{ "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": 1, "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": 2, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2025-10-20 14:15:42-- 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", "2025-10-20 14:15:42 (41.3 MB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n", "\n" ] } ], "source": [ "!wget -O FuelConsumption.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In case you're working **locally** uncomment the below line. \n" ] }, { "cell_type": "code", "execution_count": 3, "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": { "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "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": 4, "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
52014ACURARLXMID-SIZE3.56AS6Z11.97.710.028230
62014ACURATLMID-SIZE3.56AS6Z11.88.110.128232
72014ACURATL AWDMID-SIZE3.76AS6Z12.89.011.125255
82014ACURATL AWDMID-SIZE3.76M6Z13.49.511.624267
92014ACURATSXCOMPACT2.44AS5Z10.67.59.231212
102014ACURATSXCOMPACT2.44M6Z11.28.19.829225
112014ACURATSXCOMPACT3.56AS5Z12.18.310.427239
122014ASTON MARTINDB9MINICOMPACT5.912A6Z18.012.615.618359
\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", "5 2014 ACURA RLX MID-SIZE 3.5 6 \n", "6 2014 ACURA TL MID-SIZE 3.5 6 \n", "7 2014 ACURA TL AWD MID-SIZE 3.7 6 \n", "8 2014 ACURA TL AWD MID-SIZE 3.7 6 \n", "9 2014 ACURA TSX COMPACT 2.4 4 \n", "10 2014 ACURA TSX COMPACT 2.4 4 \n", "11 2014 ACURA TSX COMPACT 3.5 6 \n", "12 2014 ASTON MARTIN DB9 MINICOMPACT 5.9 12 \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", "5 AS6 Z 11.9 7.7 \n", "6 AS6 Z 11.8 8.1 \n", "7 AS6 Z 12.8 9.0 \n", "8 M6 Z 13.4 9.5 \n", "9 AS5 Z 10.6 7.5 \n", "10 M6 Z 11.2 8.1 \n", "11 AS5 Z 12.1 8.3 \n", "12 A6 Z 18.0 12.6 \n", "\n", " FUELCONSUMPTION_COMB FUELCONSUMPTION_COMB_MPG CO2EMISSIONS \n", "0 8.5 33 196 \n", "1 9.6 29 221 \n", "2 5.9 48 136 \n", "3 11.1 25 255 \n", "4 10.6 27 244 \n", "5 10.0 28 230 \n", "6 10.1 28 232 \n", "7 11.1 25 255 \n", "8 11.6 24 267 \n", "9 9.2 31 212 \n", "10 9.8 29 225 \n", "11 10.4 27 239 \n", "12 15.6 18 359 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"FuelConsumption.csv\")\n", "\n", "# take a look at the dataset\n", "df.head(13)\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": 5, "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": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# summarize the data\n", "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's select some features to explore more.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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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
92.449.2212
102.449.8225
113.5610.4239
125.91215.6359
\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\n", "9 2.4 4 9.2 212\n", "10 2.4 4 9.8 225\n", "11 3.5 6 10.4 239\n", "12 5.9 12 15.6 359" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n", "cdf.head(13)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot each of these features:\n" ] }, { "cell_type": "code", "execution_count": 7, "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(color='turquoise')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's plot each of these features against the Emission, to see how linear their relationship is:\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS, color='turquoise')\n", "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n", "plt.ylabel(\"Emission\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS, color='turquoise')\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": 10, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='turquoise')\n", "plt.xlabel(\"Cylinders\")\n", "plt.ylabel(\"Emission\")\n", "plt.show()\n", "\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": [ "#### 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": 11, "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": 12, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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Ciy++iCeeeAK33HILysrKsGnTJrS1tWHLli0AgKamJqxfvx4vvPACxo8fj6FDh2Lz5s2orKzEnj171HpJRITwHvOORqGuctgkCUc7WvBBqwlHO1piYjUkXOMiiJSmeoHyvHnzcOONN2L8+PFYunSp8/YTJ06gtrYWEydOdN6m1+sxZswYHDp0CPfddx8qKirQ2dnpFlNcXIyysjIcOnQIkyZN8vqcFosFFsv5wkiz2ftfYkQUmt50qieUoZixWuDLcREUK1RNdrZu3YqPP/4Yhw8f7nFfbW0tAKCgoMDt9oKCAnz33XfOmKSkJLcVIUeM4+u9WbFiBZYsWRLq5RORgHAc845Gwa5yOAp8PTkKfJfkIWoTHo6LoFih2p9XVVVVWLBgATZv3ozk5GSfcRqPplSSJPW4zVOgmMceewxNTU3Oj6qqKnkXT0TkIZhVjlgv8I2WcREtXV148vQJ3FP9OZ48fQItXV1hfT6KPaolOxUVFairq8OwYcOQkJCAhIQE7Nu3D//93/+NhIQE54qO5wpNXV2d877CwkJYrVaYTCafMd7o9XpkZGS4fRARhcKxyuGP5ypHPBT4qj0u4pc1X2LqqeM42GHGiU4LDnaYMfXUcfyy5suwPi/FFtWSnXHjxqGyshJHjx51fgwfPhx33303jh49igsvvBCFhYXYvXu382usViv27duHkSNHAgCGDRuGxMREt5iamhocO3bMGUNEFAnBrHLES4Hv6JRMvN5nIFYVXIQnc/thVcFFeL3PwIgkOp9b273e97m1nQkPOalWs5Oeno6ysjK321JTU5GTk+O8vby8HMuXL8eAAQMwYMAALF++HCkpKbjrrrsAAEajEXPmzMHChQuRk5OD7OxsLFq0CIMHD8b48eMj/pqIqHfrXuWAcLFxPBX46jSaHoXX4dTS1eUz0XH43NqOlq4upCWofhan17JJUlTMTIvqd8AjjzyC9vZ23H///TCZTLj66qvx/vvvIz093RmzatUqJCQkYObMmWhvb8e4ceOwceNG6HQ6Fa+ciHorOUMxWeAbvGcbxGotn22owtKC/mG+GvImmk4ZaiQpSivfIshsNsNoNKKpqYn1O