price_from_distance_duration.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Welcome To Colaboratory",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fCEDCU_qrC0"
      },
      "source": [
        "<h1>Price = f(distance, duration)</h1>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "C4HZx7Gndbrh",
        "outputId": "e14f519d-2f8c-4112-ddea-dab6fb63e7c6"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "  \n",
        "data = [\n",
        "           \n",
        "  [1000,'point E',2.1,7],\n",
        "  [1000,'fann',2.1,5],\n",
        "  [1000,'sacré coeur',2.3,9],\n",
        "  [1000,'amitié',1.8,5],\n",
        "  [1000,'castor',4.8,17],\n",
        "  [1000,'dieuppeul',5.2,15],\n",
        "  [1000,'HLM',4.1,12],\n",
        "  [1000,'colobane',3.9,7],\n",
        "  [1000,'fass',3.1,10],\n",
        "  [1000,'grand dakar',2.8,9],\n",
        "  [1000,'sicap liberté',4.4,11],\n",
        "  [1500,'ouaka',3.3,8],\n",
        "  [1500,'front de terre',5.5,16],\n",
        "  [1500,'zone de captage',5.9 ,22],\n",
        "  [1500,'liberté 6',4.1,9],\n",
        "  [1500,'biscuiterie',4.2,15],\n",
        "  [1500,'grand yoff',5.6,15],\n",
        "  [2000,'Dakar plateau',7.6,13],\n",
        "  [2000,'almadies',8.2,14],\n",
        "  [2000,'mamelles',8.1,15],\n",
        "  [2500,'Yarakh',4.8,16],\n",
        "  [2500,'Dalifort',13.4,23],\n",
        "  [2500,'Poste thiaroye',16.7,24],\n",
        "  [2500,'Camberene',12.2,23],\n",
        "  [3000,'Sicap mbao',27.2,39],\n",
        "  [3000,'Grand mbao',26.9,39],\n",
        "  [3000,'Guediawaye',17,30],\n",
        "  [3000,'Pikine',15.7,29],\n",
        "  [3000,'Thiaroye sur mer',15.7,27],\n",
        "  [3000,'Guinaw rails',18.6,29],\n",
        "  [3500,'Yeumbeul',20.4,41],\n",
        "  [3500,'Keur massar',26.8,43],\n",
        "  [3500,'Keur mbaye fall',23,35],\n",
        "  [3500,'Malika',23.2,36],\n",
        "  [3500,'Wakhinane nimzatt',18,30],\n",
        "  [4000,'Rufisque',33.7,43],\n",
        "  [4000,'Bargny',35.8,45],\n",
        "  [4000,'Diamniadio',37.6,41],\n",
        "  [4000,'Sebikhotane',50.3,67],\n",
        "  [4000,'Sangalkam',36.7,48],\n",
        "]\n",
        "dataset = pd.DataFrame(data)\n",
        "dataset\n",
        " "
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000</td>\n",
              "      <td>point E</td>\n",
              "      <td>2.1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000</td>\n",
              "      <td>fann</td>\n",
              "      <td>2.1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000</td>\n",
              "      <td>sacré coeur</td>\n",
              "      <td>2.3</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000</td>\n",
              "      <td>amitié</td>\n",
              "      <td>1.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000</td>\n",
              "      <td>castor</td>\n",
              "      <td>4.8</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1000</td>\n",
              "      <td>dieuppeul</td>\n",
              "      <td>5.2</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1000</td>\n",
              "      <td>HLM</td>\n",
              "      <td>4.1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1000</td>\n",
              "      <td>colobane</td>\n",
              "      <td>3.9</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1000</td>\n",
              "      <td>fass</td>\n",
