{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

\n", " \n", " \"Skills\n", " \n", "

\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": 2, "metadata": { "tags": [] }, "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": { "tags": [] }, "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": 3, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2025-10-17 01:32:43-- 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 0.003s \n", "\n", "2025-10-17 01:32:43 (26.0 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": 4, "metadata": { "tags": [] }, "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": 5, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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MODELYEARMAKEMODELVEHICLECLASSENGINESIZECYLINDERSTRANSMISSIONFUELTYPEFUELCONSUMPTION_CITYFUELCONSUMPTION_HWYFUELCONSUMPTION_COMBFUELCONSUMPTION_COMB_MPGCO2EMISSIONS
02014ACURAILXCOMPACT2.04AS5Z9.96.78.533196
12014ACURAILXCOMPACT2.44M6Z11.27.79.629221
22014ACURAILX HYBRIDCOMPACT1.54AV7Z6.05.85.948136
32014ACURAMDX 4WDSUV - SMALL3.56AS6Z12.79.111.125255
42014ACURARDX AWDSUV - SMALL3.56AS6Z12.18.710.627244
\n", "
" ], "text/plain": [ " MODELYEAR MAKE MODEL VEHICLECLASS ENGINESIZE CYLINDERS \\\n", "0 2014 ACURA ILX COMPACT 2.0 4 \n", "1 2014 ACURA ILX COMPACT 2.4 4 \n", "2 2014 ACURA ILX HYBRID COMPACT 1.5 4 \n", "3 2014 ACURA MDX 4WD SUV - SMALL 3.5 6 \n", "4 2014 ACURA RDX AWD SUV - SMALL 3.5 6 \n", "\n", " TRANSMISSION FUELTYPE FUELCONSUMPTION_CITY FUELCONSUMPTION_HWY \\\n", "0 AS5 Z 9.9 6.7 \n", "1 M6 Z 11.2 7.7 \n", "2 AV7 Z 6.0 5.8 \n", "3 AS6 Z 12.7 9.1 \n", "4 AS6 Z 12.1 8.7 \n", "\n", " FUELCONSUMPTION_COMB FUELCONSUMPTION_COMB_MPG CO2EMISSIONS \n", "0 8.5 33 196 \n", "1 9.6 29 221 \n", "2 5.9 48 136 \n", "3 11.1 25 255 \n", "4 10.6 27 244 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"FuelConsumption.csv\")\n", "\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": 6, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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MODELYEARENGINESIZECYLINDERSFUELCONSUMPTION_CITYFUELCONSUMPTION_HWYFUELCONSUMPTION_COMBFUELCONSUMPTION_COMB_MPGCO2EMISSIONS
count1067.01067.0000001067.0000001067.0000001067.0000001067.0000001067.0000001067.000000
mean2014.03.3462985.79475213.2965329.47460211.58088126.441425256.228679
std0.01.4158951.7974474.1012532.7945103.4855957.46870263.372304
min2014.01.0000003.0000004.6000004.9000004.70000011.000000108.000000
25%2014.02.0000004.00000010.2500007.5000009.00000021.000000207.000000
50%2014.03.4000006.00000012.6000008.80000010.90000026.000000251.000000
75%2014.04.3000008.00000015.55000010.85000013.35000031.000000294.000000
max2014.08.40000012.00000030.20000020.50000025.80000060.000000488.000000
\n", "
" ], "text/plain": [ " MODELYEAR ENGINESIZE CYLINDERS FUELCONSUMPTION_CITY \\\n", "count 1067.0 1067.000000 1067.000000 1067.000000 \n", "mean 2014.0 3.346298 5.794752 13.296532 \n", "std 0.0 1.415895 1.797447 4.101253 \n", "min 2014.0 1.000000 3.000000 4.600000 \n", "25% 2014.0 2.000000 4.000000 10.250000 \n", "50% 2014.0 3.400000 6.000000 12.600000 \n", "75% 2014.0 4.300000 8.000000 15.550000 \n", "max 2014.0 8.400000 12.000000 30.200000 \n", "\n", " FUELCONSUMPTION_HWY FUELCONSUMPTION_COMB FUELCONSUMPTION_COMB_MPG \\\n", "count 1067.000000 1067.000000 1067.000000 \n", "mean 9.474602 11.580881 26.441425 \n", "std 2.794510 3.485595 7.468702 \n", "min 4.900000 4.700000 11.000000 \n", "25% 7.500000 9.000000 21.000000 \n", "50% 8.800000 10.900000 26.000000 \n", "75% 10.850000 13.350000 31.000000 \n", "max 20.500000 25.800000 60.000000 \n", "\n", " CO2EMISSIONS \n", "count 1067.000000 \n", "mean 256.228679 \n", "std 63.372304 \n", "min 108.000000 \n", "25% 207.000000 \n", "50% 251.000000 \n", "75% 294.000000 \n", "max 488.000000 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# summarize the data\n", "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's select some features to explore more.\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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ENGINESIZECYLINDERSFUELCONSUMPTION_COMBCO2EMISSIONS
02.048.5196
12.449.6221
21.545.9136
33.5611.1255
43.5610.6244
53.5610.0230
63.5610.1232
73.7611.1255
83.7611.6267
\n", "
" ], "text/plain": [ " ENGINESIZE CYLINDERS FUELCONSUMPTION_COMB CO2EMISSIONS\n", "0 2.0 4 8.5 196\n", "1 2.4 4 9.6 221\n", "2 1.5 4 5.9 136\n", "3 3.5 6 11.1 255\n", "4 3.5 6 10.6 244\n", "5 3.5 6 10.0 230\n", "6 3.5 6 10.1 232\n", "7 3.7 6 11.1 255\n", "8 3.7 6 11.6 267" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n", "cdf.head(9)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot each of these features:\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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": 9, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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": 10, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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eN7Ip5hNPiJydl18WbRus+F+eVblFRJopFmtsbFSys7OVzZs3K0OGDFFmzJihKIqinDlzRklPT1ceffRR59iTJ08qycnJyjPPPKMoiqIcPXpUiYmJUUpLS51jqqurlXbt2imbNm2SnkN9fb0CQKmvrzfmTRGRoiiKMneuotjtiiI+9sTNbhePR5vTpxUlM9P9vbrebDZFycoS43y9futWRSkpEfenTyvK2rWtj5mVJb5/no9nZorxoVRS4vv9ut5KSkI7L2o7ZD+/LV/ZmTZtGq677jqMGDHC7fGqqirU1tZi1KhRzsfi4uIwZMgQ7Ny5EwBQWVmJU6dOuY3JyMhATk6Oc4w3zc3NaGhocLsRkfG05H5EumBWOXwl+ALAV1+JRpolJeJ+yRLgL38JjxwZq3OLiGRZ2hurtLQUH3/8MSoqKlo9V1tbCwBIS0tzezwtLQ1ff/21c0xsbCw6d+7caoz6em8WL16Mhx9+ONjpE5EENfcj2undQaUm+HrmvajBi2thPrX+jq8cGZtNfK/z8kJT40bNLaqu9j4ntR4Q20WQ1Sxb2Tl48CBmzJiBNWvWoEOHDj7H2TwuWCuK0uoxT4HGzJ8/H/X19c7bwYMHtU2eiMiDnlUOrQm+4ZYjEy7tIpqagAkTgIsvFvdNTeaejyKPZcFOZWUl6urq0L9/f7Rv3x7t27fHtm3b8P/+3/9D+/btnSs6nis0dXV1zufS09PR0tKCI0eO+BzjTVxcHDp16uR2IyIKhp4dVFqDFyvr7/hidbuIX/0KSEoC1q8H9uwR90lJ4nEilWXBzvDhw7Fnzx7s3r3bebv00ktx2223Yffu3TjvvPOQnp6OzZs3O1/T0tKCbdu2YdCgQQCA/v37IyYmxm1MTU0N9u7d6xxDRBQKelY5tAYv4Zojk5/fOreoqio0gY6XLAgA4nEGPKSyLGcnKSkJOTk5bo8lJiYiNTXV+XhhYSEWLVqE7OxsZGdnY9GiRUhISMCtt94KAEhOTsaUKVMwe/ZspKamIiUlBXPmzEFubm6rhGciIrOpqxwzZriv2GRmikDH88Nfa/ASzjkydjswdGjoztfU5DvQUVVUiHEdO4ZmTtSaGX3o9LA0QTmQefPm4cSJE5g6dSqOHDmCyy67DO+88w6SkpKcY4qLi9G+fXtMnDgRJ06cwPDhw7Fy5UrY2YGOiCyQny8ShGWaYmoNXtTVoxtuEM+5viaUOTLh4De/kR+3bp25cyHv5s0Tuwdda0DNmSNqbIV6Rya7noNdz4nIOupuLMB78OIt76WsrPXqUVaW99WjaHXxxSJHJ5DcXOCTT8yfD7mbNw94/HHfz/vqs6aV7Oc3gx0w2CGKdA6H3EpKuNITvET6ew7WhAkiGTmQ8eO5shNqLS1AQoL/qt52u6i5FewlLQY7GjDYIYpc3gKFzExxuSeSVjnaevCiVVOT2HUVSGMjc3ZC7ckngZkzA48rLg6+Bpfs53dY5+wQEfmjpSBfuDMqwbetBE0dOwIDBvhPUh4wgIGOFQ4cMHacESxvF0FEpAc7brfmq+1EKFtIhNJHH4mAxpsBA8TzFHp9+hg7zgi8jAVexiKKROXl4sM8kK1bQ7slWq9gV2R8rXL5S3Q2UlOT2PmkbjFevTp0qypWnptaC8ecHV7GIqKIFI7VhAP55hugXz/xP/mEBGDfPqBHD315R67BUbdu/le59PTMOnFC7JjZvx/IzhY7a+LjvY/1LO63Z4/IpwnV6krHjkxCDiexsWJ7ub/dWLNmhbbeDld2wJUdokgUaSs7MTHA6dOtH2/XTgQkWlZkvAVHMmS/F+PHAxs2tH48L6/1Dih/VYwBXk5qy7zV2bHbja2zw91YGjDYIYo8agfwQAX5qqqsT9D1FegE4u09+LpcJaOkBLjlFv9jfAU6KteAhzuiKBCzKyjLfn4zQZmIIlK4dNwO5Jtv9AU6QOtGoP6SsmV06+b/+RMn/Ac6gHj+xAnx79tvlzuv7DiKPrGx4hLq0qXi3opWEQCDHSKKYFZ33JbRr1/wx1DzjgJ1SQ/W3LnaxslWJmYFY7IaE5SJKKJp6UVlhePHgz+G2gg02GTrujr/z+/fL3ccdVxqqrjEFkhqqtxxiczClR0iinhqQb5bbhH34RLoAGLXlV42m2gboTYCle2S7kug12dnyx1HHfenP8mNlx1HZBYGO0QU8RwOsTvr5ZfFfTgVEty3T9/rvOUdqV3SPXOUZI7lGjT54m+rsLdxX3whN152HJFZGOwQUUQL96rBPXoA7QMkDLRrJ4IYV97yjvwlZfujKHLJ2vHx4pKgP3l5Z+vtyFzC0jKOyCwMdogo7Miu1KjbsD2TdtXeWOES8Jw65Tvgad9evL+vvhJ1cEpKxH1VlfcEa19J2Ublxaxf7zvg8ayzE45tAYi8YZ0dsM4OUTiRrSas1tnxtTspnOrsqHxVUNbDs4Ly5MnGfi9kKiiHsi0AkTcsKqgBgx2i8KClv1OkVVAOJJjia1Z+L+bN85/rM3eucdVyzS5QR5GHRQWJKKJo7WIeib2xfJk3T6yQzJwJLFsm7hMSxOMyrPxeFBWJgMZzxchuNzbQCfZ7RG0bgx0iCguBCuZ5VhOW3YYd7HZtI7W0iEThggJx39JydmXE81KQwyEel/kwt/p7UVQkWkJMmwaMGiXuGxuNDXSC/R5R28bLWOBlLKJw8PLLYjdVIGp/p0jLF/HWFFG9POfv/8Iy78HqPmF6urbLirSfM4UWL2MRUUTRujqxc2fgejoOhxhnNV8rE966nXtyOESXaH8706zsE6Z3R5zsjrunn5b7OT/9tNaZU1vCYIeIwkKggnmehfEiJWenpUWs6ATjr38NXEPIij5hWvOsVFpqIx04IDcX2XHUNrE3FhGFBXV14oYbRGDj+gHqbXXC6jwVf1y3hf/jH8ZWdFZXTLwFMKHuEyabZ7V0KZCWJubzww/AxImtAyRf74u1fMgIzNkBc3aIwom3/I+sLBHoeKuzY1Weii/e5m+0cKkhJJtn5cpu9x38eXtfzNkhf5izQ0QRKT9frpqwTJ7KE0+I1YdQ9czylb9iNNcVk2Df24kTwPTpwDXXiPsTJ+Rfq2fVzN88PXfcASKAmTXL/zFnzWKgQ/5xZQdc2SGKZL5Wgm6+WQQCZuwQ8iZQRWez6Xlv48cDGza0ftyzLYQvJ04E19XdF3XHnatg50rRiSs7RNQmeFsJWrIE+MtfQtszK1D+itm0vjdfwQMgHh8/PvAxnn1WdnbaeK4YlZUBGze2HmezicfDpQcahS+u7IArO0TRxKqeWXryV4xms4ndWCtXAnV1vhOUZVdkHntMXFby1ZqhoEBUMzaKt59NJPZAo9Dhyg4RtUlaKzHL8Fb52FM4VGpWFPHeR4zwv6V77ly54913n//WDEbugPJVD8iMnye1PQx2iCiqGF1/R7Ynk1onKNwcOgRcf717wLN/v/bjeGvNMHWq/tUUz9f5qgcUKfWUKLwx2CGiqGJk/R0tPZnsdqB/f/l5qmJifBdSNNLdd599H9nZ+o+zZMnZla3YWOCXv9T2eptN3F5+OfCOOyC86ylR5GDODpizQxRNZPNRjh8H4uN9P6+1vovMeAAYO1YkVPfpA6xeDbzzjkgsBgK3jgjWli3A8OHB76IqLhaVkWXfsytvNZP8Cdd6ShQemLNDRG2Ga58l2XyUQDuJtPZkkhkPAFdfDXzyCbBuHdCxo+82D2YoLxf38fFiy7ZeamsG2fc8bVrgFRxfrOz7RdHD0mBn+fLluPjii9GpUyd06tQJAwcOxFtvveV8fvLkybDZbG63yy+/3O0Yzc3NKCgoQJcuXZCYmIhx48bhkJX7P4kopDz7LP31r3KvC5S3orUnUzA9nDy3zxcXyx0rGOvX6w941MRk2dwfRRF1c4YO1ReUWNH3i6KLpb2xMjMz8eijj+L8888HAKxatQp5eXn417/+hX79+gEArr32WqxYscL5mliPvY+FhYV47bXXUFpaitTUVMyePRtjxoxBZWUl7Az1iaKaWrFYz+WfQHkyWnsyBdvDyW4XwQAgVkueeML/pRvXLeY1NcDs2YHPrR5ftX69uKQ1d64IXHr3Bv72N+DMGd/HsNtFYrI6DxlG5CSFuu8XRRklzHTu3Fl5/vnnFUVRlEmTJil5eXk+xx49elSJiYlRSktLnY9VV1cr7dq1UzZt2iR9zvr6egWAUl9fr3veRKRdc7OiFBcryvTp4r65Wf61p08rSmamoohwQPtt5crAc7Pb/R/Dbj87Z63jA30vXnlFUWw2cXM9hvrY2rXu34vUVP/nTk0V4wKZO9f/cebOPTv2xRflvtcvvhj4vER6yH5+h03OjsPhQGlpKY4dO4aBAwc6Hy8vL0e3bt3Qt29f/Pa3v0VdXZ3zucrKSpw6dQqjRo1yPpaRkYGcnBzs3LnT57mam5vR0NDgdiOi0JLd0u1LsBWLjxzx/7zWnkzB9HDy9r249VZg3Djfl27y8s7mKW3fDjzzjP9zP/ec3CpIUZFY6fEca7eLx4uKzj6WlRX4eFrGEZnF0stYALBnzx4MHDgQJ0+eRMeOHbFu3Tr87Gc/AwCMHj0aN954I3r27Imqqir86U9/wtVXX43KykrExcWhtrYWsbGx6Ny5s9sx09LSUFtb6/OcixcvxsMPP2zq+yIi39Qt3Z7ULd2A+4eqN8HWVenaNbjXe6POeckS98Rdu10EOn/4A5CbC3z7LZCRAWzbBjz6qO/vxYYN4vLUmDHul242bGhdVTgzUwQjJSXi8pfr41p7ZhUVAQsXigTkAwd8V1AePBhITQUOH/Z9rNRUMY7ISpZvPW9pacE333yDo0ePYu3atXj++eexbds2Z8DjqqamBj179kRpaSny8/NRUlKCO++8E83NzW7jRo4ciT59+uAZH3/qNDc3u72moaEBWVlZ3HpOFAJat3T78u67olKwXuo2bH/zjI/3n7/Srp3IefGcZ0tL60ChRw/gu++0z9Pze+ErT0nNi3nlFRHIhSKvxeEA0tICBzvffcfcGjKH7NZzy1d2YmNjnQnKl156KSoqKvDUU0/hWS/7Qrt3746ePXti/09bANLT09HS0oIjR464re7U1dVh0KBBPs8ZFxeHuLg4g98JEcnQsqW7sDAkU/Jq6VL/gQ4gnl+6tHVycGys+9zT0/UFOoD798LhEB3evf2Jqigi4Jk9O3Q1Z7Zv9x/oAOL57dtbJ0cThVLY5OyoFEVptVKjOnz4MA4ePIjuP5XK7N+/P2JiYrB582bnmJqaGuzdu9dvsENE1glmi7Yrl/Q9XQK9fscOueMEGvfjj/oDHZX6vQi3PlFs5UCRwtKVnfvvvx+jR49GVlYWGhsbUVpaivLycmzatAlNTU1YsGABrr/+enTv3h1fffUV7r//fnTp0gUTJkwAACQnJ2PKlCmYPXs2UlNTkZKSgjlz5iA3NxcjglnfJiLTBLtFWxVse4BAr09MlDtOoHFDhsgdxx/1eyEbNLzwArB2re9cG6OwlQNFjFBsDfPlrrvuUnr27KnExsYqXbt2VYYPH6688847iqIoyvHjx5VRo0YpXbt2VWJiYpQePXookyZNUr755hu3Y5w4cUKZPn26kpKSosTHxytjxoxpNSYQbj0nCp1gt2ir1K3nnluzA91sNkXJygq8DXvRIrnjLVrk/zgpKfq3x3t+L7Zu1fd61+3iRpLZ/i/zvSbSS/bz29KVnRdeeMHnc/Hx8Xj77bcDHqNDhw5YunQpli5dauTUiMgk6hZtbzuQVNOnAzfddDbBd/Vq0VrBldpG4IYbRK6KzFYLLe0F9u0LfDyZcRkZ4lKWXq7b1dXO6r6KDXqjZYebVnY7ECj9MTaWyclkvbDL2SGi6NLSIoKLggJx39Liv5ZLWpoIYtavB/bsEfdJScCvftX62L7aCGRlAQMGtB6vKKJ2jcw27KYmufcXaNy2bXLHaefxf2NvdW389YkKxLVbuVGamgLnVh04IP+9JDILgx0iMo2/woFFRWJLdXGxWMkpLgZ+8QvfybwVFb4DHte+Ulu3AhMnivHebNggV7jQqHyUlBQRwPmTlia2sLt+L44f974So7dxqGvTUqP85jfGjiMyi+V1dsKB7D59Ik8OR+T36jHrPfgqHKjyXLVoahIrOIE0Nra+pOXKqDo+q1YBkycHns/KlcCkSYHH+dp+npYG+KmB6pPrz+1//kesgAUyfbrYKm+UnBy5y339+gF79xp3XiKV7Oc3V3aIdPLstj1smPi6rMzqmckz6z20tIjLJv54XlYxapVASx0ff77/Xm4+suPuuEPb44GojUNvuUV+x5fsTjhZoWwEShQMBjtEOqhVbD1rnlRXi8cjIeAx8z3oCTiMqr9j1HF275Y7jsw4f6tcjz8u3w/Ml6lTA6/GuXYrN4psOTOWPSOrMdgh0ihQFVvgbLXbcGX2e9ATcBhVf8eo4xiVoKxnlUurYJqQBuOii4wdR2QWBjtEGoVbFVs9zH4PegKO1avlXhNonFGrHBkZcvMJNM6oy2qBFBWJTuje5OUZv+0csG5FiUgrBjtEGkVDiXyz34OeD8GOHb1vF3c1YID35GSHAygvB15+Gdi5M3BPLZlVjoED/T8vO86oy2qBlJUBGzd6f27jRnMurVq1okSkFYMdIo2ioUR+MO/BNbAoL/e+aqH3Q/Cjj3wHPAMGiOc9eUuyfuUVsZrhWbumXbvWu8B8MWplx6jLav74uyyp0npZUubnDIjvpb+fmRkrSkSahaSec5hjuwjSIlCbAtl2BFbS+x7Wrm3dHiAzUzzuzdy5rVtDyLQvaGxUlPHjFSU3V9w3Nnoft3at9/egPubZquHcc33P1dOWLXLtGLZs8X8co9pj+CPbRmLrVrnjafk5z53r/5xmtaogUhT5z28GOwqDHdJO/ZD1/KBVH5P9QLWS1vfgL7Dw956bmxWluFhRpk8X98F8qLuS6cukda6uSkrkjllSEvhYZgcERs5Vy885FIEckT8MdjRgsEN6rF0rVgpkVznCkbe/4LOyWr+HQIGF0atZp0+LVYiSEnHv7bh6mmJqmavRqyV6V7lkGDVXrT/n4mK58xYXB/8eibyR/fxmzg5RECK9WJq3VgtVVa17R4VyB5psoUO9ydOyc1Wbbvr6GdtsogfX4MFy5/XWHsNXSwit1Ln6IzNXrT/nUCVfEwVLd9fzo0eP4qOPPkJdXR3OnDnj9twdekuCEkUItSCforg/rhbke/VVuWaTkSJUO9C0fF+DTQAPNFd/XdW1dE93FRsbeKeYHna7qKTsrz3HzTcHnqvWn3Mokq+JjKCrN9Zrr72G2267DceOHUNSUhJsLn/62Gw2/Pjjj4ZO0mzsjUVaOBxipcHXX8A2m/gru6oq/PtklZWJXTyu7yUzU3zIuwZr5eVihSWQrVtFCwM9tH5f1fHV1a2DIxnFxaIvVaB+YLLfIysF+t4BYmUn0O+k1p9zMP3MoqGvHFnP1N5Ys2fPxl133YXGxkYcPXoUR44ccd4iLdAh0ioaigoC2tpFDB7sv/kmIJ6XvaTjjdbvq7ryoifQsdtFB3bZfmCe59BzTjMF+t4B5ly6e/55ufl5jouGvnIUWXQFO9XV1bj33nuRkJBg9HyIwl40FBXU2i7C4QjcFqGpKbgWGaH8vnrO01c/MDUgrK6WG28Vo753agAJtA54vF26++wzufO6jouGvnIUeXQFO9dccw127dpl9FyIIkK4FBWULfrmjdZVFPUDMBDZcd5066ZtnBqw+eN5WcTXZRJfAZ6/Qn2Kor1QX20tkJ4OdOgg7mtr5V/rj5G/k/n5Ijfq3HPdH8/MbJ2LJjt/dVw09JWjyKQrQfm6667D3Llz8emnnyI3NxcxMTFuz48bN86QyRGFI3Wp31euiJpbEswlnUCCzSPRuhKwYYPc+A0bRIXiUJC5dONwnM3N+e47cenKF9cAb+hQbZeGZPKUEhPF7ivVd9+J4CMhATh2LPDr/TH6dzI/X1SgDpRTozXI0hJk6839IvJGV7Dz29/+FgDw5z//udVzNpsNDoblFMXM2KWjhRE7wcJldcpVXZ22cbIBW1qa2Kn08sty49Xjel668kVmnGeg4+r4cfF8MAGPGb+TdnvggKNvX7ljqeOi4RIwRSZdl7HOnDnj88ZAh9oCLUv9RjLqMoDWRFRf3bQ9yY7z5pxztI3TGrBpHf/993LjA42rrfUd6KiOHw/+kpYVv5NaG75q/RkTGYVFBYl0ki3IZyTZywBLl/rP5dGaiBooN0YlO86bZ5/VNk5rwDZ4MJCa6v/Yqalnx3ftKjefQON+8Qu548iO8yc/XxTwcy1c+MUX5v1Oam346qsruyfZcUSydAc727Ztw9ixY3H++ecjOzsb48aNw/Zw32tLZDB1qf+WW8S92XVCZJf3ZbZVa10JCFQtOthq0l9+qW2c1oBNK6NWIY4elTuO7Dh/yspEAb+ZM4Fly8R9nz7m7nAqKhJ5Wt6SwT07zGv9GRMZRVews2bNGowYMQIJCQm49957MX36dMTHx2P48OEoKSkxeo5E9BM9OTT+tvTKrk49/XTg2jKKIsbppacar5aAbft24PBh/8c+fPjsDrTnnpObT6BxwQZNLS0iaCsoEPctLd7HWbmlW7YVRna23PFkxxFJ09N468ILL1SWLFnS6vEnnnhCufDCC/Uc0lJsBEqRQm3U6K0rtRHNL32ZPl3uPNOn639vjY1y52hs9P59CdQ4VGtn8NxcufG5uf7fV02N3HFqalq/VrZ5aKgbtep1/Ljc9+L4cWvnSZHD1EagX375JcaOHdvq8XHjxqGqqirI8IuIfHG9dKOFmsszY0bgFQJvgumBJFsPqGNHYMAA/8cfMMB7JWeZy4laE5SN6vuUni62l/uTkCDGuZo3T/S68vx+ORzi8Xnzzj4WKVW94+MDJ7Hn5YlxRIbSE0n16dNHeeaZZ1o9/swzzyjnn3++nkNaiis7FGny8rSt7Hi7eVsh8EXvX+Rr17ZeccjMFI/7MmCA92MPGKD726UoSuBVMc/Vj8OH5d7z4cNy509I8P76hITWY5ubW6/oePv5NTeL8VpXrazm6/c3L8/qmVGkMXVlZ/bs2bj33nvx+9//HqtXr8aaNWvwu9/9DjNmzMCcOXOMjcaIyM28efJF/vzxtkLgyz//KXdM13F6c0g++kg0jhw/HsjNFfeNjeLxYGhNaH7xRbnjyo47dkwkmKelAXFx4r6mxnt9naefDlw+wOE4myMVjnWT/Fm/XuT0TJsGjBol7o8fF48TmUJvNFVWVqZcccUVSkpKipKSkqJcccUVyvr16/UezlJc2aFIIfMXv54VHnWFwBetKweBckgA63JIvK02ZWW1Xm0KRZ6SL1Onyp176lQxnrkw1FbJfn7rqqAMABMmTMCECROMi7qIIpDDEbikvpFk/uLXSl0hKCz0PcbotgBAaNoCePv5yLZCMCpnRw/ZbfzqONkaRXPnAldcEZrfVaJwwqKCRDqVlYkaNsOGBa5pY5QDB6w57qBBcpVyBw0S/zay1YJe/n4+MgnNN98sdx7ZcVpcdpm2cbK/F3/9a+h+V4nCiXSwk5KSgh9++AEA0LlzZ6SkpPi8EUU7X/kohw6ZW9PEjFUEmePu3CmXQ7Jzp/i3Ua0W9DKi5szIkXLnkh2nRVaWtnF6fi9CUX+HKFzYFEVRZAauWrUKN998M+Li4rBy5UrY/KyzTpo0ybAJhkJDQwOSk5NRX1+PTp06WT0dCnMOh/ir2N9lmqwsUZzP6MsELS1im7KRl7LsdpEcqpb09+bll8WKQCAlJWK15KWXgNtvDzx+zRrgttvk5yoj0M9H7QAe6OeTmgr8+GPg86WkBC5WqJXMz9n156b390L2e0EUrmQ/v6VzdlwDmMmTJwc1OaJIZmU+itqL6PHHjTuma+8iX7Tm7HhWNPZFdpwWWvqHpaX5zl/p1Eku2NHy91FLi8iPOnBArMZMner9e69lJW3oUHGMMWO079JTvxdm504RWU1Xzs7HH3+MPXv2OL/esGEDxo8fj/vvvx8tGiqVLV++HBdffDE6deqETp06YeDAgXjrrbeczyuKggULFiAjIwPx8fEYOnQo9u3b53aM5uZmFBQUoEuXLkhMTMS4ceNwKNAnEVEQrM5HKSoKrru4ylvvIl+05pCoTTr9cW3SaSSj+of94Q9yx5EdN2+eWH1x7VuVkOB96//Bg3LHVMc5HEBlpdxrvJH9nhFFKl3Bzj333IPPP/8cgKimfNNNNyEhIQH/+7//i3kyRTt+kpmZiUcffRS7du3Crl27cPXVVyMvL88Z0BQVFWHJkiVYtmwZKioqkJ6ejpEjR6KxsdF5jMLCQqxbtw6lpaXYsWMHmpqaMGbMGDiM3rJC9JNwyEfR2xV62rTWvYtkKhxr7Uiu1rTx15E8mCadnlz7R/3jH9pf7y3X6sQJudfKjNNSDRnQXtdIZrXRn3Cpv0NkGj372jt16qR88cUXiqIoyqOPPqqMGjVKURRF2bFjh5KZmannkE6dO3dWnn/+eeXMmTNKenq68uijjzqfO3nypJKcnOys3nz06FElJiZGKS0tdY6prq5W2rVrp2zatMnnOU6ePKnU19c7bwcPHmSdHZK2Zo1cTZM1a4w/t0z9Gi0VdGUrHOutOSNb0yYY3vpH6