{ "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": 29, "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": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2025-10-19 05:56:26-- 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 0.002s \n", "\n", "2025-10-19 05:56:26 (33.6 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": 31, "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": 32, "metadata": {}, "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": 32, "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": 33, "metadata": {}, "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
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" ], "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": 33, "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": 34, "metadata": {}, "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": 34, "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": 35, "metadata": {}, "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": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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SLCKv5bKwM27cOFRUVKC8vNz4GDx4MGbNmoXy8nL85je/gUqlwoEDB4x1dDodSkpKMGLECADAoEGD4O/vb1JGrVbj9OnTxjJERO7kwQcBM3fuLVJUJM7WIiLruGzMTkhICBISEkyOBQcHIzw83Hg8KysLy5cvR3x8POLj47F8+XIEBQVh5i99v0qlEnPnzkVOTg7Cw8MRFhaG3NxcJCYmthjwTETkSjqdOD1cStCRy4Ht24HHHrN/u4h8gVuvoJyXl4eGhgZkZGSgtrYWQ4cOxf79+xESEmIsU1hYCD8/P8yYMQMNDQ0YN24cNm7cCDlvaBORm8jLE5fJl7r8V1ERgw6RLWSCYM1kR+9UV1cHpVIJrVbL8TtEZFd5ecDKldLqhocDf/sbx+gQtcbSz2+Xr7NDROStdDppGx/6+Ynr7vz8M4MOkT249W0sIiJPNm+etFtXly8DSqX920Pkq9izQ0TkAB98AGzcaH29IUMYdIjsjWGHiMjOpO5cPmQIcOKE/dtD5Ot4G4uIyI62bgX+4z+sq5OcLNbr1MkxbSLvpdcDR44AajUQFQWMHMnVtc1h2CEisoPycmDgQOvrLV4MrFhh9+aQDyguBhYtAi5e/PVYTAywZg0HtjfH21hERDaSyaQFnfR0Bh2SprhYXHupadABgOpq8XhxsWva5a4YdoiIbNDOvsSt6tZNvHVFZC29XuzRMbdKnuFYVpb0RSy9EcMOEZFEtgwmfuMNjq0gaY4cadmj05QgAFVVYjkSMewQEUmQlwcMHWp9Pe5cTrZSq+1bzhdwgDIRkZVs2QKC+1yRraKi7FvOF7Bnh4jIClK3gACALVsYdMh2I0eKs65aGy8mkwGxsWI5EjHsEBFZSKcTZ1BJGfg5eDAwa5b920S+Ry4Xp5cDLQOP4fvVqzkmrCmGHSIiC+TlAUFBwO7d1tcdMgQoLbV7k8iHpaWJK3V362Z6PCZGPM4xYaY4ZoeIqB2LFwOvvy6t7vXrXBmZHCMtDUhJ4QrKlmDYISJqw44d0oLOP/4BPPig/dtD1JRcDowe7epWuD+GHSKiVkjZ5woQe4IYdKg13M/K+Rh2iIjMGDIEOHnSujpyOZCdzS0gqHXcz8o1OECZiKiZwYOtDzqpqcDNmww61DruZ+U6DDtERE1MmQKUlVlXRy4Xx/YEBDimTeT5uJ+VazHsEBH9IjkZ+OQT6+tlZzPoUNu4n5VrMewQEQF49lng44+tr5eby1tX9Kv6euDRR4H+/cWv9fXice5n5VocoExEPm/RInEXcmsVFYkrKhMB4gy8potHVlQAISHiYHdLAzH3s3IMhh0i8mmDB1s/RgcQ97li0CGDtmbvlZaKyxHExIiDkc2N25HJxPPcz8oxeBuLiHzS1atAYKC0oMN9rqipbdvan7138iTw2mvif3M/K+dj2CEin6NSAeHhwK1b1tcdNIj7XNGviostD74ffMD9rFyFt7GIyKeoVMDPP0urO3kysHevfdtDnsswndxS589zPytXYdghIp9x9ar0oDN1KrBnj33bQ56tvenkzfXqJX7lflbOx7BDRD7h3/8GunaVVjcrCygstGtzyIPodMC6dWLPTK9eQEaGuK6StdPEN292TPuofQw7ROT17rkH0Gql1V24kEHHl+XlAQUFpisb5+aKC0k+8ojl1xkyBOjUyf7tI8sw7BCRV1Mqgbo6aXUHDRI3aCTflJcHrFzZ8rheLx6/e7ft6eQGgwcDJ044rp3UPs7GIiKvNX++9KAjZddz8h46ndij05bVq4HXXxf/u/l0coOtWzl7zx0w7BCRV8rNFcdZWCsgALh+nX+J+zK9XrxN1d6mnHq9OG7H3HTy2Fjgww+BmTMd106ynEvDzptvvon+/fsjNDQUoaGhGD58OP7+978bz8+ZMwcymczkMWzYMJNrNDY2IjMzExEREQgODkZycjIuWjM8noi8zvvvA6tWWV8vIABobOTYCl9WXAz06AH87/9aVt4wnfzCBeDQIXGBwUOHgMpKrpvjTlw6ZicmJgavvvoqevfuDQDYtGkTUlJS8NVXX6Ffv34AgIcffhgbNmww1glotrVwVlYWPv74YxQVFSE8PBw5OTmYMmUKysrKIOfCBUQ+p7hY+jYOV67Yty3kWYqLgccea3v8TXOcTu4ZZIJgzdvqeGFhYVi5ciXmzp2LOXPm4Nq1a9i9e7fZslqtFl26dMHmzZuR/sv/3S5duoTY2Fjs27cPEydOtOg56+rqoFQqodVqERoaaq+XQkROpteLtw+k7Bw9ZAhvXfmaplPKe/QQx+hcumR5fbkcuHlT7BEk17D089ttZmPp9Xp88MEHuHHjBoYPH248fvjwYXTt2hX33HMPRo0ahVdeeQVdf1kso