0QUUb5OYzlEou4lFt1T/TlOdAaerdY/UY//Lb4sAldEriL1vhb9/R1/zS6IqNeJxYZ8DmoX+MaqYp3YXDXROFJONJ4yjOptLCKiQKJpqTxYcra+qNuvc0ow9dRxoTiKrFAabIYLkx0iilmx3JDPk1IFvtFSEBpuaQkJuCzJ4LdI+bIkA4uTVRCNpwz5LiCimCS6VD7KYIzLX/bexMMqlxxrii7xefz8siQD1hRdosJVUTSeMmSyQ0QxKRqXykMR6oqM2qtcVrtdlRloa4ouQUtXF55tqEK1zYJinR6/zinhio6KovGUId8NRBSTonGpPJDTHR34Zd1/0CLZkKbRYU3+xShITg55RSYcq1xykpe1pmr8yVwP13LT35uqMTNC0+3TEhJ4vDyKOBps+juNFYkxIq6Y7BBRTIrGpXJ/Jn/3CTpc0gGTZMMdp79AIgBv6ZicFRmlV7nWmqqxzVwPu8tta0zVmOEleVlrqsYb5voejyEBztsjkfBQdJHbYDPcmOwQUUyKxqVyXzwTHVeB1p1EVmSUXOXylbzY0TN5sdrt+JOXWFd/MtfjHmNhRLa0KLpE0ylDvvuIKCZFy8TtQE53dPhMdESIDALN1Ir93Roozmq3Y1uA5GWbuR5We/eaz47mMwFfmXQujnonxynDcalZuDI5TbX/HpnsEFHMioWGfL+s+0/IjxFoRUb010eguJ0tDW5bV97Yz8UBwCeC09hF44jChdtYRBTTommp3JsWyRbyYwSqOzLZu4QeJ1Dcqa7A4xdc41IEt6ZE44jChckOEcW8SE/cliNNo4MphIRHpO5IqWLtPglioxUccdcbMrG7tTFg/PWGTKHHJQoXpttERGG0Jv/ikL5epO7IUaztj0jSNC0tJ+AvBe25OACoElwJEo0jChcmO0REYVSQnIzkANUyiUBIdUdKFWsnabWYkZHnN2ZGRp7zZFWNzRrw2uTEEYULt7GIKOrE23ynd0uH+Dx+ngwN3i0dEjWv2XGs3LPPjhbo0WdH7rYXkVo0khTBGetRymw2w2g0oqmpCRkZGWpfDlGvFs/znXx1UA6VTZJw56nPAvYcer3PQEU7KFvtdkyuqvR7gksL4N2SweyzQ2Eh+vubKztEFDWCne8ULasigRQkJ+PP/cq83hfKbKlwzAlL0mpxW4AtLce2l7cmhA6u216hUmv+FsU+JjtEFBWCne8UDytBcsYzeKPmnDA5216hCPV7RL0bkx0iigrBrE6oPelbLm8rE//bVCs8nsEXteeEzc0qxqyMAqxrrMHJLgv6Juhxb2YRDDqdIo8vZ4QFkTdMdogoKshdnQjHpO9w8rYy8XtTdcBxC9sEZkupPSfMc3XtCFpwsN2syOqa6AgLzt8if/jOIKKoIHd1Qs5KkNocKxOehbwip0Ps6N6u+aDVhKMdLbB5OVOi5pwwx+qa58/Csbq2v63R69fZJAlHO1r8vi5A/ggLIm+4skNEUUHu6oSadSpyiKxMBLKjpQE7zv0y91WP1D0nDBGtX4pEnZXcERZE3jDZIaKo4Fid8FaD4+C6OqF2nYookZUJOfzVI0V6Tpjo6tr25jPI0iUgR5eIRlsXlpwRr7NiLx9SApMdIooaclYn1K5TERWuFQdf9UiRnBMmumr2iun86k+g2gnP1zUtLQdrTNUBe/k4RlgQecNkh4iiiujqhMhK0P1ZRar338mFMieSPHmumAT72tpttqBPUQWzahZolcvzxF2ke/lQfGKyQ0RRR3R1wt9K0NjUTPzeVKN6/51jnW1he2zXFZNgXtuTdSdwsN3s/PwIWrCjpQGjDBlYmt8/4NdfmmiQdb2iPFeM5mYV42Snxe1aHUYZMnjsnAJiskNEMc3bSpDcupBwOh2hAmm5r80z0XF1sN2MJ+tOBEx43mk9G8ylBuS5YrS/rdHvte5va4yqfkoUfZjsEFHMc10JcsyJ8ieS/XeKdXqc6IzcSaGXG04hVaNDo73L5/ZWu83mM3lwONhuRrvN5ndLKxz1SJ51VrHWT4miE5MdIoor4ZgTJXcmk+usrimpWTjY4T+xUNIZexcW1X3j/Nzb9ta6xhqhx1rXWIMFOX193h+OE1Ce/YDC8fOk3ofJDhHFFaX778idyeSth0wCgC6hZ1Oet+2tk4IrMoHiRE5K+aKFe7Gyr5qjWOmnRNGNyQ4RxRUl++/Incnka1aXv0QnUomQ61ZP3wQ9jqAl4Nf0DbByk6TV4pIkAz63tsu+nt/k9kOmLjHgSblY6adE0Y1n9YgoroieEAoUJzqTyWrvXp8QqS3JggYjktPRP1GPUckZeLvPIPwmr1ToekPlOjrj3swioa8JFGe12/GlzEQnT5eIJXmluC41C1cmp2Hcuf/1VW/j6KcU6DHV7qdE0Y0rO0QU81xrZI51iM3Ceqf1LG7LyPN5v5yZTLdl5AnVlpggYaYx3622ZHSC9+Pz4eDY6jHodBhlyPBbpDzKkBGw345od+jpaTkoS04Nqh+Q3M7aRN6ourKzZs0aDBkyBBkZGcjIyMCIESPw7rvvOu+fPXs2NBqN28c111zj9hgWiwXz589Hbm4uUlNTMW3aNJw8eTLSL4WIVLK/rRF3nvoMD53+GkvPfO+cIRXIyc4Ov/fLnckUSm3J6JRMvN5nIFYVXIQnc/thXpj6xrhu9SzN749RhgyvcaJ9dgJ9Dx0kSAFXcPzp7qdU2mOFx7FKxGPnFIiqKzt9+/bFs88+i4svvhgAsGnTJtx0003417/+hUGDBgEAbrjhBmzYsMH5NUlJSW6PUV5ejrfffhtbt25FTk4OFi5ciClTpqCiogI6wS6gRBSbfNXIiNDA/y9duTOZQq0t8Tw+/ydzvd+VnlxtAn6d2w+N9i5kaROwoqEKZ2SOzlia3z+kDsqBvody4/yJ9Nwvii+qJjtTp051+3zZsmVYs2YNPvroI2eyo9frUVhY6PXrm5qasH79erz22msYP348AGDz5s0oKSnBnj17MGnSpPC+ACIKidwj3a5EamT8uSRAzY7cmUyhzury/F7MzSzCbxu+9/lY83P6YJgh/fznQW71GHQ6v8fL/bksKQVA4JW07rjQRXLuF8WXqKnZsdls2LZtG1pbWzFixAjn7Xv37kV+fj4yMzMxZswYLFu2DPn5+QCAiooKdHZ2YuLEic744uJilJWV4dChQz6THYvFAovl/BK12Ry5HhhE1E3ukW5PIjUy/rQGqDaRO5MplNoSb98LLbq3k760tnsdeTHKYMTRjhbnKscogxFL8kqFhqgqpSAxKXCQjDiicFE92amsrMSIESPQ0dGBtLQ0bN++HZdffjkAYPLkyZgxYwZKS0tx4sQJ/OY3v8HYsWNRUVEBvV6P2tpaJCUlISsry+0xCwoKUFtb6/M5V6xYgSVLloT1dRGRb3KPdHsTal8Vo1b5be5AU9uv0qfjpYaTbltGm8ynfX4vDrabMSM9FyNTjG5bNwfbm3Dnqc+8PsfrfQZGbKtnsD4VGVodzHabz5gMrY4npUh1qic7l156KY4ePYrGxka89dZbmDVrFvbt24fLL78ct99+uzOurKwMw4cPR2lpKd555x3ccsstPh9TkiRo/PzH/dhjj+Hhhx92fm42m1FSUqLMCyIiv0SPdN9jLPS7pZWlDe2fr7wE/6sNwV6nr9qSp+u/dVv1cQzdDOSt5jP4P5lFzufwVad0vnkgC3aJPKneZycpKQkXX3wxhg8fjhUrVuCKK67ASy+95DW2qKgIpaWl+OqrrwAAhYWFsFqtMJlMbnF1dXUoKCjw+Zx6vd55AszxQUSRIedItz9SCNcg0pcllOt01JY4TiA9Xf9twFlUIs8hOifKJoXy3RFXaWn1u6oDAGa7zdnfh0gtqic7niRJcquncdXQ0ICqqioUFXU3uho2bBgSExOxe/duZ0xNTQ2OHTuGkSNHRuR6iUgeuUe6fWm0B993WKQvi1LXKTJ0U/Q55MyJigSOcqBYoeo21uOPP47JkyejpKQEzc3N2Lp1K/bu3Ytdu3ahpaUFixcvxq233oqioiJ8++23ePzxx5Gbm4ubb74ZAGA0GjFnzhwsXLgQOTk5yM7OxqJFizB48GDn6Swiii5yj3T7Esx4ADnFukpdp+jQTZHnEE0a3mluwL62Rtkn3OTiKAeKFaomO6dPn8ZPf/pT1NTUwGg0YsiQIdi1axcmTJiA9vZ2VFZW4tVXX0VjYyOKiopw/fXX44033kB6+vnjlqtWrUJCQgJmzpyJ9vZ2jBs3Dhs3bmSPHaIoJfdIty+iR71/nVMCk71LdrHuDSlZeMUU+Gj7DSlZfu8XHbrpi+v3QjRp2NPW6Pz/ck64yRXqcXuiSFE12Vm/fr3P+wwGA957772Aj5GcnIyXX34ZL7/8spKXRkRhInKk+5a03ID9d0SOes/NLMI3nR3Ox7k8KUU42dnVZgocdC7O39gJ0aGbvrgebxdJLjzJOeEml06jQY4uwe/15OgS2PiPVKf6aSwi6n0cv3S99Za5JMmAP7ecEeq/4++o9yVJBixr+D7oPj6ioxACxd2bWSR06koL9PheeF6rSILni8gJN7nabbaAE88/t7aj3WYT7spMFA5MdohIFXOzinGPsdBtBed0pwVvekkM/K1OeDvqfaitCduaz8h6HE9KjUIQHbr5VG6pUDdpXwleIK5DS5UiWo+0rrEm6C7NREpgskMUAtdp27E6q0fN15Ck1Tp/+VrtdkyuqvQb72t1wnWMgNVux8LTXwf1OK4uSUwWeQlCcUvz++PJuhNeEx7XoZuiiYhngre3tREHBE58iZ4wE/V9gFUduXFE4cJkhyhI+9saI9qaPxyi6TXI6WvjLylQ6nGaAvSPkRvXN1EPTbt7fyDNuduD4ZrgmWxdQsmO6AkzUVbBdj6icUThEnV9dohigaOLrec2gqOL7X6X0zDRKtpeg1J9bZR6nK8Fa3ZE4hzjMTx/50vo3lZbK3Dqy59paTkB/zEXOeEm1wVJYqtfonFE4cJkh0imaOtiG4xofA1K9bVR6nHapUDrQ2JxomMnrHax5/PGccLNH9dTXUopFUxiROOIwoXJDpFM0dbFNhjR+BqUWp1Q6nGGCPaGCRSn1HiMQOZmFWOUwfvom1GGjLD02VFrRYlILiY7RDLFQ4v8aHwNoaxO2CQJRzta8EGrCZ9a23Brem5Qj+NqeoDHEI1TalstkP1tjT5PfB1sN4dlW1KtFSUiuVigTCRTPLTID+U1hPP0lr/+O7764/gqsh5lyMChdnOPguCZgn12dBoNDBqt320qg0Yb8LUrta3mj+i25CiDUfhnJfpznptVjH93tHjtt3NZkiEsK0pEcjHZIZIpHlrkB/saInF6y1v/HV89ZxxF1p7qbZ2ob+9EulaHZpfTUjm6RFyuTxG6jkpLa8B6nHbJjkpLq/NUlDdKjcfwR862pL9rdZDzc15rqvbZWPBzazvWmqqZ8JDquLZIJJOji60/IlO11RTMa4jk6S1H/50F2X1xm5+tq0CrGc0ex8LPyLhWpbb6IrHVo+S2pJyfcySKr4mUwGSHKAjdXWxLkat1XxzN1SZgSV5pTPTZcbyGPI+tqjxdYo/XEI2nt0RWM3wRuVYltyvnZhXj9oy8Hv/gagHcrsCQTqWuVe7POVLF10Sh4jYWUSg8V2+ieDXHG2+jFrzVZii9TaKEUIqnRa5V6e1KOdtzcil1rXJ/zpEqviYKVdDJTmNjI/75z3+irq4Odo8lyp/97GchXxhRNPNVK+LYJlmSh5hY3QHcO/H6Eo2nt0ItAA90rSJDN+VuV7qOx1CSTqPB2NRMv5Pkx6ZmBrxWuT/nSBRfEykhqGTn7bffxt13343W1lakp6dD4/IfkEajYbJDcS0cJ1+iXaRPoImcBBJZzfDHZOvCB60mvyeNHEM3Xz5bjTNRMFLDF5sk4cPWRr8xH7Y24heZRX7fk3J/zjekZOEVge7PN6RkCT0uUbgElewsXLgQ99xzD5YvX46UFLGTDUTxIhq3dMJtsD5V6Bi2EifQRE8Ciay8+KIF3H5JB0xePOp7pCjrjq3Ue1LudtiuNpPQ9e1qM4VlRYtIVFCbxadOncKDDz7IRId6pWjc0gk3myQJHcMOtUA5Uie+PF+Jr8d3XM8Ze5fb7WfsXVE1A02p96TcU3qs2aFYEVSyM2nSJBw5ckTpayGKCdHSVNC1a/DRjpawnoT6c4DjxXLjvJF7Ekgk3tvpJyUfX+4JtLNWK35+8nNM+74SPz/5Oc5arcJf64+S70k5p/RCqdlpt9nwUsNJ/Or013ip4STabWLT44mCEdQ21o033ohf/epX+PTTTzF48GAkJrr/RzFt2jRFLo4oGkVDU8FINPdz5WsMgbe4OzILgnoOuVsxIvF2APOyipGlS4DJ1hWwvkTu48vZrry16jjOuqwQNdssuLXmM2RrE/BWyaCAX++P0u9J0VN6wTZMfLLuhNt76ghasKOlAaMMGVia31/oGonkCCrZ+cUvfgEAeOaZZ3rcp9FoYGOGTnEsHKd05PDXNTjWToK5krsVIxqfpUvAuNQsfNAqVl8i9/FF4jwTHVdn7V24tep4SAlPON6TIqf0HA0T/Z0C82yY6JnouDrYbsaTdSeY8JDigtrGstvtPj+Y6FBvIGepX0lqNffzNU072Dhv0gT/OXLEyd26CXe8L2etVp+JjjPG3hXylpZa70k5DRPbbbaAq4QH283c0iLFsakgUZBEl/qVJLq1sr35DLJ0CYpd0y0ZefhDU61QXLD+IbhV9o92M65ONcreuhmsT0WGVgez3fcv0gytzi0+CRpY4TtxTIIm4NbQwrpv/N7vGreh72VCsb6o8Z4ExBsmrmusEXq8dY01WJDTNxyXSr1U0MnOvn378F//9V/47LPPoNFoMHDgQPzqV7/Cj370IyWvjyiqiSz1K0l0a0XWseooccomtrLhiAv3dqLVbveb6ACAFRKsdjsMOp3PmAa74HaYYFwgkX5POog0TDwpeCpLNI5IVFDbWJs3b8b48eORkpKCBx98EA888AAMBgPGjRuHLVu2KH2NRHROMCe8lDi2LTrbKJQZSH0FT/a4xsnZuqm0tPpd1QEAs92GSksrAHmrEP7kaAW3w3zEWe12vGmux0tnT+LNGB+qGczPmEgJQa3sLFu2DM8//zweeugh520LFizAypUr8dvf/hZ33XWXYhdIROeF0jU4lK7Okeincm9mEXYIJEv3Zha5fS66dSO34FipVYgX8i/ErTWfBXycF/Iv7HHbWlM1tpnr3U47rTFVY4YCw0PVEOzPmChUQa3sfPPNN5g6dWqP26dNm4YTJ06EfFFE5J1I0zdfHMekgxFKPxXRfkAGnS5ggfMoQ4bXLSPH1s241CxcmZzmNaGTW3Cs1CpEdlISsrX+/67M1iYgOynJ7ba1pmq84ZHoAN3H6d8w12Otj2P00bwSFMrPmCgUQSU7JSUl+OCDD3rc/sEHH6CkpCTkiyIi37pXMoI79RRsV+cbU7ODitvf1og7T32Gh05/jaVnvsdDp7/Gnac+87mltjS/v8/XFmoPFseqmD+uBc2zM8T6BYnEvVUyyGfC463PjtVux7YADRq3eUlk1pqqMbmqEq+YqrGjuQGvnPvcV2KkhnD+jIl8CXo21oMPPoijR49i5MiR0Gg0OHDgADZu3IiXXnpJ6WskIhdrTdXCTf48BdvV+YvOduG4K3XdxbHB9gNamt8f7TYb1jXW4GSXBX0T9Lg3syjkv/blFjTvbm8Uetzd7Y24LTHwKbS3SgbhrNWKhXXfoMHeiRxtIl7Iv7DHig7QXfsUaD3Gfi7OURTsWAnyFue4PVq2vsL1MybyJahk55e//CUKCwvxwgsv4E9/+hMAYODAgXjjjTdw0003KXqBRHSeyF/8/lyaaAjq6+TWu4Q6Gd6g04Xl6LFjirlI9+lw1CllJyUJHS8/2dkh9HiOONGVoHuMhT2Og6slXD9jIm+CPnp+88034+abb1byWohijk2SItrTROQvfn/eaT0b1PRpufUu0TIZ3tvPR7SgOZQ6pVBpIPYecsSJrgStMVWjLDk1Yv13iKIFmwoSBSnS86mA0KdHB/v1lyelQIue08Jdac/FAdExGT7QzydQkvWjpHS8IvA8P0pKD/FKe7osSWwFzhEn+nPd0dLgPA0VK/2XiJQgvJ6ZnZ2NM2fOAACysrKQnZ3t84Mo3jnqUTxXL5ToaeNPqKsIwX79p9Y2oZWDT61tANSfDK/Ez+fXZ74Vei7RODkKEsV+To64YH6u4X6vEkUT4ZWdVatWIT093fn/NVz+pF4q1HqUUIhMmfbF2/RpUXJXatScDK/UzyfSnY9dyV1JC+V9Ea73KlE0EU52Zs2a5fz/s2fPDse1EMUENetRRKZM+zLCkBF0carclRo1J8MrNT8sRaNDsxQ4fUjRiJ8gEq3xkrOSdmVyGpK0WowwZAR1Si8StVNEagvqX76PP/4YlZWVzs//8pe/YPr06Xj88cdhlTG5d82aNRgyZAgyMjKQkZGBESNG4N1333XeL0kSFi9ejOLiYhgMBlx33XU4fvy422NYLBbMnz8fubm5SE1NxbRp03Dy5MlgXhaRkPousfe4aJxcvqZMB/KltT3oaeiip7hc49Sawi1nfpi/3j93Cq6CicbJ6Tkk9z1mkyR8aRVrD+BNOGuniKJBUMnOfffdhy+//BJAdzfl22+/HSkpKdi2bRseeeQR4cfp27cvnn32WRw5cgRHjhzB2LFjcdNNNzkTmueffx4rV67E6tWrcfjwYRQWFmLChAlobm52PkZ5eTm2b9+OrVu34sCBA2hpacGUKVNgs/mfgUMUrKYA85XkxgVjblYx3i0ZjHlZxZienoPpAr9wfXVQFulw/E7rWaHr8owbnZKJ1/sMxKqCi/Bkbj+sKrgIr/cZqHii49o1+FiH/C7R3upXSg1iW2wicXJriOS+x0RWs/wJV+0UUbQI6jTWl19+iSuvvBIAsG3bNowZMwZbtmzBwYMHcccdd+DFF18UehzPkRPLli3DmjVr8NFHH+Hyyy/Hiy++iCeeeAK33HILAGDTpk0oKCjAli1bcN9996GpqQnr16/Ha6+9hvHjxwPoHlJaUlKCPXv2YNKkSV6f12KxwGI5f3rBbA6uQRv1Tkat2LaFaFywXKdMf9BqEpo55PkXvOiJslB6zoR7Cre3+VHBcq1fUaruKJgaIrnvsVBWZsJVO0UUTYJa2ZEkCfZzbcr37NmDH//4xwC6x0g4TmzJZbPZsHXrVrS2tmLEiBE4ceIEamtrMXHiRGeMXq/HmDFjcOjQIQBARUUFOjs73WKKi4tRVlbmjPFmxYoVMBqNzg+OuCA58hJ6dryVEyc6KyqQYFYzTLYu5/PubRVfbVCz54w/vuZHBct19UtkDplI3ZGcGi8Hue+xUFZmlKqdUup9TRQOQa3sDB8+HEuXLsX48eOxb98+rFmzBgBw4sQJFBSIzZNxqKysxIgRI9DR0YG0tDRs374dl19+uTNZ8Xy8goICfPddd9FjbW0tkpKSkJWV1SOmtrbW53M+9thjePjhh52fm81mJjwkLJS/+JXqzRPsasYrLjOSAv2l47raMEafgVcQeL7SGH1wM7uCEWo3aV9c62XkdFz2JZieQ3JrpETek56nu5Tss6NGzykiOYJKdl588UXcfffd2LFjB5544glcfPHFAIA333wTI0eOlPVYl156KY4ePYrGxka89dZbmDVrFvbt2+e83/OIuyRJAY+9B4rR6/XQ6yP7FyjFD51Gg7GpmX5PRI1Nzezx13Kws6I8+ZqBJFegRMn1lM4zDd8LPeYzDd/j5eIBIV9bvcWC+XVfo8neBaM2AS/nX4Q8j/9mRbtJT0/LQVlyKo51tApt9XnWy4xOycQ1yRnY2dKAU10W9EnQY1pajvDJtmB6DsmpkbotIw86jQY5ugS/yc6AxGTMze6jeLdvpd7XROEUVLIzZMgQt9NYDr/73e+gkznILSkpyZksDR8+HIcPH8ZLL72ERx99FED36k1RUZEzvq6uzrnaU1hYCKvVCpPJ5La6U1dXJzvpIhJlkyR82NroN+bD1kb8IrPI+ctEqd4v4VrN8MWx2nDaLnY6SDTOn6nfV6LF5ch3h70TM2s/R5pGi7f7DXbeLtwNWgOMS81Cl10CEDjZSfc4Su5t1eJP5nrhVYt+WrEtKdc4uTVS7TYbPg9wGuuLzg5cmmiAQcH6KTV7ThHJEVTNTlVVldvx7n/+858oLy/Hq6++isTE0Kr6JUmCxWJB//79UVhYiN27dzvvs1qt2LdvnzORGTZsGBITE91iampqcOzYMSY7FDbB1GAE8zXehDobSy7HakOB4C9s0TgHzzqPKd994pbouGqR7Jj6/fk/suTWETVLYiecXOOU6MT89BnfvYZ8xcl9besaa4TiReNEKfW+Jgq3oFZ27rrrLtx777346U9/itraWkyYMAGDBg3C5s2bUVtbi6eeekrocR5//HFMnjwZJSUlaG5uxtatW7F3717s2rULGo0G5eXlWL58OQYMGIABAwZg+fLlSElJwV133QUAMBqNmDNnDhYuXIicnBxkZ2dj0aJFGDx4sPN0FpHSgumzo9SsKNFp2EpwrTtakluKW2s+C/g1S3JLhR/f24pJIC2SHfUWC/L0eqGuwa5do9M1Yn/bOeKUWrU4bRNcFXOJuzE1262+ypcbU7vH85wUXAkSjROlds8pIlFBrewcO3YMP/zhDwEAf/rTn5ynn7Zs2YKNGzcKP87p06fx05/+FJdeeinGjRuH//f//h927dqFCRMmAAAeeeQRlJeX4/7778fw4cNx6tQpvP/++86xFUD36Irp06dj5syZGDVqFFJSUvD222/L3k6j0Kl5GiOSzx1Mnx2lZkWJTsNWguspne8Ft6dE43ytmIiYX/c1gPPdpP2ZkZHnrK0R6YbsGqfUqkWqYIdl17gvOsUaBDri+gquBInGiYqGnlNEIoJa2ens7HQW+O7ZswfTpk0DAFx22WWoqRFfJl2/fr3f+zUaDRYvXozFixf7jElOTsbLL7+Ml19+Wfh5SXlqnsaI9HMH02dHqZ4totOw5RA5paPkX/AiKyb+NNm7nP9/blb30XDPk2ladCc6jvsB+T+3051iqyCnOy2AnzqYW9JzsLIx8Ou9Jf18Y0i5K4H3ZhYJFV/fm1kUMEaOaOk5RRRIUMnOoEGDsHbtWtx4443YvXs3fvvb3wIAqqurkZMT3KBBil1qnsZQ47mD6bOj1Kwo0WnYclyTnI4Zxny/p3SU/As+1G6/Rq37P1tzs4pxj7Ew4GkpuT+3QAW/Dp9b2+G9fWm3Er1YguoaJ3cl0KDTYVSA2VijDBkwKLziHWrPKaJICWob67nnnsMf/vAHXHfddbjzzjtxxRVXAAB27tzp3N6i3kG0riEc20pqPbdjlcYfb6s0SsyKckzDVtJHHc24PCkF41KzcGVymteES269iz+hzmF6Of+iHrc5ukkvyO6L21y2rlzJ/blJEHvfBIoL5v0SzNcsze+PUQbvfY5GGTKwNL+/38cLRrD/LRBFWlArO9dddx3OnDkDs9nsduT73nvvRUpKimIXR9FPzQngaj23TqNBUoAVmCSNxmvSMDolE6MMRqHJ196ITMOWy47uU163+al/kVvv4k8o3X7TNNoe/XYAsWniclfX+iYmC11ToLhgVvVEvub+rOIer3lpfn+022xY11iDk10W9E3Q497MIsVXdORcZ7im2xPJEVSyAwA6na5H5+ILLrgg1OuhGKPUKaNYeu6Wri6cClCbcqrLipauLqQl9PxPLJRZUeE61RKor4uStRmi3YE9efbZcZBTsyWnI7Lc017+BNOJ2d/XjE3NxO9Nvh9rQU7fgNekFCW6TBOFm3Cy84Mf/AAffPABsrKyMHToUL8dij/++GNFLo6in1KnjGLpuZ9tqBKOW1qg7NZBuE61BOrromRthmh34ER0rzoZoMW6/ItRZOiZJAVTsyW6uuY47eWvW/UMH1tm3gSzquftaxptnVhypmdHazU7Foe6YkkUbsLJzk033eQ8gTV9+vRwXQ/FGKVOGcXSc5/qEut1IxonR7hOtUwwZPq9X8nvtWh3YMcztcCOn9R92eN0VSh9cERX1+Sc9hIRzKqe69fYJAl3nvLf70itjsXhnm5PFArhZOfpp5/2+v+pd1Nzz16t507TJAAIvJ3UHaescJ1q2Wg+7XfrQ6fR4JIkA+rbfSc7lyQZhL7XwUxGtwPOFRZHghHJmi3PEmS15nmrWSNHFMtCPtjR0tICs9ns9kG9ixKnjGLpuccLPqZonBwip1+C8X2AzsxWux3/8HOsGQD+0W6G1R64QHlaWk7Q//BsM9c7n0O0FquivTnoZpOOoavekp03zPVYK9Dl2JXVbseb5nq8dPYk3nR5LaLUrJEjimVB/el54sQJPPDAA9i7dy86Os7/I+mYNm6zsVtmb6Pmnn2kn/tbm9g2jGicHCKrWcGw2v0nASIzuUROdQFitTAizyFai7XZXOf8/3KKZkWGrm4z1+MeY6FQ3c5aU3WP7bA1pmpZ22Fq1sgFInIijkgtQSU7d999NwDgf//3f1FQUOC3WJl6DzX37CP53Er1XwnWp5Y2xR+zf4BmhXKncAfiqxZGznOI1BF5klPEq2SC51gh8vb1nttz/qhZI+ePmh3UiUQElex88sknqKiowKWXXqr09RBFRCh/heZpxf5qFo2Tw2q3B7UiEsiJTgt+dfprn31Z5E7hFuHZ+bi+sxMHOwJvgzueI5RVLpEiXqUSPCVXiKKxr42aHdSJRAW1dX7VVVehqkrs+C1RtNnf1og7T32Gh05/jaVnvsdDp7/Gnac+w/62RqGvPx5g8KPcODm2hyHRAYBjnW040tGCHS0N+PHJY3iy7oTb/SJ1NqI9Z1y5dj5+Kq9U9nP4qtkKRGSAp1IJnpwVIhFq1sh5UrODOpEcQa3s/M///A/mzp2LU6dOoaysDImJ7v/RDRkyRJGLI1KaEn+F1tjEGvuJxslxIECRsFIOtpvxZN0J54gBpXvOeBPsc3jWbH1n7cBrLnU6vgQq4lWqqaDSW4BA9PS14ekwihVBJTv19fX4+uuv8fOf/9x5m0ajYYEyRbVQ+rK4StGI9boRjYtWB9vNaLfZnFtaSvecAXpuJ/7i3FRuuc/hWrN1VNcilOwEKuJVKsELxxYgEB19bXg6jGJFUMnOPffcg6FDh+L1119ngTLFDKX+Ch2sT8WnnYEnYoejSLR/gh7HrMoXKPuyrrHGrf+O6IRxEf6KWt8tGRz0cyhZxKtEgqfk2IloE82nw4hcBZXsfPfdd9i5cycuvvhipa+HKGyU+is0UfCXrmicHIOSU/F2m0nxx/XlpJetFUedTSgCbyeWBv0cShfxhprgRWILUC3RejqMyFNQ/3WNHTsW//73v5W+FopxoTZMC7csrVhuHyhucFKK0OOIxjnYJAlHO1r8NsATfQ1K6RtEt+NAIlHU2l3TkuH1vlGGDNlFvK6F1LfFaGISDo7E0h9OPadoENS/nFOnTsVDDz2EyspKDB48uEeB8rRp0xS5OIodSjRMCzfRX52B4k4IFpKe6LLgh4LPKdqnJFIFyg73nquhUVIkilrXmqpx0Mf36mC7GWtN1RF7XyrdnDDacOo5xYKgkp25c+cCAJ555pke97FAufdRqmFauDXauxSJqxU8ZSUaJ+eEWLWMEzuhGmXI6NFvRwmnO8Vew+lOCxBEshNtyYWSzQmjVbScDiPyJaj/0u12u88PJjq9i+gvlmjY0lKqmLJAsFmgSJzcLZ3kCJ3wGmXIcB47V9rn1sDF3XLiPCnd1wYQ22L0JRxHz6OR43TYuNQsXJmcxkSHooqsZOfHP/4xmpqanJ8vW7YMjY2Nzs8bGhpw+eWXK3ZxFP3C8YslXESGaEa6mFLOlg4AjDKkh+U6CrQJGJ6chulpOfhb37KwJTpA+MdtKJ1chNqEMlxHz4lInKxk57333oPFcv4fiOeeew5nz551ft7V1YUvvvhCuaujqBdLf7UqVUx52i52qkskTu4JsfwAM6yCNTg5Db8ruAgLcvqGZevKVd/EZEXjPCmZXDi2GD0TUscWo0jCE67u00QkTlayI3ks3Xp+Tr1PoS5J0bhwU6LVfpHgaxGJk7u11hWm7cD+EVxVCPcv/xtTsxWJU+rUmOPouT+xevScKFZE9hwrxZ2LBP/6Fo0LxNcATzmDPUMtpuwv+Fp8xblea6Y2AekaLZol30lMhlbn3Fr7oL1R6LnlapMxezyUIapA+PvOfCHQ8NERd6XOdwG0kqfGwtF9mojEyUp2NBpNj27J7J7cu5kETziJxvnj63j22NRM7Gk+iwbpfHF8jkaHB3P7+lypCaXV/lmb2GvxFre/rREvn63GGZfXIOe/oLYwreyc7rTig1ZTwORF9Ih8IOH85V/fJXYKLlCcUo/joGT36WgUahJMFE6ykh1JkjB79mzo9d1L3h0dHZg7dy5SU7v/6nSt56HeockudvpONM4Xf8ezva0QNEg24cGecn0uOK7hc2sbJuH8Vomv1xBoM9hstzlXD4boU332jwnFnvYm7GnvPnzgK3lRYoiqq3D98lfqPRmO97YS3aejkVJJMFG4yEp2Zs2a5fb5T37ykx4xP/vZz0K7IoopRq1YMatonDcitRO+PHumCqNK/A/2lH09gr/cXONskoQXGk4G/ZyOAuUfp2ZjTWNN0I8jwlvyotQQVU/h+OWv1HsyEu/teKB0EkwUDrKSnQ0bNoTrOihG5SWIFeuKxnkjUjvhS7tkx7/amzE8xfvogGDUC25jucYd7WiBOYTVrcxzYyLeaT0bIFI5rslLJLoeK0Wp92Qk3tuxLlxJMJHS4mOzmFQTid41osezfXmvVdnBmbVdYtfjGvfvjpaQntPxa+LYuX47keDa30epIaqRoNR7Mhr7MkUbuX2iiNTCZIdCEolBgKLHs33p8HPSKRhdgieXXONCbdLwr3Pde9tDrH2Sy5G8KNV9OhKUek9yyGVgsZQEU+/GZIdCpkTvGn/660LrAVPm5S/vUNr/X5koNs3cNW5oiFs7m811WHrme1RE+C9kx/ZZrK1yKPWeDPd7O9bFUhJMvRv77JAiwjkI8Hdngy/s1QC4OT3X7bZQT44UJSUDHU1icedckZwGg0aLdoVXmcLN8dNzrHJ4K0R1iLZVDqXekxxy6ZsjCfa3lRVNSTD1Xkx2SDGh9K7x51RXR9BfO9OjOZ0SJ0dqBKeZe8YlajRoj7Gm4649ZLpXORBTR4yVek+G670d62IxCabeSdVtrBUrVuCqq65Ceno68vPzMX369B6ztWbPnu1sZuj4uOaaa9xiLBYL5s+fj9zcXKSmpmLatGk4eTL41QCKLmka+Tm5FsDtHs3plGr//7VFrEOva1ylpTWk01hq+czi3lNodEomXu8zEKsKLsKTuf2wquAivN5nYFQmOhQZ3OqjWKDqys6+ffswb948XHXVVejq6sITTzyBiRMn4tNPP3U2KgSAG264we3Ye1KS+1HP8vJyvP3229i6dStycnKwcOFCTJkyBRUVFdCFeaghhd8Nadk4ZgrcyO+65AxkJib6bE6n1PFp0aTFNU60QPOnGfkoTUrGd9YOvGauE/qacDrj5bq5ykGeuNVH0U7VZGfXrl1un2/YsAH5+fmoqKjA6NGjnbfr9XoUFhZ6fYympiasX78er732GsaPHw8A2Lx5M0pKSrBnzx5MmjSpx9dYLBa3bs9ms/IdaXsjq90ellb4fZLECpRvMub5/SWs1MmRRK0WIgeyEl1eu2iB5g8M6bgyOQ1HdS1Rkeyk8I8FEsQkmKJZVJ3GamrqLvrMznafRrx3717k5+fjkksuwS9+8QvU1Z3/JVBRUYHOzk5MnDjReVtxcTHKyspw6NAhr8+zYsUKGI1G50dJSUkYXk3vstZUjclVlXjFVI0dzQ145dzna03BdT52pdRJIKVOjkwUXJZ3jbs8KUVo0vflSd0nuERecyRcb8hU+xKIiEIWNcmOJEl4+OGHce2116KsrMx5++TJk/F//+//xYcffogXXngBhw8fxtixY50rM7W1tUhKSkJWVpbb4xUUFKC2ttbrcz322GNoampyflRVVYXvhfUCa03VeMNjoCPQvfjxhrk+5IRHp9FgbGqm35ixqZkBl8yVSppuNeb7vd9b3KfWwHPF7efigO7XfEmSQeh5wqmqi/PuiCj2Rc1prAceeACffPIJDhw44Hb77bff7vz/ZWVlGD58OEpLS/HOO+/glltu8fl4kiT5nMiu1+udw0wpNFa7Hdu8DOJ0tc1cj3uMhUFvadkkCR+2NvqN+bC1Eb/ILPKb8Kh5ckTuFprVbsc/wjDwU65qJjtEFAeiYmVn/vz52LlzJ/7+97+jb9++fmOLiopQWlqKr776CgBQWFgIq9UKk8l9JEBdXR0KCgrCds3UbWdLg9CKxc6WhqCfQ8mW9EqcHBF9La5xcrfQRL6vkaABC0yJKPapurIjSRLmz5+P7du3Y+/evejfv3/Ar2loaEBVVRWKiooAAMOGDUNiYiJ2796NmTNnAgBqampw7NgxPP/882G9fgJOCf7lLxrnzelOsa893WkBBAokQz05crJTrO+Pa9xgfSoytDq/J7kytDrnFloo3y8lRcNWGhFRqFRNdubNm4ctW7bgL3/5C9LT0501NkajEQaDAS0tLVi8eDFuvfVWFBUV4dtvv8Xjjz+O3Nxc3Hzzzc7YOXPmYOHChcjJyUF2djYWLVqEwYMHO09nUfjkCfbAEY3z5nOrWF+b/6/NjAStVih5CeXkiOhqRyirIkW66Jik3RpjHZ+JiLxRNdlZs2YNAOC6665zu33Dhg2YPXs2dDodKisr8eqrr6KxsRFFRUW4/vrr8cYbbyA9Pd0Zv2rVKiQkJGDmzJlob2/HuHHjsHHjRvbYiYDaLrFuwqJx3kiCYzQPdphxsKO7ziWcXX0vS0oBEHgr67Kk87OxRJoKmu02Z4+f/onJfmMjxajlf0NEFPtU38byx2Aw4L333gv4OMnJyXj55Zfx8ssvK3VpJKjGLlZ4KxrnTd8gfvHLGf8gV0Gi2KqLa5zcAmWzFB3dlvMSomOFiYgoFFFRoEyxq1hwu0U0zptpaTlBv1FFxj/IFcwRdrkFylla9Q9KcoAjEcULJjsUkpGGDEXjvEnSaoMulPV1Sstqt+NNcz1eOnsSb5rrYbWL16aI9MC5JMngVjMkN0GKhnmhHOBIRPFC/T8fKaY12rsUjfPGarfjS8EiZW88t5DWmqqxzaMJ4hpTNWZ4DA71dz2BeuD8o90Mq93u7C0kt8fPWcFtr1BoAYwwZOBLa3vMTDEnIgoGk51exCZJig/qEz0p9bm1HT2nlIkJteeM6xaSo9uzJ0e3ZwABEx45vYVuy8iTebXdmiIwId0O4GC7GU/n9kOmLpEDHIkobjHZ6SX2tzVi9dlqxf+C7+wSW7ERjfPmuxBWdVy3hpTq9hxMbyGbJGH1Wf9jM1afrcYogxE6jSaip6B+b6rB630GMsEhorjFmp1eYH9bI56u/65HF2LHiaX9bY1BP/ZxwQZ7R6xt+KDVhKMdLbILhr+1Bt9gz7V2Rqluz30SxEaNuMbJ7QIdyVNQot2niYhiFZOdOCe6ohDsiSVJcIPptL0TS898j4dOf407T30mK8FKCGHBwVE7AyjX7fmGlCy/93uLk3v0XGRKupJEr4+IKBYx2YlzSs6V8qYjiBxJ7opSVwhHk1xXaoJZkfFmV5vJ7/3e4uQePReZkq4k0esjIopFTHbinNwVBbkKdMGXfYmuKBWG+DZ1rNSI9OvRnovzp8oqtnXnGif36HkkV1rYT4eI4h2TnTgnd0VBLnsI859EV5Q+6mwL+jmA8ys1Iv16Lkky+C1OBoAGm1ixtWuc4+i5P65HzyO50sJ+OkQU75jsxLlguv3KUaIJ7S0ksoLRGeKGzo2p2QDE+vV8aW0P2GAwSzAx8IwbnZKJJXmlPX4eebpELMkrdTsVJ/JzC5W35yUiikc8eh7n5Dazk+uzELdbRFYwMrWJOB3CbK0vOttxpS5Nsf44JsFibm9xo1MyMcpgDNjvSKfRYGxqpteeQKH4gT4VP07PYT8dIupVuLLTC4xOycTtGXk9fthaALdn5IX0l701hGRHdEXpuax+QT8HAJyydK/mKHUaK0ewTslXnE6jwZXJaRiXmoUrk9O8Jhw2ScKHrY1CzyPHnRn5fp+XiCgeMdnpBfa3NeINj/EIwPmuwaH02TkdwhaT6IrSf5lrgn4OANh1LmlQ6jRWkWAPHNE4b0RO0cll0Ggx1JCu6GMSEcUCJjtxLtx9doJZG5BbK3Labg3iWc4zn5vLpdRprEgIx2msX+eWcDWHiHol1uzEOTl9dq5MTpP9+OkaHUxS4DlOadCiPLdvULUiBdok1AuegPKm+dycqSStFjMy8vzWwczIyAt4Gku0fiiUOiMlT2PlaHR4MLcvC5GJqNdishPn6rvEVkVE4zy9kFWKe85+EzDuv7MvQP/U4LZQluSW4taaz4L6WgBIc1l/cgz59Jx6rgWEp54XaMUSEdE4bxynsYLdyvpFRgEKkvQsRCYiApOduCc6PdtbnNVux86WBpzqsqBPgh7T0nJ6rHqsbK4VevyVzbV4OT24ZOf7ELexdB5DNedmFeMeY2HA16YmkVN0/iRotRiXKjbWgogo3jHZiXOi07M949aaqnusfqwxVfdY/RCtpwml7ibU+pVB+p6NBJO0Wr/Hy/2JxDYW4OjLgx7T6kXU2EJLEImI4gmTnTiXLVj74Rq31lTtta7FcXoLOL8dJFpPU6AN/mRSeoh19H0Tk0P6ek9KneoS4dmX51hHK3YEmMqu1HMTEcWL6Fm3p7CQBE9ZOeKsdju2BWhkt81c7+wy/GR2idDji8Z5sy/EfjOdAToiyxXpU12ufXl+mVUcMyfKiIiiBZOdGGaTJBztaMEHrSYc7Wjxenz834LTzB1xcroMA8DW1jNCjy8a580/LS1Bfy0AfNjR1OM2ke+dL0laLUYYMvzGjDBkhKUGyHGizB+RE2VERL0Jt7Fi1P62xh61HHm6RDyQXex2xFj0V7gjTm6X4W8t/mdNOYjGhUOX3f27IPq988UmSQEHmFZaWmGTpLCcglLiRBkRUW/CP/9i0P62Rjxd/12PotV6Wyeerv/OrSNyhmCBsiNObj3Kd4LJkWicN9cE0f/H1dDk8yMp5HzvfDna0QJzgFNuZrsNRztCW5HyZ25WMd4tGYx5WcWYnp6Deec+Z6JDRNQTk50YI7cjcpbgHCdHnNx6FEmgoaCcOG/+T2Zov8B/mdUHgHLdpP/V3iz0vKJxwXKcKFuQ3Re3ceuKiMgn/usYY+R0RAaAPMH5TI44uTUhHYIDI0TjvNnd3hj0144yZMCg6161kvu98+W04DFw0TgiIgovJjsxRrTnjCNusD414FZWhlbnNn18blax3ynprlslqRqxt5BonDeidUSesrUJWJrf3/m53O+dLwWCCaRoHBERhReTnRgjOjPJNa4lQH2Jt/vnZhXjr33LMD0tB8OT0zA9LQd/7VvWoyYkUzCJEY3zJk8TXB39WXsXnqw74fw8mO+dN0MFa4hE44iIKLyY7MQYx8wkf/LOzUMCgMOtTUJHyQ+3uh/P3t/WiFk1X2BHSwOOdLRgR0sDZtV80aOA92vBLsGicd7UhtAN+GC7Ge227mRO7vfOlyuT04RWy4IZrEpERMpjshNjHDOT/Hkgu9h55Pl/zaeFHtc1TokTS0qqCbH2ZV1jDQD53ztfdBoNFub09RuzMKcvh28SEUUJJjsxqHtmUmmPVYo8XSKW5JW69YppFhwE6ohT6sSSkvqGOPrge+v5Hj9yvnf+OB4nN8THCUUojRGJiHoTNhWMUZ4zk3LObb94riZkaxNQK7Aykq3tfivIObF0ZXIaijU6VAscKy/WiPX78ebezCKheVC+WD1yANHvXSBKPU4wQm2MSETUm3BlJ879ODVLVpzcE0t3ZOQLxYvGeWPQ6dAnhJNNFyT1XBlynTd1ZXJa0AmKUo8jR7RtMxIRRTtVk50VK1bgqquuQnp6OvLz8zF9+nR88cUXbjGSJGHx4sUoLi6GwWDAddddh+PHj7vFWCwWzJ8/H7m5uUhNTcW0adNw8uTJSL6UiNvf1og7T32Gh05/jaVnvsdDp7/Gnac+6/GLrjBJbOK3Iy5TK7bY54gLZqq6XFa7HTVdwRcplyg89VxN0bjNSEQU7VRNdvbt24d58+bho48+wu7du9HV1YWJEyeitfV8U7fnn38eK1euxOrVq3H48GEUFhZiwoQJaG4+3522vLwc27dvx9atW3HgwAG0tLRgypQpsNmC79obzeT8ZS+6zqDx+F/R+K3N/iekO4jGeSMynNSf/nGU7CjVGJGIqDdRtWZn165dbp9v2LAB+fn5qKiowOjRoyFJEl588UU88cQTuOWWWwAAmzZtQkFBAbZs2YL77rsPTU1NWL9+PV577TWMHz8eALB582aUlJRgz549mDRpUsRfVziJ/GX/csMppGp0aLR34at2sV96pzstgCFd9jaW6IpLKCszVdbQhoiaQxhVEW2UaoxIRNSbRFXNTlNTd6+X7OxsAMCJEydQW1uLiRMnOmP0ej3GjBmDQ4cOAQAqKirQ2dnpFlNcXIyysjJnjCeLxQKz2ez2EStE/rI/Y+/CorpvsPTM93ijVayw92B79/fAZOsSinfEtQsmEqJx3tR3hfaLOz263uYhUaoxIhFRbxI1vwUkScLDDz+Ma6+9FmVlZQCA2tpaAEBBQYFbbEFBgfO+2tpaJCUlISsry2eMpxUrVsBoNDo/SkpKlH45YROuv9gdCZTZLpbsOOLEogELEPQR6SbB4/O+HGiPnWQ2EKUaIxIR9SZRk+w88MAD+OSTT/D666/3uE/jccJFkqQet3nyF/PYY4+hqanJ+VFVVRX8hUeYaAGxXCnOcQ7yqnZEkx0b4LeQ2p9Q36TVQc7WikZKNUYkIupNoiLZmT9/Pnbu3Im///3v6Nv3fGfawsJCAOixQlNXV+dc7SksLITVaoXJZPIZ40mv1yMjI8PtI1aE61dY8rlfjoHGIDg44oK5HrlHpC8QPFHmS3IIPX6ikVKNEYmIegtVkx1JkvDAAw/gz3/+Mz788EP079/f7f7+/fujsLAQu3fvdt5mtVqxb98+jBw5EgAwbNgwJCYmusXU1NTg2LFjzph4YhLcZpLLsTmWpRNbORKN80f0iHRZiFsyowzpIX29P2p1MR6dkonNxZdhXlYxpqfnYF5WMTYXX8ZEh4jIC1VPY82bNw9btmzBX/7yF6SnpztXcIxGIwwGAzQaDcrLy7F8+XIMGDAAAwYMwPLly5GSkoK77rrLGTtnzhwsXLgQOTk5yM7OxqJFizB48GDn6ax4YgzTKoWjF02eYPM+R5wBQEuQz+naidmfgsTQxkUUhbgy5IuaXYz3tzXivxtOocEl+d3aVIcHc/ow4SEi8qBqsrNmzRoAwHXXXed2+4YNGzB79mwAwCOPPIL29nbcf//9MJlMuPrqq/H+++8jPf38X+urVq1CQkICZs6cifb2dowbNw4bN26EThdf2xcA8HVnR1ge997MIgDdBbBJ0MAK3ysUSdA4C2BD/Q6LFFw7inIDnULzJkOrC0uxrqPXkSfHFt2SPIQt6fD13A32rrA/NxFRLFI12ZEElvw1Gg0WL16MxYsX+4xJTk7Gyy+/jJdfflnBq4tONWEqtrXa7TDodLDa7X4THQCwQnLGd0IDBIj3R+SItE6jwSVJBtS3y092OsOwrSTaxXiUwah4obBNkvDsGf8F9c+eqcKoEuWfm4goVkVFgTKJ04SpRPnJ+m8BAK80iI3ZcMQZQrge0SPSVrsd/wjy+Hi7ZMe/O4LdaPNOzS7GH7c3o13y30+6XbLj4/ZmvzFERL0Jk50Yc1mSQSjuzvRcPJnbD5mCNT6n7d0djv9pEUsMHHF9Q6inET0iHeq4iH8pnOyo2cV4d6spcJCMOCKi3oDJTowRLdb9YYoR41Kz0DdBLL5A211wLLrp44hLDeJUltwj0qdC3LpTei1MzS7GbQFWdeTGERH1Bkx2YszlSSkBf2jac3EA8FROP6HHdcRdkyx2TNsRNzBRbKVpSrIRT+b2w6qCi/B6n4GyCmiLdGInxHy5IsBpL7nU7GI8RPAxReOIiHoDJjsx5lNrW8AtHfu5OADYZxGrdXHE3Z/dRyjeEVdnF9uq0ei0GJeahSuT02QXzoYytTxDqwt4tF0uNbsYT0/PDbhSpTkXR0RE3ZjsxJjTnWLTwx1xX7WJ1as44pK0WqFfpkna7rfOyU6xLSbROG9CmVq+MKdvWJIOtboYJ2m1mJmR5zdmZkae8+dDREQqHz0n+SoFTyVVtpsxKT0b+y1ip3L2W5rxGIAjbeaAdTvSubirU40w2cQSEdE4b4KZBxaJ5n6jUzIxymBEpaUVDbZO5Jzbugr3ke+5Wd2rStvM9W6rfFoAMzLynPcTEVE3Jjsx5ojgcWZHXJdgybEjbltLvVD8tpZ6XJ1qRKZW2z3lM4DMEFYaRFOHX2YWISchMWJJB9C9paX0NpmIuVnFuMdYiJ0tDTjVZUGfBD2mpeVwRYeIyAsmOzGmUzB5MdtteOnsSSRAI5TwpJ87ot5sEzvF44jrEkxFROO8EZ0H1my3IQfKn4CKVklaLW4LsKVFRERMdmLOhVo9ztrbAsa1Q8KO5gbhx30hqxQAcGlSMr7sbA8Yf+m5eVMlGi2OCTx+iSb4FQfRI9ybzXXO/x+pGVVERBT9uOYdY1pF9oyCsLK5ewjrKINRKN4R95lg4zzROG9Ejnp7csyo2t/WGPTzRjur3Y43zfV46exJvGmuh9XO3jpERN5wZSfGnLGHJ9lxzNwSPfnkiJMEd6dE47xxHPX2NvwykHDNqFLbWlN1jwLlNaZqFigTEXnBlZ0Ykyo4/kGu1nMdd5sEkylHXLrg9YjG+eLrqHcg4ZpRpaa1pmq84ZHoAN39ld4w12Otyf+QUiKi3obJToy5JS0nLI+bfK6IWW7yckNqplC8aJw/o1My8XqfgVhVcBGezO2Hu9PFinPru8R6E8UCq92ObWb/J+a2cUuLiMgNk50YU5IsNp5Brq5zb4VmwW0sR1wfvdj1iMYF4jjqPS41C5kJYqs8oqtVsUBkKKr9XBwREXVjshNjRGZjBSP7XH8Wo1ZsZccRp+acKLnXGg9OdnYoGkdE1Bsw2YkxIrOxglFybnBoXoLY0E1HnE6jwdgAW1RjUzPDUiAs91rjgUawX5FoHBFRb8BkJ8Y0hHCE259f55QAkL9SY5MkfNja6Df+w9ZG2CSxZohyyJ0AHw8uSxLbDhSNIyLqDZjsxBjRBnty9ElIQlpCdxcCuSs1lZZW1AdIwMJ1IkruBPh4UJCoVzSOiKg3YLITY8JRs2OVJOfKi9yVGtGVpnCsSNVYxepSRONigZo1UkREsYrJTowJR82O68qL3JUa0ZWmcKxIHWwXm+guGhcLHA0W/XkguzjumigSEYWCyU6MCVfNjuNxRXvSOOIuTkgWiheNk6ND8Ji8aFys8NVgMU+XiCV5pZwHRkTkgeMiYkw4VkhcH1duB+V1jWLdetc1VuPh3H7BXZwPJYnJqBCoBSpJVD7RUtvolEyMMhhRaWlFg60TOee2rriiQ0TUE5OdGOOo2VFyK8u1xiNdcDq5I+5fHS1C8aJxctybWYQdAs3z7s0sUvy5o4GjwSIREfnHZCeKWO127GxpwKkuC/ok6DEtLQdJWvfkIxw1O66nq5olsUd3xHUKHikXjZPDoNNhlCEDB9vNPmNGGTJg0MVPU0EiIpKPNTtRYq2pGpOrKvGKqRo7mhvwyrnPPYc6BlOzE+iH7Hq6KlVwZccRV6gRy5dF4+Ramt8fowwZXu8bZcjA0vz+YXleIiKKHVzZiQKOKdaeHFOsAWBuVvcJHNGanXlZxcjSJcBk68IrAaZgO05XXZmchi+t7UKP/6W1HZMBaHU6QKDMRxvG1ZWl+f3RbrNhXWMNTnZZ0DdBj3szi7iiQ0REAJjsqE50ivU9xkIkabXOPiv+jofn6RJxc3oudBoNPmg1CV2HY8VIgth2kyPOoBVbCRKNC5ZBp8OCnL5hfQ4iIopN3MZSmdwp1nI7HMvtg9NX8OSSI07NPjtEREQimOyo7FSXRVac3A7Hcjvu3piaLXQ9jrhBerG5U6JxRERESmOyEyY2ScLRjhZ80GrC0Y4Wn4Mw+ySIzTByxMntcCy34+4XnWI1O444zmoiIqJox5qdMNjf1ojVZ6vdkpI8XSIeyC7u0d12WloO1piq/W5lac/FAcHNohqdkonbM9qwzVzv9jxaADMy8tyuSe7ji9YQcVYTERGphSs7Ctvf1oin67/r8cu/3taJp+u/w/62Rrfbk7RazMjI8/uYMzLynP12gqmR2d/WiDc8Eh3g/Gkv12uS+/ic1URERNFO1WRn//79mDp1KoqLi6HRaLBjxw63+2fPng2NRuP2cc0117jFWCwWzJ8/H7m5uUhNTcW0adNw8uTJCL6K82yShNVn/R/zXn22useW1tysYtyekdfjh6EFcHtGnvPYOSC/BkfuNQUzVZuzmoiIKJqpmuy0trbiiiuuwOrVq33G3HDDDaipqXF+/O1vf3O7v7y8HNu3b8fWrVtx4MABtLS0YMqUKbDZIj/8UW49jau5WcX4a98yTE/LwfDkNExPy8Ff+5a5JTqA/JWUcNf4OIxOycTrfQZiVcFFeDK3H1YVXITX+wxkokNERKpTtWZn8uTJmDx5st8YvV6PwsJCr/c1NTVh/fr1eO211zB+/HgAwObNm1FSUoI9e/Zg0qRJil+zP8HU0zh41vkcQQsOtpu91vl0r6RAqC4o2Bof0cd3xVlNREQUjaK+QHnv3r3Iz89HZmYmxowZg2XLliE/Px8AUFFRgc7OTkycONEZX1xcjLKyMhw6dMhnsmOxWGCxnD/ybTb7nq0kR7A9Zxx1Pp4cdT5L8uA14fE19domSc7bTbauoK4pmKnars8brinckXgOIiKKL1Gd7EyePBkzZsxAaWkpTpw4gd/85jcYO3YsKioqoNfrUVtbi6SkJGRlZbl9XUFBAWpra30+7ooVK7BkyRLFr/fSRIPsONGamlEGY49f6t5WUrydBAs0Jd3XaSk5KzVyTqAFKxLPQURE8SeqT2PdfvvtuPHGG1FWVoapU6fi3XffxZdffol33nnH79dJkgSNn7/2H3vsMTQ1NTk/qqqqFLned1rPyo4TranZ3nwmYM8eXyfBAnVoDvW0lNwTaNH6HEREFJ+iemXHU1FREUpLS/HVV18BAAoLC2G1WmEymdxWd+rq6jBy5Eifj6PX66HXK9/kTm43ZEC8psZ1mKe31QyRFSLPFR4lVkVCWZmKpucgIqL4FdUrO54aGhpQVVWFoqIiAMCwYcOQmJiI3bt3O2Nqampw7Ngxv8lOuMjthgwENzPK22qGyAqRHd3T0JU8LRXKCbRoeg4iIopfqq7stLS04D//+Y/z8xMnTuDo0aPIzs5GdnY2Fi9ejFtvvRVFRUX49ttv8fjjjyM3Nxc333wzAMBoNGLOnDlYuHAhcnJykJ2djUWLFmHw4MHO01mRJLcbMiDWgdgX19UM0RWiLF0CxqVmBQ4UFMoJNFH1XVZF44iIqHdRdWXnyJEjGDp0