b251v4x6uestRqyomivsyNbB8nzFi49s4j0MrWCsqIoOHPmDABgy5Yt+PWvfw0AyMrKcu7Y0srhcKC0tBTHjh3DwIEDUVVVhdraWowaNco5Ji4uDkOGDMHOn7Z8VFZW4tSpU25jMjIykJOT4xzjzeLFi5GcnOy8ZclufSBC8Pkosr2ivAn2L/jvvjt73ldfld+xpLfmjGxXdb18rZjo5do/yqi8I63VkAH5rt82m/h5fved3HjP1wLGrbAF83tNZDo9kdSwYcOUO+64Q3nxxReVmJgYZf/+/YqiKEp5ebnSs2dPTcf65JNPlMTERMVutyvJycnKG2+8oSiKovzjH/9QACjV1dVu43/72986V5JeeuklJTY2ttUxR44cqdx9990+z8mVHQpGMNWB9fSKcqX3L3hfqwmyf/HX1ckds67OuO9zIGZUk3ZdqTGqCrSeVTHZDvCyP09vzxu5whbs7zWRXqau7Dz55JP4+OOPMX36dDzwwAM4//zzAQCvvvoqBqlVxSRdcMEF2L17Nz788EP8/ve/x6RJk/Dpp586n/fc4q4oit9t7zJj4uLinEnR6o1Ilt0utlf7c/PNrf9aNqL2i5G5Ff7+8lYU91UO2WLpRhVV//57oHdv0eG8d2/v+U+y1aSnTROrStOmyZ8b0P9z9qRnVez55+Ve4yrQ9+KSS8xZYbOq5hSRJkZGWCdOnFBaWlqCOsbw4cOVu+++Wzlw4IACQPn444/dnh83bpxyxx13KIqiKO+++64CQPnxxx/dxlx88cXKgw8+KH1O9sYiLfT8xR/oNbK5E3r+4g/mpub4ZGXJjc/KCv77m5zs/djJye7jtK6YvPii3PgXX9T/c/bm66/lzvv119rfm9ab0b2xwrkHGrUNpq7sHDx40G1790cffYTCwkK8+OKLiImJCTb4QnNzM3r37o309HRs3rzZ+VxLSwu2bdvmXD3q378/YmJi3MbU1NRg7969mleYiGRpqbMj+xrPlRRfZLc5G0VdSerRQ2687DiV6y6qJ58UNWvq672Pra8Hzjnn7NdaV0xkC/+p4/T8nL351a/kzus6zqxK2XPnGns8o75HRGbT1Qj01ltvxd13343f/OY3qK2txciRI9GvXz+sWbMGtbW1ePDBB6WOc//992P06NHIyspCY2MjSktLUV5ejk2bNsFms6GwsBCLFi1CdnY2srOzsWjRIiQkJODWn0q5JicnY8qUKZg9ezZSU1ORkpKCOXPmIDc3FyNGjNDz1ogC+vpr7eNk65gEGvdTxQfTqZV11To4GzeKisKBaNkSP28esGSJtkTW+npxmalrV1GQb86cwFWGp04V/5aZv+s4o+opHT0qdxzXcffcI+rwGG3/fmOPZ3XNKSJZulZ29u7di1/99GfI//zP/zh3P5WUlGDlypXSx/nuu+/wm9/8BhdccAGGDx+Of/7zn9i0aRNG/tRsZt68eSgsLMTUqVNx6aWXorq6Gu+88w6SkpKcxyguLsb48eMxceJEXHHFFUhISMBrr70GO2ufh5yVuzE8Vwg01LbUbP167eO0ViD2JTFR7jjB8LZL55NP5F4rOy6YXVTqCohaTdof1+rQWld2jKqnJLvY7TpOts6OVrK7vGRZXXOKSJqea2SJiYlKVVWVoiiKMnbsWGctnK+//lrp0KGDnkNaijk7wbNyN4a3Oit2u3jcDCNHyuVHjBx59jVqboPNFlzOzsqV5ufpeNulY2RtoWB3USUmuh9P9uev9T38/e9y4//+d//v94475I7zUyqioijG7rpzvRmds2NlzSkiRTE5Z6dfv3545plnsH37dmzevBnXXnstAODbb79FquxaMUUNI3YZ6aW1Mq0R+vbVPk6tKAy0riqspd5Jz55y59aquNj/Lh0j/4KX3UXlS9eu7l8XFYlq0MXFratDu9JaN0f2klygcZdcIncc13FmVDTOywPi4409ppU90Ig00RNJbd26VTnnnHOUdu3aKXfeeafz8fnz5ysTJkzQc0hLcWVHP6N2GemhpzKtEY4f1/9XdLAVhc2qLaPuQPJF604mf4LdaaS3lo/WnUN6VvC80fN7GmglUH2N59wHDPA+Ni9P3/fM6O8pkdFMXdkZOnQofvjhB/zwww/4+9//7nz87rvvxjPPPGNQGEaRwKhdRnroqUxrhPh4oH2A1P727b3/FR1sRWGZbth6BMoR0Zrv4k8wO42Sk1uv7ABy+WJa+3XpWcHzRmtuketc1Xl5ztNmE78/rqtZX3wBfPSRWNWaNg0YNUrcHz8un2emlev31Nc8jeyBRqRbiIKvsMaVHf1kcws8ezIZQW+/pmBZWU1YNkdC603tsRTseWVyM/TWCvKss6PSmi8mu7oWzAqeN3pyy3zNde7c8KpYHIoeaETeyH5+S289/+Uvf4l3330XnTt3xiWXXOK3QvHHH39sQBhGkcCoXUZ66O3XFCwtdVOqqow9t1m7WgLt0jEyN0O2OnCHDsDp00BSErBnj/djq/liiuL+uJov9uqrrVfN8vNF/sr27WKrf/fuYou95+pDfLwYt2GD7zlqyYMpKgIWLhQrjQcOiN/LqVPdV3Q8eZvr998DN92k7T2bTfZ7SmQVm6J4/ifj3cMPP4y5c+ciISEBDz/8sN+xDz30kCGTC5WGhgYkJyejvr6erSM0amkBEhIC1zo5ftz//9T1OHFCnDuQ48eNTcxMTBTHDCQhATh2zLjzAsBLLwG3327sMQFx+SklxffzDgfQq5f/S5ZZWSK4C/QBV1AALFumbX52u7jU45p0HGhOaq0gmTn5M36894AnL8+8y0O+hOo9E0UK2c9v6ZUd1wAm0oIZMo9MDonDIcYNHWrsuWVrkfzzn8aeOy5OLtiJizPunCqzdrVMmQKsW+f7ebsd6N/ff7Dzy1/KfcDqWWlTd9cBZwMeLfliwfz8+/YVQYTrn4U2m3xOj5FC9Z6Joo2uBGVXTU1NaGhocLtR22FUZeBIOveYMcaO02LwYPGXu9ECVdZtaQFef93/mNdflyvmOHWq/lWHJUvOnkP25/ruu/oLXaqlDTzXvxVFX2mDYItfWvnfG1Ek0xXsVFVV4brrrkNiYiKSk5PRuXNndO7cGeeccw46d+5s9BwpjFmZs2PVub/80thxWrju0jFSoMttRu58k9mdJHMO2Z/rwoXArbcCw4aJOkWydZ9aWkRw5Y9r8BXIvHni0ubMmeIy3syZ4mstAZOV/70FYmUFdaJAdPXGuu222wAAf//735GWluY3WZmim7rSUF3d+q9foHWPpWg4t2z+j9EF3FQffmj8Mc87z//zBw7IHUd2nHopSmtvLNdzBPr5e1NdDVx/PbB2beAkXi0BXmGh/3HqCpG313tenvPHyv/e/CkrA2bMcL/ElpkpAvNQJ0sTeaVnq1diYqLyf//3f3peGpa49Tw4a9eK4meeBdDUx8zcfqr33KdPK8rWrWJL/Nat2oqe3XWX3Jbku+4y4h26a272X2hO761XL0XJzVWU8ePF1nBPxcVyxyku1v5+iotFeYDx47Wfw9fPP9AtNTXwz9yo0gZGF7+08r83f/PxfE9WzYfaFtnPb13BztChQ5XNmzfrmlg4YrATvFD3p3KltcZHsH28Lr9c7kPw8suNe4+qoiLjAx1vtwED3M8bimrVes/h7ecpc9uyxf98jArwzAgUw6WujZUV1IkUxeRg54svvlBGjBihrFy5Utm1a5fy73//2+0WaRjsBMfXX3bq/+xC8T9g2ZUaI/4Klf1gzcw08h0KV1wRmmDHW8Azd67/8UYEtnrP4frzv/12uff3xz/6n4tRAZ5ZxS+DWZ00ytatcu9t69bQz43aBsOLCrr6/vvvceDAAdx5553Ox2w2GxRFgc1mg4OZaW2GwyGu1SuK7zGFhaImiZl1P+z2wFtt/c1VUUS+g8xcZUsxaS3Z5HCEV1G2igqgqQno2FF87SvPxlsNHFktLe5F9hYu1HcO15//p59qn4c3aiK1t1wblWebB2/MKn4p8ztvNu4Oo4ihJ5K66KKLlPz8fOXDDz9UqqqqlK+++srtFmm4sqNfJP1lZ9RcZ82SO86sWfJzk720dvfdoVvZAUQejSfXPJviYv2Xrvxd+gzmHFu2yL23QJexZOYpw6qGtaEQSf/9U3QydWXn66+/xsaNG3H++ecbG3lRxImkv+yMmqtM1WYt47S0PLjySuC55+SOawRvu6tiYwPvPgrEqN1J3gwdCqSm+m9Kmpoqvyqip82DK6NWiMJRuO4OI/Kkq87O1VdfjX//+99Gz4UiUDjX/fDUrZsx4wYNkjuOr3Gu9Ujefdf/pTVABBbqJZ0uXeTObRSj+4oBxtev8WS3Bw4In3tO2yVCNcBbulTcR2JgYoZA3dkBdj2n8KBrZWfs2LGYOXMm9uzZg9zcXMTExLg9P27cOEMmR+FP/csuUM+kaPrLTjYn5NNPgdGj3R/zVo/EH0VxL/+/caOmqQZt9Wrjj2lk/RpfAtUi+vDD0NV/kQ3uFi6MzCAqP1+sPnqrs/Pkk6yzQ+FBV7Dzu9/9DgDw5z//udVzTFBuW4zsmWS2ujpjxn31ldxxPMf5ulwlQ720Jlu0zwgDBpxNTjbSZ58ZO85TSwvwxBP+xzzxROiCi1AEd1Zj13MKd7qCnTNnzhg9D4pQWnomWf1Xq1GX3Hr2lDuO6ziZXWv+qJfWOnTQ93qtBgwAPvrInGPX1ho7ztPSpUCg/0WdOSPGzZ4td8xgdsoZXX06XIXD7jAiXzTl7Pz6179GfX298+tHHnkER48edX59+PBh/OxnPzNschT+jOyZZDb1kpuv7iY2m3mX3AJ1q5Z10UXBH8Objh2B3Fxg/HigsdG8QAcwP89rxw5jx5WVAb16id5aao+tXr3ke2yZtfWciORpCnbefvttNDc3O79+7LHH8OOPPzq/Pn36ND7Tu/ZMESmS/mo1Kpny66/lzuc6LtjdaOqltfa61mIDy8gAPvkEWLfOnEtXrvr2NXacJ9n5y4xTLz16BqqHDonHZQIemS7vdrsYR0Tm0BTsKB5r8J5fU9vTq5ex48ymJlOee67745mZ7lu8/cnKkjuX6zjZnWC+qKscZgUioVxVMPvD/9ZbjRkX6NKjorjvlPNFpst7pG49J4oUJv2dSG1Fbq6x4wLxlTuhJaci2GTKqipjxwWSmnr20tp77xlzTE8nT8qPDbbSs9l1Z7TUv/FH5tKj6045f8yoPk1E