Task1_supervised_ML.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Task1_supervised ML.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOXahnsc59YJ6VcoHvMRoCR",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shreyash0502/MLmodels/blob/main/Task1_supervised_ML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UsyH1CyJ9Eh2"
      },
      "source": [
        "<h1><b>This is my project for prediction using Supervised Machine Learning.</h1>\n",
        "\n",
        "```\n",
        "#Here, I will implement the Linear Regression ML algorithm for my model.\n",
        "```\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I7b4S0oq-amw"
      },
      "source": [
        "First, we import all the necessary libraries for developing the Linear Regression model.\n",
        "\n",
        "---\n",
        "```\n",
        "# Here, we will use the sci-kit learn python library.\n",
        "# We will also use python libraries- pandas(for data formatting and manipulation) and numpy(for array manipulation).\n",
        "# Finally, I will use matplotlib library for visualization. \n",
        "```\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_msvJGngJJoM"
      },
      "source": [
        "from sklearn import linear_model\n",
        "from sklearn.model_selection import train_test_split\n",
        "from matplotlib import pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import metrics\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lplxUUJTAlvp"
      },
      "source": [
        "Now, we will import the data from the link and store it in a variable. \n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "udRiGh7CVHVN",
        "outputId": "209b9f1b-25ec-4778-a3d3-7dd8892827b9"
      },
      "source": [
        "data = pd.read_csv(\"http://bit.ly/w-data\")\n",
        "display(data.head(5))\n",
        "print(\"Data Imported Successfully!\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "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>Hours</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.5</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.1</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.2</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.5</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.5</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Hours  Scores\n",
              "0    2.5      21\n",
              "1    5.1      47\n",
              "2    3.2      27\n",
              "3    8.5      75\n",