6ysDLdv30ZSUpKxfHR0NBISEnDs2LFWw05jYyMaGxuN39dJHcFIRG5DpxNvP92+bX1dBh3fY25KubWysxl0PIXLw05FRQWGDx+OW7duoVOnTti1axfuv/9+AMCkSZMwffp0xMXFobKyEi+88ALGjh2LsrIyKBQKaDQaBAQEoHPnzibXjIyMhEajafU58/Pz8dJLLzn0dRGR87Q2Rbg9HTqI6+9wjI7vuHoV6NPHtluWcrkYdFassF+7yLFcPhvr3nvvRXl5OY4fP44///nPmD17Nr755hsAQHp6OiZPnoyEhARMnToVf//73/H999/jk08+afOagiBA1to8QABLliyBVqs1Pqqqquz6mojIOfR64PHHpQWdjAyxPoOO7zBsAGtL0Jk/X7x1xaDjWVwedgICAtC7d28MHjwY+fn5GDBgANa0sopXVFQU4uLicPbsWQCASqWCTqdDbW2tSbmamhpERka2+pwKhcI4A8zwICLPUlwMdO8O7Nhhfd2cHMtn25B36NpV+r5ogLiOTmysuMgkb115HpeHneYEQTAZT9PUlStXUFVVhaioKADAoEGD4O/vjwMHDhjLqNVqnD59GiNGjHBKe4nI+QyzZqwZTGqwY8evC8GRb8jMFPdGk8pwo2D1au5O7qlcOmZn6dKlmDRpEmJjY3H9+nUUFRXh8OHD+PTTT1FfX49ly5Zh2rRpiIqKwoULF7B06VJERETg0UcfBQAolUrMnTsXOTk5CA8PR1hYGHJzc5GYmIjx48e78qURkYPo9eKtBCnzSLt0AWbMsH+byH3l5QFr19p2jZgYMehw3RzP5dKw8/PPP+PJJ5+EWq2GUqlE//798emnn2LChAloaGhARUUF3nvvPVy7dg1RUVEYM2YMduzYgZCQEOM1CgsL4efnhxkzZqChoQHjxo3Dxo0bucYOkZdSKoEbN6TVPXPGvm0h92bJlg/myOXAvn3i2J6oKGDkSPboeDq3W2fHFbjODpFnCA0Vt3KQQqkErl2za3PIza1eDTz7rPX1Fi/mAGRP4XHr7BARtaa+HmjSoWs1Bh3fdP68deU7dBAHrzPoeB+GHSJyaw8+KH3X6NBQ4Nw5cawO+R7DVg6WCAkBLl/mTCtv5XazsYiIDAYNkhZ05swRN/TUahl0vJ1OJ96uyswUv+p0v57LyLBsrE2XLkBdHYOON2PYISK3NGUKcOqU9fW6dAE2bOAHly/IywOCgsRxOWvXil+DgsTjgPhvIDu77WssWADU1Di+reRaDDtE5HZSU4F2FkpvFWdc+QbDFiHN97bS68XjhsCzYoU44Lh5D49cLh7/n/9xTnvJtTgbC5yNReROGhrEv86l4EBk76bXA0eOAFVV4q3Ku3dbL9t8R/KmO5z36iXe4mLvn+fjbCwi8ki/rBlqtZAQBh1vVlwMLFoEXLxoWXm9Xgw3WVni9wEBv/43+R6GHSJyG9u3A599Zn09uVwcYEreybA9iLX3Iaydek7ei2N2iMilGhrEQaJduwIzZ1pfPyAAuHPH/u0i96DXiz06UgZcWDP1nLwbe3aIyGVSU4GPPpJe/+JFoFs3uzWH3NCRI5bfumpKLhfH5RAB7NkhIhexJehMniz+pc+g4/3Uamn1srM5AJl+xZ4dIi9gmKWiVnvGxoUNDdKDzu9+B+zda9/2kPuKirKuvFwuBh1u+UBNMewQeThzs1RiYoA1a4C0NNe1qy1xcdLqRUcDJSX2bQu5t5EjxX/P1dXmx+3IZOKSAzNnAvHxnFJO5kkOO9euXcOJEydQU1ODu80WO/jDH/5gc8OIqH2tzVKprhaP79zpfoFn8GDg3/+WVvd//se9e6zIOpasfSOXi8H9scfEYNP037pMJn5dv979/p2Te5G0qODHH3+MWbNm4caNGwgJCYHM8C8OgEwmw9WrV+3aSEfjooLkifR6oEeP1gdvymTiX8SVle4REK5eBbp3B27ckFZ/2zbgiSfs2yZyPsMt11WrgH37TBcGbOsWlLkezNhYcT8sBh3fZennt6Sw06dPHzzyyCNYvnw5gqQudepGGHbIEx0+DIwZ0365Q4eA0aMd3Zq2qVTAzz9Lr5+cbNusLXIPli4MuHix+cDjaWPTyPEcuoJydXU1Fi5c6BVBh8hTWTpLRepsFnuxNeikpAC7d9utOeQiH3wAzJhhWdmCAuDll83f0nJ1cCfPJGnq+cSJE3Hy5El7t4WIrGDpLBVrZ7PY09Wr0oKOvz8wf764txGDjufbudO6W5CGrR6I7EVSz87kyZOxePFifPPNN0hMTIS/v7/J+eTkZLs0johaZ8kslZgYsZyrhIdLq3f1KtCpk33bQq5RXAxMn259PW71QPYkKew89dRTAIC//vWvLc7JZDLo9XrbWkVE7bJklsrq1a4b09Bk3oJVhgxh0PEGOh2wdi3w4ovS6nOrB7InSbex7t692+qDQYfIedLSxFsEzVcSjolx7bTz7t2l1Rs0CDhxwr5tIefLywOCgoCcHKC+3vr63OqB7I2LChJ5uLQ0cRCvu8xS6dULqKqyvt4jjwCffGL/9pBz5eUBK1fadg1u9UD2JjnslJSU4PXXX8e3334LmUyGvn37YvHixRjpygECRD7KXWapLFwI/PCD9fU448o76HTiTCqpuNUDOYqk21hbtmzB+PHjERQUhIULF2LBggUIDAzEuHHjsG3bNnu3kYg8wMKF4grH1uKMK++xbp04k8oaMhkwcSJQWCj+W2DQIUeQtKhg37598fTTT+PZZ581OV5QUIC3334b3377rd0a6AxcVJDINr16SevR+eor4IEH7N4ccpHMTHFQsjU++EAcZE8khaWf35J6dn744QdMnTq1xfHk5GRUVlZKuSQReaCffgI6dJAWdAAGHW9jzQyq2Fjgww8ZdMg5JIWd2NhYfP755y2Of/7554iNjbW5UUTk/vz9xd3Lre8bFkmtR+4rI6P9gfEyGbB/v7hnG/e0ImeRNEA5JycHCxcuRHl5OUaMGAGZTIajR49i48aNWLNmjb3bSERupkMH6WElNlbsESLvExAgDjBuazZWbi4wYYLz2kQESAw7f/7zn6FSqbBq1Sq8//77AMRxPDt27EBKSopdG0hE7qVjR+lB5ze/4cq43s4wwLigwHSwMmdakStJGqDsbThAmcgygYHArVvS6mZmAm+8Yd/2kPvS6cTZWefPi2N5MjK4dg7Zn0N3PSci39OjB4MOWS4gAMjKcnUriEQWh52wsDB8//33iIiIQOfOnSFrY+Obq1ev2qVxROQe4uKkj7P5zW8YdIjItSwOO4WFhQgJCTH+d1thh4i8R1AQ0NAgrW6vXsC5c/ZtDxGRtThmBxyzQ9QaW4LOtWuAUmnX5hARmXDoooKnTp1CRUWF8fuPPvoIqampWLp0KXQ6ncXXefPNN9G/f3+EhoYiNDQUw4cPx9///nfjeUEQsGzZMkRHRyMwMBCjR4/GmTNnTK7R2NiIzMxMREREIDg4GMnJybh48aKUl0