KIYOHQoAePjhhzF06FA89dRT0Ol0qKysxE033YRLLrkEs2bNwiWXXIJ//OMfSE9Pdz7GqlWrMH36dMycOROjRo1CSkoK3n77beh0uoi/HrndkAGxvja+uK5mqDV9PBLP22QX65m0r7UJL509iTfN9bDaA1UqERFRb6GRJB/Vrr2I2WyG0WhEU1MTMjIyQn68taZqn3OoPJsE+vsaEU/m9sO41CzYJAl3nvos4Iyq1/sMVLSuJRLPu7vlLJY3yCsiD/T9JiKi2Cf6+zumCpRjxdysYtxjLMTOlgac6rKgT4Ie09Jy3FZ0XDlmVwXDc0aVt349DuGYUaXTaDA2NdPv9Y9NzQzpefMSkmR/jWPuFwAmPEREvVxMFSjHkiStFrdl5GFBdl/c5rF15UrkpJEv3mZU+ZuxFY5eNDZJwoetjX5jPmxt9HlcXoTIvC5ftnFLi4io12OyozKRk0a+eK7UyJlurpRInJQKpa7JDmBnS0PQz01ERLGPyY7Kgjml5G2aeLAT10MVidNYgO/J6iJE+x8REVF8Ys2OykRPKc3LKkaWLsHnPCg5KyxKDuuM5Ckwz3ldxzpasUNg1Ua0/xEREcUnJjsqE+mzk6dLxM3puX6LfCO1wuJJ9Pq9zd4Khuu8rh8ZjNjZ0iCrrxEREfU+3MZSmUg9isgpqnCssNgkCUc7WvzO5FLq+oMRTF8jIiLqfbiyEwW661EQ0kRvpVdY5EwYV+L6g3W5PiWk+4mIKP6xqSCUbyoYLJskOetRfNXm+OOYDO6LZ1Gz0o8T6vXLpVYjRSIiig6iv7+5vh9FHPUo41KzcGVymuxf0L5OLHk7veWLWqe6gsEBoUREJILbWHHG88SS3BWWYE91ydn2UopaRdlERBRbmOzEIdcTS3IFk0D42vaqt3Xi6frvsCQPYUl41Bp+SkREsYXbWOQmSyuW/zri1Nz2EhkjoeSxdyIiik1MdsiNaEriiFOzbkbNY+9ERBQ7mOyQm0Z7l6w4tetmlCjKJiKi+MaaHXIjtw4mGupmQi3KJiKi+MZkh9zIbU4Y6XERvoRSlE1ERPGN21jkRm4dDOtmiIgo2jHZoR7k1sGwboaIiKIZt7HIK7l1MKybISKiaMVkh3ySWwfDuhkiIopG3MYiIiKiuMZkh4iIiOIat7HIJ5skyarBkRtPREQUCUx2yCu5U8zVmHpOREQkgttYccgmSTja0YIPWk042tEiewinY4q5Z6NAxxTz/W2NIcUTERFFEld24kyoKyyiU8xHGYzQaTSy44mIiCKNKztxRIkVFrlTzNWcek5ERCSCyU6cEF1hCbSlJXeKudpTz4mIiAJhshMnlFphicWp50RERP4w2YkTSq2wOKaY++Nt6rloPBERUaQx2YkTSq2wcOo5ERHFGyY7cULJFRZOPScioniiarKzf/9+TJ06FcXFxdBoNNixY4fb/ZIkYfHixSguLobBYMB1112H48ePu8VYLBbMnz8fubm5SE1NxbRp03Dy5MkIvorooPQKy+iUTLzeZyBWFVyEJ3P7YVXBRXi9z0CfiYvceCIiokhRNdlpbW3FFVdcgdWrV3u9//nnn8fKlSuxevVqHD58GIWFhZgwYQKam5udMeXl5di+fTu2bt2KAwcOoKWlBVOmTIHNZovUy4gaSq+wOKaYj0vNwpXJaQETJbnxREREkaCRJJntdcNEo9Fg+/btmD59OoDuVZ3i4mKUl5fj0UcfBdC9ilNQUIDnnnsO9913H5qampCXl4fXXnsNt99+OwCguroaJSUl+Nvf/oZJkyYJPbfZbIbRaERTUxMyMjLC8voiiTOqiIioNxD9/R21NTsnTpxAbW0tJk6c6LxNr9djzJgxOHToEACgoqICnZ2dbjHFxcUoKytzxnhjsVhgNpvdPuIJV1iIiIjOi9pkp7a2FgBQUFDgdntBQYHzvtraWiQlJSErK8tnjDcrVqyA0Wh0fpSUlCh89URERBQtojbZcdB4rEpIktTjNk+BYh577DE0NTU5P6qqqhS5ViIiIoo+UZvsFBYWAkCPFZq6ujrnak9hYSGsVitMJpPPGG/0ej0yMjLcPoiIiCg+RW2y079/fxQWFmL37t3O26xWK/bt24eRI0cCAIYNG4bExES3mJqaGhw7dswZQ0RERL1bgppP3tLSgv/85z/Oz0+cOIGjR48iOzsb/fr1Q3l5OZYvX44BAwZgwIABWL58OVJSUnDXXXcBAIxGI+bMmYOFCxciJycH2dnZWLRoEQYPHozx48er9bKIiIgoiqia7Bw5cgTXX3+98/OHH34YADBr1ixs3LgRjzzyCNrb23H//ffDZDLh6quvxvvvv4/09HTn16xatQoJCQmYOXMm2tvbMW7cOGzcuBE6nS7ir4eIiIiiT9T02VFTvPXZISIi6g1ivs8OERERkRKY7BAREVFcU7VmJ1o4dvLirZMyERFRPHP83g5UkcNkB3AOFmUnZSIiotjT3NwMo9Ho834WKAOw2+2orq5Genp6wO7McpjNZpSUlKCqqqrXFD7zNfM1xyu+Zr7meBXLr1mSJDQ3N6O4uBhare/KHK7sANBqtejbt2/YHr83dmnma+4d+Jp7B77m3iFWX7O/FR0HFigTERFRXGOyQ0RERHGNyU4Y6fV6PP3009Dr9WpfSsTwNfcOfM29A19z79AbXjMLlImIiCiucWWHiIiI4hqTHSIiIoprTHaIiIgorjHZISIiorjGZCdM9u/fj6lTp6K4uBgajQY7duxQ+5LCasWKFbjqqquQnp6O/Px8TJ8+HV988YXalxVWa9aswZAhQ5yNuEaMGIF3331X7cuKmBUrVkCj0aC8vFztSwmrxYsXQ6PRuH0UFhaqfVlhd+rUKfzkJz9BTk4OUlJScOWVV6KiokLtywqbCy64oMfPWaPRYN68eWpfWth0dXXhySefRP/+/WEwGHDhhRfimWeegd1uV/vSFMcOymHS2tqKK664Aj//+c9x6623qn05Ybdv3z7MmzcPV111Fbq6uvDEE09g4sSJ+PTTT5Gamqr25YVF37598eyzz+Liiy8GAGzatAk33XQT/vWvf2HQoEEqX114HT58GOvWrcOQIUPUvpSIGDRoEPbs2eP8XKfTqXg14WcymTBq1Chcf/31ePfdd5Gfn4+vv/4amZmZal9a2Bw+fBg2m835+bFjxzBhwgTMmDFDxasKr+eeew5r167Fpk2bMGjQIBw5cgQ///nPYTQasWDBArUvT1FMdsJk8uTJmDx5stqXETG7du1y+3zDhg3Iz89HRUUFRo8erdJVhdfUqVPdPl+2bBnWrFmDjz76KK6TnZaWFtx999344x//iKVLl6p9ORGRkJDQK1ZzHJ577jmUlJRgw4YNztsuuOAC9S4oAvLy8tw+f/bZZ3HRRRdhzJgxKl1R+P3jH//ATTfdhBtvvBFA98/49ddfx5EjR1S+MuVxG4vCoqmpCQCQnZ2t8pVEhs1mw9atW9Ha2ooRI0aofTlhNW/ePNx4440YP3682pcSMV999RWKi4vRv39/3HHHHfjmm2/UvqSw2rlzJ4YPH44ZM2YgPz8fQ4cOxR//+Ee1LytirFYrNm/ejHvuuUfR4dDR5tprr8UHH3yAL7/8EgDw73//GwcOHMCPf/xjla9MeVzZIcVJkoSHH34Y1157LcrKytS+nLCqrKzEiBEj0NHRgbS0NGzfvh2XX3652pcVNlu3bsXHH3+Mw4cPq30pEXP11Vfj1VdfxSWXXILTp09j6dKlGDlyJI4fP46cnBy1Ly8svvnmG6xZswYPP/wwHn/8cfzzn//Egw8+CL1ej5/97GdqX17Y7dixA42NjZg9e7balxJWjz76KJqamnDZZZdBp9PBZrNh2bJluPPOO9W+NMUx2SHFPfDAA/jkk09w4MABtS8l7C699FIcPXoUjY2NeOuttzBr1izs27cvLhOeqqoqLFiwAO+//z6Sk5PVvpyIcd2OHjx4MEaMGIGLLroImzZtwsMPP6zilYWP3W7H8OHDsXz5cgDA0KFDcfz4caxZs6ZXJDvr16/H5MmTUVxcrPalhNUbb7yBzZs3Y8uWLRg0aBCOHj2K8vJyFBcXY9asWWpfnqKY7JCi5s+fj507d2L//v3o27ev2pcTdklJSc4C5eHDh+Pw4cN46aWX8Ic//EHlK1NeRUUF6urqMGzYMOdtNpsN+/fvx+rVq2GxWOK+cBcAUlNTMXjwYHz11VdqX0rYFBUV9UjYBw4ciLfeekulK4qc7777Dnv27MGf//xntS8l7H71q1/h17/+Ne644w4A3cn8d999hxUrVjDZIfJGkiTMnz8f27dvx969e9G/f3+1L0kVkiTBYrGofRlhMW7cOFRWVrrd9vOf/xyXXXYZHn300V6R6ACAxWLBZ599hh/96EdqX0rYjBo1qkfriC+//BKlpaUqXVHkOA5XOIp241lbWxu0WvfSXZ1Ox6PnJK6lpQX/+c9/nJ+fOHECR48eRXZ2Nvr166filYXHvHnzsGXLFvzlL39Beno6amtrAQBGoxEGg0HlqwuPxx9/HJMnT0ZJSQmam5uxdetW7N27t8fJtHiRnp7eowYrNTUVOTk5cV2btWjRIkydOhX9+vVDXV0dli5dCrPZHHd/+bp66KGHMHLkSCxfvhwzZ87EP//5T6xbtw7r1q1T+9LCym63Y8OGDZg1axYSEuL/1+PUqVOxbNky9OvXD4MGDcK//vUvrFy5Evfcc4/al6Y8icLi73//uwSgx8esWbPUvrSw8PZaAUgbNmxQ+9LC5p577pFKS0ulpKQkKS8vTxo3bpz0/vvvq31ZETVmzBhpwYIFal9GWN1+++1SUVGRlJiYKBUXF0u33HKLdPz4cbUvK+zefvttqaysTNLr9dJll10mrVu3Tu1LCrv33ntPAiB98cUXal9KRJjNZmnBggVSv379pOTkZOnCCy+UnnjiCclisah9aYrTSJIkqZNmEREREYUf++wQERFRXGOyQ0RERHGNyQ4RERHFNSY7REREFNeY7BAREVFcY7JDREREcY3JDhEREcU1JjtEREQU15jsEFHM2bhxIzIzM1W9huuuuw7l5eWqXgMRiWEHZSJSzOzZs7Fp06Yet0+aNEnRmWHt7e1obm5Gfn6+Yo8p19mzZ5GYmIj09