              "4    3.5      30"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Data Imported Successfully!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e6c996ttBYwt"
      },
      "source": [
        "I will now manipulate the data variable and store the features and labels in separate variables \"X\" & \"y\".\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AmmEElvkgo4i"
      },
      "source": [
        "#X = data.iloc[:, :-1].values  \n",
        "#y = data.iloc[:, 1].values\n",
        "X = data[[\"Hours\"]].values\n",
        "y = data[[\"Scores\"]].values\n",
        "#y = y.T\n",
        "#y = y.reshape((-1,))\n",
        "#print(X.shape, y.shape)\n",
        "#print(X, y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2BR9V3oCFA_L"
      },
      "source": [
        "Visualization of data is an important aspect. With visualization, we can understand better and develop human insights like developing trends and patterns in data. \n",
        "\n",
        "Now, we will visualize the data.\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "zjLeV3d8h_Lq",
        "outputId": "120799b3-09b4-4ca4-d053-c8750c8964c8"
      },
      "source": [
        "l_reg = linear_model.LinearRegression()\n",
        "plt.figure(figsize = (10, 8))\n",
        "ax = plt.axes()\n",
        "ax.set_facecolor(\"#f3faff\")\n",
        "plt.grid()\n",
        "plt.scatter(X, y, s = 100)\n",
        "plt.legend([\"Scores\"])\n",
        "plt.title('Hours vs Percentage')  \n",
        "plt.xlabel('Hours Studied')  \n",
        "plt.ylabel('Percentage Score')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QPcp57xyEq2-"
      },
      "source": [
        "Now, we have successfully imported the data and visualized it. We can now train our Linear Regression model as shown below. \n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hf5Nc0tPipIU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "35277a09-defc-4e11-d9a8-97026fcbf79d"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
        "model = l_reg.fit(X_train, y_train)\n",
        "print(\"Training complete!\")\n",
        "m = l_reg.coef_\n",
        "b = l_reg.intercept_\n",
        "line = m*X + b\n",
        "plt.figure(figsize = (10, 8))\n",
        "ax = plt.axes()\n",
        "ax.set_facecolor(\"#f3faff\")\n",
        "plt.grid()\n",
        "plt.scatter(X, y, s = 100)\n",
        "plt.legend([\"Scores\"])\n",
        "plt.title('Hours vs Percentage') \n",
        "plt.plot(X, line) \n",
        "plt.xlabel('Hours Studied')  \n",