              "      <td>3.1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1000</td>\n",
              "      <td>grand dakar</td>\n",
              "      <td>2.8</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1000</td>\n",
              "      <td>sicap liberté</td>\n",
              "      <td>4.4</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1500</td>\n",
              "      <td>ouaka</td>\n",
              "      <td>3.3</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1500</td>\n",
              "      <td>front de terre</td>\n",
              "      <td>5.5</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1500</td>\n",
              "      <td>zone de captage</td>\n",
              "      <td>5.9</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1500</td>\n",
              "      <td>liberté 6</td>\n",
              "      <td>4.1</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1500</td>\n",
              "      <td>biscuiterie</td>\n",
              "      <td>4.2</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1500</td>\n",
              "      <td>grand yoff</td>\n",
              "      <td>5.6</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2000</td>\n",
              "      <td>Dakar plateau</td>\n",
              "      <td>7.6</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2000</td>\n",
              "      <td>almadies</td>\n",
              "      <td>8.2</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2000</td>\n",
              "      <td>mamelles</td>\n",
              "      <td>8.1</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2500</td>\n",
              "      <td>Yarakh</td>\n",
              "      <td>4.8</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2500</td>\n",
              "      <td>Dalifort</td>\n",
              "      <td>13.4</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2500</td>\n",
              "      <td>Poste thiaroye</td>\n",
              "      <td>16.7</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2500</td>\n",
              "      <td>Camberene</td>\n",
              "      <td>12.2</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>3000</td>\n",
              "      <td>Sicap mbao</td>\n",
              "      <td>27.2</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>3000</td>\n",
              "      <td>Grand mbao</td>\n",
              "      <td>26.9</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>3000</td>\n",
              "      <td>Guediawaye</td>\n",
              "      <td>17.0</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>3000</td>\n",
              "      <td>Pikine</td>\n",
              "      <td>15.7</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>3000</td>\n",
              "      <td>Thiaroye sur mer</td>\n",
              "      <td>15.7</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>3000</td>\n",
              "      <td>Guinaw rails</td>\n",
              "      <td>18.6</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>3500</td>\n",
              "      <td>Yeumbeul</td>\n",
              "      <td>20.4</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>3500</td>\n",
              "      <td>Keur massar</td>\n",
              "      <td>26.8</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>3500</td>\n",
              "      <td>Keur mbaye fall</td>\n",