HTVroGIxMT/pDMiiqgbbrgBGzZscLtNr9cr+hwGg0H1AbPZ2dmqPj8RieM2FhEpSq/Xo7Cw0O0jKyvLeb9Go8H//M//4Oabb0ZKSgoGDBiAnTt3uj3Gzp07MWDAABgMBlx//fXYtGkTNBoNGhsbAfTcxlq8eDGuvPJKvPbaa7jgggtgNBpxxx13oLm52RkjSRKef/55XHjhhTAYDLjiiivw5ptv+n0tv//97zFgwAAkJyejoKAAt912m/M+122svXv3QqPR9PiYPXu2M/7tt9/GsGHDkJycjAsvvBBLlixBV1eXzO8uEQWDyQ4RRdySJUswc+ZMfPLJJ/jxj3+Mu+++G2fPngUAfPvtt7jtttswffp0HD16FPfddx+eeOKJgI/59ddfY8eOHfjrX/+Kv/71r9i3bx+effZZ5/1PPvkkNmzYgDVr1uD48eN46KGH8JOf/AT79u3z+nhHjhzBgw8+iGeeeQZffPEFdu3ahdGjR3uNHTlyJGpqapwfH374IZKTk53x7733Hn7yk5/gwQcfxKeffoo//OEP2LhxI5YtWyb3W0dEwVB15joRxZVZs2ZJOp1OSk1Ndft45plnnDEApCeffNL5eUtLi6TRaKR3331XkiRJevTRR6WysjK3x33iiSckAJLJZJIkSZI2bNggGY1G5/1PP/20lJKSIpnNZudtv/rVr6Srr77a+RzJycnSoUOH3B53zpw50p133un1tbz11ltSRkaG22O6GjNmjLRgwYIet585c0a66KKLpPvvv995249+9CNp+fLlbnGvvfaaVFRU5PWxiUhZrNkhIkVdf/31WLNmjdttnvUtQ4YMcf7/1NRUpKeno66uDgDwxRdf4KqrrnKL/+EPfxjweS+44AK3YuGioiLnY3766afo6OjAhAkT3L7GarVi6NChXh9vwoQJKC0txYUXXogbbrgBN9xwg3PrzZfOzk7ceuut6NevH1566SXn7RUVFTh8+LDbSo7NZkNHRwfa2tr8PiYRhY7JDhEpKjU1FRdffLHfmMTERLfPNRoN7HY7gO7aGo1G43a/JHBo1N9jOv73nXfeQZ8+fdzifBVPp6en4+OPP8bevXvx/vvv46mnnsLixYtx+PBhn8fef/nLX+L777/H4cOHkZBw/p9Xu92OJUuW4JZbbunxNcnJyQFfGxGFhskOEUWVyy67DH/729/cbjty5EhIj3n55ZdDr9fj+++/x5gxY4S/LiEhAePHj8f48ePx9NNPIzMzEx9++KHXpGXlypV444038I9//AM5OTlu9/3gBz/AF198ETAJJKLwYLJDRIqyWCyora11uy0hIQG5ublCX3/fffdh5cqVePTRRzFnzhwcPXoUGzduBIAeKz6i0tPTsWjRIjz00EOw2+249tprYTabcejQIaSlpWHWrFk9vuavf/0rvvnmG4wePRpZWVn429/+BrvdjksvvbRH7J49e/DII4/glVdeQW5urvP1GwwGGI1GPPXUU5gyZQpKSkowY8YMaLVafPLJJ6isrMTSpUuDek1EJI6nsYhIUbt27UJRUZHbx7XXXiv89f3798ebb76JP//5zxgyZAjWrFnjPI0VSr+e3/72t3jqqaewYsUKDBw4EJMmTcLbb7+N/v37e43PzMzEn//8Z4wdOxYDBw7E2rVr8frrr2PQoEE9Yg8cOACbzYa5c+e6ve4FCxYA6G6q+Ne//hW7d+/GVVddhWuuuQYrV65EaWlp0K+HiMSxgzIRRb1ly5Zh7dq1qKqqUvtSiCgGcRuLiKLO73//e1x11VXIycnBwYMH8bvf/Q4PPPCA2pdFRDGKyQ4RRZ2vvvoKS5cuxdmzZ9GvXz8sXLgQjz32mNqXRUQxittYREREFNdYoExERERxjckOERERxTUmO0RERBTXmOwQERFRXGOyQ0RERHGNyQ4RERHFNSY7REREFNeY7BAREVFc+/8Bw+9f4RGNrdYAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='turquoise')\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": 15, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coefficients: [[39.64157726]]\n", "Intercept: [123.40260366]\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": { "tags": [] }, "source": [ "#### Plot outputs\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot the fit line over the data:\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Emission')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='turquoise')\n", "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n", "plt.xlabel(\"Engine size\")\n", "plt.ylabel(\"Emission\")" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "#### Evaluation\n", "We compare the actual values and predicted values to calculate the accuracy of a regression model. Evaluation metrics provide a key role in the development of a model, as it provides insight to areas that require improvement.\n", "\n", "There are different model evaluation metrics, lets use MSE here to calculate the accuracy of our model based on the test set: \n", "* Mean Absolute Error: It is the mean of the absolute value of the errors. This is the easiest of the metrics to understand since it’s just average error.\n", "\n", "* Mean Squared Error (MSE): Mean Squared Error (MSE) is the mean of the squared error. It’s more popular than Mean Absolute Error because the focus is geared more towards large errors. This is due to the squared term exponentially increasing larger errors in comparison to smaller ones.\n", "\n", "* Root Mean Squared Error (RMSE). \n", "\n", "* R-squared is not an error, but rather a popular metric to measure the performance of your regression model. It represents how close the data points are to the fitted regression line. The higher the R-squared value, the better the model fits your data. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse).\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean absolute error: 22.34\n", "Residual sum of squares (MSE): 822.19\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": [ "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": "markdown", "metadata": { "tags": [] }, "source": [ "## Exercise\n" ] }, { "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": "code", "execution_count": 18, "metadata": { "tags": [] }, "outputs": [], "source": [ "train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n", "\n", "test_x = test[[\"FUELCONSUMPTION_COMB\"]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now train a Linear Regression Model using the `train_x` you created and the `train_y` created previously\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n", " normalize=False)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regr = linear_model.LinearRegression()\n", "\n", "regr.fit(train_x, train_y)\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": 20, "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": 21, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean Absolute Error: 20.46\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.

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