        "plt.ylabel('Percentage Score')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training complete!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WBuaqnnNEJcC"
      },
      "source": [
        "We can verify our model has trained correctly by comparing its result on the test-set with the actual values. \n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "GMF0hrZokxoW",
        "outputId": "366bac9a-2459-46c9-8dce-78e42cc61d0a"
      },
      "source": [
        "y_pred = model.predict(X_test)\n",
        "#print(y_test.shape, y_pred.shape)\n",
        "y_pred = y_pred.reshape((-1,))\n",
        "y_test = y_test.reshape((-1,))\n",
        "#print(y_test.shape, y_pred.shape)\n",
        "#y_pred.reshape(1, -1)\n",
        "#print(y_pred)\n",
        "#print(y_test)\n",
        "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
        "df"
      ],
      "execution_count": null,
      "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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>75</td>\n",
              "      <td>86.468742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>41</td>\n",
              "      <td>46.778668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>42</td>\n",
              "      <td>34.871646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>95</td>\n",
              "      <td>90.437749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>25</td>\n",
              "      <td>28.918135</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Actual  Predicted\n",
              "0      75  86.468742\n",
              "1      41  46.778668\n",
              "2      42  34.871646\n",
              "3      95  90.437749\n",
              "4      25  28.918135"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cst5QN0IDtFO"
      },
      "source": [
        "Now that we have trained the model and cross-checked it on test-set, We can make predictions using our own data and get the output. \n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rKKKFZzw1hck",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "outputId": "2c8a1cc8-61da-4698-b2c5-514dbe93fa4a"
      },
      "source": [
        "#on custom data\n",
        "hours = input(\"Enter hours of study: \")\n",
        "#hours = 9.25\n",
        "hours = np.array(hours, dtype='float64')\n",
        "prediction = model.predict(hours.reshape(1, -1))\n",
        "print(\"Prediction = {}\".format(prediction[0][0]))\n",
        "#plt.figure(facecolor = \"blue\")\n",