              "      <td>23.0</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>3500</td>\n",
              "      <td>Malika</td>\n",
              "      <td>23.2</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>3500</td>\n",
              "      <td>Wakhinane nimzatt</td>\n",
              "      <td>18.0</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>4000</td>\n",
              "      <td>Rufisque</td>\n",
              "      <td>33.7</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>4000</td>\n",
              "      <td>Bargny</td>\n",
              "      <td>35.8</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>4000</td>\n",
              "      <td>Diamniadio</td>\n",
              "      <td>37.6</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>4000</td>\n",
              "      <td>Sebikhotane</td>\n",
              "      <td>50.3</td>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>4000</td>\n",
              "      <td>Sangalkam</td>\n",
              "      <td>36.7</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       0                  1     2   3\n",
              "0   1000            point E   2.1   7\n",
              "1   1000               fann   2.1   5\n",
              "2   1000        sacré coeur   2.3   9\n",
              "3   1000             amitié   1.8   5\n",
              "4   1000             castor   4.8  17\n",
              "5   1000          dieuppeul   5.2  15\n",
              "6   1000                HLM   4.1  12\n",
              "7   1000           colobane   3.9   7\n",
              "8   1000               fass   3.1  10\n",
              "9   1000        grand dakar   2.8   9\n",
              "10  1000      sicap liberté   4.4  11\n",
              "11  1500              ouaka   3.3   8\n",
              "12  1500     front de terre   5.5  16\n",
              "13  1500    zone de captage   5.9  22\n",
              "14  1500          liberté 6   4.1   9\n",
              "15  1500        biscuiterie   4.2  15\n",
              "16  1500         grand yoff   5.6  15\n",
              "17  2000      Dakar plateau   7.6  13\n",
              "18  2000           almadies   8.2  14\n",
              "19  2000           mamelles   8.1  15\n",
              "20  2500             Yarakh   4.8  16\n",
              "21  2500           Dalifort  13.4  23\n",
              "22  2500     Poste thiaroye  16.7  24\n",
              "23  2500          Camberene  12.2  23\n",
              "24  3000         Sicap mbao  27.2  39\n",
              "25  3000         Grand mbao  26.9  39\n",
              "26  3000         Guediawaye  17.0  30\n",
              "27  3000             Pikine  15.7  29\n",
              "28  3000   Thiaroye sur mer  15.7  27\n",
              "29  3000       Guinaw rails  18.6  29\n",
              "30  3500           Yeumbeul  20.4  41\n",
              "31  3500        Keur massar  26.8  43\n",
              "32  3500    Keur mbaye fall  23.0  35\n",
              "33  3500             Malika  23.2  36\n",
              "34  3500  Wakhinane nimzatt  18.0  30\n",
              "35  4000           Rufisque  33.7  43\n",
              "36  4000             Bargny  35.8  45\n",
              "37  4000         Diamniadio  37.6  41\n",
              "38  4000        Sebikhotane  50.3  67\n",
              "39  4000          Sangalkam  36.7  48"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_kCnsPUqS6o"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpIx82OBiBbA",