8jQFOzabDTaP9X/Pr6lt+f57Y8f5423bdmYmcMstwEsvAd9+e/bxjAyRgOprpSaYZMovvtA+TnYnWCAuKXKGOnRI1P0JFLz4+hk89ZS2LcZmfvgblQBdXS13HNlxwRYnDHfh1u6EyJWmYEdRFEyePBlxcXEAgJMnT+J3v/sdEhMTAcAtn4fahlAFO762bR865H2F4NtvgeuvB9auNb7Ox4kT2scFU1Tx8OGzqwcDBgC7duk/li/795+9rOMreNFS6VmGWR/+Rv1OmvG7bUT16XBkVBBMZBZNOTuTJk1Ct27dkJycjOTkZNx+++3IyMhwft2tWzfccccdZs2VwlDXrsaO8yaYbduTJgXOqdAqO1v7uEGDgHa66pULaoKz2ijTTGrw4pp8G6iJKiCXv+LJjMrERv1OhuJ3Oxr4SuL29ntEZBVNKzsrVqwwax4UoTwTfYMd500w27abmkRLhlGj9J/f0//9n/Zx27cHrv3ij5rgfP/9+o8hy1sH+EA/A89Kz1Yy6ncyFL/bkS5QEOz5e0RklSD+1iQ6W7vGn2Br1wS7bdvolgd6cnbKy40599atxhwnENfgBYishq9G/U6G4nc70mkJgomsxGCHgqLWrrHZvNeusdmCbwQYbBPRpqbgXu9J9lKNkZfP1Maheptj6qUGL5HU8NWo38lQ/G5HukgKgqltY7BDQTOido0/l1wS3OuvvLL1Y66dx8vLtQUmerqeB3tpZ+FCkUAs25fLKOrlMyurT+th1O+k2b/bkS6SgmBq22wKKwOioaEBycnJqK+vR6dOnayeTsQya+vphAnA+vX6XtuundgV5Zr4GuzOkcceA/7wh8DjHn0UuO8+8W+HAzjnHONXmcy2ZQswfLj4t5qICrjnaKgBUDh++Bv1O8lt1d45HKJgaHW197wdm038t1VVxe8XmUP285tFBckwZjUClM2R8Wb27NaBTrDbp/V2PY+Li7xgx7V2kbrK4S1QfPLJ8At0AON+J9nk0jv1Ut8NN4jAxlsQ3NYv9VF4sPQy1uLFizFgwAAkJSWhW7duGD9+fKveWpMnT3YWM1Rvl19+uduY5uZmFBQUoEuXLkhMTMS4ceNwyIiui6RJMJeG/NGz2Ga3A3PnuhenM2r79O7dcnNwHbd9u6iXE2k++MD96/x8EcRt3QqUlIj7qqrwDHQoNHipjyKBpSs727Ztw7Rp0zBgwACcPn0aDzzwAEaNGoVPP/3UWagQAK699lq3be+xHsU4CgsL8dprr6G0tBSpqamYPXs2xowZg8rKStj5J0VImFlU7IorgJ07A48bNgzo1893cTqjtk/LBi2u4yI1QdN1ZUfFVQ7yFGwLFiKzWRrsbNq0ye3rFStWoFu3bqisrMRVV13lfDwuLg7p6elej1FfX48XXngBq1evxogRIwAAa9asQVZWFrZs2YJrrrmm1Wuam5vdqj03NDQY8XbaLH/VjfVU1vX0U8HugK64Avjv//b9vFE7R2Tn4zouUhM0mcJmvUjJF2IQTOEsrHZj1dfXAwBSUlLcHi8vL0e3bt3Qt29f/Pa3v0WdS6OhyspKnDp1CqNcqsZlZGQgJycHO30sByxevNhZ9Tk5ORlZsm2sqRWjOkP7I/s/0EDjjNo5cvvtcsdxHTdoUHh+QAVyyy1Wz6BtKysTCcDDhondeMOGia9ZlZhIm7AJdhRFwaxZs3DllVciJyfH+fjo0aPx0ksv4b333sMTTzyBiooKXH311c6VmdraWsTGxqJz585ux0tLS0Otj05/8+fPR319vfN28OBB895YlNPSGVqvoUOBDh38j+nQIXCwY9T26Zkz/T/vbdzOnca3rQiF//zH6hm0XWzDQGScsNmNNX36dHzyySfYsWOH2+M33XST8985OTm49NJL0bNnT7zxxhvI93NtRFEUnx3Z4+LinM1MKThff23sOG8cjsDF9FpaxDh/qydG7RzRU1QwUnN2DhywegZtE9swEBkrLFZ2CgoKsHHjRmzduhWZAeqzd+/eHT179sT+/fsBAOnp6WhpacGRI0fcxtXV1SEtLc20OZMgW/9Gb50cQHTFDtRX6swZMS4QI3aOzJ0beIznuEjN2fG1CkbmYhsGImNZGuwoioLp06ejrKwM7733Hnr37h3wNYcPH8bBgwfR/adPj/79+yMmJgabN292jqmpqcHevXsxSLbULel27Jix47z5/HNjxwW7fVrPfAYPBlJT/Y9PSRFF/EpKgEsvlTuH2QYMsHoGbRPbMBAZy9LLWNOmTUNJSQk2bNiApKQkZ45NcnIy4uPj0dTUhAULFuD6669H9+7d8dVXX+H+++9Hly5dMGHCBOfYKVOmYPbs2UhNTUVKSgrmzJmD3Nxc5+4sMk+vXsaO80b2f+jbtwMFBb63nrsKZueIS1UEQ8apbDYxJ7XL+K5dmqdmOI8FUwoRtmEgMpalKzvLly9HfX09hg4diu7duztvr7zyCgDAbrdjz549yMvLQ9++fTFp0iT07dsXH3zwAZKSkpzHKS4uxvjx4zFx4kRcccUVSEhIwGuvvcYaOyEg22wkmKYkPqoOtLJ3L7BsmUgMTkgA5s3Tf05/xo/XPk6mqODhw2cvS4wbp2dmxuva1eoZtE2R1ouMKNxZurITqC1XfHw83n777YDH6dChA5YuXYqlS5caNTWS9M03xo7z5oILtL/G4QAef1z827WKsuvzemuX9OypfZzWyxLhsqLimdtEocE2DETGCosEZYpc551n7Dhvpk7V/z/1JUta7+QKtnaJ+le3P55/dWu9LBEovycUuHJgLbZhIDIOgx0KiuzllmAuy8TGAr/8pb7XOhzuu7R81S5Rqz3LBDx2e+DgJT3dPUDTelliz57A8zCTzcaVg3DAXmRExmCwQ0H54Qdjx3nT0gJ8/LH+16u1Yoyq9nziBFBR4X9MRYUYp1IvS/g7t2tw8eWX/o9vlD59Wq9SZWVx5SCcqMn0t9xyNoGdiLRhsNOGmNGV/J//NHacN08/Hdxc+/QR90ZVe9ZTZ0erUNW3OXAAuOkmrhwQUXRjsNNGmNVj5/hxY8d589ln+l9rt4ucH0AEMjICjdNTZ0ddVfJFrYirBnWXXSZ3DiM8+aTo3cWVAyKKVgx22gAze+xs3So37s039a8oBZO/8stfnq23Y9QqlJ46O1or4oayN61nXhMRUbRhsBPlAvXYAYLrSh6oZ5Xqu+/0rygF08bs44/PztGomkC//rXccVzHad16HsqVHYA9sIgoujHYiXJm99jREyRpXVH6qcG9Lq6rFtnZcq8JNO7NN+WO4zpO69bzZ5+VG28UNa+JiCgaMdiJcmb32JENIFxpXVEKpkYPcHbV4p575MYHGtfQIHcc13Fat56HcqXFNa+JiCgaMdiJcmb32Pmp+bxmWlaUXn9d3zlU6qqFUTk7dXVyx3Edp249B1oHPN4q4oZypWXWLP99xIiIIh2DnSinp9pvKMmsKJ08Gdw5/uu/xL1Ru7G6dJE7juc4LRVxg6kaLctuF9vjvbXTICKKJgx2opzdLrYU+3Pzzfo/WI8d0/c6lcyKUocOwZ3j+efFvVErO7I5RN7GyVbEDaZqtD/Z2cD06UBxsSgHwECHiNoCSxuBkvkcDrHl25/SUmDxYn0BT2Ii0NSk/XU2m1jRkFlRuu46YM0a7edQ7dsn7o3ajSXbt8rXOLUirj/BVo325amngNGjjT8uEVE448pOlDOqarAvetpAaO3avGOH9nO4+vBDcW/UbqxOneSOIzvOm2CrRnvToQMwapSxxyQiigQMdqJcdbWx4zy10/EbpLVr86lT2s/h6vRpcS+TByOzMykpSe68suO8MWM31jXXsDoyEbVNDHai3PffGzvOU8eO8uP09l7q1UvX1JzUla3YWLHzyB+ZnUmyAUMwgYUZu7Fef12+CCQRUTRhsBPlgs0vCWT+fLlxDz6ov/fSxo2ap+WmvUtmWlGR2IHkOQctO5N695Y7r+w4b8zYjcW2EETUVjHYiXKHD+sf19Ii8moKCsS9t1UB2Q/PYD5kP/lE/2uB1u0miorETqTi4vDdmSSzCqUH20IQUVvE3VhRrmtXfePmzQOWLHFPkp0zR3wAuwYFeqoJa6W3urPquutaPxYbKyo46/H118aO80X9Pnv+HILBthBE1BZxZSfKpadrHzdvHvD4460/YB0O8fi8eWcfM7tCMwCcc47+1wLAhRcG93pPsgGDEYGF5ypUTo7+Y7EtBBG1VQx2opzsioA6rqVFrCT4s2TJ2Uta48fLHV92nDf/+7/6XwsEv5vLk1G7umSpq1BLlwIffKD/OGwLQURtFYOdCOZwAOXlomhgebn3wEa2fo46Tqa+i2ui60svyR1fdpw3a9fqfy3gvaiiTD6SL7GxwJgx/seMGWNOYNGxo/YVI7aFIKK2jjk7EaqsDJgxw71gYGamqJDruq37zBm546njZBNY1XHHj8uNlx3nTbD5Kp6BjGw+kr/5BCp0uGOHGGfGjqpA7SoyM4GZM8UW/z59xAoTV3SIqC3jyk4EKisDbrihdWXk6mrxeFnZ2cdSUuSOqY7Tmo8SimAnN1f/awHg6qvP/ltLPpIv5eWBd7kdPizGGU2mIvahQ6Kv1tKl4vIXAx0iausY7EQYh0Os6Hjr36Q+Vlh49sNca4JyqPNRZARbZ0fNQdKaj+TLe+/JnVd2nBayO9OC3cFGRBRNGOxEmEB/2SuKe6+rc8+VO646TmuVYbUVQyCy47wJJt8nLw+Ijxf/1pqP5Ms338idW3acFqHY/UZEFG0Y7EQYrX/ZDx4cuDpyaqp79/GiIhEkeJOX557XovUymR56C+ElJwPr12s/TqBxPXrIHUd2nBZ6fp5ERG0dg50Io+cv+yNH/I/1fL6szPulI5tNPO6aE6S3aKEWGRn6XldfD/zqV2e/Nqo+jmsOkBHjiIjIXDZF8Zb90bY0NDQgOTkZ9fX16NSpk9XT8aulBUhI8H85xm4XCcGxscCGDXI1btavF6s2DodovOnvUllWltjpY7eLAEiW3t+0ceOA117T91oAaGwUW7a1fu98cTiAtDT/ScqpqcB33xm/G6u8HBg2LPC4rVtFHzIiomgm+/nNlZ0Is3OnXN7Jzp3i33Pnyh1XHSez28c1JygUvvoquNf/5jfi3siu588953/Mc88ZH+gATFAmItKDwU6E0fphd/So3Hh1XHW13HjZcUYItu3C/v1n/21E13NA1DJau1