        "plt.figure(figsize = (10, 8))\n",
        "ax = plt.axes()\n",
        "plt.legend([\"Scores\"])\n",
        "plt.title('Hours vs Percentage') \n",
        "plt.plot(X, line) \n",
        "plt.axvline(x = hours, linestyle = \"dashed\")\n",
        "ax.set_facecolor(\"#f3faff\")\n",
        "#plt.annotate(\"Input Hours\", (hours, 10))\n",
        "plt.annotate(s = \"(\"+str(hours)+\", \"+str(\"{:.2f}\".format(prediction[0][0]))+\")\", xy = (hours, prediction[0][0]), xytext =(hours+2, prediction[0][0]-2), arrowprops = dict(facecolor ='green', shrink = 0.005))\n",
        "plt.axhline(y = prediction[0][0], linestyle = \"dashed\")\n",
        "plt.xlabel('Hours Studied')  \n",
        "plt.ylabel('Percentage Score')\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter hours of study: 5.5\n",
            "Prediction = 56.701186623516726\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4WVHHFLoSS8FLRCSB+ndq4XUJIgflgy83M2pqQaQ98fzjOaNHGw8rykwKXiIiCbRsww4AurZq7HElIvGLnVpcPO50auVke1RNZtNdjSIiCTRh1udMmPW512WIxK289VwKXcmj4CUiIuJD3+7YGxW6WjSorfVcNUBTjSIiIj7zy6cW8p9lGyPt6aMH0K1NEw8r8g8FLxERER/RVhHe0lSjiIiIDwSDTqErBWjES0QkgW48tYvXJYgcYPZnG7h5+uJI+9qTjuSGU/S76gUFLxGRBOrZvpnXJYhEiR3lKrhtKPVq68+/V/SdFxFJoCVffwcogElq0NRi6tEaLxGRBLp/9nLun73c6zLE57bs2hcVumrnZCl0pQiNeImIiGSQsTM/4pWP10faj1/dn16HawQ2VSh4iYiIZAhNLaY+TTWKiIikOee0VUS6UPASERFJY0s3l9D9jlmR9qX9Oyp0pTBNNYqIJNDY4cd4XYL4SOwo1we3nkajurkeVSPxUPASEUmgrq0ae12C+ISmFtOTphpFRBJo3sotzFu5xesyJIPt2FOk0JXGNOIlIpJAD81ZAUD/Ti08rkQy0e9fXsqMgq8j7UdG9iVrw6ceViTVpeAlIiKSBmJHuZbeNRwzo2CDRwXJQUnqVKOZ3Wxmn5nZp2Y2zczqmFlHM1tgZl+a2Qwzq5XMGkRERNJdeVOLZuZRNXIokha8zKwN8Eugj3OuG5ANXAxMBO51zh0JbAOuSlYNIiIi6azgqy1Roevc3u20nivNJXuqMQeoa2bFQD1gA3AycEn4/FRgPPCvJNchIiKSVmJHud4beypN62uSKN0lLXg559ab2V+Ar4G9wJvAImC7c64k/LB1QJtk1SAiUtPGnd3d6xIkA+iuxcxlzrnkXNisKfAccBGwHXgWmAmMD08zYmbtgNfCU5Gxzx8FjALIy8vr/fS06Qmpa/euXdRv0CAh10o3fu27X/sN/u27X/sN6nu6931PseMX/9kTdezRofUrfU4m9PtgpWrfh5w8eJFzrk9555I51XgKsMo5txnAzP4NDACamFlOeNSrLbC+vCc75yYBkwB69e7j8gcOSkhRBXPnkKhrpRu/9t2v/Qb/9t3Lfs9ZthGAQV3zPHl9v/7MIf37Pv7FT5i5cG2k/cBP+8T1e5Tu/T4U6dj3ZAavr4F+ZlaP0FTjEGAh8F/gfGA6cDnwYhJrEBGpUVPe/wrwLnhJeqpoqwjJPEm7q9E5t4DQ1OJiYGn4tSYBY4BfmdmXQHNgcrJqEBERSXXaKsJfknpXo3NuHDAu5vBXQH4yX1dERCTVfbhqK1c8Oj/Szu/YnEev7OdhRVITtHO9iIhIDYsd5XrrNyfzg8Z1PapGapKCl4iISA3SVhH+puAlIpJA95x/vNclSIraVVhMv7vfjDqm0OU/Cl4iIgnUStNFUo6bpi3irc+/jbRvPrUrV53YycOKxCsKXiIiCfTa0m8AGNa9tceVSKqInVr8+M7hZGfprkW/UvASEUmgGQVrAAUvCdF6LomVtH28RERE/Gr+yi1RoSs7yxS6BNCIl4iISELFjnK9eMOJdDqsoUfVSKpR8BIREUkQTS1KVTTVKCIicoj2FgUUuiQuGvESEUmgey/u7XUJUsNuf/5jnl