        "outputId": "dbe57fdd-b827-446d-d265-f91e65320da0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "\n",
        "X = dataset.iloc[:,2:3].values  \n",
        "y = dataset.iloc[:,0].values\n",
        "y"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
              "       1500, 1500, 1500, 1500, 1500, 1500, 2000, 2000, 2000, 2500, 2500,\n",
              "       2500, 2500, 3000, 3000, 3000, 3000, 3000, 3000, 3500, 3500, 3500,\n",
              "       3500, 3500, 4000, 4000, 4000, 4000, 4000])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tC0vNyi1jNlt",
        "outputId": "2905a8bb-036a-48b6-daab-47fdb6141713",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "source": [
        "\n",
        "# fitting the linear regression model\n",
        "from sklearn.linear_model import LinearRegression\n",
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(X,y)\n",
        " \n",
        "# visualising the linear regression model\n",
        "plt.scatter(X,y, color='red')\n",
        "plt.plot(X, lin_reg.predict(X),color='blue')\n",
        "plt.title(\"price = f(distance)\")\n",
        "plt.xlabel('Distance (km)')\n",
        "plt.ylabel('Price')\n",
        "plt.show()\n",
        " \n",
        "# polynomial regression model\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg = PolynomialFeatures(degree=2)\n",
        "X_poly = poly_reg.fit_transform(X)\n",
        "  \n",
        "X_poly     # prints X_poly\n",
        " \n",
        "lin_reg2 = LinearRegression()\n",
        "lin_reg2.fit(X_poly,y)\n",
        " \n",
        " \n",
        "# visualising polynomial regression\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg = PolynomialFeatures(degree=4)\n",
        "X_poly = poly_reg.fit_transform(X)\n",
        "lin_reg2 = LinearRegression()\n",
        "lin_reg2.fit(X_poly,y)\n",
        "  \n",
        "X_grid = np.arange(min(X),max(X),0.1)\n",
        "X_grid = X_grid.reshape(len(X_grid),1) \n",
        "plt.scatter(X,y, color='red') \n",
        "  \n",
        "plt.plot(X_grid, lin_reg2.predict(poly_reg.fit_transform(X_grid)),color='blue') \n",
        "  \n",
        "plt.title(\"price = f(distance)\")\n",
        "plt.xlabel('Distance (km)')\n",
        "plt.ylabel('Price')\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZzVZd3/8dfHAYRREgQkY5lxQRR/LuiY611kKmrd4V253FGSSyhqaWrmUml6kyZ3kmapmGuOClYqt6lI7pHbgBuoJCrDIpsCopKyzOf3x3UdzzmznRnmnDnb+/l4nMd8v9d3+3yHYT5zXdf3e13m7oiIiLRms3wHICIihU/JQkREMlKyEBGRjJQsREQkIyULERHJSMlCREQyUrKQkmBm/2Fmc/MdR4KZjTOzZWb2kZn1iWV3mdlRLexfbWZuZl3i+kNmNqYzY95UZra7mf0z33FIbpnesxDJLjPrCqwB9nP3l2PZ7sDdwK7ezH86M6sG3gG6uvuGdlzLgSHuPi8LoW8yM3sQuM7d/y+fcUjuqGYhRS/x13gB6Q90B+aklJ0C1DaXKEpELeEepUQpWUhBMrP5ZnaBmb1mZqvM7BYz6x63jTCzRWb2UzNbCtySKEs5fpCZ/dXMVpjZ+2Z2bcq2E83s9XjeaWZWlcW4dwISzWGrzeyxuHwE8GTKfhVm9r9m9p6ZvQ18rdF5njCzk+Pyjmb2pJl9EPefHMufiru/HJu7jjWz3mb2QLzvVXF5YKPzXmZmM8zsQzN7xMz6pmw/yMz+aWarzWyhmX0/lm8e410Qm9euN7MeKSE/AXzVzDbv+HdRCpGShRSy0cBIYAdgJ+BnKds+D2wNVAFjUw8yswrgAaAeqAYGEJqAMLNRwIXAN4F+wNPAXS0FEH9ptvQ5v/H+7v4vYNe42svdDzazLYDtSCYRgB8AXweGAzXAt1v5PlwGPAL0BgYCv4vX+lLcvoe7b+nukwn/p2+J35fBwL+Baxud7zvACcA2QDfg3HivVcBD8fz9gD2Bl+IxVxD+DfYEdiR8T3+Rct+LgfXA0FbuQ4qZu+ujT8F9gPnAqSnrRwJvxeURwDqge8r2EcCiuLw/sALo0sx5HwJOSlnfDFgLVGUx9mrAE9cn/GL1RvE+1uj+Dmt0zBPAyXH5dmASMLCZazmwYyux7AmsSll/AvhZyvppwMNx+QLg3mbOYcDHwA4pZfsD7zTabzHwpXz/7OiTm49qFlLIFqYs1wNfSFlf4e6ftHDcIKDem+8orgKuTtQOgJWEX4YDshFwC1bHrz1Tyr5A0/tryXmEGJ83szlmdmJLO5pZpZndYGb1ZrYGeAroFWtbCUtTltcCW8blQcBbzZy2H1AJzEz5vj0cy1P1JHmvUmKULKSQDUpZHgy8m7LeWkfxQmBwCx3fC4FT3L1XyqeHuzf76GfsC2jpc2FbbsLdPyb8Et4ppXhJM/fX0vFL3f0H7v4FQifyH8xsxxZ2P4fQFLSvu38OSDRVWRtCXUho8mvsPUJz1q4p37Ot3D2RZDCzAYQmrYJ5fFmyS8lCCtnpZjbQzLYGLgImt/G45wm/jK8wsy3MrLuZHRi3XQ9cYGa7ApjZVmZ2dEsn8tAX0NLnV+24lweBL6esTwF+FO+vN9Ck/yPBzI5O6aReRUiUDXF9GbB9yu49Cb/YV8fv28XtiLEWOMTMjjGzLmbWx8z2dPcG4EZgopltE2MaYGYjU479MvCYu3/ajutJEVGykEJ2J6Fj923CX+b/05aD3H0j8J+EjtgFwCLg2LjtXuDXwN2xmWY24UmlXJsEjDazxF/4NwLTgJeBWcBfWzl2H+A5M/sImAqc6e5vx22XALfF5qFjgN8CPQi1gWcJzUVt4u4LCH1D5xCa514C9oibfwrMA56N37e/k96ZPZqQiKVE6aU8KUhmNp/Qwfv3fMeSLWZ2JzDF3e/LdyzZZOGFwxvcff98xyK5U2gvM4mULHf/Tr5jyAV3f4XwdJSUMDVDiYhIRmqGEhGRjFSzEBGRjEqyz6Jv375eXV2d7zBERIrKzJkz33P3xi9bAiWaLKqrq6mrq8t3GCIiRcXMWhxJQM1QIiKSkZKFiIhkpGQhIiIZKVmIiEhGShYiIpKRkoWIiGSkZCEiIhkpWYiIlIANG+CXv4TXXsvN+UvypTwRkXJy001w8slheeVKuPrq7F9DyUJEpEjNnw/bbZdcP/RQmDgxN9dSshARKTINDTByJPw9ZWqw+fOhqip311SfhYhIEbnzTqioSCaKP/4R3HObKEA1CxGRorB4MQwcmFw/4AB46qmQODqDahYiIgXMHY46Kj1R/OtfMGNG5yUKULIQESlY994Lm20G998f1q+9NiSPIUM6PxY1Q4mIFJjly6F//+T67rtDXR107Zq/mHJaszCz+Wb2qpm9ZGZ1sWxrM5tuZm/Gr71juZnZNWY2z8xeMbO9Us4zJu7/ppmNyWXMIiL54g6jR6cnitmz4eWX85sooHOaob7i7nu6e01cPx941N2HAI/GdYAjgCHxMxa4DkJyAS4G9gW+CFycSDAiIqXioYdCk9Odd4b1K68MyWPXXfMbV0I+mqFGASPi8m3AE8BPY/nt7u7As2bWy8y2jftOd/eVAGY2HTgcuKtzwxYRyb6VK6FPn+T69tvDnDnQvXv+YmpOrmsWDjxiZjPNbGws6+/uS+LyUiBR4RoALEw5dlEsa6k8jZmNNbM6M6tbsWJFNu9BRCQnTjklPVHMmgVvvVV4iQJynywOcve9CE1Mp5vZl1I3xlqEZ+NC7j7J3WvcvaZfv37ZOKWISE48/jiYwaRJYf3ii0OT0/Dh+Y2rNTlthnL3xfHrcjO7l9DnsMzMtnX3JbGZaXncfTEwKOXwgbFsMclmq0T5E7mMW0QkF9asCZ3Xn3wS1rfZBt55Byor8xtXW+SsZmFmW5hZz8QycBgwG5gKJJ5oGgPEJ4iZChwfn4raD/ggNldNAw4zs96xY/uwWCYiUjTOOQe22iqZKJ55BpYtK45EAbmtWfQH7jWzxHXudPeHzewFYIqZnQTUA8fE/R8EjgTmAWuBEwDcfaWZXQa8EPe7NNHZLSJS6J55JgzNkXDuuTBhQv7i2VQWug1KS01NjdfV1eU7DBEpYx9/DNXV8N57Yb2yEpYsgc99Lq9htcrMZqa85pBGw32IiGTZL34BW26ZTBRPPBGSRyEnikw03IeISJbMmgV7751cHzcO/vCH/MWTTUoWIiId9MknsPPOUF+fLFu5EnqX0FgTaoYSEemAX/8aevRIJoqHHw7vTJRSogDVLERENsns2bDbbsn144+HW28NL9uVIiULEZF2WLcO9torjN+UsHw5lPrAEWqGEhFpo9/9DjbfPJko7rsvNDmVeqIA1SxERDL6179g6NDk+je/CX/+c+k2OTVHyUJEpAUbNsCBB8LzzyfLFi+GL3whfzHli5qhRESa8cc/htnpEonirrtCk1M5JgpQzUJEJM0774QJiBIOOyw5i105U7IQEQEaGuDQQ+Gxx5Jl8+dDVVXeQiooZZ4rRUTCvNcVFclEcfPNoclJiSJJNQsRKVuLF8PAgcn1Aw6Ap54KiUPSqWYhImXHHUaNSk8Ub74JM2YoUbREyUJEysq994bO6qlTw/rvfx+Sx4475jeuQqdmKBEpC8uWwec/n1zfYw944YXweKxkppqFiJQ0dxg9Oj1RzJkDL72kRNEeShYiUrIS70fceWdYnzAhJI9hw/IbVzFSM5SIlJyVK6FPn+T6DjuE2sTmm+cvpmKnmoWIlJRTTklPFLNmwbx5ShQdpWQhIiXh8cfDKLCTJoX1Sy4JTU7Dh+c1rJKhZigRKWoffADbbBMmJYLQkf3WW1BZmd+4So1qFiJStM4+G3r1SiaKZ5+FJUuUKHJByUJEis4//xmanCZODOs/+Uloctp33/zGVcrUDCUiRePjj2Hw4PC0E8CWW8K770LPnvmNqxyoZiEiReHnPw/JIZEonnwSPvxQiaKzqGYhIgVt5kyoqUmun346XHtt/uIpV0oWIlKQPvkEdtoJFi5Mlq1cCb175y+mcqZmKBEpOFdcAT16JBPFtGmhA1uJIn9UsxCRgjF7Nuy2W3J9zBi45Zbw5JPkl5KFiOTdunXhTevXXkuWLV8O/frlLyZJp2YoEcmra64J4zYlEsX994cmJyWKwqKahYjkxdy5sPPOyfVvfQvuuUdNToVKNQsR6VQNDTB2bHqiePdd+POflSgKmZKFiHSamTNh//3hxhvD+t13hyanbbfNb1ySmZKFiOTcqlXhZbp99oH6evjTn0IN49hj8x2ZtFXOk4WZVZjZi2b2QFzfzsyeM7N5ZjbZzLrF8s3j+ry4vTrlHBfE8rlmNjLXMYtIdjQ0wG23wdChcP31cMYZ8MYb8N3vqsmp2HRGzeJM4PWU9V8DE919R2AVcFIsPwlYFcsnxv0ws2HAccCuwOHAH8ysohPiFpFUtbVQXR0mta6uDuuteOUV+NIuy/n+92GHFc9Q1/9rXLNvLb16tXCudp5fGsn198/dc/YBBgKPAgcDDwAGvAd0idv3B6bF5WnA/nG5S9zPgAuAC1LO+dl+LX323ntvF5EsuuMO98pK99DFED6VlaG8kQ8+cD/rLPeKzTZ6H1b4HznRN2LJY8aNa3qubt3cu3Zt0/mlGe3492kNUOct/F7Ndc3it8B5QENc7wOsdvcNcX0RMCAuDwAWAsTtH8T9Pytv5hgR6QwXXQRr16aXrV0byiN3uPPO0OR09dVwcuVd/IudOImb2QxPHjNpUtNzrVsH69e3en5pRRv+fToqZ8nCzL4OLHf3mbm6RqPrjTWzOjOrW7FiRWdcUqR8LFjQavlrr8FXvwqjR8PAgfDcc3D9x99ja1Y1PWbjxo5fV9Jl+PfJhlzWLA4EvmFm84G7CU1RVwO9zCzxMuBAYHFcXgwMAojbtwLeTy1v5pjPuPskd69x95p+evVTJLsGD262+KOBO/PTn8Iee8BLL8F114WpTffZp+VjqGhHl2NL55B0LX2fsvj9y1mycPcL3H2gu1cTOqgfc/fRwOPAt+NuY4D74/LUuE7c/lhsQ5sKHBefltoOGAI8n6u4RaQZ48enTWztwJ+7fYddPn6BK6+E448Pb2SfempKLmh0DBDWx45tWt6tG3Tt2nTf8eOzfislqaXvdTa/fy11ZmTzA4wAHojL2xN+2c8D7gE2j+Xd4/q8uH37lOMvAt4C5gJHZLqeOrhFcuCOO9yrqvxfDPHDuj/p4L7HHu4zZmQ+xs3C10SHa3PlLe0rbZOF7x+tdHBb2F5aampqvK6uLt9hiJSUtWvh8svhyiuhe3e47DI47TToohHmSoaZzXT3mua26Z9ZRDKaOhXOPBPmzw8v1E2YAJ//fL6jks6k4T5EpEXvvAP/+Z8wahRssQU88UQYqkOJovwoWYhIE598ApdeCsOGhQTxv/8LL74IX/5yviOTfFEzlIikefhh+OEPYd48OOYYuOoqGKDXYMueahYiAoT3t771LTjiiDC80PTpMHmyEoUEShYiZW7dOrjiCthlF3joIfjVr8IggIccku/IpJCoGUqkjD32WJhn4o034Kij4Le/haqqfEclhUg1C5Ey9O678N//HcZzWr8e/vY3uPdeJQppmZKFSBlZvz50WA8dGpLDJZfA7Nlw5JH5jkwKnZqhRMrE00+HN64TyeGaa2CHHfIdlRQL1SxEStyyZTBmDHzpS/Dhh3DfffDAA0oU0j5KFiIlauNGuPba0OR0111w4YVh3olRozT/tbSfmqFEStCzz4YmpxdfDI/AJpKGyKZSzUKkhLz3HvzgB7D//rB8OUyZAo88okQhHadkIVICGhrC1NZDh8Ktt8K558Lrr8PRR6vJSbJDyUIKW20tVFeH8Seqq8N6Zxybeo6+fcNvXLOwvCnnyVV8wMyZoSZxyimw225hetMJE6Bnz+yEKQJ0zkx5nf3RTHkl4o473Csr3SH5qaxs2wxgHTk29Rxdu6afA9y7dev4LG5ZiG/lSvdx48LEaP37h0MbGjoWlpQ3NFOeFKXqaqivb1peVRVm4cnVsZnO0d7ztOfcbThvQwPcfjucdx68/z6ccUYYTnyrrTY9HBHQTHlSrBYsaF95to5ty77tOU97js9w3pdfDmM5zZgRmp4eeQT23LNjoYi0hfospHANHty+8mwd25Z923Oe9hzfQvmaNXDWWbD33jB3Ltx8M/zjH0oU0nmULKRwjR8PlZXpZZWVoTyXx6aeo2vXpuXdurXvPB2Izx3uvDM85XTNNeGx2Llz4YQTQr+4SKdpqTOjmD/q4C4hd9zhXlUVenGrqtrfQb2px6aeo0+fZCd0nz4d79xuY3xz5riPGBEuW1Pj/vzz2bmsSEtQB7dI8fjoo9BhPXFiePz18svh5JOhoiLfkUmpUwe3SBFwh7/8BX78Y1i0CE48Mcxg169fviMTUZ+FSEGYODH0QRx9dHjv75//hJtuUqKQwqGahUgevfxy+hNNw4bBCy9AF/3PlAKjH0mRPHBv+jTTww/DyJH5iUckkzY1Q5nZTmb2qJnNjuu7m9nPchuaSGk67rimicJdiUIKW1v7LG4ELgDWA7j7K8BxuQpKpBS9+WYYi3Dy5GTZ0qUhUYgUurYmi0p3f75R2YZsByNSqsxgp52S6xdcEJJE//75i0mkPdraZ/Geme0AOICZfRtYkrOoRErEGWfA73+fXqaahBSjtiaL04FJwM5mthh4B/huzqISKXILFzYd5mn+/DCorEgxalOycPe3gUPMbAtgM3f/MLdhiRSvxjPTnXoqXHddfmIRyZa2Pg31KzPr5e4fu/uHZtbbzP4n18GJFJOf/7xponBXopDS0NYO7iPcfXVixd1XAUfmJiSR4rJ8eUgS/5Py59Prr6tvQkpLW5NFhZltnlgxsx7A5q3sL1IWzNKfaDr66JAkdt45fzGJ5EJbO7hrgUfN7Ja4fgJwW25CEil8V10F55yTXtbQ0LQZSqRUtLWD+9dm9grw1Vh0mbtPy11YIoXpgw+gV6/0slmzYPjw/MQj0lnaPOqsuz/k7ufGT8ZEYWbdzex5M3vZzOaY2S9j+XZm9pyZzTOzyWbWLZZvHtfnxe3VKee6IJbPNTMNilCMTjstjI5nFr6edlr69tpaqK4O42BUV4f15rR1vxwwS08UX/lKaHLqUKJI3E/i+2LW6fcl0iYtzYoUJ0X6R/z6IbAm5fMhsCbDsQZsGZe7As8B+wFTgONi+fXAuLh8GnB9XD4OmByXhwEvE/pItgPeAipau7Zmyisw48YlZ5pL/YwbF7bfcYd7ZWX6tsrKpjPStXW/LPvJT5qG3tCQhRM3dz+deF8ijZHvmfLMrBL4BzAO+BvweXffYGb7A5e4+0gzmxaXnzGzLsBSoB9wPoC7Xx7P9dl+LV1PM+UVmC5dYOPGpuUVFbBhQ/hLur6+6faqqvAmW0Jb98uStWthiy3Sy55+Gg46KEsXaOl+EnJ0XyItaW2mvIzNUGZWYWZvbOKFK8zsJWA5MJ1QK1jt7olxpRYBA+LyAGAhQNz+AdAntbyZY1KvNdbM6sysbsWKFZsSruRKc4kitXzBgua3Ny5v635ZYNY0UbhnMVFA5rhzcF8imypjsnD3jcBcMxucad/mjnX3PYGBwBeBnD1Q6O6T3L3G3Wv6aXqxwtLS5NGJ8sbjYiQ0Lm/rfh1wzz1Nn2jasCFH70xkijuL9yXSUW3t4O4NzIlzWkxNfNp6EQ8v9D0O7A/0is1MEJLI4ri8GBgEELdvBbyfWt7MMVIMxo5tvXz8eKisTN9WWRnKU7V1v02wbl1IEscckyx78MGQJFrKdR3W3P0kZOm+RLKmpc6M1A/w5eY+GY7pB/SKyz2Ap4GvA/eQ3sF9Wlw+nfQO7ilxeVfSO7jfRh3cxWfcOPeKitB5W1GR7NxOuOMO96oqd7PwtaXO3bbu1w79+6f3Lffr1+FTtl3ifhLfF8jafYm0F5vawW1m3YFTgR2BV4GbPNnf0Coz253w4l4FoQYzxd0vNbPtgbuBrYEXge+6+6fxWn8ChgMrY0J5O57rIuBEwhwaZ7n7Q61dWx3c0hbTpsHhh6eXffopdOuWn3hE8q21Du5MyWIyYXa8p4EjgHp3PzMnUWaRkoW0ZuPG8IBWqsmT05ugRMpRa8ki