bUtHGVmSked611ZCQmKBMRaceighFG64dd167A998HHq/m1MiMdR3Xp49c4m8wAcvq1UBSkv7XHzvm/nVREbBwodh1deCA/sJ7+fni0t/27SK47N5dJAabsaKjGjxYBFSBLjMyQZmI6CwGOxFG/bCrrvaeA2OziefVD7tZs4D/+q/Ax1Uv72hNOJ4/X+748+fLHdebjh1Fjsx33+l7fe/erR8Lpuu5K7s9tLkxdjtwyy2iAKIvN99sbsBFRBRpLL2MtXjxYgwYMABJSUno1q0bxo8fj88++8xtjKIoWLBgATIyMhAfH4+hQ4di3759bmOam5tRUFCALl26IDExEePGjcOhQIknEcpuFy0hfCX7Koro9aR+2PXqJXdcdZzWIoTdusmNlx3nTUsL8MMP+l9vdNdzKzkc3nt9uSotDb7FBhFRNLE02Nm2bRumTZuGDz/8EJs3b8bp06cxatQoHHO57lBUVIQlS5Zg2bJlqKioQHp6OkaOHInGxkbnmMLCQqxbtw6lpaXYsWMHmpqaMGbMGDj4f3zTPfqoseO8kSkG6M+4cfpfG27CMYGciCjcWXoZa9OmTW5fr1ixAt26dUNlZSWuuuoqKIqCJ598Eg888ADyf8r4XLVqFdLS0lBSUoJ77rkH9fX1eOGFF7B69WqMGDECALBmzRpkZWVhy5YtuOaaa0L+vsyktovwxWYTzycnA3V1wO7dcsdVP0C//VZuvDpOdqdUMDuq/vMf/a8FAtcZiiTcjUVEpF1Y7caqr68HAKT8VG63qqoKtbW1GDVqlHNMXFwchgwZgp0/7a2urKzEqVOn3MZkZGQgJyfHOcZTc3MzGhoa3G6RQqZdxKFDwIgRwK23yu8uUgsF/uMfcuPVcVp3e+nxySf6XwsEl9wcbrgbi4hIu7AJdhRFwaxZs3DllVciJycHAFBbWwsASEtLcxublpbmfK62thaxsbHo3LmzzzGeFi9ejOTkZOctKyvL6LdjGrP+Yle3ku/dKzdeHXfypNz448dFrkl5ufZLUoE6jAcSqCZOJFET1H0Vc7TZuBuLiMhT2AQ706dPxyeffIKXvWRf2jz+z64oSqvHPPkbM3/+fNTX1ztvBw8e1D/xEAsm0defjh3FvWyVY3XcmTPy57j1VlH9t1cv95YTgWjdEu6pqiq414cTNUEdaB3wqF+7JqgTEVGYBDsFBQXYuHEjtm7dikyXKm3pP2358Vyhqaurc672pKeno6WlBUc8EjNcx3iKi4tDp06d3G5tXV2duA/UZFIlO86b6mrghhvkA55Bg/SfCxD5S9EkPx949dXWHe0zM8XjZhU0JCKKVJYGO4qiYPr06SgrK8N7772H3h4FUXr37o309HRs3rzZ+VhLSwu2bduGQT99Avbv3x8xMTFuY2pqarB3717nmGiiBiVGUze3eX6A+iI7zht1VaiwUO6SVrCXZKZMCe71/jgc4tKc3kt0euXni4KIxcXA9Oni/osvGOgQEXlj6W6sadOmoaSkBBs2bEBSUpJzBSc5ORnx8fGw2WwoLCzEokWLkJ2djezsbCxatAgJCQm49dZbnWOnTJmC2bNnIzU1FSkpKZgzZw5yc3Odu7OiSTArKv707CnuL7hAbrw6Li4OaG7Wfj5FObtFOlBRvmBTqrwVFTRCWZnY+eaaMJ6ZKS4zmR10lJUBBQXuu+cefxxYupQBDxFRK4qFAHi9rVixwjnmzJkzykMPPaSkp6crcXFxylVXXaXs2bPH7TgnTpxQpk+frqSkpCjx8fHKmDFjlG+++UZ6HvX19QoApb6+3qi3Zpq//EVRRKhg7O3oUXH85ma58c3NYnxMTHDnLSkJ/J5Pn1aUzEx9x09NFa832tq1imKztT6fzSZua9caf07Xc/t7z2aem4gonMh+ftsURTYlNXrJtogPB1OnAsuXG3/cmhpRFfnECSAhIfD448eB+HigXTv5pGZvtm6Va7cwfjywYYP243fsKLa9G5mw63CIJGtfJQDUlh1VVcYnCjsc4j352wXXoQPQ1MQkZSKKfrKf32GRoEzyzNp6/otfiPvp0+XGq+Pa67wQqmWLdEsL8Prr+s7T1CRyaYwkU+vIrCrGmzcH3u5/8qQYR0REAoOdCCPbu0ortejfxo1y49Vxsjk+rrRukQ62XYTRwY6VVYyXLDF2HBFRW8BgJ8LoCS5knHOOuJetm6OOO+887efSukX6wAHt5zCTlVWMZVtfRFOLDCKiYDHYiTD33GPOcT/6SNwPHCg3Xh33q1/JjZ88GSgpETk6VVXadgwFu5vqqquCe70nK6sYDxhg7DgioraAwU6E+ec/zTmuWuCvtFRuvDpOdj6HDwO33CKSkbUmzubmahvvqZ3Bv+VWVjF+4gljxxERtQUMdiKMWZ0t1O7osq0Z1HGyl5iCuRT1ww/6XwsAPlqkBcWqKsbx8UBenv8xeXliHBERCQx2Isz775tz3JIScf/kk3Lj1XGyTTqDaeYZbD+w778P7vW+5OcDX30lLs3pvUSnx/r1vgOevDzxPBERnWVpBWXSbssWc457+rS4X71abvzq1cC8eUBKCvDdd4HHp6Ton1uwunY179h2u1ydIKOtXy9qIs2dC+zfD2RniwrKXNEhImqNwU6E0dOaQUZiorhvaZEbr46TzUsJJn8l2H5gwfTxCmfx8cCyZVbPgogo/PEyVoQJNlnXl3ffFfeyqxTqOLUYYSCy47wJZgu3WbuiiIgocjDYiTBmJSiPGyfuJ0yQG6+OUxObA5Ed502grd7+3Hxz9LZNaGkRuVMFBeJedlWOiKitYbATYWTyY/RQi9BpTTjWWoRQD39bvQMpLQ2u+nK4mjdP9DCbOVNcypo5U3w9b57VMyMiCj8MdiKMWX1K1YBAdueSOk62hk2wtW58bfUOxKweVVaaN08kI3sGcQ6HeJwBDxGROwY7Eeb++805rrpbKjVVbrw6btAgufGy4/zx3Or9hz/Ivc6sS39WaGkJ3PdqyRJe0iIicsVgJ8Lo6UUl48QJca/1MtZFF8mNlx0XiLrV+5ZbgMZGudeYVXXaCjJNUR0OMY6IiAQGOxHGrPwT9fKYbE0addzUqYETgO12Mc5oimLsuEiwf7+x44iI2gIGOxHGrPwTtdmmbE6MOi42Fhgzxv/YMWPk21BokZ1t7LhIIJugrWfnGhFRtGKwQwCAN94Q94MGya3UqDk4DgdQWel//Mcfm7MiJdsB3qxO8Va47DJjxxERtQUMdiKMGa0J0tKA5GTx75075XJCdu4U/96+HTh0yP94s3ZEyebiRFPOTlaWseOIiNoCBjsRxohdTZ5iY88GONXVcq9Rx9XUyI2XHafF118bOy4SqAUW/WHVaCIidwx2Isyzzxp/TNeVF611dmRbOQTT8sEX2e7e0dQFXC2w6Csnx2YT1ZSjtWo0EZEeDHYijFm7bNSVF611di69VG687Dgtjh0zdlykUAsseq7wZGWJx/PzrZkXEVG4YrATYczaZaOuvGitszN3rtx42XFa9O1r7LhI4llgcetWoKqKgQ4RkTftrZ4AaTNggPHHdM3x0Lqy8957cuNlx2nx+OPAX/8qNy4aqQUWiYjIP67shBGZLtZqw04juXYG17qyI9uWwIz2BfHxQF6e/zF5eWIcERG1XQx2woRsF2vZCsdauHYG79xZ7jXquJ495cbLjtNq/XrfAU9eXnQlJxMRkT4MdsKAli7WWrt+y3DdjVVRIfcadVx7yQuhsuP0WL8eOH4cmDYNGDVK3B8/zkCHiIgE5uxYTLaL9cKFoh6OWmclUCE/rdTdWLKVjtVxiYly42XH6RUfL1bEiIiIPHFlx2Jau1jb7aLjt9HU3Viy9VnUcRkZcuNlxxERERmNwY7FDhzQNs7hAF5+2bjz22zuu7Fkd3up42QrOptR+ZmIiEgGL2OZxOEQeTA1NWLVZPBg76smffrIHU8dJ9OLSivXiruyu73UcezVRERE4Y4rOyYoKwN69QKGDQNuvVXc9+olHvc0dapcl/GpU8W/jewxZbcDc+a4F6KT3e2ljmOvJiIiCncMdgxWVgbccEPr1ZfqavG4Z8ATGwvMmuX/mLNmiXGAsT2mzpwB/vIX9znJ7vZSx7FXExERhTtLg533338fY8eORUZGBmw2G9Z77BWePHkybDab2+3yyy93G9Pc3IyCggJ06dIFiYmJGDduHA4ZfZ1HksMBzJgBKErr59THCgtbJyQXFYl2Cp4Bgd0uHi8qOvuYupLir22Et+N4421OelZq2KuJiIjCmaXBzrFjx/Dzn/8cy/zsGb722mtRU1PjvL355ptuzxcWFmLdunUoLS3Fjh070NTUhDFjxsAhu4faQIHyaRTFvaaNq6IioLHRvVZMY6N7oAOcXUkBWgc8Npu4vfzy2Z5JxcX+d3t5zknvSg17NRERUdhSwgQAZd26dW6PTZo0ScnLy/P5mqNHjyoxMTFKaWmp87Hq6mqlXbt2yqZNm6TPXV9frwBQ6uvrtU7bTUmJoojwwf+tpKT1a9euVZTMTPdxmZnicW+8jc/Kaj1e75xkj09ERGQV2c/vsN+NVV5ejm7duuGcc87BkCFD8Mgjj6Bbt24AgMrKSpw6dQqjRo1yjs/IyEBOTg527tyJa665xusxm5ub0dzc7Py6oaHBkLnK5tN4jlPzfDwvf6l5Pt4uBeXni3YI3nZ8ue4E++47fXPyd3xfZHegBSMU5yAiougS1sHO6NGjceONN6Jnz56oqqrCn/70J1x99dWorKxEXFwcamtrERsbi84eDZ3S0tJQW1vr87iLFy/Gww8/bPh8L7tM+7hAeT42m8ipycvznovj2fW6rEwcz/VymhoAeWOziVwbb7ultHTV9nbezExxScyoS1mhOAcREUWfsN6NddNNN+G6665DTk4Oxo4di7feeguff/453njjDb+vUxQFNj8ZvPPnz0d9fb3zdvDgQUPm++yz2sfJ5vksXSpyccrLfQcuvnaC+Qt0gOB3S2ndgRau5yAiougU1sGOp+7du6Nnz57Yv38/ACA9PR0tLS044lEJr66uDmlpaT6PExcXh06dOrndjKC1GjIgXzdn5kz/NXv8rRCpPAOazMzgd0vp3YFm5DkUJfhzEBFR9IqoYOfw4cM4ePAguv+UYNK/f3/ExMRg8+bNzjE1NTXYu3cvBlnQn0BrNWRAX90cb6sZMpWVHQ6xO8vI3VLB7EAz6hxA8OcgIqLoZWmw09TUhN27d2P37t0AgKqqKuzevRvffPMNmpqaMGfOHHzwwQf46quvUF5ejrFjx6JLly6YMGECACA5ORlTpkzB7Nmz8e677+Jf//oXbr/9duTm5mLEiBEhfz9aqyEDcnVzPHlbMZFdIUpLE41Ehw41JrFX9rzBVH6urjZ2HBERtS2WBju7du3CJZdcgksuuQQAMGvWLFxyySV48MEHYbfbsWfPHuTl5aFv376YNGkS+vbtiw8++ABJSUnOYxQXF2P8+PGYOHEirrjiCiQkJOC1116D3YItOlqrIQP+6+b447lioncnWLBCcd7vv5cb9+qrQEGByEFqadF/PiIiii42RfGX5dE2NDQ0IDk5GfX19Ybk78ybByxZ4p5DYreLQMezSKC/18goKRErNQ6HyOWprvae26LuuqqqMnartnpef5eZsrKCO+9LLwG3367tNYG+30REFPlkP78jKmcnUhQVAcePi/yY6dPF/fHjvj94y8pEjyo9CbbqikmgysqAOT2q7HYRbPlz883BnVe2X5crhwN4/HERRBIRUdvGlR0Yv7KjhczKiDe+Vmr0rCoFIxQrO3q/R4A45/Hj7pcOiYgoOnBlJ0LI7DTy5GulxtcKkcPRuru5UUKxUypQvy5/HA7g6af1n5uIiCIfgx2L6dml5K0+jkydHTNq0YRiNxbgu7O6DNn6R0REFJ3Cul1EWyC7S6m4WGwb99UPSku9G9kWEDJCuQvMs1/XP/4B/PWvgV8nW/+IiIiiE3N2EB45O8Huonr5ZVFhORB195ZRrNoFBojt5QkJ/lermLNDRBS9mLMTIYzaRWXGCovDIXpx+evJZdUuMEBfXSMiImp7GOyEATUfxXOLtZbeVYEqMdtsYleUt+7m3pSViRWbYcP89+Qyav56XX55cM8TEVH042UsWHsZy5XDcTYfxVdujj9qZ3DA/ZKSGgDJBh7qcTx/MwIdJ9j5axVoS7qZl9CIiMh6vIwVgex2kTyst3eVESssoehibpRQNCElIqLIx91YUcZzx5LWFRa9u7rKykSQ5PrazEyRz2PWZaxQbXsnIqLIxmAngvm6bKSuEOmhJ4Dwddmrulo8blbejlXNT4mIKLLwMlaE0pJArEW3btrGWXnZy+ikbCIiik4MdiKQupLieblJXUkxoy2EL1bmzVi57Z2IiCIHg50IY/ZKSl2dtnFW581Yue2diIgiA3N2IozZbSG05sGEQ95MsEnZREQU3RjsRBizV1LUPJhA7R/UPBit480STFI2ERFFN17GijBmr6RozYNh3gwREYU7BjsRJhQ7kLTmwTBvhoiIwhnbRSB82kXIMqotRCBa2z+Eul0EERG1bbKf3wx2EHnBDuC9YnFWlrhkxJUUIiJqC2Q/v5mgHKG4A4mIiEgOg50Ixh1IREREgTHYIZ+Ys0NERNGAwQ55pbWLuRVdz4mIiGRw63kUcjiA8nLg5ZfFvdbWEVp7b4VTry4iIiJP3I2FyNyN5UuwKywOh+ie7qslhVoRuapKXKLSOp6IiMgosp/fXNmJIkassGjtYm5l13MiIiIZDHaihFHd0LX23rK66zkREVEgDHaihFErLJHY9ZyIiMgfBjtRwqgVFq29t0LRq4uIiCgYDHaihFErLOx6TkRE0YbBTpQwcoWFXc+JiCiaWBrsvP/++xg7diwyMjJgs9mwfv16t+cVRcGCBQuQkZGB+Ph4DB06FPv27XMb09zcjIKCAnTp0gWJiYkYN24cDvlLXolSRq+w5OcDX30FbN0KlJSI+6oq34GL1vFEREShYmmwc+zYMfz85z/HsmXLvD5fVFSEJUuWYNmyZaioqEB6ejpGjhyJxsZG55jCwkKsW7cOpaWl2LFjB5qamjBmzBg4tFbSiwJGr7CovbduuUXcBwqUtI4nIiIKhbApKmiz2bBu3TqMHz8egFjVycjIQGFhIe677z4AYhUnLS0Njz32GO655x7U19eja9euWL16NW666SYAwLfffousrCy8+eabuOaaa6TOHU1FBQH2qCIiorYh4osKVlVVoba2FqNGjXI+FhcXhyFDhmDnzp0AgMrKSpw6dcptTEZGBnJycpxjvGlubkZDQ4PbLZpwhYWIiOissA12amtrAQBpaWluj6elpTmfq62tRWxsLDp37uxzjDeLFy9GcnKy85aVlWXw7ImIiChchG2wo7J5ZNsqitLqMU+BxsyfPx/19fXO28GDBw2ZKxEREYWfsA120tPTAaDVCk1dXZ1ztSc9PR0tLS04cuSIzzHexMXFoVOnTm43IiIiik5hG+z07t0b6enp2Lx5s/OxlpYWbNu2DYMGDQIA9O/fHzExMW5jampqsHfvXucYIiIiatvaW3nypqYmfPHFF86vq6qqsHv3bqSkpKBHjx4oLCzEokWLkJ2djezsbCxatAgJCQm49dZbAQDJycmYMmUKZs+ejdTUVKSkpGDOnDnIzc3FiBEjrHpbREREFEYsDXZ27dqFYcOGOb+eNWsWAGDSpElYuXIl5s2bhxMnTmDq1Kk4cuQILrvsMrzzzjtISkpyvqa4uBjt27fHxIkTceLECQwfPhwrV66EnVuQiIiICGFUZ8dK0VZnh4iIqC2I+Do7REREREZgsENERERRzdKcnXChXsmLtkrKRERE0Uz93A6UkcNgB3A2FmUlZSIiosjT2NiI5ORkn88zQRnAmTNn8O233yIpKSlgdWYtGhoakJWVhYMHD7aZxGe+Z77naMX3zPccrSL5PSuKgsbGRmRkZKBdO9+ZOVzZAdCuXTtkZmaadvy2WKWZ77lt4HtuG/ie24ZIfc/+VnRUTFAmIiKiqMZgh4iIiKIagx0TxcXF4aGHHkJcXJzVUwkZvue2ge+5beB7bhvawntmgjIRERFFNa7sEBERUVRjsENERERRjcEOERERRTUGO0RERBTVGOyY5P3338fYsWORkZEBm82G9evXWz0lUy1evBgDBgxAUlISunXrhvHjx+Ozzz6zelqmWr58OS6++GJnIa6BAwfirbfesnpaIbN48WLYbDYUFhZaPRVTLViwADabze2Wnp5u9bRMV11djdtvvx2pqalISEjAL37xC1RWVlo9LdP06tWr1c/ZZrNh2rRpVk/NNKdPn8Yf//hH9O7dG/Hx8TjvvPPw5z//GWfOnLF6aoZjBWWTHDt2DD//+c9x55134vrrr7d6Oqbbtm0bpk2bhgEDBuD06dN44IEHMGrUKHz66adITEy0enqmyMzMxKOPPorzzz8fALBq1Srk5eXhX//6F/r162fx7MxVUVGB5557DhdffLHVUwmJfv36YcuWLc6v7Xa7hbMx35EjR3DFFVdg2LBheOutt9CtWzccOHAA55xzjtVTM01FRQUcDofz671792LkyJG48cYbLZyVuR577DE888wzWLVqFfr164ddu3bhzjvvRHJyMmbMmGH19AzFYMcko0ePxujRo62eRshs2rTJ7esVK1agW7duqKysxFVXXWXRrMw1duxYt68feeQRLF++HB9++GFUBztNTU247bbb8Le//Q0LFy60ejoh0b59+zaxmqN67LHHkJWVhRUrVjgf69Wrl3UTCoGuXbu6ff3oo4+iT58+GDJkiEUzMt8HH3yAvLw8XHfddQDEz/jll1/Grl27LJ6Z8XgZi0xRX18PAEhJSbF4JqHhcDhQWlqKY8eOYeDAgVZPx1TTpk3DddddhxEjRlg9lZDZv38/MjIy0Lt3b9x888348ssvrZ6SqTZu3IhLL70UN954I7p164ZLLrkEf/vb36yeVsi0tLRgzZo1uOuuuwxtDh1urrzySrz77rv4/PPPAQD//ve/sWPHDvz617+2eGbG48oOGU5RFMyaNQtXXnklcnJyrJ6Oqfbs2YOBAwfi5MmT6NixI9atW4ef/exnVk/LNKWlpfj4449RUVFh9VRC5rLLLsOLL76Ivn374rvvvsPChQsxaNAg7Nu3D6mpqVZPzxRffvklli9fjlmzZuH+++/HRx99hHvvvRdxcXG44447rJ6e6davX4+jR49i8uTJVk/FVPfddx/q6+tx4YUXwm63w+Fw4JFHHsEtt9xi9dQMx2CHDDd9+nR88skn2LFjh9VTMd0FF1yA3bt34+jRo1i7di0mTZqEbdu2RWXAc/DgQcyYMQPvvPMOOnToYPV0Qsb1cnRubi4GDhyIPn36YNWqVZg1a5aFMzPPmTNncOmll2LRokUAgEsuuQT79u3D8uXL20Sw88ILL2D06NHIyMiweiqmeuWVV7BmzRqUlJSgX79+2L17NwoLC5GRkYFJkyZZPT1DMdghQxUUFGDjxo14//33kZmZafV0TBcbG+tMUL700ktRUVGBp556Cs8++6zFMzNeZWUl6urq0L9/f+djDocD77//PpYtW4bm5uaoT9wFgMTEROTm5mL//v1WT8U03bt3bxWwX3TRRVi7dq1FMwqdr7/+Glu2bEFZWZnVUzHd3Llz8Yc//AE333wzABHMf/3111i8eDGDHSJvFEVBQUEB1q1bh/LycvTu3dvqKVlCURQ0NzdbPQ1TDB8+HHv27HF77M4778SFF16I++67r00EOgDQ3NyM//znPxg8eLDVUzHNFVdc0ap0xOeff46ePXtaNKPQUTdXqEm70ez48eNo1849dddut3PrOclramrCF1984fy6qqoKu3fvRkpKCnr06GHhzMwxbdo0lJSUYMOGDUhKSkJtbS0AIDk5GfHx8RbPzhz3338/Ro8ejaysLDQ2NqK0tBTl5eWtdqZFi6SkpFY5WImJiUhNTY3q3Kw5c+Zg7Nix6NGjB+rq6rBw4UI0NDRE3V++rmbOnIlBgwZh0aJFmDhxIj766CM899xzeO6556yemqnOnDmDFStWYNKkSWjfPvo/HseOHYtHHnkEPXr0QL9+/fCvf/0LS5YswV133WX11IynkCm2bt2qAGh1mzRpktVTM4W39wpAWbFihdVTM81dd92l9OzZU4mNjVW6du2qDB8+XHnnnXesnlZIDRkyRJkxY4bV0zDVTTfdpHTv3l2JiYlRMjIylPz8fGXfvn1WT8t0r732mpKTk6PExcUpF154ofLcc89ZPSXTvf322woA5bPPPrN6KiHR0NCgzJgxQ+nRo4fSoUMH5bzzzlMeeOABpbm52eqpGc6mKIpiTZhFREREZD7W2SEiIqKoxmCHiIiIohqDHSIiIopqDHaIiIgoqjHYISIioqjGYIeIiIiiGoMdIiIiimoMdoiIiCiqMdghooizcuVKnHPOOZbOYejQoSgsLLR0DkQkhxWUicgwkydPxqpVq1o9fs011xjaM+zEiRNobGxEt27dDDumVj/++CNiYmKQlJRk2RyISE70dzojopC69tprsWLFCrfH4uLiDD1HfHy85Q1mU1JSLD0/EcnjZSwiMlRcXBzS09Pdbp07d3Y+b7PZ8Pzzz2PChAlISEhAdnY2Nm7c6HaMjRs3Ijs7G/Hx8Rg2bBhWrVoFm82Go0ePAmh9GWvBggX4xS9+gdWrV6NXr15ITk7GzTffjMbGRucYRVFQVFSE8847D/Hx8fj5z3+OV1991e97efrpp5GdnY0OHTogLS0NN9xwg/M518tY5eXlsNlsrW6TJ092jn/ttdfQv39/dOjQAeeddx4efvhhnD59WuN3l4j0YLBDRCH38MMPY+LEifjkk0/w61//Grfddht+/PFHAMBXX32FG264AePHj8fu3btxzz334IEHHgh4zAMHDmD9+vV4/fXX8frrr2Pbtm149NFHnc//8Y9/xIoVK7B8+XLs27cPM2fOxO23345t27Z5Pd6uXbtw77334s9//jM+++wzbNq0CVdddZXXsYMGDUJNTY3z9t5776FDhw7O8W+//TZuv/123Hvvvfj000/x7LPPYuXKlXjkkUe0fuuISA9Le64TUVSZNGmSYrfblcTERLfbn//8Z+cYAMof//hH59dNTU2KzWZT3nrrLUVRFOW+++5TcnJy3I77wAMPKACUI0eOKIqiKCtWrFCSk5Odzz/00ENKQkKC0tDQ4Hxs7ty5ymWXXeY8R4cOHZSdO3e6HXfKlCnKLbfc4vW9rF27VunUqZPbMV0NGTJEmTFjRqvHf/jhB6VPnz7K1KlTnY8NHjxYWbRokdu41atXK927d/d6bCIyFnN2iMhQw4YNw/Lly90e88xvufjii53/TkxMRFJSEurq6gAAn332GQYMGOA2/le/+lXA8/bq1cstWbh79+7OY3766ac4efIkRo4c6faalpYWXHLJJV6PN3LkSPTs2RPnnXcerr32Wlx77bXOS2++nDp1Ctdffz169OiBp556yvl4ZWUlKioq3FZyHA4HTp48iePHj/s9JhEFj8EOERkqMTER559/vt8xMTExbl/bbDacOXMGgMitsdlsbs8rEptG/R1TvX/jjTdw7rnnuo3zlTydlJSEjz/+GOXl5XjnnXfw4IMPYsGCBaioqPC57f33v/89vvnmG1RUVKB9+7P/ez1z5gwefvhh5Ofnt3pNhw4dAr43IgoOgx0iCisXXngh3nzzTbfHdu3aFdQxf/aznyEuLg7ffPMNhgwZIv269u3bY8SIERgxYgQeeughnHPOOXjvvfe8Bi1LlizBK6+8gg8++ACpqaluz/3yl7/EZ599FjAIJCJzMNghIkM1NzejtrbW7bH27dujS5cuUq+/5557sGTJEtx3332YMmUKdu/ejZUrVwJAqxUfWUlJSZgzZw5mzpyJM2fO4Morr0RDQwN27tyJjh07YtKkSa1e8/rrr+PLL7/EVVddhc6dO+PNN9/EmTNncMEFF7Qau2XLFsybNw9//etf0aVLF+f7j4+PR3JyMh588EGMGTMGWVlZuPHGG9GuXTt88skn2LNnDxYuXKjrPRGRPO7GIiJDbdq0Cd27d3e7XXnlldKv7927N1599VWUlZXh4osvxvLly527sYKp1/Pf//3fePDBB7F48WJcdNFFuOaaa/Daa6+hd+/eXsefc845KCsrw9VXX42LLroIzzzzDF5++WX069ev1dgdO3bA4XDgd7/7ndv7njFjBgBRVPH111/H5s2bMWDAAFx++eVYsmQJevbsqfv9EJE8VlAmorD3yCOP4JlnnsHBgwetngoRRSBexiKisPP0009jwIABSE1NxT/+8Q88/vjjmD59utXTIqIIxWCHiMLO/v37sXDhQvz444/o0aMHZs+ejfnz51s9LSKKULyMRURERFGNCcpEREQU1RjsEBERUVRjsENERERRjcEOERERRTUGO0RERBTVGOwQERFRVGOwQ0RERFGNwQ4RERFFtf8PvJlBIb+scX0AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "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": { "tags": [] }, "source": [ "## Practice\n", "Plot __CYLINDER__ vs the Emission, to see how linear is their relationship is:\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# write your code here\n", "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='Green')\n", "plt.xlabel(\"Cylinders\")\n", "plt.ylabel(\"Emission\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
Click here for the solution\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", "
\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": 15, "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": 16, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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": 17, "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: [[39.25604145]]\n", "Intercept: [125.23211126]\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": 18, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Emission')" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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": 19, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean absolute error: 22.99\n", "Residual sum of squares (MSE): 911.17\n", "R2-score: 0.77\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": 20, "metadata": { "tags": [] }, "outputs": [], "source": [ "train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n", "\n", "test_x = test[[\"FUELCONSUMPTION_COMB\"]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
Click here for the solution\n", "\n", "```python \n", "train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n", "\n", "test_x = test[[\"FUELCONSUMPTION_COMB\"]]\n", "\n", "```\n", "\n", "
\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": 22, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n", " normalize=False)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regr = linear_model.LinearRegression()\n", "\n", "regr.fit(train_x,train_y)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
Click here for the solution\n", "\n", "```python \n", "regr = linear_model.LinearRegression()\n", "\n", "regr.fit(train_x, train_y)\n", "\n", "```\n", "\n", "
\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": 23, "metadata": { "tags": [] }, "outputs": [], "source": [ "predictions =regr.predict(test_x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
Click here for the solution\n", "\n", "```python \n", "predictions = regr.predict(test_x)\n", "\n", "```\n", "\n", "
\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": 24, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean Absolute Error: 20.42\n" ] } ], "source": [ "print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
Click here for the solution\n", "\n", "```python \n", "print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n", "\n", "```\n", "\n", "
\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", "Joseph Santarcangelo\n", "\n", "Azim Hirjani\n", "\n", "##

© IBM Corporation. All rights reserved.

\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 }