VETTzwgLSgI5OJs7UYdIjIXUgKO/PmzcP3338PQFxNOT09HUFBQfjggw+Ql5dn8XViYmLw6quv4uTJkzh58iTGjh2LlJQUY6BZsWIFCgoKsHbtWpSWlkKlUmHChAm4fv268RpZWVnYtWsXioqKcPToUdTX12PKlCnQW7tBCxEZJSYC//yn9fUUCuDuXfu3h4jIJoIEoaGhwrlz5wRBEIRXX31VSEpKEgRBEI4ePSrExMRIuaRR586dhXfeeUe4e/euoFKphFdffdV47tatW4JSqRTeeustQRAE4dq1a4K/v79QVFRkLFNdXS106NBB+PTTT1t9jlu3bglardb4qKqqEgAIWq3WprYTeQOZTBDEvhnrHh07urrlRORrtFqtRZ/fknp2BEHA3V/+fDt48CAeeeQRAOI2EpcvX5YUuvR6PYqKinDjxg0MHz4clZWV0Gg0SEpKMpZRKBQYNWoUjh07BgAoKyvD7du3TcpER0cjISHBWMac/Px8KJVK44NbXJAz6PXA4cPA9u3iV3fsfDTcgrJWXJz0sT3kHBoNoFKJi0KqVOL3RL5CUtgZPHgwXn75ZWzevBklJSWYPHkyAKCyshKRkZFWXauiogKdOnWCQqHAM888g127duH++++H5pffxObXi4yMNJ7TaDQICAhA586dWy1jzpIlS6DVao2Pqqoqq9pMZK3iYnGdmjFjgJkzxa89eojH3YXUCZbduwMXLti1KWRnwcFAVBTw889AY6P4NSpKPE7kCySFndWrV+PUqVNYsGABnn/+efTu3RsAsHPnTowYMcKqa917770oLy/H8ePH8ec//xmzZ8/GN998YzzffIq7IAjtTntvr4xCoTAOijY8iByluFjc2bn5uPnqavG4qwPPiRPSg05gIPDjj/ZtD9lXcDBw86b5czdvMvCQb5C0gnL//v1NZmMZrFy5EvL2trxtJiAgwBiWBg8ejNLSUqxZswbPPfccALH3Jioqyli+pqbG2NujUqmg0+lQW1tr0rtTU1NjdegicgS9Hli0yPytIUEQQ0ZWFpCS0v5u0Y5gy3JZgYGtf4iSe9Bo2n+Pbt789RYXkbeS1LNTVVVlMr37xIkTyMrKwnvvvQd/f3+bGiQIAhobG9GzZ0+oVCocOHDAeE6n06GkpMQYZAYNGgR/f3+TMmq1GqdPn2bYIbdw5EjLHp2mBAGoqhLLOZstQWfAAAYdd9V0bNj991tW54EHHNkiIteT1LMzc+ZMPP3003jyySeh0WgwYcIE9OvXD1u2bIFGo8F//dd/WXSdpUuXYtKkSYiNjcX169dRVFSEw4cP49NPP4VMJkNWVhaWL1+O+Ph4xMfHY/ny5QgKCsLMmTMBAEqlEnPnzkVOTg7Cw8MRFhaG3NxcJCYmYvz48VJeGpFdqdX2LWcvtgSdhASgvNxuTSE7Ki4WexKtXWrs2jWHNIfIbUgKO6dPn8aDDz4IAHj//feRkJCA//f//h/279+PZ555xuKw8/PPP+PJJ5+EWq2GUqlE//798emnn2LChAkAgLy8PDQ0NCAjIwO1tbUYOnQo9u/fb9y2AhC3rvDz88OMGTPQ0NCAcePGYePGjVbfTiNyhCZ3YO1Szh5sCToyGWDmDja5gfffB9LTpdW95x67NoXI7UjaLqJTp044ffo0evTogeTkZPzud7/Dc889h59++gn33nsvGjxsDiq3iyBH0evFWVfV1ebH7chkQEwMUFnpnDE73buLt82k4uYy7ik3F1i1Snp9tZpjdsgzWfr5Lalnp1+/fnjrrbcwefJkHDhwAP/93/8NALh06RLCw8OltZjIS+j14hgctVrssSkoEP/ibr6GjaGHZfVq5wSduDgGHW/R0AAsXgycPQvU1Nh2WzEoiEGHvJ+ksPPaa6/h0UcfxcqVKzF79mwMGDAAALBnzx7j7S0iX2RuzERMjPiX9/btLY+vXg2kpTm+XYGBwK1b0usz6LiP1FTgo4/sc62gIODGDftci8idSd71XK/Xo66uzmTK94ULFxAUFISuXbvarYHOwNtYZA+G9XSa/0YZenDefx+IiPi1x2fkSOf06Pj7A3fuSKv71VecqeNObA06nTuLs+juuUfsDWKPDnk6Sz+/JYcdb8KwQ7YyjM1pbRaMs8fmNH1eqfh/BvfS0CD2xEgVG+v8f39Ejmb3MTu//e1v8fnnn6Nz584YOHBgmysUnzp1yrrWEnkonQ5Ytw4oKbF8PZ3Ro53TNgYd77J4sW31nTU2jMgdWRx2UlJSoFAoAACpqamOag+Rx8jLEwcfW7Ohp7PW0+kgablQEYOO+zCE6fPngSZrp1ptxw7njA0jcle8jQXexiLr5eUBK1daX+/QIcf37CgU4oektWJjgZ9+sn97SBopYdqcnBzg9dft0yYid+PQqedN1dfX4+7duybHGBjIm+l04oeQNQxjdkaOdEybDKqrpQWd7t25oac7MEwp37vX9vdDLgeys4EVK+zTNiJPJinsVFZWYsGCBTh8+DBuNZnPathtXG/rnyJEbmzePOv+2nbGejrV1UBiIlBba33djh0ZdNyBPaaUJySIPYe9egEZGUBAgD1aRuT5JIWdWbNmAQDeffddREZGtjlYmcibfPABsHGjdXUcvZ6O1NtWAODnJ/YmkGvZI+ikpAC7d9ujNUTeR1LY+frrr1FWVoZ7773X3u0hcls7dwJPPGF5+f/4D2DuXMeup9Ohg20Dim/ftl9bSJqGBmlBJyEBiI4G4uPF8WOBgfZvG5G3kBR2hgwZgqqqKoYd8hnFxcD06ZaXl8uB9esdexshIMC2oMOpCe5B6pTyuXOBrCy7NoXIa0kKO++88w6eeeYZVFdXIyEhAf7+/ibn+/fvb5fGEbkDvV7cAsIa2dmODToKhfReGZkMaDangJyo6XTyXr2A776z/hpyuTgmh4gsIyns/Pvf/8b58+fxxz/+0XhMJpNxgDJ5pSNH2l4wsLn0dMfOgJE648qAQcd17DWd3NFhmsjbSAo7f/rTnzBw4EBs376dA5TJ61mzEGC3bsDWrY5rCyAOeJbKWYsaUks5OdYvWdDadTidnMg6ksLOjz/+iD179qB37972bg+R24mKsrzsG284dkl+W/6uCArixo+uUF8vbqZ6/rzt19qxA5gxw/brEPkaSYvKjx07Fv/85z/t3RYit6HXA4cPA9u3i/8dE9N20JDLxV3NHTW9/KefbA86N27Yrz3UPr0e6NsXCAmxPejExgIffsigQySVpJ6dqVOn4tlnn0VFRQUSExNbDFBOTk62S+OIXKG4WByQ3HScTni4OHtJJjM/i6moCHjsMce0x98fuHNHWt3ISKC8nD06zlZcDDz+uPWDyOfNE9c++v57IDhYXH8nLs6xyxcQ+QJJe2N1aGOXQU8coMy9sciguFgMLc1/KwwhJzwcuHLl1+OxsY5dMFBq0PH3t20QM0lXXAxMmyatbmEhp5MTWcOhe2M13wuLyBsYppibi/+GXp3AQODgQaCmRhzL48i/uP/zP6X36DDoOF99PTBrFrBvn7T6nE5O5DhWjdl55JFHoNVqjd+/8soruHbtmvH7K1eu4P7777db44icqb0p5oIgnpfLxZWUR492XNDJyxMXJZTi7Fn7toXa9+CD4ticPXukB1ROJydyHKvCzmeffYbGxkbj96+99hquXr1q/P7OnTv417/+Zb/WETmRpdOyHT19W6cTl/+XQiYDOEnSuR58ECgtte0a3J2cyLGsCjvNh/dIGO5D5LYsnWJuzVR0KRQK6XV5h9m56uvtE3RWrbJPe4jIPEljdoi80ciR4hTz6mrz43ZkMvH8yJGOef6KCkDqTiucWu4cDQ3iXlZnz4obcP74o23XW7yYPTpEzmBV2JHJZC1WS+bqyeQt5HJgzRpxNlbzKeaGf+arV9t/nE5ZGTB4sPT6SiXQZOgcOUhqqunu5Pv3S79WbCxw7hzH6BA5i1VhRxAEzJkzB4pf+tlv3bqFZ555BsHBwQBgMp6HyBOlpQE7d7ZcZycmxjFTzG39W6GmBujSxT5todY1DzpS+PkBAwcCX3wBdOpkl2YRkYWsWmen6cafbdmwYYPkBrkC19mh5vR6cXaWWu24Kea2Bp3vvgPuvdc+bSHz9HpxqYGHH7btOnv3itfgwoBE9mXp57ekRQW9DcMOOZutt64A8+OKyH7MraQtxZAhwIkT9mkTEZly6KKCRGQbBh33tmOHuN2DrRh0iNwDww6Rk9l6+4pBx7Fyc6VNBf/P/wQuXxY3/ezVC9i8mWNziNwFww6Rk3zzDdCvn/T6MhnX0XEUnQ5Ytw7YtEncOFWKN94QtxMhIvfDsEPkBLb25pw9y5WR7c2wZs7evbavl5OSwqBD5M4YdogcjLet3I89ppIbpKQAu3fb51pE5BhWbRdhb/n5+RgyZAhCQkLQtWtXpKamtthba86cOcbFDA2PYcOGmZRpbGxEZmYmIiIiEBwcjOTkZFy0dQoFkR188430ul9/zaDjCPYIOgoF8Oc/AzdvMugQeQKXhp2SkhLMnz8fx48fx4EDB3Dnzh0kJSXhRrN17x9++GGo1WrjY9++fSbns7KysGvXLhQVFeHo0aOor6/HlClToNfrnflyiFqQOkbnu++AxET7toXEW1f26NHZtk0c48NbV0SewaW3sT799FOT7zds2ICuXbuirKwMv//9743HFQoFVCqV2WtotVqsX78emzdvxvjx4wEAW7ZsQWxsLA4ePIiJEye2qNPY2Giy2nNdXZ09Xg65AWcsBmgpW7YC4GKBjrF4se3XKCqy/0raRORYLu3ZaU6r1QIAwsLCTI4fPnwYXbt2RZ8+ffDUU0+hpqbGeK6srAy3b99GUlKS8Vh0dDQSEhJw7Ngxs8+Tn58PpVJpfMTGxjrg1ZCzFRcDPXoAY8YAM2eKX3v0EI87m0wG3L4trS5vXTnO2bO21c/JAdLT7dMWInIetwk7giAgOzsbDz30EBISEozHJ02ahK1bt+KLL77AqlWrUFpairFjxxp7ZjQaDQICAtC5c2eT60VGRkKj0Zh9riVLlkCr1RofVVVVjnth5BTFxeIGns2HalVXi8edGXhsGZDMoONY8fHS6snlYq/Q66/btz1E5BxuMxtrwYIF+Prrr3H06FGT4+lN/oxKSEjA4MGDERcXh08++QRpbfQlC4LQ6o7sCoXCuJkpeT69XlzW31xQEAQxfGRlibNmHHVL61//Esfn2DJMjEHH8VauBP73fy0vP3Ag8Ic/ABkZ3KGcyJO5Rc9OZmYm9uzZg0OHDiEmJqbNslFRUYiLi8PZX/qjVSoVdDodamtrTcrV1NQgMjLSYW0m93HkSNv7FwkCUFUllnMEmQy47z7pQYezrpwnMFAMve0x9OScOiUGZQYdIs/m0rAjCAIWLFiA4uJifPHFF+jZs2e7da5cuYKqqipERUUBAAYNGgR/f38cOHDAWEatVuP06dMYMWKEw9pO7kOttm85a9i6ho5azVlXzrZ7d+uBp2dPoLBQnFK+YoVTm0VEDuTS21jz58/Htm3b8NFHHyEkJMQ4xkapVCIwMBD19fVYtmwZpk2bhqioKFy4cAFLly5FREQEHn30UWPZuXPnIicnB+Hh4QgLC0Nubi4SExONs7PIu/2Se+1WzlLNloSyWlAQ0MokQ3Kw3bt/XUH57FlxLM/KlZxKTuStZILgug701sbUbNiwAXPmzEFDQwNSU1Px1Vdf4dq1a4iKisKYMWPw3//93yYzqG7duoXFixdj27ZtaGhowLhx47Bu3TqLZ1lZukU8uSe9Xpx1VV1t/naQTAbExACVlfYds2NLr46/v7gfExERSWfp57dLw467YNjxfIbZWIBp4DEEkp077bs2SocOto2z4W+dbQwbdxp2GOcAYiLfZOnnt1sMUCayVVqaGGi6dTM9HhNj/6AjkzHouFJenngL8NlngbVrxa9BQeJxIiJz3GbqOZGt0tLEgaeOXEGZm3q6Vl6eOLamOb3+1+McWExEzfE2FngbiyzDoONaOp3Yg9PWFH+5XJxJxVtaRL6Bt7GI7Ehq0OnQAThzhkHHHtata38tI71eLEdE1BRvYxG1Q2rQ+fFHoHt3+7bFl50/b99yROQ7GHbIrbl61o3UoOPnx6BjC3Nr4PTqZVldS8sRke/gmB1wzI67yssDCgpMb13I5UB2tnMGoUoNOjIZcPeufdviK3Q6YNAg4PTpluemTgX27eOYHSL6FcfskEczzLpp/sFmmHXj6GnGtgxGZtCRJi8PUCjMBx0A+PhjcfHItmRnM+gQUUsMO+R2dDqxR6ctBQWOW4HYlqDDflJpWptS3tz58+K6Os2XEzBs3Mlp50RkDsMOuZ1581wz66akhEHHFSwJt83L37wpbti5YAE37iSi9nGAMrkNnQ545hlg40bLyttz1g3X0HEevd504cdTp9oPt02dPSveqsrKclgTicjLMOyQW8jLA1atsm68i71m3TDoOE9xMbBoEXDx4q/HOnWy7hrx8fZtExF5P87GAmdjuZql4zWastesG4XCtrE//O2x3PvvA+nptl/n5k0gMND26xCR5+NsLPII1o7XMLDHrBsGHefJzbVP0ElJYdAhIuvxNha5lCVbADSXnm77YFTeunIewy1KW6WkALt3234dIvI9DDvkUtYOMu7WDdi61bbnZNBxHmt77mSylj/fBx4Ajh1jjw4RScewQy5lzSBjmQx4442Wa6xYo7xcel2AQcda1vbcffaZuHGqq7YHISLvxLBDLpWRIY7naO8DMSYGWLMGSEuT/lwVFcDAgdLqfvcdcO+90p/bV1nTcxcbC4wdC0yY4Lj2EJFv4gBlcqmAAHGwcVvS04ELF2wLOjIZ0L+/tLqCwKAjlTU9d6tX29ZrR0TUGoYdcrkVK8Sl/lvbAqCoyLYPQaljdAICeNvKVhkZlr13O3bYFmaJiNrCdXbAdXacpfnKuSNHmn4Q6nTiGA97jtewJeg0Ntr23CRqbx2lnBzg9ded1x4i8h6Wfn5zzA45hbmVc5uPw7H3FgC29AYx6NiPYZmAggLTsVlyuXgLk3taEZGjsWcH7NlxtOJi4LHHWt4SMvS67Nxp/1sY3NDT/Tii546IfJuln98MO2DYcSS9HujRw7RHpymZTOzhqay03+BUudy6PbaaUqsBlco+7SAiIsfidhHkFo4caT3oAGIvSlWVWM4etm6VHnSCghh0iIi8EcfskEOp1fYt15ryculr6ABi0Llxw7Y2EBGRe2LYIYeKirJvOXNs3f6Bt66IiLwbb2ORQ40cKY7JaS2QyGTiyrkjR0q7vj32uWLQISLybgw75FByuTi9HGgZTAzfS105V6mU3q4OHTjriojIVzDskMOlpYnTy7t1Mz0eEyN92rlKBdTVSW+TNZtTEhGRZ+OYHXKKtDQgJaXtFZQtdfUq8PPP0trRoQODDhGRr2HYIaeRy4HRo22/Tni4tHpbtgCzZtn+/ERE5Fl4G4s8ii0Dkhl0iIh8k0vDTn5+PoYMGYKQkBB07doVqamp+Ne//mVSRhAELFu2DNHR0QgMDMTo0aNx5swZkzKNjY3IzMxEREQEgoODkZycjIttrWRHHuebb7gFBBERSePSsFNSUoL58+fj+PHjOHDgAO7cuYOkpCTcaLK624oVK1BQUIC1a9eitLQUKpUKEyZMwPXr141lsrKysGvXLhQVFeHo0aOor6/HlClToOfgDIfR6cRZVJmZ4ledznHPJZMB/fpJq3vkCIMOEZHPE9xITU2NAEAoKSkRBEEQ7t69K6hUKuHVV181lrl165agVCqFt956SxAEQbh27Zrg7+8vFBUVGctUV1cLHTp0ED799FOLnler1QoABK1Wa8dX470WLxYEuVwQxBghPuRy8bi9NX0Oax6hofZvCxERuRdLP7/dasyOVqsFAISFhQEAKisrodFokJSUZCyjUCgwatQoHDt2DABQVlaG27dvm5SJjo5GQkKCsUxzjY2NqKurM3mQZfLygJUrW85o0uvF43l59nsuW25b/fJPyafp9cDhw8D27eJXdnQSka9ym7AjCAKys7Px0EMPISEhAQCg0WgAAJGRkSZlIyMjjec0Gg0CAgLQuXPnVss0l5+fD6VSaXzExsba++V4JZ0OWLWq7TIFBfa5pWVL0PnhB9uf39MVF4u7zY8ZA8ycKX7t0UM8TkTka9wm7CxYsABff/01tm/f3uKcrNknnyAILY4111aZJUuWQKvVGh9VVVXSG+5DHn64/R3F9Xpg3Trbnuf/+/+k1+3QAejZ07bn93TFxcBjj7Xcbb66WjzOwENEvsYtwk5mZib27NmDQ4cOISYmxnhc9cumRc17aGpqaoy9PSqVCjqdDrW1ta2WaU6hUCA0NNTkQW3LyQEOHbKs7Pnztj3XiBHS6/r6rRq9Hli0yPygbMOxrCz+nIjIt7g07AiCgAULFqC4uBhffPEFejb7k7xnz55QqVQ4cOCA8ZhOp0NJSQlG/PKJOGjQIPj7+5uUUavVOH36tLEM2SYnR7w9ZalevaQ9T1kZp5fb6siRlj06TQkCUFUlliMi8hUuXUF5/vz52LZtGz766COEhIQYe3CUSiUCAwMhk8mQlZWF5cuXIz4+HvHx8Vi+fDmCgoIwc+ZMY9m5c+ciJycH4eHhCAsLQ25uLhITEzF+/HhXvjyvkJdnXdCRy4GMDOue49w5ID7eujrNMeiI1Gr7liMi8gYuDTtvvvkmAGB0sz0ENmzYgDlz5gAA8vLy0NDQgIyMDNTW1mLo0KHYv38/QkJCjOULCwvh5+eHGTNmoKGhAePGjcPGjRshl7LxEhnpdNYFHQDIzgYCAiwvb+vu42fOAPffL72+t4mKsm85IiJvIBME/k1cV1cHpVIJrVbL8TtNrF4NPPus5eXHjAG++MLy8rYGHf7LbUmvF2ddVVeb//nIZOJu85WV0jZhJSJyJ5Z+frvFAGVyLw0NwIIF1t+++vRTy8ufO8eg4whyObBmjfjfzcc/Gb5fvZpBh4h8C8MOmUhNBYKCgP/9X3Egq6WsvX1lyxidH3+UXtcXpKUBO3cC3bqZHo+JEY+npbmmXURErsLbWOBtLIOUFGDPHuvrZWe3v9hgUwqF9IUH/fyA27el1fU1er0460qtFsfojBzJHh0i8i6Wfn67dIAyuY/t2x0fdDQa2wbGMuhYRy4Hmo39JyLySbyNRXj/fXFLAWvI5cDixZYHneBg24LOjz8y6BARkTTs2fFxubnW3YKKjRV7czIyLB+jExwM3LwprX0AByMTEZFt2LPjw/LyrAs6AJCcLG43YGnQ0WikB52TJxl0iIjIdhygDN8coKzTibOurN0j6eZNIDDQ8vJSt3+oqQG6dJFWl4iIfAPX2aE2rVtnfdBJSXFO0FEqGXSIiMh+GHZ8lLU7kycnA7t3W15eatAJCACuXZNWl4iIyByGHR9lzc7k27YBH31kWdmffrJt5/LGRul1iYiIzGHY8VEZGZYtMLdjB/DEE+2X0+vFdXDi4qS3iaPHiIjIERh2fFRAgDiFvC05OcCMGe1fq7hYDDrWjgEy+Mc/GHSIiMhxuM6OD1uxQvxaUGAaVORyMQgZzreluBiYNk16G44dAx58UHp9IiKi9nDqOXxz6nlTOp04O+v8eXEsj6ULBur14uwsW1Y29sZ/fdyTiojIObg3FlksIEBcKNBaDz3EoNNccTGwaBFw8eKvx2JigDVruNs4EZGrcMwOSZKUBBw/Lr2+twadxx4zDToAUF0tHi8udk27iIh8HcMOWS0oCDhwQFpdmcw7g45eL/bomHtthmNZWdIHcRMRkXQMO2SVoCCgoUFa3bNngbt37dsed3HkSMsenaYEAaiqEssREZFzccwOWax7d2lBx8/PtrE97qj5oO7wcMvqqdWObRcREbXEsEMW+c1vxJ4Ja02YAOzfb//2uFJeXsvp+h0s7CONinJMm4iIqHUMO9Su3/4WqKy0vt6UKcDHH9u/Pa5gmE6+ahWwd2/L8+3dnpPJxFlZI0c6pn1ERNQ6hh1q029/C3z1lfX1vCnomJtO3pbmg7ANe4WtXs31doiIXIFhh1rVsydw4YL19SZM8K6g89hj1s0gCw0FtNpfv4+JEYMO19khInINhh1qQasFIiOl7UAeGOg9Y3Tamk7ellmzgOnTuYIyEZG7YNghE717izOMpAgMBG7etG97XKm96eStiY8HRo+2e3OIiEgirrNDRr16SQ86sbHeFXQAadPE5XJxbzEiInIfDDsEQLxd88MP0ur27An89JN92+MOpEwTz862bBNVIiJyHoYdQl4e8MYb0uoOHCg9JLm7kSPFwcWG2VRtkcuBxYuBFSsc3y4iIrIOw46P0+nEBfKkGDgQOHXKvu1xJ3K5uFs50DLwGL6fNAkoLBRv4THoEBG5J4YdH6bTAenp0jan7NHDu4OOQVoasHMn0K2b6fGYGODDD4F9+8QNPnnriojIfXE2lo8yt+WBpXr1As6ds3+b3FVaGpCSIs7O4nRyIiLPw7Djg/LygJUrra+nUAA//wwolfZvk7uTyzmdnIjIU7n0NtaXX36JqVOnIjo6GjKZDLt37zY5P2fOHMhkMpPHsGHDTMo0NjYiMzMTERERCA4ORnJyMi5KWRzFR0gdo/Ob3wC3bvlm0CEiIs/m0rBz48YNDBgwAGvXrm21zMMPPwy1Wm187Nu3z+R8VlYWdu3ahaKiIhw9ehT19fWYMmUK9FLuz3i5hgZg7Fjrb10tXCh9/R0iIiJXc+ltrEmTJmHSpEltllEoFFCpVGbPabVarF+/Hps3b8b48eMBAFu2bEFsbCwOHjyIiRMn2r3Nnio1FfjoI+vqyOXiujGcZURERJ7M7WdjHT58GF27dkWfPn3w1FNPoaamxniurKwMt2/fRlJSkvFYdHQ0EhIScOzYsVav2djYiLq6OpOHN5s61fqgk5rK6dREROQd3DrsTJo0CVu3bsUXX3yBVatWobS0FGPHjkXjLztUajQaBAQEoHPnzib1IiMjodFoWr1ufn4+lEql8REbG+vQ1+FKU6cCe/daV0cuB3bs4HRqIiLyDm49Gys9Pd343wkJCRg8eDDi4uLwySefIC0trdV6giBA1sayt0uWLEF2drbx+7q6Oq8MPMnJ1gcdgFseEBGRd3Hrnp3moqKiEBcXh7NnzwIAVCoVdDodamtrTcrV1NQgMjKy1esoFAqEhoaaPLzNs88CH39sXR1ueUBERN7Io8LOlStXUFVVhahfdmgcNGgQ/P39ceDAAWMZtVqN06dPY8SIEa5qpsvl5QGrV1tX53e/4xgdIiLyTi69jVVfX49zTZbiraysRHl5OcLCwhAWFoZly5Zh2rRpiIqKwoULF7B06VJERETg0UcfBQAolUrMnTsXOTk5CA8PR1hYGHJzc5GYmGicneVrpK6jc+AAb10REZF3cmnYOXnyJMaMGWP83jCOZvbs2XjzzTdRUVGB9957D9euXUNUVBTGjBmDHTt2ICQkxFinsLAQfn5+mDFjBhoaGjBu3Dhs3LgRch9by7++HnjySeD4cevX0UlJAQIDHdMuIiIiV5MJgiC4uhGuVldXB6VSCa1W65Hjdx58ECgtlVZ3yhTrx/YQERG5A0s/v916Nha1b8gQ4ORJaXUZdIiIyBd41ABlMrV1q/SgM3Uqgw4REfkGhh0PVVwM/Md/SKublQXs2WPX5hAREbkthh0PpNcDixZZX8+wjk5hof3bRERE5K44ZscDHTkCXLxoefkePcRwlJHB6eVEROR7GHY8jF4PfP65dXUqKoBOnRzTHiIiInfHsOMh9HrglVeANWuAq1ctrzdkCIMOERH5NoYdD1BcDDz9NHDlinX1Bg8GTpxwTJuIiIg8BQcou7niYmDaNOuDzpYt0hcaJCIi8iYMO25MrwcWLrSuTmws8OGHwKxZjmkTERGRp2HYcWOzZgHV1ZaX/8tfgMpKIC3NcW0iIiLyNAw7bkivBx5/HNixw7p648aJa+kQERHRrxh23ExxMRAXZ33QiY0FRo50TJuIiIg8GWdjuZHiYuCxxwBr96GXyYDVq9mrQ0REZA57dtyEYQsIa4NOeDiwcyfH6RAREbWGPTtuwtotIABg4kTgk0/Yo0NERNQW9uy4CbXauvJyubhzOYMOERFR2xh23ERUlHXls7O5qScREZElGHbcxMiRQEyMONi4LR06AIsXAytWOKddREREno5hx03I5eImn0DrgeePfwQaGhh0iIiIrMGw40bS0sSZVd26mR43bAHx7ru8dUVERGQtzsZyM2lpQEqKODtLrRbH8owcyYHIREREUjHsuCG5HBg92tWtICIi8g4MO06g0wHr1gHnzwO9egEZGbwdRURE5CwMOw6WlwcUFIgrJBvk5opTxznQmIiIyPEYdhxErwdmzTK/oadeD6xcKf43Aw8REZFjcTaWA1i6c3lBgXiLi4iIiByHYcfODDuXV1e3X1avF8fyEBERkeMw7NiRlJ3Lz593XHuIiIiIYceupOxc3quXY9pCREREIoYdO5Kyc3lGhmPaQkRERCKGHTvizuVERETuh2HHjrhzORERkftxadj58ssvMXXqVERHR0Mmk2H37t0m5wVBwLJlyxAdHY3AwECMHj0aZ86cMSnT2NiIzMxMREREIDg4GMnJybho7cAZO+HO5URERO7HpWHnxo0bGDBgANauXWv2/IoVK1BQUIC1a9eitLQUKpUKEyZMwPXr141lsrKysGvXLhQVFeHo0aOor6/HlClToG+6ZLETcedyIiIi9yITBGsmSjuOTCbDrl27kJqaCkDs1YmOjkZWVhaee+45AGIvTmRkJF577TXMmzcPWq0WXbp0webNm5Geng4AuHTpEmJjY7Fv3z5MnDjRoueuq6uDUqmEVqtFaGioXV6PXs+dy4mIiBzJ0s9vtx2zU1lZCY1Gg6SkJOMxhUKBUaNG4dixYwCAsrIy3L5926RMdHQ0EhISjGXMaWxsRF1dncnD3gw7lz/xhPiVQYeIiMg13DbsaDQaAEBkZKTJ8cjISOM5jUaDgIAAdO7cudUy5uTn50OpVBofsbGxdm49ERERuQu3DTsGsmYjfQVBaHGsufbKLFmyBFqt1vioqqqyS1uJiIjI/bht2FGpVADQooempqbG2NujUqmg0+lQW1vbahlzFAoFQkNDTR5ERETkndw27PTs2RMqlQoHDhwwHtPpdCgpKcGIESMAAIMGDYK/v79JGbVajdOnTxvLEBERkW/zc+WT19fX49y5c8bvKysrUV5ejrCwMHTv3h1ZWVlYvnw54uPjER8fj+XLlyMoKAgzZ84EACiVSsydOxc5OTkIDw9HWFgYcnNzkZiYiPHjx7vqZREREZEbcWnYOXnyJMaMGWP8Pjs7GwAwe/ZsbNy4EXl5eWhoaEBGRgZqa2sxdOhQ7N+/HyEhIcY6hYWF8PPzw4wZM9DQ0IBx48Zh48aNkHP6ExEREcGN1tlxJUess0NERESO5fHr7BARERHZA8MOEREReTWXjtlxF4Y7eY5YSZmIiIgcw/C53d6IHIYdwLixKFdSJiIi8jzXr1+HUqls9TwHKAO4e/cuLl26hJCQkHZXZ7aXuro6xMbGoqqqioOi3RzfK8/B98oz8H3yHO7+XgmCgOvXryM6OhodOrQ+Moc9OwA6dOiAmJgYlzw3V3D2HHyvPAffK8/A98lzuPN71VaPjgEHKBMREZFXY9ghIiIir8aw4yIKhQIvvvgiFAqFq5tC7eB75Tn4XnkGvk+ew1veKw5QJiIiIq/Gnh0iIiLyagw7RERE5NUYdoiIiMirMewQERGRV2PYcaJly5ZBJpOZPFQqlaubRQC+/PJLTJ06FdHR0ZDJZNi9e7fJeUEQsGzZMkRHRyMwMBCjR4/GmTNnXNNYH9feezVnzpwWv2fDhg1zTWN9XH5+PoYMGYKQkBB07doVqamp+Ne//mVShr9brmfJ++Tpv1cMO07Wr18/qNVq46OiosLVTSIAN27cwIABA7B27Vqz51