFSEJk2aYNiwYaipqUG/fv0wb948WKNqkpiIYkV+vtwDJxQVwB0LdVoszonKUZsjE6C6Ojnr5EthoUyz8ViiY9Uq4NprnY/961/A3/9uWB8pcrBcBFgugoiiS3GxXJXlmKycmysDnVjcrNGTZ5+VU1b+FBW5VLivrgYyMoATJxqOJSUBFRVAixYG95Lq6uRejHv2AGefLQvhqtSHUxV15SKIiEiNmTkykWLPngDaPf44kJzsHOisXCm/Z6BjOEPyqQwS0dNYRETkmbbZYGN19tk62u3eDXTq5PzA4MFypZWfrUwoMFOnAk895X7cZms4rloY1wicxgKnsYiIok1dnRwl8LWBYpO4etT2uRZxH69xfuCHH4AOHULbwUZM5WdjtcoBtWCntDiNRUREMSshAZg40fvjN+BdnKq3Ogc6Tz8ts7oZ6ITU7Nn+d/G22WS7cOE0FhFRDLHZQrMSLBJp0yCFhQ0frsmowlG0QhwcJi1SU+XW0s2ahb+TjVBA+VQhxpEdIqIYUVwMtG8vKx3cfru8b98+vMVBw23mTDkdUlQEvN/jIVQhxTnQ+egjuSMyA52w0ZVPFSbM2QFzdojIfMGOyBQXy/13XP9H1/JvQ126oboa+OMfG5YYv/ZaGBc4/e9/wAUXOB+75RbgjTeYgGwC5uwQEZGbYEdkzK6GfumlckX38uXAjh3yPjlZHg8pmw248kr3QOfHH2W1cgY6pvCXTwXIx43cb8cfBjtERGFSXg5kZgJNm8r78vKGERnXauZlZfK4t4DHZgPWrQMWLwaef974auiqxTUvvRQoKfH8WElJCAOepUuBJk2ATz9tOPb88/LFtm0boouSqpkzgSlT3EcnrVZ5PJzLzgFOYwHgNBYRhV7z5s572amwWGT18NJS5w8NTzsoq1i0CBg+3H+7qVOdk34Bef2JE50/pKqr5QiOP8eOGTildeSITDh2dOaZwPffyyiSIgp3UCYiaiQCCXQAzyMy3kaCVKhUQ9c2g3Od8tI2g3Pc/faPf1S7rmo7v6ZMcQ90NmyQbwYDnYiUkCCnUJ9/Xt6Hc+rKEYMdIqIQKi8PLNBxdOCAvPeVm+OLxSJrZ111le92qsU1tSmtsC0x/uor+SKefrrh2J13AvX1MmeHyA8GO0REIfS73wV/Dm1ERhvE0ENPNXS9m8GFfInx6dNAt27ub2JZGTB/PhOQSRmDHSKiEDp6NPDnuo7IaCM8euTkqC871ztS8+qrau1V2zlZvBiIjwe2bm049p//yGGt7OwATkiNGXdQJqKoF8m7BrdqBfzyi/7neRqRUcm5AYAxY+Tz9SaE6h2pWbBArf2CBTJfQ8nBg0B6uvOxjh2BnTvNS/igqMeRHSKKapG+a/C2bWrtMjKcv/c0InPVVfK4r9kbqxV44QVg1ixgwgQZmKi+F6NH+w8SrVbZDghBzs64ce6BzhdfAN99x0CHgsJgh4iiVqB71IRTZqb/SgXNmsk+r10rl4evXSuXm7tOPVmtwHPPya+9BTyuOTd63gu9m8EZlrOzZYt8QbNmNRwbNUpOWYV8Z0JqDLjPDrjPDlE0stnkCI63hF1ve9SYxdvy82bNgOPH9Z3L0z47Vqv35GK974XqPjtBlwU4dQro2hX45hvn47/84j7CQ+QB99khopjmb2VSILsGh9Lx4zKnKCMDSEyU9wcO6A90ADnis3dvw0hQUZHvgEPve+FYXHPsWHl/4oT7rrdBlQWYO1c+4BjozJ8vO8tAhwzGBGUiikqqK5MCWcEUKlqJCE/0JllbrUCfPvLrxYvVrq/nvdA2g/NHC4BURoIAyDfANdO6a1dg82a5+sqHUO/GS7GLwQ4RRSXVlUmq7czkaVoqJ0fm56gsGTf7vZg5E3j0UYVA5E9/cl+HvnUrcPHFfq/haWpt8mQvARWRC05jEVFUuuoqIC3Nd5u0NP+7BpvNiCRrf6u0VHdQDobVKvf+69VL3juNSn32meyEY6CjbQWtGOiolrAg8oQJymCCMlE0stlk3suhQ97bpKXJXNdISFD2xMgkay1oApzLSWgBkOrGgoHwNjL1/NO1GHr/ucCPPzo/4ddf/Ueqvwk6CZpiGhOUiSimbdjgO9AB5OORkqDsiZFJ1vn5MqA580zn43p2UA6Et5GpQT+/iKG3NXUOdJYskS9KMdAB9JewIPKEOTtEFJWiMUHZlWrfPvpILXE5Px/IywvfbtKeCpOeiZ/xM3Kd2okel8Ly2SanjqgmZIet2CjFNAY7RBSVzE7KDURNDTBlCrB7t6yAMHiw2vMefbTha3+Jy46rtELNeWRKYCH+gDuwyKlNF2zH8zO7oI9DIKMnITvkxUapUWDODpizQxSNtHyXsjLnkQWNmZsKehq1uOkmYMUK97ZNmwK1tZ5fgyfhyMFRtXixLNFxNdZjPfo4PTYDf8M0zAAg9wIaPlwe16a9XF+vt9fFnB3yhTk7RBTTfJVO8FREM1w81epq0cJzoAMAJ0/KD35f9a4caUFCQYH/XJZQOzO1BuXIcAt0WuGIPdABGkbXPE17aby9rqA2LiT6DYMdIopa/pJy8/KAdevkCMS6daEPDrwl65486f+5qsEO0JC4/Pzzwb+2ujoZFI4bJ+/r6hSf+NxzuPq6ZshAhf3QUCyDBQKVaGU/5rj8P9CE7Jkz5fRfnMsnVlycPM59dsgf5uwQUVTzlpS7YoX7sm49G/Xp5WvUQkV9vf7nTJjQ8HUgry2gjfp+/FG+sQ7Wog/64SMIP38/B5NUftll8mdbVtZwLCtLHifyhyM7RBT1tKTc4cPl/YoV4a+G7m/UItT0vjbdG/UJAdx4o1ugcx7+h2uw1mug47j8P9Ckcm3EzDHQAYD9+yOnuj1FNiYogwnKRLHErGroWrKumSwWOaU3bx5QUeF9SbdK0q/FIkd3fvwR6HN6DW56cYBzg+nTsbjTw0qvWUtQDiSpPNqq21N4qX5+cxqLiGKKnrwQI5doR8ISdyHka+/fv+GYp+ktlY36hAAennIcB5CFljjW8EBCgtwBOTkZWevU+qW9N1pS+c03yyDF007PrknlZv08KbZwGouIYkooNhtUSeL1V5/KLJ6mt1Q24JuKJ3EcLZwCnd/jPUwdXwskJwMIrCaX3p2eY2HzSDIfgx0iiilGbzY4daqc8pkwAZg1S943a+ae06KNWuhNDGjaNLQBkhDy5rik29cGfGfjewhY8CT+Zj/2Pq6HBfX4AL9HYWFDsOfvNQvhefl/fj6wdy+wdq2c4lq7Vk5DeUqujsbNIynyMNghophy1VVyXxtfkpPVKoDrTeL9/HPf5xs/HhgzBhg4UN6fOAG8/rp8LNQjQo5LukeP9pTfIvABrsP36Oh09Gx8jxvwPgDZQdc6VP5es7fHXZPKveXbREJFd4p+TFAGE5SJYkldHZCU5Hspd1ycLN3gayM6vTv3BrPTr6fyCaGwcCFwxx3yay2QA4Dr8T7exw1Obe/HE5iJ+z2eZ+xYucdPuHY3NrOiO0U27qBMRI2GzdaweeDEif73rKmv918lW2+17WCqc7tO6xQV+T5PoA4ebPh65kzg7/dVoQ7xToFOJVqiOaq9BjpAwzRYuCqSm1XRnWKHqcHOnDlz0LVrV7Rs2RItW7bE5Zdfjg8++MD++F133QWLxeJ0u8xlB6na2lqMGzcObdq0QfPmzTFkyBD8bOZmF0QUVq7lGV54Qe15u3f7flxvte1gq3M7TuuMG+d/6iYnB1izRgZH06apXfuMMxy+mT4d//p3CuJx2n7o//15NVLjKnECzb2ew2qV02CA//dQo9rOFz15PkSuTF16npOTgyeeeALnnHMOAGD+/PnIy8vDl19+iQsvvBAAcN1112Hu3Ln25yS4jIUWFBTgnXfewZIlS5CWloZJkyZh0KBB2LJlC6zcdIEopnkrKqnCX46M3mrbRlbnVlmi/dxzQL9+8ut164DHH/d/3jPPBPDNN8D55zs/8NvQyTCLBZtbNUxveeJYh0o1z8iofKRwVnSnGCMiTOvWrcUrr7wihBBixIgRIi8vz2vbo0ePivj4eLFkyRL7sbKyMhEXFydWrlypfM3KykoBQFRWVgbcbyLS7/RpIdauFWLRInl/+rS+5+bkaGuN9N/mzfN9/tpaIaxW3+ewWmW7QNqrWLrU/TXm5srjet+Ltjk2UX/V1e4P7N3rdt0pU9xfi9UqjztasEDtvV6wQP01E+mh+vkdMTk7NpsNS5YswfHjx3H55Zfbj69btw7p6eno1KkT/vznP6OioqHo3JYtW3Dq1CkMHDjQfiw7OxudO3fGpk2bvF6rtrYWVVVVTjciCi9P1cHbt1ff+j/Y8gxHjvh+XG+17WCrc3vay0d16kYbCfI2gjIUy/Hjz1ZYNnzScPDZZ2Us0q6dW/uZM2VScVGRTEYuKpLfu9bLys31/Xr1tiMKmTAFX15t375dNG/eXFitVpGSkiLee+89+2NLliwR7777rtixY4d4++23xUUXXSQuvPBCcfLkSSGEEK+//rpISEhwO+eAAQPEX/7yF6/XfPjhhwUAtxtHdojCY+lSISwW9xEAi0XeXEcuPFm0KPBRHUCIhQv9X2PKFN/ncB3p0J6jMioS7HM8jYq5jgSl4Ih7pzMzhThxwv+LV3D6tBBpab7fo7Q0fSN2RHqojuyYHuzU1taK3bt3i5KSEvG3v/1NtGnTRuzatctj2/3794v4+Hix9Lf/Cb0FO/379xejRo3yes2TJ0+KyspK+23fvn0MdojCxN+Ui8Uip2r8fUCuXRtcsLNmje/zBzMtVVsrRFGREGPHynutzaFDQnTuLERqqrw/dCiwgMrT9FZOjjyuBUG7Bt/vfrL16/3/gHRgsENmi5pgx1W/fv18jsqcc8454oknnhBCCPHRRx8JAOLw4cNObbp27Soeeugh5WsyZ4cofFSDlLVrfZ9HC5o8jRAZEewUFamdp6hI7XVnZATWT9eAyt+o2OrC7e4P/uEPQtTXq3VUB6N+lkSBirqcHY0QArW1tR4fO3ToEPbt24es3/YF79atG+Lj47F69Wp7mwMHDmDnzp3o1atXWPpLRPoYVetIy1MBAlvt45D+55GRy6ozM4FfflE7nyvHfWpsNrn5oBDu7SzChi9ED/Sf2NX5gZ9/Bl57LSRbNLNuFUULU4OdadOmYcOGDdi7dy927NiBBx98EOvWrcMdd9yB6upqTJ48GZ999hn27t2LdevWYfDgwWjTpg1uvPFGAEBKSgpGjhyJSZMm4aOPPsKXX36JP/zhD+jSpQv6O5b9JaKIYWStI2+bzRlxfqOWVR8+HHigo9H25vGWlD0Mb8CGJuiBzQ0HX3xRRkWBvDmKWLeKooWp++z88ssv+OMf/4gDBw4gJSUFXbt2xcqVKzFgwADU1NRgx44dWLBgAY4ePYqsrCz07dsXb7zxBpJ/q7gLAEVFRWjSpAmGDRuGmpoa9OvXD/PmzeMeO0QRSqt1VFbmZYTitw3zVGsd5ecDeXkyEDhwAEhPB+66K/jz9+yptkFhz56+H+/d2/85/NH25nEdIUnFIRxCG6djP6ADFvztfzi3ZSKy1snXGar/Do3+WRKFTHhm1SIbc3aIwkvLO3HNPdGzGivU51+zxpjcn9TU4BKpHXN2HHNkijDerXFPfOb2fC1xOVSWLvXd/1Bemyhqc3aIKPb5q3V0/fVyf5drr5X3NTWBnT872/n4mWeGv5aSax/0ctyb56qrgGvTv4SABQV4zt7mFYyEBQJf4DK355eVyZ2YVfcv0mvBguAeJwoHBjtEZApvG+YtWCArab/wArBqlbxv1gwYOlTf+T//3H3aZ/9+eVxFebkx7davVztPnMv/xlYrMGWKw0Z+p07B+rsuWFlxiVO7TBzAn/GK1/Nq00sFBf6LdupVUwOsWOG7zYoV+oNVIqMx2CGikHKsSL5unfMHrmPxyz59gJtu8v7huWKFesAzdaqs7+Ra/by+Xh6fOtX/ORwrhAfTLjUVyMjw3SYjQwYEXncsXrBADu/s3Gl/zsTWc2GBwC/I9NtHIYB9+2Rek5GmTDG2HVGomJqgTESxrbhYLpV2XEGUkyOXjLtOJekZJUhK8t6mrg4oLPR9nsJC4NFHvZduAIC0NN/n0NOuvNz78vOMjIbRoYIClwd/+UU+0dEFFwDbtuGpuHgM+S0p++uv5evxx+gl4N99Z2w7olDhyA5RI+dr5CUYWkVy16XS3nJIjBolmD3b/2tw3LvGm0OH1Pqj2q68XAYbGRlAYqK8P3DAxzTYqFHugc7mzcCuXUB8vNOomFb93B+jl4A3b25sO6JQYbBD1IgFW4zTG1+b33nLITFqEz9tTxp//LVr3VrtPKrtpk6Vo1q//ALU1sr7nBwPU2pffCHXbL/8csOxsWPlG9etm8dza0vAve35Y7HIYpxGLwFXnVbUm29FZDQGO0SNlN6RFz38VST3lEPSsaPauf210/ak8cdfu5IStfOotNNyiFxHnGw2hxyiujrZqctcVlQdPAg8/7zP8/vaTVr7/tlnjd9vx0PB9KDaEYUKgx2iRiiQkRc9Aikj8NRTas/x1270aP8f6larbOeL6mv3104lh6jq6Zfl3NYPPzQcXLRI/jDatPH+RAfelvOHcrm9NqLkSyhGlIj0YrBD1AgFMvKiRyBlBJKS5E7IvuTleU5Odsw72rTJQ6KvC8e9a7xRHQXx185XDlEW9kPAghfFqIaD3bsDp07JZJwAuAawngJao2gjShaL5xEliyU0I0pEejHYIWqEQl3AMdAckuXLvQc8eXnycVee8o7eeEO2d/2Qddu7xgd/ZSBU23nODRJYgD9iP1yGYb76Ss6LNdG/UFabliwrcz6+f39oNxXMzwcmT3bfJyguTh4P5waORN5w6TlRIxRMAUebraEOVVaW59pL2l/8N98sAxvH0QV/OSTLl8vl5VOmyGTkjh3l1JWnER3tA9519KKsTN5efx349FP/5/FEdedjf+1cc4OuwEZshHOUNxNTkFA0EwUuBctV+ZuWtFjkaJenANDXOf39nAH5M3j6afdr22zy+GWXMeChCBCm8hURjbWxqLE5fVrWTHKtHeVYQyo3V7ZztHSpfJ5q7SVP7XNzjamXpL0Gf3WlAq0TZVRtrNpa2Y9E1IgyZLmdoBUOO9W/CoRjzSxft7Vr1c6n+nP29zPw9ntEZBTWxiIirwJZvRPI6i1vJSGM+EvfX94R4J4ro2elWUWFWj/8tUtIAN7q8zxOIgnZaJgXzMdSWCBwFK2Vcoh8MXJaUs/POdS5X0RGYbBDFIRQbcgXDv6KcToGJMGs3nItCaEyjaLyvgaST6RnpVkwU312P/0EWCwY+tF99kMbcCWsOI1lyNeVQxTyvkL/zznUuV9ERmGwQxSgUG3IF06qIy/h/Ate9X0NdDdg1b4GtVGfEMAtt7htMFO37WtsKdqA0WOt7vWvgmDUEnC9P2ejgiyiUGOwQxSAUG7IF4nC9Re8nvfVXzDij7++BrxR38cfy6VIb73VcOwf/wCEQMJF56OgQO4RWFAQ3NSVa1/9rVS/7Tb/o2p6f85XXQW0aOG7bXIy99kh8zHYIdIp1BvyhZPRoyjB/AWv9331FYyoUOmrnqk+nDgha0c4FqqKiwMqK4F//lN/B3Ww2eSUny9Llhg/dWezyZfty/Hj0fFvgWIbgx0inWIlKVPvKIq/6t5pacH9BR/I++otGPE1gmGxyGBFCxD85Vrl58u9coqKZImqoiLg++9dAp2nn5bVLo8ebTj29tvyxC1bej+5QVSStUMxdTd7NlBf7/uc9fWei65Gc74bRR/us0OkUywkZYZiX5ZgBfq+5ufLfjruCXPwIHDrrfJx1z1+hJD7+PTv33A8J0eOEnlaJVZcLN8rx2DimWd+a/+7H9w30hkwAFi50n2XvRAy6ndS7/5I336rdl3Xdp7eU18/A6JgcWSHSKdYSMrUO4qyYQNw6JDvcx46FNxoVjDvq+uKr1tu8Tzik5ra0FdH3nKtvI1+/fyzQOJNN7gHOt99B6xaFdZABzD2d1LP1F15udp1Hds1tnw3igwMdoh0CmqVjoGCmQbQOxKwb59ae9V2nvTqpVbAs1cvtfO5rjRbs8b77smecoK8jX5dhw8gEIcb8H7Dwccflw39lGQ/eBDo0EEm9XboIL83gtG/k6qr9ALJ8YmVfDeKLgx2iHQKeJWOgYJd9q73Q+qLL9Taq7bzZNMm/x9yNptsp8pxxMdq1T+a5di+BY7hJBLxAX5vP3YMLbDh/WPAAw/47UurVkB6ugwijh+X9+np8niwQvE7qbI/UqdOaufS2sVKvhtFHwY7RAHQtUrHYEZMA+gdCVCtnB1Mhe1Q50LpPb9jQc2/4184hpZIRJ392EB8iJY4hp8O+1l7DRnQVFZ6fqyy0piAx4zfydGj1UbjRo+WX8dCvhtFJyYoEwXIU2Kst2KJRjEqsVhvIqqf2Rk71XaeaPk0RrVzpXc06+BBoCO+w3c41+nx5cjDjVgGwGJv58vBg94DHU1lpWx3xhlqffQm3L+TCQnAxImywKo3jqUw0tPVzqvajkgVR3aIghBIKYRgqE4DPP+8/1wePSMBo0f7z7mNi2v4Cz4QK1YY286VruXz9fW47T/XuAU67VGKG7EcWqAD+A9QLr1UrX+q7SLNzJmy5IXr775RpTCIjMCRHaIoojq8P2FCw9e+lvSqjgRYrUCzZkB1tfdrNmsWXLC3Z4+x7QL2ztvAjXnIdDg0Ec+gCBM9Nvc3BaWahGxEsrJZS7pnzgQefVTup7Nnj1ykNnq0+w7RRhVXJdKLwQ5RFAlkObuWy+Mtb0MbnfJlwwbfgQ4gH9+wwf+5vOnYUa7aVmkXCH/L51uiEr8eagXc2HCsAmegHX7ESXhZxgXg5ZeBG27wft4zzpAJyf4EO4Wl5XK5TnH6+/kbJSFBTqH6EgvbNlB04jQWURQJpB6UEUt6w5FY6ivvI5B2rnz17VE8iEq0cjp2T4e1yECFz0AHkEuyffnvf9X6562dyhYD0bKkO1K2baDGh8EOURQJtB5UsEt6w/EXeVKSnFLzJS/P+145/njq2wXYBQELHsTj9mO/XHMbUF+PIxf1UTqv676Crs44A0hJ8d0mJcXzyI7qFgPRsqQ7ErZtoMaJwQ5RlNESi7Oz9T/3o48C24QwmNpYejY/XL7ce8CTlycfD5TjqEIcbNiEy7ELnZ3a9Mz6CW1WLQYsFrz2mtp5VdodPeo94ElJcS6ppdGzxUA0Lek2c9sGarwY7BBFqUAqfT/6aGCbEAYqkM0Ply+XlbTHjAEGDpT3J04EF+gADsvtxZuwoQkux+f2x8ZiFuIsAvfPyrWPKrgm13qj2u7oUZl42769rBnavr383lOg429aSgjnaaloy4VR3aGZyDCCRGVlpQAgKisrze4KkV9LlwphsWgfeYHfLBZ5W7rU/zXXrlU759q1/vup57qGOnTIrTM/Ilck4KTIzXXvT1GR2msuKjK+q3rf79OnhWjRwnfb5GTZjiiWqH5+c2SHKIr4+otfLz2Jq3qnSfSOTITcxIlu83Bb/70Rny76CR+uTfQ4qmDmUnjH3ZtV2tlscvTLl+PHzU9QJjILl54TBcFmC+8Oyv4SUfVyTFz1tWRc7zSJSj9Vrhss25ZtsHa/2PngXXcBc+fiEgCX+Hiuv8Rjve300Ls3z+zZQH2977b19TLmu+KK8PyuEkUSjuwQBSjYYpyBCFWCqb/z6q1IrndkwnCnT+No+4vcAp1uWftRPHiu0inuvFPtUqrt9FDdc0drpzq69MIL4c3ZIooUDHaIAmBEMc5AhCrB1N959VYkD+euwW4WLgTi49Hqx+32QyPxCiwQ+LI8S/nnM3Kk2uVU2+nhulLJX7tARpdC/btKFEkCnsY6evQo/vvf/6KiogL1LuOnd4biTx2iCGFUMc5AaMuny8qMyduxWOT5/G3ipjdnR+/IhCEOHnSrIPkNzkVXbMcpyCVTen4+33+vdlnVdnpoI2m+AkzHkbTRo4HJk/Xl5IT6d5UokgQU7Lzzzju44447cPz4cSQnJ8PisAbWYrEw2KGYpmcDN6PzUXxVK9dLzyZuenN29I5MBG30aGDOHKdDPfBfbEYPt6aqPx9/GwHqbaeHnpG0Pn3Uqo97EsrfVaJIEtA01qRJk3DPPffg2LFjOHr0KI4cOWK/HT58WPk8c+bMQdeuXdGyZUu0bNkSl19+OT744AP740IITJ8+HdnZ2UhKSkKfPn2wa9cup3PU1tZi3LhxaNOmDZo3b44hQ4bgZyMzOIlcmL2Bm69N2Tz9hW61yuM5Oe7tVTdxu+oqoEUL322SkxtGiLQRKF8MKQtQUiKjNodAZ3f/v8IC4THQceRvg8VQTGOpbrAYyO/YZZep9yPQ6xFFq4CCnbKyMtx3331o1qxZUBfPycnBE088gc2bN2Pz5s245pprkJeXZw9oZs6cicLCQsyaNQslJSXIzMzEgAEDcOzYMfs5CgoKsGzZMixZsgQbN25EdXU1Bg0aBBvXWFKIuMyUBN0uUK6jOkLIZNkTJ4CiImDsWHmvbcgXzCZuepc2ayNQvmogBVUWoK4O6NQJuPRS5+MVFSh7cLbSKfxtsNihg1pXVNvpSWhv00btnFo7bWo1UJGy2SBRyASyic+NN94o3njjjUCe6lfr1q3FK6+8Iurr60VmZqZ44okn7I+dPHlSpKSkiBdffFEIIcTRo0dFfHy8WLJkib1NWVmZiIuLEytXrlS+JjcVJD3WrFHb8G3NmtBc3+jN+k6flpvTLVok7z1tPBfoBntLlwqRk+PcxtMGfrq88orbhU/NWyiKioQYO1aIZ54R4swz9W+86PrenT7t3nfXW26u2kZ9en9mzzyj1udnnpHtVTch9HR91ddAFIlUP78Dytm54YYbMGXKFHz99dfo0qUL4uPjnR4fMmSI7nPabDa8+eabOH78OC6//HKUlpaivLwcAwcOtLdJTExE7969sWnTJowaNQpbtmzBqVOnnNpkZ2ejc+fO2LRpE6699lqP16qtrUVtba39+6qqKt39pcarosLYdnoYnRxdXCzP5zjzm5MjR2UcR30C3WAvP1/2xZC9iA4ccC8I9rvf4W/9SvD0yCZOU0JxcQ3vh2pek7bRofbeOeZHeTqH6uhUID8zf5XUNVq7QKahWHiTGpVAIimLxeL1FhcXp+tc27dvF82bNxdWq1WkpKSI9957TwghxKeffioAiLKyMqf2f/7zn8XAgQOFEEK8/vrrIiEhwe2cAwYMEH/5y1+8XvPhhx8WANxuHNkhFYGUTojEa+sZbTCzdIIQQoi77nK/2JdfiilTfPfHXwkFlfcu2NGpQH5met/vQEZ2gh5hI4oAIS0XUV9f7/WmN1fm3HPPxbZt2/D555/jr3/9K0aMGIGvv/7a/rjFZdJfCOF2zJW/Ng888AAqKyvtt3379unqMzVujtWzPbFYfCff6qkC7kr1L/jHHgOuvVbm7dTUuF/3o498jzYAzuUc7r5b7bqq7ZRt2iTf0HnzGo5NmAAIgboLfofCQt9PP3ECWLVK5ilNm6Z2SdeNDvPz5YiVYx7U99+r5zsFkmw8apTac7R2Kr+TOTnAmjWhK7wZzO81UciFJfTSoV+/fuIvf/mL2LNnjwAgtm7d6vT4kCFDxJ133imEEOKjjz4SAMThw4ed2nTt2lU89NBDytdkzg7ptXSp77+avf3F7GmUICfH+FEC11uPHv7zT3yNNowZo9Z+zBgj3l0hRE2N5w4fOmRvonf0w8i8o1D8zBxHdgItvBrI76QRgn2PiAIV8kKg69evx+DBg3HOOeegY8eOGDJkCDZs2GBE8IXa2lp06NABmZmZWL16tf2xuro6rF+/Hr1+20mrW7duiI+Pd2pz4MAB7Ny5096GKFIYseuyyhJwT0pKAquppY027N6t1l61nU8vvAAkJTl1uHbh/5Ofoamp9mN684gC2ejQqJ+Zn8FoWCzOI4GBjAZ9/rnvtv4eD5S39+jnn7lDM0WQQCKp1157TTRp0kQMGzZMPPfcc+LZZ58Vw4YNE/Hx8eL1119XPs8DDzwgPvnkE1FaWiq2b98upk2bJuLi4sSqVauEEEI88cQTIiUlRRQXF4sdO3aI4cOHi6ysLFFVVWU/x7333itycnLEmjVrxNatW8U111wjLrroInFax/ICjuyQHoGs0vH3HNVVMbW1QsTFBTa6E8gtrCM7P/3kdsJPcbmIw2kBCJGX59xc70iN3lV0Rq3GqqhQu25FRcNz9I7s1NYKYbX6bmu1ynZGMnLFGlEgVD+/Awp2zjvvPFFYWOh2/JlnnhHnnXee8nnuuece0a5dO5GQkCDOOOMM0a9fP3ugI4QQ9fX14uGHHxaZmZkiMTFRXH311WLHjh1O56ipqRFjx44VqampIikpSQwaNEj89NNPul4Pgx3SI1zTEp6ofsAHe3MNvk6cUHveiRP63svTp4VY+3G92HvZMLeTXYCdbud3DHj0fsDrDXaM+pm1b692nvbtnd+XnBzvy+ddfz5mJZCbmaxPJESIp7F++OEHDB482O34kCFDUKq6ZhLAq6++ir1796K2thYVFRVYs2YNBgwYYH/cYrFg+vTpOHDgAE6ePIn169ejc+fOTudo2rQpnn/+eRw6dAgnTpzAO++8g9zc3EBeFpGSH3/U386oXZcNmSbyw9OS5IQEoGlT389r2lS2U1VcDNyWuQ59rolDu8//n/34Y5gGCwS+xoVuz1mxoiHhWiuR4MvEiQ19Ki9X65fWzqjK7YEURdWWvQPuU2Cefj6Bbg0QLNOr2xMpCijYyc3NxUcffeR2/KOPPmKg0YiZuRojnNdevlx/O721pbzxl/thBE9lJDZsAE6e9P28kydlOxXLF9fg6pva4M1f+zodT8FR/B2P+XzulCkNX8+cKb/3VCJjyhT5uEZv0GFU5XbVANC1na+yIK4/H9Wq54FUR/fF1Or2RDoEtKngpEmTcN9992Hbtm3o1asXLBYLNm7ciHnz5uE57c8RalRUN6eLhWsfP66/nb9q5arVx3v2lPm7RsrJkSu7Kyq8b/pn5F/w9U8XYuiUSU7HhmAF3oHaZqSuo1szZ8rSD7Nny5GLs8+WdUFdgwe9CcqtW6u199cuL8955byvdq5UN2VUqXputcp2RjKluj1RAAIKdv76178iMzMTzzzzDP7f/5PDz+effz7eeOMN5Hn6F0sxTVuN4fohrq1YUS02GS3X7tQJcFgA6LOdxle1cj072YZi4HT4cKBfP99tDPkLvrQUOOssp+HkNeiHgVgFoWOQuWNH92MJCXJfIF/0VmIvKVFrX1ICjBjh/fGLLlI7j7d2Vqv/iuQqVc8dp/SMEvbq9kSBClMOUURjgnLgjFplFE3XDiZZN9jdeFWScvXeVN6jBQvUzrVggYcn19cLMXiwW+OO+Dag/upNgtboXTk0erRaf0aP9n3dcK6UmjLF/VpWqzweClyNRWYL+T47RIAcXve1f4sQwL596rkc0XDtpCSgRw/fbXr0kO1c5ecHV3180ybj85FU3qNDh9TO5dZu1SpZqOqdd+yHSu/5FywQ2I1O0Csvz/P7qkJvJXZPI0ie+GunN5E6GDNneq5675i7ZCTH99RTInXQ1e2JDKI8jZWamorvvvsObdq0QevWrX2WYzh8+LAhnaPIZ9Qqo2i6ts0GbN3qu83WrbKdp//kVaYlvAnF+6hyXt25GdXVQEaG/KTVNG0KVFSgbbNkpK1QD6A0eXnek8NtNrVio1rSr2uOV26u/FB2DDqNzIPRgo3CQufzWa0y0AkkGPH2mlWm9Izk7T3NyXF/T4nMohzsFBUVITk52f61v/pU1DgYtcoomq79wQf+R1dsNtlu0CBjr52ebuz5NP7eI125GTNmuBei+uAD4Lrr5NcKI1OpqcB558lRp7ZtgffeA1JSPLfVm6CumvRrdB6MaiK1CjMXBHhiaHV7olAI07RaRGPOTuBOnxYiLc33nH1aWuhydsy4dqdOarkcnToZe10h1DfG03Nr0cL/e6SSm3FV5nfuB2+4QebsOAikvpe3vBM9ldsDNWWK+67VocyD8Sccr5koWoQ0Z2fr1q3YsWOH/fsVK1Zg6NChmDZtGurq6gwKw4gik+osbShmcysqjD9ndbX/kSqrFejWzfNjFtRjNfrjk3KXHJw9e4B333VL5ghkKs5mkyMsU6c6H9NTuT0Yrteorw/+nIEI52smiiUBBTujRo3Cd999B0DupnzrrbeiWbNmePPNNzHV8X8jinkbNvjPvTh0KHQJymZcWzVBNtBEWl9CMR0IAP/+t+/H6+pk3OJqEN5BPazoD4dNRmfOlJ+8Z53l8VzBvIbCQtkXQD1B/fnnA99scupUGWS5BhdCuAdf4WDmggCiaBZQsPPdd9/hd7/7HQDgzTffRO/evbFo0SLMmzcPS5cuNbJ/FOEaY4KyatXxQKqT+6NtTmg0f7tCz57tHCi0RCUELE4bAR5Ga8x68rjzFsceaK8hkLQ/m032BVD/uU6YANx+O9C3L9C+vXoV7ro6GVz54hh8qZ7z2WeBcePkvd6BcDP/vRFFs4CCHSEE6n8bx12zZg1+//vfA5BlJH799VfjekcRrzEmKKuODoRiKsFqlZsAGs1f4OFYU+kRPIRKtHJ6/Bp8hDQcxrf7mvm9lq+6Tyq0vgTyc/35Z7mxo0rA4xrgeeIYfPkzdSrQrJkMvmbNkvfNmukbHTLz35s/ZpaLIfInoGCne/fuePTRR/Haa69h/fr1uOGGGwAApaWlyMjIMLSDFNn8/ZVuschlvf7KIETTta+80th2eths8sPEaP42Pj/7bOA8/A8CFjyEf9mPv4mbYUE91uIaezsV3uo+qdCuEegIkRBqeS1GFtfUpsNcr+kpF8kXM/+9+VJcLEfN+vYNbBSNKNQCCnaeffZZbN26FWPHjsWDDz6Ic845BwDw1ltvoVevXoZ2kCKb3urMkXLtYP4KVf1wDcXuDP5yNgK1Zw9w7bVyIzqtqridzYb73rwS/8MFTofb4kcMw5sA5AvVW3vJdYNFbf9BXxyvEcwIkUpei1HFNY2cDjPz35s3WskW199LrWQLAx6KCEYuAaupqRF1dXVGnjIsuPQ8eOHept6R3hIMntrn5Kgv2e3eXW25dPfuxr1GzcKFxi8993TLy/vtgm+95fbgWPzb43OM+FlPmeK7X96Wn/tbFu/ptnCh774YVeahqEitP0VF6u9TsGVHjGJmuRgiIdQ/vwMKdn766Sexb98++/dffPGFGD9+vHjppZcCOZ3pGOwEx9u+H9p/duH4D/j0abl/y6JF8t7bf65G7FHSv7/ah1f//ka+Qkn1gzPYWyscdj+YnS0emFAT8qA2kMDZ8ec/ZoxxwUUgwZersWPV+jN2rOo75P6aff3Oh5Lqnklr14a/b9Q4hDTYufLKK8WC3yr+HThwQLRs2VJcfvnlIi0tTTzyyCOBnNJUDHYCF01/2RnV1xkz1P6DnzFDf//8fXgFO7KTkiLEwIFC/PnP3ts8CQ+f8Bs22PtQWysDhbFj5X0wBSy9nSuYa6i+R/5GdjTBjlqGYmQnUixapPbaFi0yu6cUq0Ia7LRq1Up88803QgghnnvuOdGrVy8hhBAffvih6NChQyCnNBWDncBF0192RvV12jS180ybpt431am1QHYfVr11wVduBz8/9063HZCNEqqpz1D8TgYTfIWz6nm4RdO/f4pNqp/fyrWxHJ06dQqJiYkA5NLzIUPkXhvnnXceDnCDh0Ylmvb9MKqv/pJo9bbTEjyFcD6uJXi+9VZDvaOrrpL1NE+eVDu3CitO4wv0RDc4VzfNRhm6tMvGhyFItNZWJ7nSVicBgVfq1lYs+Urk1rtiKZjimkbX2Iok2ntdVub++wvIpOmcnPCvDiNyFdBqrAsvvBAvvvgiNmzYgNWrV+O63wr87d+/H2lpaYZ2kCJbJO/74Uq1iKa/dqoLDlXa6d3+v67O2EDnNizGacQ7BTp/wUuwQOAAstGxo3HX0oRisz5HjiuWvAn3iqXLLgvu8UgViavDiDwJKNh58skn8dJLL6FPnz4YPnw4LrroIgDA22+/jUsvvdTQDlJki9R9P0Lp66+Na6d3+38/mxMrS8OvELBgMW63H9uNc5CAWvwHf7Ef8zUaESijN+vz5PPPg3vcSFpA643FEt31rLztmZST4zwqSWSmgKax+vTpg19//RVVVVVo3bq1/fhf/vIXNGvmfwdVih3aX3Y33eT5cSEi5y871SKa/trt3at2HpV2eqfWdu9Wa+/L8xiLsXjB6VhPfI7/oqfTsby80NT3+vZbY9u5Uh05evTR8Ewd6Qlo+/QJfX9CIT9f/r5s2CB/V7Oy5B84kfDvnggIMNgBAKvV6hToAED79u2D7Q9RyBg15WbUZnMq13Jt17Gj3HwvEN2wGZvRw+nYS/gL7sVLbm3z8vzXywpUebmx7VzpGTkKNA9Hj2jKawuG1Rq9wRrFPuVg55JLLsFHH32E1q1b4+KLL4bFx5alW7du9foYxRbVIfq8PPP/yjMqmXLUKFnXyJ9Ro/y36dVLvi++Ppyt1ob8nyeeAF54wXtbT5rgFLajK87HN07HM1COCsjyLhdcIF97x45y6ioUIzqaUOd5qY5+6Rkls9kCH7WIprw2olilHOzk5eXZV2ANHTo0VP2hKBNNQ/TalNvNN8vAxjHg0ZNM+cUXatf74gv/r3nTJrVRiE2b5Lk2b1a7tuYuzMVc3ON07E7Mx2u40+lYp07AsmX6zh2oTp2MbefK6HIexcUyoHf8Pc/Jkb9LKvkoXLFEZD7lYOfhhx/2+DU1btE2RK8lU3r68Hr2WbUPLyNfs95zlZWptc9AOcrhPFSwA51xCbbiNOLd24exfu/o0cDkyf5Hs/TU2XLUs6fa6FfPnv7beNsWQKuerpKAa1SQTUSBC2g1lqPq6mpUVVU53ajxMGo5dzi5FqBcuxYoLVVfNdKqlXHt9E5xqBQB/Q/+5BboXIIt6IodHgMdwPOIQ6ho+874Esy+M9nZxrTztS0AII+rrqLiiiUicwWUoFxaWoqxY8di3bp1OOmw6YcQAhaLBbZoXUNJUUtvTkUwyZRvv63e7vrr3Y879jU9HUhNBQ4f9n6etLSGKY41a7y3uwyf4TM4b+7zLMZjAp7129djx/w2MZS2YWBhoXOwYLXKQCfQDQWNpFJhXs8ULVcsEZknoGDnjjvuAAD83//9HzIyMnwmK1NsM2o5dzCCzanQ6/vvA2/nqa+qOy0DwNGj7scSUItvcS7a40en42n4FYehtsnn8ePqfTDKzJly+ffs2cCePXL12ujRwS8HN2q1l+qUoWo7gCuWiMwSULCzfft2bNmyBeeee67R/aEoY/ZKEyNyKvSqqQmsnbe+1tf7Ps+hQw2jBz16OCcpj8KLeBF/dWp/K5bg/+FWtU7+5uBBYPHi8I82BFOGwZuDB41pZ9R5GotgVqwRhVpAOTs9evTAvn37jO4LRSEzd1A2MqdCD9UY37Gdv776oyUoa9M72SiDgMUp0PkCl8KK07oDHQD47DPg9tuBvn2B9u1lYBatzjjDmHZGnacxKC6Wvzd9+8bO7xHFloCCnVdeeQVPPvkk5s+fjy1btmD79u1ON2o8zKyNoyenwki+8mu8tVPpqy/ayNjmEoGFuANlyHF6vAu24zJ8gXoE/0ZrBUij9YPKNQk40HZGnSfWaSOWrr/f0f57RLEloGmsgwcPYs+ePbj77rvtxywWCxOUGykjlnOrqqmR9aF271YfsTF6ELJNG/3t9OR1uLJvKvjJJ+hzTW+nx2bgb5iGGYGf3AMhImszSL2MqnoeiurpscZfIdto/j2i2BJQsHPPPffg4osvxuLFi5mgTADCs9Jk6FBgxQr9z/viC+CPfzSuH6pFJB3bBZPXEW+rATLbA0ecs7xb4Qgq0SrwE/sQSZtB6uW4rw0Q+L42Rp0nlkXTpqLUuAUU7Pz44494++23cc455xjdH4pioVxpEmigAxi/h4zq+RzbBZrXMR7P4llMAI40HLsRxViOGwM7oU6RshmkXkaNNoZz1DIaRdumotR4BRTsXHPNNfjqq68Y7FBY1NQEHugAst6Tkc45B9i1S62dRm9eR1v8iB/R3unYOvTGNfgYIvi9QJVF0maQehk12sj9cbwzezUmkaqAgp3BgwdjwoQJ2LFjB7p06YL4eOddWYcMGWJI54gAmaMTKG9lB4JZJjtqlFrw5VgIVCX/QxIoRj5uxHKno+fhf/gW56l1kOyMGm3k/jiese4XRYuAgp17770XAPDPf/7T7TEmKJPRvvsu8Od6KjsQ7CaEgWyk6Jj/4W0arB/WYA0GOB2bjofxCKarXTAE9u837dIUBVj3i6JFQOPh9fX1Xm96Ap0ZM2agR48eSE5ORnp6OoYOHYpvv/3Wqc1dd90Fi8XidLvsssuc2tTW1mLcuHFo06YNmjdvjiFDhuDnYNb5UkRp3lz/c6xWOSLkWnbAiGWyy5er9cG1nZb/keO8ahy5qcdRiZZOgc4pNEFLVJoa6ADqFd6p8WLdL4oGuoKd3//+96isrLR//9hjj+Gow/71hw4dwgUXXKB8vvXr12PMmDH4/PPPsXr1apw+fRoDBw7EcZe966+77jocOHDAfnv//fedHi8oKMCyZcuwZMkSbNy4EdXV1Rg0aBBHmMLMZgPWrZM78a5bZ9xmfkOHqrUbPhwYOxYoKgJOnHAPdPwtkwXUNiFUrSPlqV1+viyNUFQk+7r+90/ip8Mt0BINjW/Au0jAKRxDS7ULhVA4C4RS9Aq2uC5RyAkd4uLixC+//GL/Pjk5WezZs8f+fXl5uYiLi9NzSicVFRUCgFi/fr392IgRI0ReXp7X5xw9elTEx8eLJUuW2I+VlZWJuLg4sXLlSqXrVlZWCgCisrIy4L43dkuXCpGTI4T8eJS3nBx5PFhr1zqf19tt7drwnOeKK9TOc8UV7s/V3qcu+MrtCe/jOgHUK507XLeiooB+ZEREYaH6+a1rZEe4/Jnn+n2wtFGj1NRUp+Pr1q1Deno6OnXqhD//+c+ocEiG2LJlC06dOoWBAwfaj2VnZ6Nz587YtGmTx+vU1taiqqrK6UaB8zY1pNWnCnYHVS0J0heVzd2MWibbpYvaeVzbFRcDt9xUj30/W7AdFzk9dg524/f4AEDk7FllsXhO7qbwCtWIKVFjEr41rH4IITBx4kRceeWV6Ny5s/349ddfj9dffx0ff/wxnnnmGZSUlOCaa65BbW0tAKC8vBwJCQlo3bq10/kyMjJQ7qWs8YwZM5CSkmK/5ebmhu6Fxbhw1KdyLEnhjUoSpFHLZAOtjfXVPc/B5lLO4X48AQsE9oDbOJA71pwiMoauYEdLEHY9ZoSxY8di+/btWLx4sdPxW2+9FTfccAM6d+6MwYMH44MPPsB3332H9957z+f5xG+lKzx54IEHUFlZab+xqGngzKpPFQijipaqjnbY2/36K6xNLHikssDp8dY4jJm4X+1kJhACmD3b7F40Xqw5RWQcXUvPhRC46667kJiYCAA4efIk7r33XjT/bbmMNtqi17hx4/D222/jk08+QY6f+YqsrCy0a9cOu3fvBgBkZmairq4OR44ccRrdqaioQK9evTyeIzEx0f4aKDg//mhsO0+00SNfVOrvGLVMVnWUymaD/FN83Tqn46PwIl7GKI/PiTS//TOjMGPNKSJj6RrZGTFiBNLT0+3TP3/4wx+QnZ1t/z49PR133nmn8vmEEBg7diyKi4vx8ccfo0OHDn6fc+jQIezbtw9Zv801dOvWDfHx8Vi9erW9zYEDB7Bz506vwQ4ZJ9Bl2HoYOXpkxDJZlU0Or8BGJDWzuAU6FtRHTaADeB8Fo9DSU3OKiPzTNbIzd+5cQy8+ZswYLFq0CCtWrEBycrI9xyYlJQVJSUmorq7G9OnTcdNNNyErKwt79+7FtGnT0KZNG9x44432tiNHjsSkSZOQlpaG1NRUTJ48GV26dEH//v0N7S+5c9klIOh2nqjOMqq2C3b7f1+bHMbBBpuHf1a2LdvQPu8iWHzsNHvmmcC8eXIzwsJCYPNmtf6EUo8eZvegcWLNKSJjmZqgPGfOHFRWVqJPnz7Iysqy39544w0AgNVqxY4dO5CXl4dOnTphxIgR6NSpEz777DMkJyfbz1NUVIShQ4di2LBhuOKKK9CsWTO88847sHJ8N+Q6dTK2nSeqG9vp2QBP2/5/+HB5r+dXxdsmh3/DDPdA59ZbASFgveQie5K162iJ9v1zzwH9+sk+9eyp3p9QOnLEfxsyHmtOERnLIoxePx6FqqqqkJKSgsrKSrRsaf5GbtGkshJo1cp/u6NHgZSUwK4xZoxaouwVVwAXXwycfbZMDnYtE2GU+fOBu+5q+D4D5SiH+6fO63OqcMe9yU7HPJWqyM11r6BdXQ0kOz/VFAsXAnfcYXYvGh+bTa668ldzqrSUOTvUuKl+fkfM0nOKTqozm8HMgKpWLf/0U2DWLGDCBKBZM2Dq1MCv6Uu7dg1f/xc93AKdOzEfFgiceZ57tKK602wkTGEB+qu1kzEct1vwNhLImlNE6hjsUFD27DG2nSejR+v/T91mA556ynvAE8xGbVddBdza5iMIWNADDVHJSSTCgnq8hjt9LmFXmUKLhFwMlWX4FDqsOUVkHAY7FJT27Y1t50lCgqxeHojCQqCuzvlYUBu1nToFaxMLlvzqnPx+AXYhCSeh7YAc7F/daWmBP9cIFgtHDiIBa04RGYPBDgUl0NIJ4WKzOef7BFXa4h//cEsEegUjYYHA/+BcAPfzz333yd+o0o4dPvphkDPPlMvo3aqw53LkIJIEk0xPRJKupedErg4eNLadJ3V1coQmUNoUmmppC7eN2vbtA9q2dWvfDMdRg2Yez1VYCDz6qHuStKcE5ZwcmZ/hGFz88IP/1xUsbaps797Al+ETEUUDjuw0IqEoKBiOYGf27OD6evbZ8j6gzQnPP98t0HnvzjdggfAa6ADuI0qAvu3/w7GZX329zGt64AGOHBBRbGOw00iEqqCg6nLyQJedA7438fPHam2oUaVrc8L335cRxzffNDyQlgYIgfdbDFM6j2OpBX/b/wPOBVPDuc+Op7wmIqJYwmCnEQhlQcG33za2nSfBrEy65JKGqSSVTQcTUIs/3mkBbrjB+YHdu4FffwWgPuri2E7v9v+5uWrXMIKnUSgioljCYCfG6R1R0Kuqyth2nqSnB/7crVsbRi38bZ/5NCahFk2dD953n3ziOefYD118sdq1Hdvp3f5fq9AeLsFsDUBEFOkY7MS4UBcUVC10r9rOk4qKwJ/rOGrhbXPCDvgBAhZMgksWdE1Nw85uDt59V+3aju30bv+vbSoXrkKcWl4TEVEsYrAT40JdUPD8841t50mwe85ooxajPBQbL0M2foDzJ33tm2/LKLBpU/cnILDRLG2kxlvwYrG4b+KnbSoX6hEex7wmIqJYxGAnxoW6oOBvaSyGtfNET4FPT7RRC8fz3IhiCFiQjYYobw/OggUCn7UZ7PN8qiNNju0C3f7fdVO5e+9Vu7YeEyeGro4YEVEkYLAT4wIZUdBDtW5qMPVVg10i/6c/yft9+4CmqIGABcW4yalNO+zFOdhjb+eLag6Razsjtv83crNBq1VuKjhzpnHnJCKKRAx2Ypw2ouBrI71gygJ88omx7TwJdt+XV16R9x2evNdtb5wZ+BssEPgJDdU9/Y0kNW+udl1P7fRu/++6ZcCnn6pd25tLLgHGjgWKioATJxjoEFHjwB2UKSinTxvbzpNevYCdOwN//rHN3wKW83Cly/EE1OIU3Odv/K3aOv984J13/F/XW56Stv2/P9qWAf76o8eMGcDAgcadj4goGnBkJ8ZpS8+9sViCW3qemWlsO08CLyIqcAwt8I/Xz3M6OhAfwgLhMdABvK/a0qjmtwSTB+OvtEUgWrQA+vUz7nxERNGCwU6MC/XS819+MbadJyqjKK6GYxEE4tACx+3H6rt0RROrwGp4H9pQWZl0xRVqfVBt54lKaQu95s9nKQgiapwY7MS4UC89P3zY2Hae6PnQb45qCFiwCHc4P1BWhrjtX2HiRN/PV1mZ9PXXan1RbedJMLtGe+OrEjsRUSxjsBPjQr30vEULY9t54qHguEfzcSeqkex07B/4J5JbCCA7G4BMyJ0yxX2EQ8/KpNJStf6otvMk0J+HL6yBRUSNFYOdGNerl/+pC6tVtgvEgw8a284Tf3W1LsROCFhwJ15zOt4Ep/Ao/oEmLmn4M2fKlUhFRYGtTOrQwdh2nvjbMiAQrIFFRI0Vg50Yt2mT/+Rjm022C8THHxvbzpPt2709IiBgwU50cTraG+tggYDtt8WGrVq5PzMhQSZmP/+8vNeTTNyli/82etp54msTwmCwBhYRNUYMdmJcMDk7dXVyD55x4+S9pymQ48fdj3mi2k61b/fgVQiXX9+NuAIWCHyC3k7Hp00L/NqehGPXaMD7JoTBYA0sImqMuM9OjAt0t9+pU2WOh+Oo0OTJMoHXcbrnrLPUzq/azhPH2lgtUYlKtHJrk45fcBCeX2y7dh4PByzUeVCO8vOBvDy5OuvAAeCFFwLfWJA1sIioseLIDrmZOhV46in36S+bTR6fOrXhWDjyV7QSCcW40S3QmYSnYYHwGug4Pt8ooS7B4UrbhHD4cGDlysDPwxpYRNRYMdiJcXqLVtbVyREdXxxX9Xz/vdr5Vdt5curzLRCw4EYsdzoeBxsKMcnv843OUwl1CQ5fWrQAevTQ9xzWwCKixo7TWFHMZmuY3sjKkiMJrh+weqdcZs9WS2iePVsm9qru3RLQHi9CAHFx+JvL4cvwGb7AZcqn8ZTzo/LeRaq//Q246Sbvj7/xBrB/vwzyzj5bTl1xRIeIGjMGO1GquFiWE3DccC8nR444OBaV1KZcyso8j0RYLPJxbcpFdRREa6daZkJ3OYpZs2RmtIOVuBbXQ/88jmupCtX3zht/JTgAGQjm5RkfQKmU/5g8We7xEy3BGxFRqHEaKwppBSJddxYuK5PHi4sbjvlawqx97zjlorpaR2tn+A7Khw7JjrkEOqk4FFCgAwDnntvwtZ73zhuVUg7BlOAI5trBlv8gIopFDHaijK8Ckdox18Ke3pYw5+TI446jGaNHq21CqK3qSUlR67dSuwEDgDZtnI+98AJqTggcQarahVw49jWQ986TsjK1a6u20yPU5T+IiGIRg50oE+hf9vn5wN69wNq1wKJF8r601H3aJiEBuupHHTyo1m+f7T77TI7mrFnjfLy+Hhg9Gi+9pHYNTxz7atSoiCGvOUDhXPZORBQrmLMTZYL5y76mRk5paYmr3bt7rlmlrdp55hkZb2ji4oBJk5xX9SQlAUeO+O9PUpKHgzYb3Go5AMCWLcAll9i/DWQ1ldXqvieQUaMiZ5yhdh7VdnpcdZXcd+jQIe9t0tKMW/ZORBQLOLITZQL9y/7SS4HkZGD5crnvzPLl8vtLL/X8/Msucz9HVpY87sg1+dcbt3ZPPeUe6Nx0kxxecQh0gMA2BXQM0jRGjYqo7mhs5M7HREQUOAY7UUb7y94X17/sL70UKCnx3LakxD3g0ZJ4XXNO9u93T+LdvVut3/Z2FRVyyspxZ0IAqKyUCUQGEcJ9A0SjNgPUzuOLkZsKOtqwwfeoDiAfZ4IyEVEDBjsxrrrae6CjKSmR7QD/SbxCOCfxas9T6QcuvxzIyHB+4P/+T560ZUuvz/3xR7VreOK4AaLelWneaOexWDyfx2IJ3aaCTFAmItKPwU6U0fuX/R//qHZerZ3eZdVxCr9BvbEO9cLivLOg1Srnmu6+2+/zgyleqW2AqNGzMs0Xo86jFxOUiYj0Y4JylNH7l73ecg56l1Wfcw7w7bee21hxGqcR7/7Azp3AhReqXQhy6fjkyQFsTPibb75x/t61uGagOygbdR499G4SSUREHNmJOnr/ste7D47eZdXnn+/58YfwiHugM2KE/ITWEegAasvhfdm50/2YY3HNPn0CD1CMOo+e65lVl4uIKFpxZCfK6P3LfuRI4NNP/Z935Eh5r3dZ9WuvyVVdmmyUoQzu2bvV5dVokdFc7eQeuK4C06Np08CfS0RE0c/UkZ0ZM2agR48eSE5ORnp6OoYOHYpvXeZEhBCYPn06srOzkZSUhD59+mDXrl1ObWprazFu3Di0adMGzZs3x5AhQ/Czv8STKKU3ybZtW7Xzau30LiV3HEF4GpPcAp3bsBgWCFhbBh7oqNSi8qVTp8CfG2lUamOp7AJNRNSYmBrsrF+/HmPGjMHnn3+O1atX4/Tp0xg4cCCOHz9ubzNz5kwUFhZi1qxZKCkpQWZmJgYMGIBjx47Z2xQUFGDZsmVYsmQJNm7ciOrqagwaNAi2GP0f36zkWE+mTAG64isIWDAJhfbjR9AKFgi8gdvs7QKlkjTty5AhgT830rA2FhGRfqZOY61c6VzYce7cuUhPT8eWLVtw9dVXQwiBZ599Fg8++CDyf/sEnz9/PjIyMrBo0SKMGjUKlZWVePXVV/Haa6+hf//+AICFCxciNzcXa9aswbXXXhv21xUOqsmxqkGC1m7/frX2+/cDOH0aBfO7Yxa+cnqsI77D9+jodOzrr9XO68m+fYE/F1Db4TlacOk5EZF+EZWgXFlZCQBITZVFH0tLS1FeXo6BAwfa2yQmJqJ3797YtGkTAGDLli04deqUU5vs7Gx07tzZ3sZVbW0tqqqqnG6xavlyfe2++EKtff2ChUB8PM6pbgh0/oT/wALhFugAwQU7qn3yJpaWYXPpORGRfhGToCyEwMSJE3HllVeic+fOAIDy8nIAQIbLRnQZGRn48bed5srLy5GQkIDWrVu7tdGe72rGjBl45JFHjH4JYVVcLHM3HEducnJkPo/jNJbDbJ9PWrtTp3y3a4ODOIh0wKFm53foiM7YiVNI8Pq8YFYHnT4d+HMB4OKLg3t+JOHScyIi/SJmZGfs2LHYvn07Fi9e7PaYxSUTVwjhdsyVrzYPPPAAKisr7bd9wc6ThJlWzsF1iqqszL2cg+NKKV+0dhUV3tvMwhgZ6Di4ttUXOBff+Qx0AP9BlC+//BL4cwHgrruCe34kMWoXaCKixiQigp1x48bh7bffxtq1a5HjUHQo87clP64jNBUVFfbRnszMTNTV1eGIS2KGYxtXiYmJaNmypdMtWvgr5wA4r8ZxmN3zSWuXnu7+WDdshoAFY9CwFfH68+8FhMB3rbxUEnXRPPDFWMorxLxR3VgxWkRSgjoRUTQwNdgRQmDs2LEoLi7Gxx9/jA4dOjg93qFDB2RmZmL16tX2Y3V1dVi/fj169eoFAOjWrRvi4+Od2hw4cAA7d+60t4klelfjOJZK8EVr5ziyE486fINzsRk9nNqm4xc8e+4cAPoToAMR7CiF6saK0SQ/H9i7F1i7Fli0SN6XljLQISLyxNScnTFjxmDRokVYsWIFkpOT7SM4KSkpSEpKgsViQUFBAR5//HF07NgRHTt2xOOPP45mzZrh9ttvt7cdOXIkJk2ahLS0NKSmpmLy5Mno0qWLfXVWLNG7GufoUbX2WjttFOVu/B/+DyOd2vwRC7AQf3RqFw49ewIvvBD480eO9N8mUDZbeMtFONJ2byYiIt9MDXbmzJGjA31c/seeO3cu7vot0WLq1KmoqanB6NGjceTIEfTs2ROrVq1CskMySlFREZo0aYJhw4ahpqYG/fr1w7x582CNwcSFtDR97dq1UxtVaddO3l+cVQ4B56U823ARumMzbA6/LueeK+9btQJ+/dX/+Vu18t/Gm9zcwJ8LAC4DhoZRTRInIiJzWYTwVmWn8aiqqkJKSgoqKysjPn/nmWdkUUx/nn4amDQJqKxUCzSOHgVSJtwDzJ3rdPx3+BJf4XdOxywW4ORJWbMqI8N3UrMmPT3wRGObDWjfPrCpsLQ0eV2j414tSdz1X4+WJByO3JmaGrlZ4+7dQMeOwFNPAUlJob0mEVEkUf38jogEZVK3Z4++dnV1/ttejk1IaWVxCnQKMQEWCLdAx5XeabJAOK5AigR6k8RDYehQoFkzOb23apW8b9ZMHiciImcMdqKM3pydS30slkpALfaiHTbhCqfjafjVqfSDKyEaEppVV1kFsxoLAD7/PLDnHTpkfOkEs0s2DB0KrFjh+bEVKxjwEBG5YrATZVQTg202YPFiwMu+irgXc1CLpmiHnxoOvvEGRv1F4DD8Jwb973/yftw4tf6otvOkrg4o9B57+VVWFvhzPTGzZENNjfdAR7NihWxHREQSg50ooyUG+/POO8Dtt8vcGkdn4mcIWDAHo+3HPsNlOLvdaWDYMLgUlPdKa9eihVp71XaezJ4d3JTQwYOBP9cTM0s2qBZUDabwKhFRrGGwE2VGjwbiAvqpCSzGbfgZzkubOmMHeuEzfF4iM3hVE1y1dj/95LudRrWdJ6p5St6ccUZwz3ellWzwtom3xSJXkIWiZMPu3ca2IyJqDBjsRBmrVf/Kot5YB4E43IY37McexwOwQGAXZB2y32qvKo8cae2ys9Xaq7bz5OyzA38uYPyeQGaWbOjoXmM1qHZERI0Bg50o8/HH6nWmmqIGFTgD69DX6XgrHMGDeNzpmJYH8thjaufW2n3yiVp71XaejB4dXOAQilVRZpVseOopY9sRETUGDHaizGuvqbUrQBFq0AxnoGHHvzwshwUClWjl1v7uu+W9yzY7XmntPv1Urb1qO08SEoBBgwJ/fqhWRZlRsiEpCcjL890mL4/77RAROTJ1B2XS7/Bh34+3RylKcZbTsY/RF/2xBsJHbKut3vnmG7V+qLYzgs0GbNkSvuvpYUbJhuXLvS8/z8uTjxMRUQOO7ESZH3/09ojAcuS5BTrn4hv0w8c+Ax0AaN1a3u/cqdYPrd2VV6q1V23nib99bfyJxfpRy5cDJ04AY8bIivVjxsjvGegQEbnjyE4M6I/VWI2BTscewiP4Fx5SPsdXX8n7pk3V2mvtRo8GPvjAf/vRo/238SaY/WrS0mIz2AHkVNWsWWb3gogo8jHYiTJt2zaMqjTDcfyCDLTAcfvjJ5GIM3AQ1Uj2cgbPEhLkfadOwEcf+W/fqZO8P3RI7fyq7TwJZr+al18OXxVyIiKKTJzGijLa0ua/YQaOo4VToHM93kcSTqIaybjsMpk027692nl795b3Q4aotdfaqU6bBDO94m9fG0+ys4GlS2O7+nhdnVziPm6cvFepg0ZE1Bgx2Iky9d99DwELZmCa/dh7+D0sqMdKXG8/Vl0NDB8uq56r0EoqHDmi1l5rd+yYWnvVdp6o7GvjutHiL78EXk8rGkydKgt/Tpggp7ImTJDfT51qds+IiCIPg51oUV8PXHst3t/tvFvc2fgeg/AeAOcoICVF3qvuyaO101sKwbUchTeq7bzxtq+NVmC0vt75uM0m95qJxQ//qVPla3PdPyiWXzMRUTAY7ESDd9+VwxurVtkPTcWTsEDgB3jeXviKK2QhUNVq4xkZ8r5XL/85LlarbAcAXbqonV+1nS+u+9p8+KH/gpeFhbE1vaNSFDXWXjMRUbAY7ESyqioZWQwebD90qkUrNMNxPAXff77PnCkLgf7yi9qltP17Nm3yv+OwzSbbAfrLSwRL29dm+HDg66/V+jp7tjHXjgQqRVFj7TUTEQWLwU6kevhhORflOD+zZg3EoSOojWtm+OWa/XZK1WXeWjuVUg5Wa3BLz71RLRAabCHRSNIYXzMRUbAY7ESa//1PZt3+858Nx26+WQY9/fphwwb3/BQjaKu29ObsJCQAEyf6bjtxYsPSdiOpFggNtpBoJFFdXafajoioMWCwEynq6+Ua6wsucD7+44/Am2/alx2tWxeay7/3nrz3t8zbYgFyc2U7zWWX+T63v8cDNXq0+yosV3FxoRlVMks4c6SIiGIFg51IUFws53o2bmw49txzgBByF8EQO/vshtVb2jJvITy3FULu6aJNXdlswPjx3s9tsQAFBaGpPG61Nky/edOsWWxtKhiOTRyJiGINgx0zHTkio4Gbbmo4lp0tlxjdd5/Hp4Si9MHMmYE/11/dKiGAfftCU3l8wwa5n5Av1dWhq3puBr3TjERExGDHPFOnAqmpzsc++UTu7uejQJW25NsoriMvekdq9CY0G8l7UdTA2kWDQKYZiYgaOwY74bZ9u/xEeuqphmN//GNDzo4fL71kbHdcR170jtSYOdIQjlIVkUZlN2nHaUYiImKwEz6nTwPduwMXXeR8vKwMWLBAufBTqJYUayMvekdqundXa6/aTo/jx/230dMuWnjbTTonRx6P5XpgRESBYLATDosXA/HxwJYtDcdeekkOk2Rn6zpVqJZRayMvekdqpkxRa6/aTg+t8rpR7aKJ627Sa9cCpaUMdIiIPLEI4W3dTeNRVVWFlJQUVFZWomXLlsad+MQJ93oNZ50lt/5NTAzolHV1QFKScXvtWCxyRKC0VE592GyydISv1TxpaXJnZqsVOO884Ntv/V/n3HOBb74xps+amhr/q7EA+WNISjL22kREZD7Vz2+O7ITSrFnO33/2mZyHCjDQAdSWW+vhupRcr/h4Y9vpkZQE5OX5bpOXx0CHiKixY7ATSv36Ad26AX/6k4wq/OyuV1cnA49x4+S9p2KOKsutg7Fhg/89Wg4dakhQVt0wMFQbCy5f7j3gycuLreRkIiIKTBOzOxDTunUDNm9Wajp1qqxW7bj53uTJstSC4z44Ri/h1paS5+XJ0Z19+9Sep7VLS1Nrr9ouEMuXyymtKVOA3buBjh3lYjeO6BAREcBgJyJMneq8El1jszUc1wIeo5dwOy4l79MH+OILted98YVcMV9WptZetV2gkpLcZw2JiIgATmOZrq5Ojuj4UljYMKXlb1O5QGkjRqplHbR2qtUswlD1goiIyCMGOyabPdt/gGGzyXaA/9pVgdJGjFQTlbV211yj1l61HRERkdEY7JhMdZPAUG0m6FpeoGdPtedp7fr08Z+Pk5YWmppeREREKhjsmEx1k0Ctnb/aVXp4Ki+guseh1s5qBV5+2Xfbl19m+QIiIjIPg50QsdmAdevk5snr1nmfqho92n8gYLXKdoD/2lW+xLn8tM8805jyAvn5wNKlMpfIUU6OPM5dfYmIyEwMdkKguBho3x7o2xe4/XZ53769PO4qIUEuL/dl4kTZDghs6bk2gtO6tf+2FRVq53Rt56l8wd69DHSIiMh8XHpusOJi4Oab3ROIy8rkcU8jKdqyctd9dqxW9312All6npoqNwJ03SzQU5+CqWJutTI3h4iIIo+pIzuffPIJBg8ejOzsbFgsFix32e72rrvugsVicbpd5rIVb21tLcaNG4c2bdqgefPmGDJkCH4OdJ4nSFo+jaeVUtqxggLPU1ozZ8oaTkVFwNix8v7ECedAB/C/9FyrdbVmjRxhWbPG++Z6nvqkcn7HhGYiIqJIZ2qwc/z4cVx00UWY5WM3uOuuuw4HDhyw395//32nxwsKCrBs2TIsWbIEGzduRHV1NQYNGgSb6oYxBvKXT+O4gZ8nCQky8Hj+eXmvTV050paeA+4Bifb9c8/JShXDh8v2evqkcv5gamkRERGFm6nTWNdffz2uv/56n20SExORmZnp8bHKykq8+uqreO2119C/f38AwMKFC5Gbm4s1a9bg2muvNbzPvqjm0wRb8iE/X049jR/vHMjk5MhAxHGaLJA+6Tk/ERFRpIv4nJ1169YhPT0drVq1Qu/evfHYY48hPT0dALBlyxacOnUKAwcOtLfPzs5G586dsWnTJq/BTm1tLWpra+3fV1VVGdLXYPJd9MrPl/WsNmyQgUpWlpxach1xCbRPqucnIiKKdBEd7Fx//fW45ZZb0K5dO5SWluIf//gHrrnmGmzZsgWJiYkoLy9HQkICWrssM8rIyEB5ebnX886YMQOPPPKI4f296iqgRQvfVclbtDAu38VbQrDN1hCkpKfLEZmyMs+5RFqOj6c+6U04drxuqIKjcFyDiIhiS0QHO7feeqv9686dO6N79+5o164d3nvvPeT7mEsRQsDio3jUAw88gIkO672rqqqQm5sbdH9tNplU7MuJE7JdqD6gi4vdp5/S0mSgY7E4BzxG5uB4um5Ojsz/MWraKxzXICKi2BNV++xkZWWhXbt22L17NwAgMzMTdXV1OHLkiFO7iooKZGRkeD1PYmIiWrZs6XQzwuzZQH297zb19Q11roymLXt3TUg+fFjep6Y6H8/JMWZTQW/X1Za2e9pfKBKvQUREsSmqgp1Dhw5h3759yPotwaRbt26Ij4/H6tWr7W0OHDiAnTt3olevXmHvn5l1rvwte7dY5BJ0bUn62rVAaWnwgU4wy+2NuoYQwV+DiIhil6nTWNXV1fj+++/t35eWlmLbtm1ITU1Famoqpk+fjptuuglZWVnYu3cvpk2bhjZt2uDGG28EAKSkpGDkyJGYNGkS0tLSkJqaismTJ6NLly721VnhpLfOlZFUlr3//LOcrho+PLzX1Za2B7rhoEqJjGCvQUREscvUYGfz5s3o27ev/Xstj2bEiBGYM2cOduzYgQULFuDo0aPIyspC37598cYbbyA5Odn+nKKiIjRp0gTDhg1DTU0N+vXrh3nz5sFqQtbq6NHA5Mm+Rxgc61wZKVzL3s24blmZse2IiKhxMTXY6dOnD4SnuYnffPjhh37P0bRpUzz//PN4/vnnjexaQLQ6V0895b2NY50rI4Vz2Xu4r3vwoLHtiIiocYmqnJ1oMHMmMGWK++omq1Uedy3/YBSzyjyE47pnnKHW7uBB/1XmiYio8WGwEwKqda6MZFaZh3Bc98wz1do9/rj/KvNERNT4WISveaRGoqqqCikpKaisrDRsGbpZpk5Vq55uNE974OTmGlNewmaTwYue+q5aoGXE0noiIopMqp/fDHYQO8GOtheNt52SQ/3BH8rdjX29Nm+03aFLS7nLMhFRLGKwo0MsBDv+Rj9i4YPf0+iRirVruSSdiCgWqX5+M2cnRujZ7yZa5ecDe/fK4GXRIuDvf1d7ntHL7YmIKLow2IkRZu2zE25acdLhw4F+/dSeY/RyeyIiii4MdmKEWfvsmElb9u5LKJbbExFRdGGwEyNCsd+NzSb3rInUvWtUSl/cdlv05igREZExGOzECKP3uykulgnPfftG7t41NpsMxHxZsiTygjQiIgovBjsxJD9fLi933YQvJ0ffsnNtmbdrwnNZmTweKQGPngKhRETUeJlaG4uMl58P5OUFvt+NzSaXd3vakEAIOUpUUCCv4XrOUO6z40ljScomIqLgMNiJQdqKpUDoWcLueA1Pe+Dk5MiptVBtZNgYk7KJiEg/TmNFsVAkEAcyWmLWtJdZxU+JiCi6MNiJUqFKIE5P19fO37QXIKe9QpEkbFbxUyIiii4MdqJQJCUQm71zs1FJ2UREFLuYsxNlgkkgVlFRoa9dJCQJB5uUTUREsY3BTpQJNIFYld6k30hJEg4mKZuIiGIbp7GiTKhHUvQm/TJJmIiIIh2DnSgT6pEUvUm/TBImIqJIx2AnyoRjJEVv0i+ThImIKJJZhPCU6tq4VFVVISUlBZWVlWjZsqXZ3fFLW40FOCcqawGQUQGG3h2Rw72DMhERNW6qn98MdhB9wQ7gecfi3Fw5ZcSRFCIiagxUP7+5GitKcbk1ERGRGgY7UYzLrYmIiPxjgjIRERHFNAY7REREFNM4jUVecTUWERHFAgY75JGn1V45OXIDQU+rvfS2JyIiChdOY8Ugmw1Ytw5YvFje22z6nq+3qnokVWEnIiJyxX12EJ377HgT7AiLzQa0b++92KjFIs9XWiqnqPS2JyIiMorq5zdHdmKIESMseqqqB9KeiIgo3BjsxAibTY7oeBqn044VFPif0tJbVT3UVdiJiIiCxWAnRhg1wqK3qnqoq7ATEREFi8FOjDBqhEVvVfVwVGEnIiIKBoOdGGHUCIvVKpOZAfcARvv+2Wcbko31ticiIgo3BjsxwsgRlvx84K23gDPPdD6ekyOPu67q0tueiIgonLj0HLGz9FxbjQU4JyprAZDewIM7KBMRUSSLiqXnn3zyCQYPHozs7GxYLBYsX77c6XEhBKZPn47s7GwkJSWhT58+2LVrl1Ob2tpajBs3Dm3atEHz5s0xZMgQ/OwrUzeGGT3ColVVHz5c3vsLXPS2JyIiCgdTg53jx4/joosuwqxZszw+PnPmTBQWFmLWrFkoKSlBZmYmBgwYgGPHjtnbFBQUYNmyZViyZAk2btyI6upqDBo0CDa92wbHiPx8YO9eYO1aYNEieV9ayqkkIiJqvCJmGstisWDZsmUYOnQoADmqk52djYKCAtx///0A5ChORkYGnnzySYwaNQqVlZU444wz8Nprr+HWW28FAOzfvx+5ubl4//33ce211ypdO1amsYiIiBqTqJjG8qW0tBTl5eUYOHCg/VhiYiJ69+6NTZs2AQC2bNmCU6dOObXJzs5G586d7W08qa2tRVVVldONiIiIYlPEBjvl5eUAgIyMDKfjGRkZ9sfKy8uRkJCA1q1be23jyYwZM5CSkmK/5ebmGtx7IiIiihQRG+xoLC5rqYUQbsdc+WvzwAMPoLKy0n7bt2+fIX0lIiKiyBOxwU5mZiYAuI3QVFRU2Ed7MjMzUVdXhyNHjnht40liYiJatmzpdCMiIqLYFLHBTocOHZCZmYnVq1fbj9XV1WH9+vXo1asXAKBbt26Ij493anPgwAHs3LnT3oaIiIgatyZmXry6uhrff/+9/fvS0lJs27YNqampaNu2LQoKCvD444+jY8eO6NixIx5//HE0a9YMt99+OwAgJSUFI0eOxKRJk5CWlobU1FRMnjwZXbp0Qf/+/c16WURERBRBTA12Nm/ejL59+9q/nzhxIgBgxIgRmDdvHqZOnYqamhqMHj0aR44cQc+ePbFq1SokJyfbn1NUVIQmTZpg2LBhqKmpQb9+/TBv3jxYuaMdERERIYL22TET99khIiKKPlG/zw4RERGREUydxooU2uAWNxckIiKKHtrntr9JKgY7gL3WFjcXJCIiij7Hjh1DSkqK18eZswOgvr4e+/fvR3Jyst8NC/WoqqpCbm4u9u3b12hygfia+ZpjFV8zX3OsiubXLITAsWPHkJ2djbg475k5HNkBEBcXh5ycnJCdvzFuXMjX3DjwNTcOfM2NQ7S+Zl8jOhomKBMREVFMY7BDREREMY3BTgglJibi4YcfRmJiotldCRu+5saBr7lx4GtuHBrDa2aCMhEREcU0juwQERFRTGOwQ0RERDGNwQ4RERHFNAY7REREFNMY7ITIJ598gsGDByM7OxsWiwXLly83u0shNWPGDPTo0QPJyclIT0/H0KFD8e2335rdrZCaM2cOunbtat+I6/LLL8cHH3xgdrfCZsaMGbBYLCgoKDC7KyE1ffp0WCwWp1tmZqbZ3Qq5srIy/OEPf0BaWhqaNWuG3/3ud9iyZYvZ3QqZ9u3bu/2cLRYLxowZY3bXQub06dP4+9//jg4dOiApKQlnnXUW/vnPf6K+vt7srhmOOyiHyPHjx3HRRRfh7rvvxk033WR2d0Ju/fr1GDNmDHr06IHTp0/jwQcfxMCBA/H111+jefPmZncvJHJycvDEE0/gnHPOAQDMnz8feXl5+PLLL3HhhRea3LvQKikpwcsvv4yuXbua3ZWwuPDCC7FmzRr791ar1cTehN6RI0dwxRVXoG/fvvjggw+Qnp6OPXv2oFWrVmZ3LWRKSkpgs9ns3+/cuRMDBgzALbfcYmKvQuvJJ5/Eiy++iPnz5+PCCy/E5s2bcffddyMlJQXjx483u3uGYrATItdffz2uv/56s7sRNitXrnT6fu7cuUhPT8eWLVtw9dVXm9Sr0Bo8eLDT94899hjmzJmDzz//PKaDnerqatxxxx34z3/+g0cffdTs7oRFkyZNGsVojubJJ59Ebm4u5s6daz/Wvn178zoUBmeccYbT90888QTOPvts9O7d26Qehd5nn32GvLw83HDDDQDkz3jx4sXYvHmzyT0zHqexKCQqKysBAKmpqSb3JDxsNhuWLFmC48eP4/LLLze7OyE1ZswY3HDDDejfv7/ZXQmb3bt3Izs7Gx06dMBtt92GH374wewuhdTbb7+N7t2745ZbbkF6ejouvvhi/Oc//zG7W2FTV1eHhQsX4p577jG0OHSkufLKK/HRRx/hu+++AwB89dVX2LhxI37/+9+b3DPjcWSHDCeEwMSJE3HllVeic+fOZncnpHbs2IHLL78cJ0+eRIsWLbBs2TJccMEFZncrZJYsWYKtW7eipKTE7K6ETc+ePbFgwQJ06tQJv/zyCx599FH06tULu3btQlpamtndC4kffvgBc+bMwcSJEzFt2jT897//xX333YfExETceeedZncv5JYvX46jR4/irrvuMrsrIXX//fejsrIS5513HqxWK2w2Gx577DEMHz7c7K4ZjsEOGW7s2LHYvn07Nm7caHZXQu7cc8/Ftm3bcPToUSxduhQjRozA+vXrYzLg2bdvH8aPH49Vq1ahadOmZncnbByno7t06YLLL78cZ599NubPn4+JEyea2LPQqa+vR/fu3fH4448DAC6++GLs2rULc+bMaRTBzquvvorrr78e2dnZZnclpN544w0sXLgQixYtwoUXXoht27ahoKAA2dnZGDFihNndMxSDHTLUuHHj8