0xvcw9x9t3iSm4DGs+WJFJXdd4dXX00v04B/Ipll6uBen1hoa/OTSCGaMSN0YKcmio8+UqIQaatMNYs9zGxNXDagR1w3wN39czmNTqSD3MPIIKluvBFOPjk/8YgUq1aThbvn6qFBkZz76lfhscfSy1STENk0bR2iXKRovPgi7LVXetmqVU1HixWRtmvzqLMihc499EukJooJE0K5EoVIx6hmISXh2GNhypT0MjU5iWSPkoUUtblzm05humwZbLNNfuIRKVVqhpKiZZaeKC66KNQmlChEsk81Cyk6p50G112XXqYmJ5HcUrKQorFgQZgPKFV9vUbyFukMaoaSomCWnijGjQu1CSUKkc6hmoUUtPvvh6OOSi9Tk5NI51OykIK0Zg1stVV62RtvwNCh+YlHpNypGUoKzsiR6YnihhtCbUKJQiR/lCxyobYWqqvDCHbV1WG9WLQWey7uK+Wc0/t/FzN45JGwqW9faGhoeVZWEek8aobKttra8Ntt7dqwXl+f/G03enT+4mqL1mKH7N9XvN7atc4WNMDy5KYFC2DQoJYPFZHO1epMecUqrzPlVVeHX6SNVVXB/PmdHU37tBY7ZP++qqux+vRjf8PZnF3118L/XomUoI7MlCfttWBB+8oLyabEvon3deONMLZRomjAMIAFtknnFJHcUZ9FtrX04H8xvBDQWuxZuq+PPw7vTKS2bj3NQXgiUWzCOUUk95Qssm38eKisTC+rrAzlha612LNwX126wJZbJtf/38DVeOUWHMSMTT6niHQOJYtsGz0aJk0KbfmJ144nTSr8zm1oPfYO3FddXThk48Zk2YYN8OrCXsX7vRIpM+rglpzZsAG6dk0ve/xxGDEiL+GISAatdXCrZiE5cfbZ6YninHPCi3VKFCLFSU9DSVbNng277ZZetn596K8QkeKlmoVkRUND6HZITRQvvBBqE0oUIsVPyUI67JJLoKIiuX7yySFJ1DTb8ikixUh/88kmmzcPhgxJL/v0U+jWLT/xiEjuqGYh7eYempxSE8XTT4dyJQqR0qRkIe3ym9+EQWcTjjkmJImDDspfTCKSe2qGkjZpbv7rtWuhR4/8xCMinUs1C2mVO2y9dXqieOSRUK5EIVI+lCykRTfcEJqcVq0K6yNHhiRx6KH5jUtEOp+aoaSJpUth223Ty9asgZ498xOPiOSfahaS5sEH0xPFffeF2oQShUh5U7IQIEyC91//BV/7Wlg/5JCQJEaNym9cIlIYlCzK3Lp1cPnlsMsuoeP68svDi3XTp+c7MhEpJOqzKGN//zuccQbMnQvf/CZMnKhJ6kSkeTmrWZjZIDN73MxeM7M5ZnZmLN/azKab2Zvxa+9YbmZ2jZnNM7NXzGyvlHONifu/aWZjchVzwamtherq8EhS376h48AsfPr2Dds3weLFcOyx4ammDRvgoYfgL39RohCRluWyGWoDcI67DwP2A043s2HA+cCj7j4EeDSuAxwBDImfscB1EJILcDGwL/BF4OJEgilptbVhour6+tB58P778NFHye3vvw8nnNCuhLF+fXgDe+edYepUuPTSMKT44YfnIH4RKSk5SxbuvsTdZ8XlD4HXgQHAKOC2uNttwFFxeRRwuwfPAr3MbFtgJDDd3Ve6+ypgOlD6v94uuii8It2a9evDfm3w5JMwfDice26YgGjOHPj5z6F7946HKiKlr1M6uM2sGhgOPAf0d/clcdNSoH9cHgAsTDlsUSxrqbzxNcaaWZ2Z1a1YsSKr8efFggVZ2W/pUvje90KC+OgjuP9++L//g+2373iIIlI+cp4szGxL4C/AWe6+JnWbhwnAszIJuLtPcvcad6/p169fNk6ZX23tQGhhvw0b4He/g6FDYcoU+NnP4LXX4BvfyGKMIlI2cposzKwrIVHUuvtfY/Gy2LxE/Lo8li8GBqUcPjCWtVRe2saPh8rK1vfp2jXs18gzz8A++8CPfgT77QevvgqXXZb5dCIiLcnl01AG3AS87u5XpWyaCiSeaBoD3J9Sfnx8Kmo/4IPYXDUNOMzMeseO7cNiWWkbPRomTQoj+JlBnz6w5ZbJ7X36wC23hP2iFSvgpJPggAPgvffgnnvg4Ydhp53yEL+IlJRcvmdxIPA94FUzeymWXQhcAUwxs5OAeuCYuO1B4EhgHrAWOAHA3Vea2WXAC3G/S919ZQ7jLhyjR6clg5Zs3Ag33ggXXggffgjnnRc6r1Nzi4hIR1joNigtNTU1XldXl+8wOkVdHYwbF76OGAG//z0MG5bvqESkGJnZTHevaW6bhvsoUitXhiTxxS/CokXhdYvHHlOiEJHcULIoMg0NcPPN4SmnG2+EM88Mw3V85zuha0NEJBeULIrI2WdDRUXoxB46FGbNCuM5fe5z+Y5MREqdBhIsAq++Crvvnlw/+2yYMCEMGSUi0hmULApYQ0OoSaSqq4O9985PPCJSvvS3aYG6+OL0RDF2bBhPUIlCRPJBNYsC8+abTV+i+/RT6NYtP/GIiIBqFgXDPTzNlJoo/vGPUK5EISL5pmRRAH7zm/TO6uOOC0niwAPzF5OISCo1Q+VRfX2YCC/V2rXQo0dewhERaZFqFnngDr17pyeK6dNDuRKFiBQiJYtO9u1vhyan1avD+uGHhyRxyCH5jUtEpDVqhuokb7wBu+ySXrZmDfTsmZ94RETaQzWLTmCWnih+8YtQm1CiEJFioZpFDu2yS6hRpCrBEeFFpAyoZtFetbWhZ3qzzcLX2trw6ds3VCHMqO+9J2bpieL1436JV3T5bB/MkseLiBQ41Szao7Y2jLuxdm1Yr6+HE04Igzht3AiA4bA6ecjw4TBrv9Pguuuanq++PpwP2jQjnohIvqhm0R4XXZRMFAnr18PGjRzBgyFRpPCqambNIsyl3ZK1a8N5RUQKmGoW7bFgQZOipfRnW5amldWxN3szCyLAU74AAApgSURBVBbE2YhiraM95xURKSSqWbTH4MFpq4anJYoDmIFjIVGk7t94nPEM5xURKTRKFu0xfjxUVjKBc5s2OWHM4KBkQbduYX9I9ks0p7IyuZ+ISIFSsmiHtf81Glv7Mecx4bOyl8b/Db+jFvr0Se7Yp0+YKDvRaf2HP8C4cU1rGFVVoT9DndsiUuDMS/DB/5qaGq+rq8vqOU84AW69Nbl+7LFw991ZvYSISF6Z2Ux3r2lumzq4M1ixAs4/P5kounWDTz4Jr0mIiJQLNUO14NNP4eCDYdAguP12+MEPYMmSUK5EISLlRjWLZpx6KtxwQ3L9tdeaDgIoIlJOlCxSuKfPWAfhFYnGZSIi5Ua/BlOsXJlcnjev+eQhIlKOVLNI0aePRoUVEWmO/m4WEZGMlCxERCQjJQsREclIyUJERDJSshARkYyULEREJCMlCxERyUjJQkREMlKyEBGRjHKWLMzsZjNbbmazU8q2NrPpZvZm/No7lpuZXWNm88zsFTPbK+WYMXH/N81sTK7iBaC2Fnr2DMPKpn769g0fM+jSJXytrg7719Ymt5mFCY5S90scu9lmyWNERIpMziY/MrMvAR8Bt7v7/4tlVwIr3f0KMzsf6O3uPzWzI4EfAkcC+wJXu/u+ZrY1UAfUAA7MBPZ291WtXXuTJj+qrYXvfx82bGj7Md26hZEGN25s+zGVlZodT0QKUmuTH+WsZuHuTwErGxWPAm6Ly7cBR6WU3+7Bs0AvM9sWGAlMd/eVMUFMBw7PScAXXdS+RAGwbl37EgXA2rXhWiIiRaSz+yz6u/uSuLwU6B+XBwALU/ZbFMtaKm/CzMaaWZ2Z1a1YsaL9kS1Y0P5jNlVnXktEJAvy1sHtof0ra21g7j7J3WvcvaZfv37tP8HgwdkKpbCuJSKSBZ2dLJbF5iXi1+WxfDEwKGW/gbGspfLsGz8+dEq3R7duoUO7PSorw7VERIpIZyeLqUDiiaYxwP0p5cfHp6L2Az6IzVXTgMPMrHd8cuqwWJZ9o0fDrbfClls23danT/hAMjlUVcHNN8NttyW3QXK2pMR+iWPNwjHq3BaRIpTLp6HuAkYAfYFlwMXAfcAUYDBQDxzj7ivNzIBrCZ3Xa4ET3L0unudE4MJ42vHufkuma2/S01AiImWutaehcpYs8knJQkSk/fLy6KyIiJQOJQsREclIyUJERDJSshARkYxKsoPbzFYQnrZK6Au8l6dw8k33Xp7K9d7L9b4hO/de5e7NvtVcksmiMTOra6mHv9Tp3nXv5aRc7xtyf+9qhhIRkYyULEREJKNySRaT8h1AHuney1O53nu53jfk+N7Los9CREQ6plxqFiIi0gFKFiIiklHJJwszO9zM5prZvDjvd8kys5vNbLmZzU4p29rMppvZm/Fr73zGmAtmNsjMHjez18xsjpmdGcvL4d67m9nzZvZyvPdfxvLtzOy5+HM/2cy65TvWXDCzCjN70cweiOtlcd8AZjbfzF41s5fMLDFKd85+5ks6WZhZBfB74AhgGPDfZjYsv1Hl1K00naP8fOBRdx8CPBrXS80G4Bx3HwbsB5we/53L4d4/BQ529z2APYHD45wwvwYmuvuOwCrgpDzGmEtnAq+nrJfLfSd8xd33THm/Imc/8yWdLIAvAvPc/W13XwfcDYzKc0w54+5PASsbFY8CbovLtwFHdWpQncDdl7j7rLj8IeGXxwDK497d3T+Kq13jx4GDgT/H8pK8dzMbCHwN+GNcN8rgvjPI2c98qSeLAcDClPVFsayc9I+zDgIsBfrnM5hcM7NqYDjwHGVy77Ep5iXCNMXTgbeA1e6+Ie5Sqj/3vwXOAxrieh/K474THHjEzGaa2dhYlrOf+XZOOi3FzN3dzEr2WWkz2xL4C3CWu68Jf2gGpXzv7r4R2NPMegH3AjvnOaScM7OvA8vdfaaZjch3PHlykLsvNrNtgOlm9kbqxmz/zJd6zWIxMChlfWAsKyfLzGxbgPh1eZ7jyQkz60pIFLXu/tdYXBb3nuDuq4HHgf2BXmaW+GOwFH/uDwS+YWbzCc3LBwNXU/r3/Rl3Xxy/Lif8kfBFcvgzX+rJ4gVgSHxCohtwHDA1zzF1tqnAmLg8Brg/j7HkRGyrvgl43d2vStlUDvfeL9YoMLMewKGEPpvHgW/H3Uru3t39Ancf6O7VhP/Xj7n7aEr8vhPMbAsz65lYBg4DZpPDn/mSf4PbzI4ktG1WADe7+/g8h5QzZnYXMIIwVPEy4GLgPmAKMJgwbPsx7t64E7yomdlBwNPAqyTbry8k9FuU+r3vTujIrCD88TfF3S81s+0Jf3FvDbwIfNfdP81fpLkTm6HOdfevl8t9x/u8N652Ae509/Fm1occ/cyXfLIQEZGOK/VmKBERyQIlCxERyUjJQkREMlKyEBGRjJQsREQkIyULKRtmtjGO0DknjtJ6jpltFrfVmNk1rRxbbWbf6bxom1y/h5k9GYf2GJEYZXUTz3W3mQ3JZnxS+pQspJz8O47QuSvh5bUjCO+i4O517v6jVo6tBvKWLIATgb/GoT066jrCmEoibaZkIWUpDpEwFjjDgs/+WjezL8cayEtxroSewBXAf8SyH8eaxtNmNit+DojHjjCzJ8zsz2b2hpnVxjfMMbN9zOyfsVbzvJn1jDWFCWb2gpm9YmantBDyaJp5Gzee80Uz28HMLjGz22Jc9Wb2TTO7Ms558HAcEgXCC4yHpAyLIZKRkoWULXd/m/Dm8zaNNp0LnO7uewL/AfybMC/A07FmMpEw5s6h7r4XcCyQ2oQ1HDiLMIfK9sCBcbiZycCZce6JQ+J5TwI+cPd9gH2AH5jZdqnBxGO3d/f5jcoPAK4HRrn7W7F4B8I4Sd8A7gAed/fd4rW+Fu+7AZgH7NG+75iUM/1lIdLUDOAqM6slNP0sSh3BNuoKXGtmewIbgZ1Stj3v7osA4tDh1cAHwBJ3fwHA3dfE7YcBu5tZYjyjrYAhwDsp5+sLrG50/V2AScBh7v5uSvlD7r7ezF4lJMKHY/mrMY6E5cAXgJmtfytEAiULKVtxfJ2NhF+cuyTK3f0KM/sbcCQww8xGNnP4jwnjb+1BqKF/krItdSyijbT+/8yAH7r7tFb2+TfQvVHZklg2HEhNFp/Ge2gws/WeHM+noVEc3eN5RdpEzVBSlsysH6EJ51pvNECame3g7q+6+68JIxfvDHwI9EzZbStCTaEB+B7hr/jWzAW2NbN94jV6xj6DacC4RH+Cme0URxH9jLuvAirMLDVhrCY0K12+ifM57EQYpVSkTVSzkHLSIzYLdSXM2/0n4Kpm9jvLzL5C+Gt8DvBQXN5oZi8T5jr/A/AXMzue0NTzcWsXdvd1ZnYs8Ls4lPi/Cf0WfyQ0D82KHeEraH4qzEeAg4C/p5xzmYVJgB4ysxPb9B0AzKw/4cmwpW09RkSjzooUATPbC/ixu38vC+f6MbDG3W/qeGRSLtQMJVIE3H0W8LiZZWruaovVhDkwRNpMNQsREclINQsREclIyUJERDJSshARkYyULEREJCMlCxERyej/Ax5zNg0pkgAUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3vaivyx8lE4t",
        "outputId": "26106c40-c2a7-4d10-a021-fda45be30ddb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "lin_reg2.coef_"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.00000000e+00,  2.52346741e+02, -8.53235354e+00,  1.63897871e-01,\n",
              "       -1.30360123e-03])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    }
  ]
}