esWIGCggKsXbsWpaWlUKlUmDBhgnFfNXKe9t4rAHj44YdNfs/27dvnxBaSQUlJCebPn4/jx4/jwIEDuHPnDpKSknDjxg1jGf5uuZ4l7xPg4b9XAjnNiy++KAwYMMDVzaB2ABB27dpl/P7u3buCSqUSXn31VeOxW7duCUqlUnjrrbdc0EIyaP5eCYIgzJ49W0hJSXFJe6htNTU1AgChpKREEAT+brmr5u+TIHj+7xV7dpzs7NmziI6ORs+ePfH444/jhx9+cHWTqB2VlZXQaDRISkoyHlMoFBg1ahSOHTvmwpZRaw4fPoyuXbuiT58+eOqpp1BTU+PqJhEArVYLAAgLCwPA3y131fx9MvDk3yuGHScaOnQo3nvvPXz22Wd4++23odFoMGLECFy5csXVTaM2aDQaAEBkZKTJ8cjISOM5ch+TJk3C1q1b8cUXX2DVqlUoLS3F2LFj0djY6Oqm+TRBEJCdnY2HHnoICQkJAPi75Y7MvU+A5/9ecddzJ5o0aZLxvxMTEzF8+HD06tULmzZtQnZ2tgtbRpaQyWQm3wuC0OIYuV56errxvxMSEjB48GDExcXhk08+QVpamgtb5tsWLFiAr7/+GkePHm1xjr9b7qO198nTf6/Ys+NCwcHBSExMxNmzZ13dFGqDYcZc8780a2pqWvxFSu4nKioKcXFx/D1zoczMTOzZsweHDh1CTEyM8Th/t9xLa++TOZ72e8Ww40KNjY349ttvERUV5eqmUBt69uwJlUqFAwcOGI/pdDqUlJRgxIgRLmwZWeLKlSuoqqri75kLCIKABQsWoLi4GF988QV69uxpcp6/W+6hvffJHE/7veJtLCfKzc3F1KlT0b17d9TU1ODll19GXV0dZs+e7eqm+bz6+nqcO3fO+H1lZSXKy8sRFhaG7t27IysrC8uXL0d8fDzi4+OxfPlyBAUFYebMmS5stW9q670KCwvDsmXLMG3aNERFReHChQtYunQpIiIi8Oijj7qw1b5p/vz52LZtGz766COEhIQYe3CUSiUCAwMhk8n4u+UG2nuf6uvrPf/3ypVTwXxNenq6EBUVJfj7+wvR0dFCWlqacObMGVc3iwRBOHTokACgxWP27NmCIIhTZF988UVBpVIJCoVC+P3vfy9UVFS4ttE+qq336ubNm0JSUpLQpUsXwd/fX+jevbswe/Zs4aeffnJ1s32SufcJgLBhwwZjGf5uuV5775M3/F7JBEEQnBmuiIiIiJyJY3aIiIjIqzHsEBERkVdj2CEiIiKvxrBDREREXo1hh4iIiLwaww4RERF5NYYdIiIi8moMO0REROTVGHaIiIjIqzHsEDnZnDlzIJPJWjzOnTuH0aNHIysrq0Wd3bt3QyaTGb/fuHGj2Wt07NjR5HlSU1PbbMtXX32F6dOnIzIyEh07dkSfPn3w1FNP4fvvvzcpt2nTJjz44IMIDg5GSEgIfv/732Pv3r0mZQ4fPgyZTIaEhATo9XqTc/fccw82btxo8rxTpkxB165d0bFjR/To0QPp6em4fPmyybWuXbvWos0PPPAAli1bZvy+R48ekMlkKCoqalG2X79+kMlkJs9tKC+TyRAUFISEhAT83//7fwEAo0ePNvtzNTx69OhhLNf8fTpz5gxmzJiBLl26QKFQID4+Hi+88AJu3rxpUs7w/MePHzc5npWVhdGjR7d4Da2pq6vD888/j/vuuw8dO3aESqXC+PHjUVxcjKYL41vbLik/R7lcjujoaMydOxe1tbUWvwYiZ2HYIXKBhx9+GGq12uRhyU7DTYWGhra4xo8//mhx/b1792LYsGFobGzE1q1b8e2332Lz5s1QKpV44YUXjOVyc3Mxb948zJgxA//85z9x4sQJjBw5EikpKVi7dm2L654/fx7vvfdeq89bU1OD8ePHIyIiAp999hm+/fZbvPvuu4iKimrxAWyp2NhYbNiwweTY8ePHodFoEBwc3KL8X//6V6jVanz99ddITU3FM888gx07dqC4uNj4szxx4gQA4ODBg8ZjpaWlZp//+PHjGDp0KHQ6HT755BN8//33WL58OTZt2oQJEyZAp9OZlO/YsSOee+45Sa8VAK5du4YRI0bgvffew5IlS3Dq1Cl8+eWXSE9PR15eHrRaraR2Sf05/vTTT9i6dSu+/PJLLFy4UPLrInIU7npO5AIKhQIqlcqma8hkMsnXuHnzJv74xz/ikUcewa5du4zHe/bsiaFDhxp7VI4fP45Vq1bhjTfeQGZmprHcK6+8glu3biE7OxspKSmIjY01nsvMzMSLL76IJ554wqSnyeDYsWOoq6vDO++8Az8/P+Pzjh07VtJrAYBZs2ahsLAQVVVVxra8++67mDVrltngFRISYvzZvfzyy3j//fexe/dupKenG8vcunULABAeHt7mz1kQBMydOxd9+/ZFcXExOnQQ/4aMi4tDnz59MHDgQBQWFpqEm3nz5uHNN9/Evn378Mgjj1j9epcuXYoLFy7g+++/R3R0tPF4nz59jD93Ke2y5efYrVs3/OEPfzDbM0TkauzZIfJBn332GS5fvoy8vDyz5++55x4AwPbt29GpUyfMmzevRZmcnBzcvn0bH374ocnxrKws3Llzx2yvDwCoVCrcuXMHu3btgr32IY6MjMTEiROxadMmAGKY27FjB/70pz9ZVL9jx464ffu2pOcuLy/HN998g+zsbGOgMBgwYADGjx+P7du3mxzv0aMHnnnmGSxZsgR379616vnu3r2LoqIizJo1yyToGHTq1Al+fn6S2mXLz7G6uhp79+7F0KFDrXo9RM7AsEPkAnv37kWnTp2Mj+nTp1t9Da1Wa3KNTp06ISkpyaK6Z8+eBQDcd999bZb7/vvv0atXLwQEBLQ4Fx0dDaVS2WJ8T1BQEF588UXk5+cbb6c0NWzYMCxduhQzZ85EREQEJk2ahJUrV+Lnn3+2qO2t+dOf/oSNGzdCEATs3LkTvXr1wgMPPNBmnTt37mDjxo2oqKjAuHHjJD2v4fX37dvX7Pm+ffu2+BkBwF/+8hdUVlZi69atVj3f5cuXUVtba9F7J6Vd1vwcn3vuOXTq1AmBgYGIiYmBTCZDQUGBVa+HyBkYdohcYMyYMSgvLzc+3njjDauvERISYnKN8vLyFuMtWmOvHhVBEEwGThvMnTsXEREReO2118zWe+WVV6DRaPDWW2/h/vvvx1tvvYX77rsPFRUVktsyefJk1NfX48svv8S7777bZm9E0w/p+fPnY/HixWZ7r+yhtZ9Rly5dkJubi//6r/9qMXamvesBMHtNe7TLmp/j4sWLUV5ejq+//hqff/65sX7zAepErsawQ+QCwcHB6N27t/ERFRUFQBx0bK435Nq1awgNDTU51qFDB5Nr9O7dG926dbPo+fv06QMA+O6779otd/78ebMfxpcuXUJdXR3i4+NbnPPz88PLL7+MNWvW4NKlS2avHR4ejunTp2PVqlX49ttvER0djddffx0AjK+1tZ+FUqk0+5xPPvkkXnzxRfzjH//ArFmzWn1dhg/pH3/8EfX19VixYkWLWz2WMvwsv/nmG7Pnv/vuO7M/IwDIzs5GQ0MD1q1bZ/HzdenSBZ07d8a3337rkHZZ83OMiIhA7969ER8fj7Fjx2L16tU4duwYDh06ZPHrIXIGhh0iN3Lffffh5MmTLY6Xlpbi3nvvtdvzJCUlISIiAitWrDB73jBA+fHHH0d9fb1xanZTr7/+Ovz9/TFt2jSz15g+fTr69euHl156qd32BAQEoFevXrhx4wYAID4+Hh06dGgx+0mtVqO6urrVn8Wf/vQnlJSUICUlBZ07d271+Qwf0tHR0Tb3kDzwwAO47777UFhY2GL8zT//+U8cPHgQTzzxhNm6nTp1wgsvvIBXXnkFdXV1Fj1fhw4dkJ6ejq1bt5oNkjdu3MCdO3dsapelP8fm5HI5AKChocHiOkTOwNlYRG4kIyMDa9euxfz58/H0008jMDAQBw4cwPr167F582aTsoIgQKPRtLhG165djb0UWq0W5eXlJufDwsLQvXt3vPPOO5g+fTqSk5OxcOFC9O7dG5cvX8b777+Pn376CUVFRRg+fDgWLVqExYsXQ6fTITU1Fbdv38aWLVuwZs0arF692mQmVnOvvvoqJk6caHJs7969KCoqwuOPP44+ffpAEAR8/PHH2Ldvn/E2XEhICObNm4ecnBz4+flhwIABuHTpEp5//nn07du31bFJffv2xeXLlxEUFNTuz9peZDIZ3nnnHSQlJWHatGlYsmQJVCoV/vGPfyAnJwfDhw83u3aSwdNPP43CwkJs377d4sG9y5cvx+HDhzF06FC88sorGDx4MPz9/XHkyBHk5+ejtLQU99xzj+R2WfpzvH79OjQaDQRBQFVVFfLy8hAREYERI0ZY9DqInEYgIqeaPXu2kJKS0ur5kydPChMnThS6du0qhIaGCoMHDxa2b99uUmbDhg0CALMPtVptfB5z52fPnm28TmlpqZCWliZ06dJFUCgUQu/evYWnn35aOHv2rMnzrV+/Xhg8eLAQGBgoBAUFCQ899JCwZ88ekzKHDh0SAAi1tbUmx5OSkgQAwoYNGwRBEITz588LTz31lNCnTx8hMDBQuOeee4QhQ4YYzxvcunVL+Otf/yr07dtXCAwMFOLi4oQ5c+YYX59BXFycUFhY2OrPU6lUmly7vfIGlZWVAgDhq6++anFu1KhRwqJFi0yOff3118K0adOE8PBwwd/fX+jVq5fwl7/8Rbhx40a77d22bZsAQBg1alS77TK4du2a8H/+z/8R4uPjhYCAACEyMlIYP368sGvXLuHu3bt2aVdT5n6OTf9ddenSRXjkkUfM/ryIXE0mCHYaqUhERETkhjhmh4iIiLwaww4RkZtpvn5S08eRI0dc3Twij8PbWEREbubcuXOtnuvWrRsCAwOd2Boiz8ewQ0RERF6Nt7GIiIjIqzHsEBERkVdj2CEiIiKvxrBDREREXo1hh4iIiLwaww4RERF5NYYdIiIi8mr/P6f/QGEmLQBcAAAAAElFTkSuQmCC\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": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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\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": {}, "source": [ "## Practice\n", "Plot __CYLINDER__ vs the Emission, to see how linear is their relationship is:\n" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n", "plt.xlabel(\"Cylinders\")\n", "plt.ylabel(\"Emission\")\n", "plt.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "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": [ "We can plot each of these features:\n" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "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": 39, "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": 40, "metadata": {}, "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": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coefficients: [[39.60619393]]\n", "Intercept: [123.82649696]\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": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Emission')" ] }, "execution_count": 42, "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": 43, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean absolute error: 23.44\n", "Residual sum of squares (MSE): 1029.97\n", "R2-score: 0.75\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": 44, "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": 45, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n", " normalize=False)" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regr = linear_model.LinearRegression()\n", "\n", "regr.fit(train_x, train_y)" ] }, { "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": 46, "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": 47, "metadata": { "jupyter": { "source_hidden": true }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean Absolute Error: 19.74\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 }