Pbbb+OTTz5BTk6O2d0JuYSEBHuCcvfu3VFSUoLnnnsOL730ksk9M96WLVtQUVGBbt262Y/ZbDZ88sknmDVrFmpra2M+cRcAmjdvji5dumD37t1mdyVksrKy3AL2888/H0uXLjWpR+Hz448/Ys2aNSguLja7KyE3ZcoU/O1vf8Ntt90GQAbzP/74I2bMmMFgh8gTIQTGjRuHZcuWYd26dejQoYPZXTKFEAK1tbVmdyMk+vXrhx07djgdu/vuu3Heeefh/vvvbxSBDgDU1tbif//7H6666iqzuxIyV1xxhdvWEd999x3atWtnUo/CR1tcoSXtxrITJ04gLs45dddqtXLpOamrrq7G999/b/++tLQU27ZtQ2pqKtq2bWtiz0JjzJgxWLRoEVasWIHk5GSUl5cDAFJSUpCUlGRy70Jj2rRpuP7665Gbm4tjx45hyZIlWLdundvKtFiRnJzsloPVvHlzpKWlxXRu1uTJkzF48GC0bdsWFRUVePTRR1FVVRVzf/k6mjBhAnr16oXHH38cw4YNw3//+1+8/PLLePnll83uWkjV19dj7ty5GDFiBJo0if2Px8GDB+Oxxx5D27ZtceGFF+LLL79EYWEh7rnnHrO7ZjxBIbF27VoBwO02YsQIs7sWEp5eKwAxd+5cs7sWMvfcc49o166dSEhIEGeccYbo16+fWLVqldndCqvevXuL8ePHm92NkLr11ltFVlaWiI+PF9nZ2SI/P1/s2rXL7G6F3DvvvCM6d+4sEhMTxXnnnSdefvlls7sUch9++KEAIL799luzuxIWVVVVYvz48aJt27aiadOm4qyzzhIPPvigqK2tNbtrhrMIIYQ5YRYRERFR6HGfHSIiIoppDHaIiIgopjHYISIiopjGYIeIiIhiGoMdIiIiimkMdoiIiCimMdghIiKimMZgh4iIiGIagx0iijrz5s1Dq1atTO1Dnz59UFBQYGofiEgNd1AmIsPcddddmD9/vtvxa6+91tCaYTU1NTh27BjS09MNO6dehw8fRnx8PJKTk03rAxGpif1KZ0QUVtdddx3mzp3rdCwxMdHQayQlJZleYDY1NdXU6xOROk5jEZGhEhMTkZmZ6XRr3bq1/XGLxYJXXnkFN954I5o1a4aOHTvi7bffdjrH22+/jY4dOyIpKQl9+/bF/PnzYbFYcPToUQDu01jTp0/H7373O7z22mto3749UlJScNttt+HYsWP2NkIIzJw5E2eddRaSkpJw0UUX4a233vL5WmbPno2OHTuiadOmyMjIwM0332x/zHEaa926dbBYLG63u+66y97+nXfeQbdu3dC0aVOcddZZeOSRR3D69Gmd7y4RBYLBDhGF3SOPPIJhw4Zh+/bt+P3vf4877rgDhw8fBgDs3bsXN998M4YOHYpt27Zh1KhRePDBB/2ec8+ePVi+fDneffddvPvuu1i/fj2eeOIJ++N///vfMXfuXMyZMwe7du3ChAkT8Ic//AHr16/3eL7Nmzfjvvvuwz//+U98++23WLlyJa6++mqPbXv16oUDBw7Ybx9//DGaNm1qb//hhx/iD3/4A+677z58/fXXeOmllzBv3jw89thjet86IgqEqTXXiSimjBgxQlitVtG8eXOn2z//+U97GwDi73//u/376upqYbFYxAcffCCEEOL+++8XnTt3djrvgw8+KACII0eOCCGEmDt3rkhJSbE//vDDD4tmzZqJqqoq+7EpU6aInj172q/RtGlTsWnTJqfzjhw5UgwfPtzja1m6dKlo2bKl0zkd9e7dW4wfP97t+K+//irOPvtsMXr0aPuxq666Sjz++ONO7V577TWRlZXl8dxEZCzm7BCRofr27Ys5c+Y4HXPNb+natav96+bNmyM5ORkVFRUAgG+//RY9evRwan/ppZf6vW779u2dkoWzsrLs5/z6669x8uRJDBgwwOk5dXV1uPjiiz2eb8CAAWjXrh3OOussXHfddbjuuuvsU2/enDp1CjfddBPatm2L5557zn58y5YtKCkpcRrJsdlsOHnyJE6cOOHznEQUPAY7RGSo5s2b45xzzvHZJj4+3ul7i8WC+vp6ADK3xmKxOD0uFBaN+jqndv/ee+/hzDPPdGrnLXk6OTkZW7duxbp167Bq1So89NBDmD59OkpKSrwue//rX/+Kn376CSUlJWjSpOG/1/r6ejzyyCPIz893e07Tpk39vjYiCg6DHSKKKOeddx7ef/99p2ObN28O6pwXXHABEhMT8dNPP6F3797Kz2vSpAn69++P/v374+GHH0arVq3w8ccfewxaCgsL8cYbb+Czzz5DWlqa02OXXHIJvv32W79BIBGFBoMdIjJUbW0tysvLnY41adIEbdq0UXr+qFGjUFhYiPvvvx8jR47Etm3bMG/ePABwG/FRlZycjMmTJ2PChAmor6/HlVdeiaqqKmzatAktWrTAiBEj3J7z7rvv4ocffsDVV1+N1q1b4/3330d9fT3OPfdct7Zr1qzB1KlT8cILL6BNmzb215+UlISUlBQ89NBDGDRoEHJzc3HLLbcgLi4O27dvx44dO/Doo48G9JqISB1XYxGRoVauXImsrCyn25VXXqn8/A4dOuCtt95CcXExunbtijlz5thXYwWzX8+//vUvPPTQQ5gxYwbOP/98XHvttXjnnXfQoUMHj+1btWqF4uJiXHPNNTj//PPx4osvYvHixbjwwgvd2m7cuBE2mw333nuv0+seP348ALmp4rvvvovVq1ejR48euOyyy1BYWIh27doF/HqISB13UCaiiPfYY4/hxRdfxL59+8zuChFFIU5jEVHEmT17Nnr06IG0tDR8+umneOqppzB27Fizu0VEUYrBDhFFnN27d+PRRx/F4cOH0bZtW0yaNAkPPPCA2d0ioijFaSwiIiKKaUxQJiIiopjGYIeIiIhiGoMdIiIiimkMdoiIiCimMdghIiKimMZgh4iIiGIagx0iIiKKaQx2iIiIKKb9fwLbwgzkGoqlAAAAAElFTkSuQmCC\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": {
|
||
"tags": []
|
||
},
|
||
"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": 96,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Mean absolute error: 23.55\n",
|
||
"Residual sum of squares (MSE): 1018.06\n",
|
||
"R2-score: 0.76\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": 103,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Coefficients: [[16.40024771]]\n",
|
||
"Intercept: [67.43562042]\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"msk = np.random.rand(len(df)) < 0.8\n",
|
||
"train = cdf[msk]\n",
|
||
"test = cdf[~msk]\n",
|
||
"\n",
|
||
"plt.scatter(train.FUELCONSUMPTION_COMB, train.CO2EMISSIONS, color='blue')\n",
|
||
"plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
|
||
"plt.ylabel(\"Emission\")\n",
|
||
"plt.show() \n",
|
||
"\n",
|
||
"from sklearn import linear_model\n",
|
||
"regr = linear_model.LinearRegression()\n",
|
||
"train_x = np.asanyarray(train[['FUELCONSUMPTION_COMB']])\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_)\n",
|
||
"\n",
|
||
"plt.scatter(train.FUELCONSUMPTION_COMB, train.CO2EMISSIONS, color='blue')\n",
|
||
"plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
|
||
"plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
|
||
"plt.ylabel(\"Emission\")\n",
|
||
"\n",
|
||
"train_x = train[[\"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": "code",
|
||
"execution_count": 104,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Coefficients: [[16.40024771]]\n",
|
||
"Intercept: [67.43562042]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"regr = linear_model.LinearRegression()\n",
|
||
"train_x = np.asanyarray(train[['FUELCONSUMPTION_COMB']])\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_)\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": "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": 101,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[[127.21873633]\n",
|
||
" [127.21873633]\n",
|
||
" [127.21873633]\n",
|
||
" [165.33604443]\n",
|
||
" [103.39541876]\n",
|
||
" [103.39541876]\n",
|
||
" [103.39541876]\n",
|
||
" [135.15984218]\n",
|
||
" [171.68892912]\n",
|
||
" [138.33628452]\n",
|
||
" [138.33628452]\n",
|
||
" [119.27763047]\n",
|
||
" [103.39541876]\n",
|
||
" [103.39541876]\n",
|
||
" [166.9242656 ]\n",
|
||
" [103.39541876]\n",
|
||
" [103.39541876]\n",
|
||
" [103.39541876]\n",
|
||
" [103.39541876]\n",
|
||
" [103.39541876]\n",
|
||
" [119.27763047]\n",
|
||
" [119.27763047]\n",
|
||
" [119.27763047]\n",
|
||
" [119.27763047]\n",
|
||
" [141.51272687]\n",
|
||
" [141.51272687]\n",
|
||
" [141.51272687]\n",
|
||
" [141.51272687]\n",
|
||
" [141.51272687]\n",
|
||
" [141.51272687]\n",
|
||
" [119.27763047]\n",
|
||
" [141.51272687]\n",
|
||
" [119.27763047]\n",
|
||
" [128.8069575 ]\n",
|
||
" [103.39541876]\n",
|
||
" [128.8069575 ]\n",
|
||
" [128.8069575 ]\n",
|
||
" [128.8069575 ]\n",
|
||
" [170.10070794]\n",
|
||
" [170.10070794]\n",
|
||
" [128.8069575 ]\n",
|
||
" [128.8069575 ]\n",
|
||
" [109.74830345]\n",
|
||
" [109.74830345]\n",
|
||
" [139.9245057 ]\n",
|
||
" [155.80671741]\n",
|
||
" [155.80671741]\n",
|
||
" [147.86561155]\n",
|
||
" [128.8069575 ]\n",
|
||
" [139.9245057 ]\n",
|
||
" [139.9245057 ]\n",
|
||
" [155.80671741]\n",
|
||
" [170.10070794]\n",
|
||
" [100.21897642]\n",
|
||
" [155.80671741]\n",
|
||
" [109.74830345]\n",
|
||
" [128.8069575 ]\n",
|
||
" [128.8069575 ]\n",
|
||
" [128.8069575 ]\n",
|
||
" [128.8069575 ]\n",
|
||
" [162.15960209]\n",
|
||
" [173.27715029]\n",
|
||
" [128.8069575 ]\n",
|
||
" [128.8069575 ]\n",
|
||
" [128.8069575 ]\n",
|
||
" [173.27715029]\n",
|
||
" [103.39541876]\n",
|
||
" [103.39541876]\n",
|
||
" [109.74830345]\n",
|
||
" [128.8069575 ]\n",
|
||
" [128.8069575 ]\n",
|
||
" [128.8069575 ]\n",
|
||
" [ 93.86609174]\n",
|
||
" [ 93.86609174]\n",
|
||
" [ 93.86609174]\n",
|
||
" [ 93.86609174]\n",
|
||
" [103.39541876]\n",
|
||
" [157.39493858]\n",
|
||
" [144.68916921]\n",
|
||
" [179.63003497]\n",
|
||
" [130.39517867]\n",
|
||
" [ 97.04253408]\n",
|
||
" [103.39541876]\n",
|
||
" [ 97.04253408]\n",
|
||
" [103.39541876]\n",
|
||
" [157.39493858]\n",
|
||
" [103.39541876]\n",
|
||
" [127.21873633]\n",
|
||
" [130.39517867]\n",
|
||
" [130.39517867]\n",
|
||
" [130.39517867]\n",
|
||
" [130.39517867]\n",
|
||
" [151.04205389]\n",
|
||
" [ 97.04253408]\n",
|
||
" [103.39541876]\n",
|
||
" [ 95.45431291]\n",
|
||
" [ 97.04253408]\n",
|
||
" [130.39517867]\n",
|
||
" [128.8069575 ]\n",
|
||
" [128.8069575 ]\n",
|
||
" [155.80671741]\n",
|
||
" [155.80671741]\n",
|
||
" [155.80671741]\n",
|
||
" [166.9242656 ]\n",
|
||
" [166.9242656 ]\n",
|
||
" [147.86561155]\n",
|
||
" [155.80671741]\n",
|
||
" [109.74830345]\n",
|
||
" [128.8069575 ]\n",
|
||
" [155.80671741]\n",
|
||
" [109.74830345]\n",
|
||
" [100.21897642]\n",
|
||
" [109.74830345]\n",
|
||
" [ 95.45431291]\n",
|
||
" [127.21873633]\n",
|
||
" [127.21873633]\n",
|
||
" [127.21873633]\n",
|
||
" [103.39541876]\n",
|
||
" [103.39541876]\n",
|
||
" [109.74830345]\n",
|
||
" [103.39541876]\n",
|
||
" [130.39517867]\n",
|
||
" [130.39517867]\n",
|
||
" [160.57138092]\n",
|
||
" [127.21873633]\n",
|
||
" [151.04205389]\n",
|
||
" [151.04205389]\n",
|
||
" [151.04205389]\n",
|
||
" [151.04205389]\n",
|
||
" [109.74830345]\n",
|
||
" [122.45407281]\n",
|
||
" [122.45407281]\n",
|
||
" [109.74830345]\n",
|
||
" [122.45407281]\n",
|
||
" [103.39541876]\n",
|
||
" [109.74830345]\n",
|
||
" [109.74830345]\n",
|
||
" [162.15960209]\n",
|
||
" [128.8069575 ]\n",
|
||
" [103.39541876]\n",
|
||
" [109.74830345]\n",
|
||
" [109.74830345]\n",
|
||
" [109.74830345]\n",
|
||
" [103.39541876]\n",
|
||
" [ 97.04253408]\n",
|
||
" [103.39541876]\n",
|
||
" [ 97.04253408]\n",
|
||
" [109.74830345]\n",
|
||
" [ 97.04253408]\n",
|
||
" [109.74830345]\n",
|
||
" [109.74830345]\n",
|
||
" [103.39541876]\n",
|
||
" [119.27763047]\n",
|
||
" [119.27763047]\n",
|
||
" [151.04205389]\n",
|
||
" [111.33652462]\n",
|
||
" [144.68916921]\n",
|
||
" [127.21873633]\n",
|
||
" [162.15960209]\n",
|
||
" [127.21873633]\n",
|
||
" [127.21873633]\n",
|
||
" [103.39541876]\n",
|
||
" [130.39517867]\n",
|
||
" [130.39517867]\n",
|
||
" [ 95.45431291]\n",
|
||
" [103.39541876]\n",
|
||
" [103.39541876]\n",
|
||
" [111.33652462]\n",
|
||
" [103.39541876]\n",
|
||
" [103.39541876]\n",
|
||
" [127.21873633]\n",
|
||
" [127.21873633]\n",
|
||
" [127.21873633]\n",
|
||
" [103.39541876]\n",
|
||
" [104.98363993]\n",
|
||
" [158.98315975]\n",
|
||
" [158.98315975]\n",
|
||
" [158.98315975]\n",
|
||
" [104.98363993]\n",
|
||
" [127.21873633]\n",
|
||
" [158.98315975]\n",
|
||
" [170.10070794]\n",
|
||
" [170.10070794]\n",
|
||
" [ 97.04253408]\n",
|
||
" [ 97.04253408]\n",
|
||
" [ 97.04253408]\n",
|
||
" [ 97.04253408]\n",
|
||
" [103.39541876]\n",
|
||
" [103.39541876]\n",
|
||
" [103.39541876]\n",
|
||
" [ 90.6896494 ]\n",
|
||
" [ 90.6896494 ]\n",
|
||
" [103.39541876]\n",
|
||
" [ 97.04253408]\n",
|
||
" [135.15984218]\n",
|
||
" [125.63051516]\n",
|
||
" [125.63051516]\n",
|
||
" [131.98339984]\n",
|
||
" [131.98339984]\n",
|
||
" [125.63051516]\n",
|
||
" [114.51296696]\n",
|
||
" [119.27763047]\n",
|
||
" [178.0418138 ]\n",
|
||
" [178.0418138 ]\n",
|
||
" [ 92.27787057]\n",
|
||
" [111.33652462]\n",
|
||
" [103.39541876]\n",
|
||
" [111.33652462]\n",
|
||
" [128.8069575 ]\n",
|
||
" [111.33652462]\n",
|
||
" [128.8069575 ]\n",
|
||
" [135.15984218]\n",
|
||
" [100.21897642]\n",
|
||
" [100.21897642]\n",
|
||
" [135.15984218]\n",
|
||
" [127.21873633]\n",
|
||
" [127.21873633]\n",
|
||
" [135.15984218]\n",
|
||
" [144.68916921]\n",
|
||
" [127.21873633]\n",
|
||
" [111.33652462]\n",
|
||
" [111.33652462]\n",
|
||
" [100.21897642]\n",
|
||
" [128.8069575 ]\n",
|
||
" [128.8069575 ]\n",
|
||
" [122.45407281]]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"predictions = regr.predict(test_x)\n",
|
||
"print(predictions)"
|
||
]
|
||
},
|
||
{
|
||
"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": 105,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Mean absolute error: 113.18\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - 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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python",
|
||
"language": "python",
|
||
"name": "conda-env-python-py"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.7.12"
|
||
},
|
||
"prev_pub_hash": "20d6dc1d9e74df451be22381c972d7921c93657bea402a00c749dca52bb85996"
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|