+8LtK+9qQjueGULh5WJKlMwUtEJIGa1q/ldQlSg2JHuZaMH0ZutiaTpGIKXiIiCfTC4rUAjOjVzuNKJNk0tSgHQ7FcRCSBXliyjheWrKv6gZK2Pvp6m0KXHDSNeImIiMQpNnA9c91Ajmnd2KNqJB0peImIiMRBo1ySCJpqFBERqcS+Ym0VIYmjES8REZEK/GbGYl7/dEOkfcZxrZl4QU8PK5J0p+AlIpJA/7o03+sSJEFiR7kW3nE6dXKzPapGMoWCl4hIAtWtpT/MmUBTi5IsWuMlIpJA0xesZvqC1V6XIQdpydffKXRJUmnES0QkgUrXA13ct4O3hUi1xQauhy7PZ8CRLT2qRjKVgpeIiPieRrmkpmiqUUREfKs4EFTokhqlES8REfGlu15ayjMffh1p53dszqNX9vOwIvEDBS8REfGd2FGuBbcNpX5t/UmU5NNvmYhIAk25qr/XJUgVNLUoXtIaLxER8YVlG3YodInnNOIlIpJAj81dCcAVAzt5XImUFRu47vtJb0455gceVSN+puAlIpJA7yzfBCh4pRKNckkq0VSjiIhkpEDQKXRJytGIl4iIZJx731zG5PdWRtpdftCI564/wcOKREIUvEREJKPEjnK9f8upNK5Xy6NqRKIpeImIJFCd3GyvS/A1TS1KqlPwEhFJoAcvy/e6BF9atXkXV76xO+qYQpekIgUvERFJa7GjXH88rwdnH9/Wo2pEKqfgJSKSQA/+dwUAowd39rgSf9DUoqQbbSchIpJA87/awvyvtnhdRsZz7sCtIh4dWt+jakTip+AlIiJp5eF3vqT7HbMi7bxGdTTSJWlDU40iIpI2Yke55vxuCC0a1vGoGpHqS9qIl5l1MbOPynx8b2Y3mVkzM5ttZivCn5smqwYREckc5a3nUuiSdJO04OWcW+6cO945dzzQG9gDPA+MBd52znUG3g63RUQyQpN6tWiizToTav22PVpELxmjpqYahwArnXNrzOwcYFD4+FRgDjCmhuoQEUmq+37S2+sSMkrP8a9RHAhG2redeSwX9+3gXUEih6imgtfFwLTw13nOuQ3hr78F8mqoBhERSSOxo1xL7xqOmXlUjUhimHMuuS9gVgv4BjjWObfRzLY755qUOb/NOXfAOi8zGwWMAsjLy+v99LTpCaln965d1G/QICHXSjd+7btf+w3+7buX/Z75vyIAzj/Km+nGTPiZO+e46s09Ucfi2SoiE/p+MPzab0jdvg85efAi51yf8s7VxIjXMGCxc25juL3RzFo55zaYWStgU3lPcs5NAiYB9Ordx+UPHJSQYgrmziFR10o3fu27X/sN/u27l/3+v+XzAMgf2N+T10/3n/m0Bau5+5XPIu1aOVksHjcsrueme98Pll/7DenZ95oIXj9h/zQjwEvA5cCE8OcXa6AGERFJcbFTi2/+ejCtm9TzqBqR5Ehq8DKz+sCpwLVlDk8AnjGzq4A1wIXJrEFERFKf7loUv0hq8HLO7QaaxxzbSuguRxER8bnNOwsZ/Ke3o44pdEkm0871IiIJlNdIG3rGa8if32bj94WR9k2nduHqE4/0sCKR5FPwEhFJoIkX9PS6hLSgrSLErxS8RESkRmk9l/hZ0t4ySETEjybM+owJsz6r+oE+9OKSdQpd4nsa8RIRSaBlG773uoSUFBu4Xv7lSXRsmXobX4okm4KXiIgklUa5RPbTVKOIiCTF9j1FCl0iMTTiJSIiCXfuA+/yv407I+2rTujEzad19bAikdSg4CUikkAdWlT9Zs6ZLnaU65M7h5OVpa0iREDBS0Qkocafc5zXJXhKU4sildMaLxEROWSzP9ug0CUSB414iYgk0PgXPwl99tHIV2zgmvnzE+jaqpFH1YikNgUvEZEEWr1lt9cl1CiNcolUj6YaRUSk2nbvK1HoEjkIGvESEZFqueLReXy46rtI+6L89tx+VncPKxJJHwpeIiISt9hRriXjh5GbrckTkXgpeImIJFAmLyrX1KLIoVPwEhFJoLHDj/W6hIR7f8Vmrn28IOqYQpfIwVHwEhGRCsWOcj15zY84vn1Tj6oRSX8KXiIiCTTm2SUATLygp8eVHDpNLYoknoKXiEgCbfy+0OsSDllhcYA+d70edUyhSyQxFLxERCTipmmLeOvzbyPtYd1b8+cL03/0TiRVKHiJiAhw4NTiojtOp3ZutkfViGQmBS8REdF6LpEaouAlIpJAPdql1x1/i9d8x2WPzIs6ptAlkjwKXiIiCXTzaV29LiFusaNcj4zsS79OLTyqRsQfFLxERHxIU4si3tAbbImIJNBN0xZx07RFXpdRoeKSoEKXiIc04iUikkDb9xR5XUKF7njhE/69aG2kPeDIljx0eb6HFYn4j4KXiIgPxI5yFdw2lHq19SdApKbpvzoRkQynqUWR1KE1XiIiGerzb3YodImkGI14iYgkUL8jUmM7htjA9Y9L+jD46DyPqhGRUgpeIiIJNHpwZ69L0CiXSArTVKOISIYIBB1XvrE76phCl0hqiWvEy8wGAp2dc4+ZWUuggXNuVXJLExFJP6MfLwDgwctqdpuGP7/+OVPf3//Pcrc2jZk+emCN1iAiVasyeJnZOKAP0AV4DMgFngQGJLc0EZH0U1gcqPHXjJ1afP/W02hcN7fG6xCRqsUz1fhj4GxgN4Bz7hugYTKLEhGR+MSGrkeH1lfoEklh8QSvIuecAxyAmdVPbkkiIlKVlZt2ahG9SBqKZ43XM2b2ENDEzK4BrgQeTm5ZIiJSkdjA9acLjmf4cW08qkZEqqPS4GVmBswAugLfE1rndYdzbnY8FzezJsAjQDdCI2ZXAsvD1+wArAYudM5tO7jyRURSy0ldDkvq9TXKJZLeKg1ezjlnZrOcc92BuMJWjPuB151z55tZLaAecCvwtnNugpmNBcYCYw7i2iIiKeeKgZ2Scl3nHN3vmBV1TKFLJP3Es8ZrsZn9sLoXNrPGwInAZADnXJFzbjtwDjA1/LCpwIjqXltExE8e/O+KqNDVtmk9hS6RNBXPGq++wE/NbA2hOxuN0GDYcVU8ryOwGXjMzHoAi4AbgTzn3IbwY74F9B4WIpIxRk6eB8CUq/on5HqxU4vvjDmF5g1qJ+TaIlLzLHTDYiUPMDu8vOPOuTVVPK8PMB8Y4JxbYGb3E1ondoNzrkmZx21zzjUt5/mjgFEAeXl5vZ+eNr2qvsRl965d1G/QICHXSjd+7btf+w3+7buX/Z5YsBeAMfl1D/lasbvQPzq06pvK/fozB//23a/9htTt+5CTBy9yzvUp71yVI17OuTXhEasTwofec859HMfrrgPWOecWhNszCa3n2mhmrZxzG8ysFbCpgtedBEwC6NW7j8sfOCiOl6xawdw5JOpa6cavffdrv8G/ffey3w2Xh0a88gce/IjXum17OP1v/406Fu/Uol9/5uDfvvu135CefY9n5/obgWuAf4cPPWlmk5xz/6jsec65b81srZl1cc4tB4YAn4c/LgcmhD+/eCgdEBHJJN3veJWyExF3nN2NC39Y7sSDiKSheNZ4XQX0dc7tBjCzicA8oNLgFXYD8FT4jsavgCsILeh/xsyuAtYAFx5M4SIimUZbRYhkvniClwFl33wsED5WJefcR4Te5zHWkHieLyKSbk7v1qraz9FWESL+EU/wegxYYGbPh9sjCG8RISIi0S7u26Faj39i3iomzvo80m5QO4f5tw1NcFUikiriWVz/NzObAwwMH7rCObckqVWJiKSpvUWhCYK6tbKrfGzs1OLs35xMq8aHfjekiKSueBbX9wM+c84tDrcbmVnfMncriohI2HVPFABV7+Ol9Vwi/hTPzvX/AnaVae8KHxMRkWra+H2hQpeIj8W1uN6V2WXVORc0s3ieJyIiZZw4YTbf7S6KtH89tGvS3ttRRFJTPCNeX5nZL80sN/xxI6GtIUREJE7dbn81KnQtvWs4/Zvt46c/PY+jOrX1sDIRqUnxjFyNBv4O3BZuv0X4rXxERKRqsVOLT53bmnPPG87c9+ZQdFwRud/V8qgyEalp8dzVuAm4uAZqERFJeyN67h+9+veitdzxwieRduG6L+i04SVOfXghhT8sxF3voASyP45n8kFEMkGFwcvMrgHmOOdWmJkR2rvrPEK7zY8svctRRET2G9GrHbB/lMs5R+Gaj2n51XNsWrWcD/rugeuA3PATisCysigqKiI7O5usrCxC/+SKSCaqbMTrRmBK+OufAD2AI4CewP3sf9NsEREJ27a7iBMmzMY5x96VBWyfN5Vct5VNfXfD6UDs9l45sK+4kJZN6hIMBMFBVnZWzEd2KJRlZ4U/Z5OdEzqWnZ1Fdk4OOTk5ZGdnU7SvkIaNGpOdk0NuTg7Z2aFzOTm54c855ObkkpObG/qcnUNubi65ObWolZtLbm6tMsdyyQ5ft/S5OTk5ZJW2s/e/bnaZ82UfH/X87HKO5VR+DQVRyTSVBa8S51xx+Oszgcedc1uBt0Wfwr0AACAASURBVMzsT8kvbb9VW3YzcvK8qGOnd2vFxX07sLcoENk3p6wRPdsyolc7tu0u4ubpiyLHd+7YS8Pl87go/3CGdW/Nhh17uWXmRwc8f+SAIxjUNY9Vm3dx50tLDzh/7aDO9O/UgmUbdjChzK7TpW48tQs92zdjydffcf/s5QecHzv8GLq2asy8lVt4aM6KA86PO7s7HVs2YM6yjUx5/8B7Ge45/3haNa7La0u/YUbBmgPO33txb5rWr8ULi9fywpJ1UX0H+Nel+dStlc30Bat5/dMNBzy/dA+ix+au5J3lm6LO1cnN5sHL8gF48L8rmP/VlqjzTerV4r6f9A7V8eYyPl67Lep8XqM6TLygJwATZn3Gsg3fR53v0KI+4885DoDxL37C6i27o853bdWIscOPBWDMs0vY+H1h1Pke7Zpy82ldAbhp2iLWbtjfb4B+R7Rg9ODOAIx+vIDC4kDU80/qcljkTrPY3zs4+N+9UjX5uzexILrv4M3vXlmZ/LvXtVVjnpy3it3L57L9gykEbDvuhEKKj6HiW5nqQMlvS/a3gxB0QYLBIAQ58MOVc6y6jykCCuO5hkHQsGAW5oxsssgmCwJQXOLC5yz8eKN2Vuh8oMSxryiIBcEFXeSatbOzyHJGSUmQ4uIALggEHQQdLujIwXBBRzAQJBAIhs45B1mGZWWRk5NNdk4WzrJC711nWVh2FpaVjWVlUS/XqF+/HvsCsKfEhc9nh89n0ymvEbVya7F1TwnbCwOR41lZOWRl5zDgqFbkZOfw1da9bNxZQlZWTuh8di61cnL5cZ8OZGVn8+Hq7azdXki95j+gzfEnef67t3PHXgbuWRb17972PUVRz8/Uf/dK/66l4t/cilQWvIJm1grYRui9Fe8uc05bK4uIlLFi404Wrv4OV1LMd/95CJf1Pe7iAPygmhcqDWhVb3xfAxzgcAQjWa64kkeXVHIunvNF5R0MAs7hggGKgwGKKwmZhUH4rpLzi6oIqTO2ljmeE/5cEu50ED5928gKZuGChtsLObPrRoKXSLyszBZd0SfMzgQeIvSf/8vOuWvCx08Cfuecq7Ed/3r17uPenb8wIdcqmDuH/IGDEnKtdOPXvvu13+Dfvtd0v2PvWuzZpj4/LFzEhAl3UdK+mL0D90KLSi5QmkiyAAt/SGrbBs2eac6aNVuqfmyS+fW/c0jdvjesZYucc33KO1fhiJdz7hUzOxxo6JwrO2a6ELgowTWKiKSl2NDVp0MzAH45+tdcefW1PPCPe7nv3okEOgco/FEhNI25wHawv4em0oIloemfrOzQFFpWtmHhabasbMOyDSz02bJK22DZQJZBFgQCJWTXygmFuCwgy+Es9LXLcmAOZw6XFfoczHI4C4aOmSNoQYIWLPN89gfC2GOJPF/RY1I1iAYhKyslhiUlzVS6nYRzroTQVGPZY7sreLiIiG/MXbGJ0Y9/GGk3rJPDvP83NGp9TIMGDRh7y+2Mvu4G/vaXe3jwwQcIHhNg34/2QaPwgwqhzRHt+OLz0LqRYDBIIBCgpKQk8hEIBAiUaR9wLFASec6niz+k8zHdD7hGsGw7UPE1S0pKCJSUUFxSTElJMUXFxRQXF1FUUkRJoISS4mKKi4tD5wMllJQUU1wY+hx6fjHF4WsEAgGKi4sJBgOUlATCx4KR1w4EAgQDQYKBAIFAkGBJgGAwGFnjFbrZwO0PolmloTMURENrv/YH0aALkJ2bExVEDwxzDgxcRUHUHC6riiBqwF7IzokOXnv37uXHZ57Oq2/+h+zsbBrXyebYbt0BaNuuPc88/9IBv0dPPj6F28b+ltat2wAw6ue/YOSVV1f6uzfslEF8u2EDdeuGVv2MGzcucu7fzz7DH38/HjOj+3E9ePSJp6Oeu3PnToYO3n9v3Pr167j4kp8x8a/3sW/fPkZdcRkfLVlEs2bNmfLUDA7v0IHPli7l7/f9lYcmT6m0LomP3vpHRKSaYke5XrnxJDq0aACEFhHHatKkCXf9YSI33PgbJk64i6kPP0KgR4Di/sXgov+AZ2VlkZWVRW5u7gHXiUdWoCglp14OVjxBtPT8RwUfcPRxvQgEKwmdcQbRsq8ZCD+muLgoEkRLAiV0Of+YqFqfmPIoZ484l+zs0M+zbt26fLDwwIXksc674CL+ev8D1fq+TH78KXr1Ds1kFcydA8CXK1bw1z/dw+x33qdp06Zs3rTpgOc1bNgwqqYT+vbmrBHnAvD4Y5Np0rQpH3/xJTNnTOeOW8cw9ekZHNu9O9+sX8far7+mXfv21apTDqTgJSJSDVW9wfWw7q0rfG7Lli35y1//wa9+NZbf/+F2Zj44jeIOxeTk6J/iilQniG7fvIHuPXrUQFXlmzHtKR59/OmqH5gkUyY/zDXXXU/TpqH57JaHHVbp41f8739s3ryJAQNDI2Cvvvwit9w+HoAR553Pr2/6Bc45zIxhZ5zFzGemc/NvfpfUPvhBldslW8jPzOyOcLu9meUnvzQRkdSxe19JlaELYMOOvWzYsbfSa7Vu04Z//etRPlz4Oed0O48unY9OaK1S84qKili96isO79AhcqywsJAT+/Vh8MB+vPziCxU+98Xnn6Nfr+P42UXns27t2rhe77qrr+BHfY5n4t2/p/QmuS9X/I8v//c/TjlpAIMH9mP2G69Xeo3nnpnOuRdcFNkn7Zv162nbNrQBcE5ODo0bN2br1q0A9Ozdhw/mvhdXbVK5eP436/8I3VR7MnAXsBN4DvhhEusSEUkZv3tmCbOWfhNp3zDkKK4d1Lncx5buUVS6H1llOnTsyNSpMxJTpHhq65YtNG7cJOrY51+uoXWbNqz66ivOHHoyx3brzhGdOkU9ZtgZZ3HBRT+hdu3aPPrwQ1x71eW8+uZ/Kn2tyVOfonWbNuzcuZOfXXQexfv20PeEwZQESlj55Qpee2sO69et4/QhJzJ/8VKaNGlS7nVmPjOdh6c8EVf/WrY8jG83fFP1A6VK8bxBWF/n3PWEttsjfIej3tFVRHyh2+2vRoWuj+8cXmHoEv+qU7cu+/ZFb6jbuk1owXzHI45g4ImD+OSjJQc8r3nz5tSuXRuAy6+8mo8WH7j5aKzS6zZs2JALLr6EZV8sA6BNm7YMP+tscnNz6dCxI0d2PoqVXx64WSjA0o8/pqSkhJ69ekddd9260IhbSUkJO3bsoHnz5gDsKyykTl1t4ZkI8QSvYjPLJrTNHGbWktAImIhIRitvajE7KxX3NhCvNW3alEAgQGFhKHxt27aNffv2AbBlyxYWzHufrkcfc8Dzvt2w/90bXn35JY7qun/auVe3rgc8vqSkhC1bQnuHFRcX8/qrr9CxY0cAzjx7BO+9Myfyml+u+B8dOh5Rbr3PzpjGBRf9JOrY8DPP5uknpgLwwnMzOWnQyZFpyC9X/I9jju1W9TdCqhTPVOPfgeeBw8zsbuB84LakViUi4qGFq7cycvL8qGPlrecSKevkU05j3vtzGTzkFJYv+4Ibf34tWVlZBINBbv7tWLoeEwpefxh/Bz179+GMs87mXw/8nVmvvEROTg5NmzXjwUemAKHgVN4G5/v27ePHZwyluDi0fcfgIacw/IzQ7+Yppw3l7bfepM9xx5Cdnc0f7vlzZMTqR32Oj7qb8fnnnmHmi7Oirn3ZFVdxzchL6XH0kTRt2ozHnpweOffuO/9l6DD9N5AIVQYv59xTZraI0NsGGTDCOfdF0isTEfFA7CjXc9efQJcfNKrg0SL7jbruev55/70MHnIK/fr/iAVLDnzPQYDbxt8V+frOu+/hzrvvOeAxHy6Yz6jrrj/geP369XlvQfR0ZOl2EmbGhD//Df78twOeF7utxdLlB74fYZ06dXhi+rMHHN+3bx+LFy1k4l/vK7c/Uj1VBi8zawZsAqaVOZZb5g20RUQyQjx3LVZl5IDyp3Yk8x3fsxcnDBpMIBCI7OV1sIadcWaCqjp0a7/+mrvunqBtTxIknu/iYqAdoR3sDWgCfGtmG4FrnHNVrwQUEUlhhcUB+twVfev9wU4tDuqal4iSJE1dNvJKr0tIuCM7d+bIzrqhJFHiCV6zgZnOuTcAzOw04DzgMUJbTfRNXnkiIsl110tLeebDryPtKwYewa+HHvy+Wqs27wKgY8sGh1ybiGSeeIJXP+fcNaUN59ybZvYX59y1ZlY7ibWJiCRV7NTiknHDyM2J52bvit35UmhdTzz7eImI/8QTvDaY2Rig9PaGi4CN4S0mtK2EiKSlRKznEhGprnj+1+4SoC3wQvijffhYNnBh8koTEUm8peu2K3SJiGfi2U5iC3BDBae/TGw5IiLJExu4pl07gO5ty387FRGRZIhnO4mWwO+AY4E6pcedcycnsS4RkYTSKJeIpIJ41ng9BcwAzgRGA5cDm5NZlIhIohSVBOh1Z2K2ioiH3sdRRCoTT/Bq7pybbGY3OufeAd4xsw+TXZiIyKG6ZeZHvPzx+kj7pC6H8c+f/TCpr9m/U4ukXl9E0ls8wat0h/oNZnYG8A3QLHkliYgcutipxQ9vP526tQ5tN/F4LNuwA4CurRon/bVEJP3EE7z+YGaNgV8D/wAaATcltSoRkUNw5Ru7o9o1uZ5rwqzPAe3jJSLliyd4bXPO7QB2AIMBzGxAUqsSETkIn67fzsUPvh99TIvoRSSFxBO8/gH0iuOYiIhnYqcW//mzPpzURe+bKCKppcLgZWb9gR8BLc3sV2VONSK0eaqISEqIDV2PDq1PvkKXiKSgynaurwU0IBTOGpb5+B44P/mliYhUriQQ1P5cIpJWKhzxKrN1xBTn3JoarElEpEr3vPoZT81fHWn3aNeEp0Z5v/z0xlO7eF2CiKSweNZ41TazSUCHso/XzvUi4pXYUa55/+80GtbJ9aiaaD3ba7cdEalYPMHrWeBB4BEgUJ2Lm9lqYGf4eSXOuT5m1ozQTvgdgNXAhc65bdW5roj4V6pPLS75+jtAAUxEyhdP8Cpxzv3rEF5jcPiNtkuNBd52zk0ws7Hh9phDuL6I+MCKjTv58QPvRh1LtdAFcP/s5YD28RKR8sUTvF42s58DzwP7Sg865747yNc8BxgU/noqMAcFLxGpROwo118u7Mnp3Vt7VI2IyMEz51zlDzBbVc5h55w7osqLh567DXDAQ865SWa23TnXJHzeCG3Q2qSc544CRgHk5eX1fnra9Co7E4/du3ZRv0GDhFwr3fi1737tN2RG32N3oX90aP0qn+NlvycW7AVgTH5dT14/E37mB8uvffdrvyF1+z7k5MGLnHN9yjtX5YiXc67jIbz2QOfcejM7DJhtZstiru3MrNzk55ybBEwC6NW7j8sfOOgQytivYO4cEnWtdOPXvvu135DefQ8GHceNmxV1LN6pRS/73XD5PADyB3oz1ZjOP/ND5de++7XfkJ59r2wfLwDMrJ6Z3Ra+sxEz62xmZ8Zzcefc+vDnTYSmKvOBjWbWKnytVsCmgy1eRDLT719eGhW6crItJddziYhUVzxrvB4DFhHaxR5gPaE7HV+p7ElmVh/Ics7tDH99GnAX8BJwOTAh/PnFgytdRDJR7HquOb8bQouGdTyqpvrGDj/G6xJEJIXFE7w6OecuMrOfADjn9oTXZlUlD3g+/NAc4Gnn3Otm9iHwjJldBawBLjzI2kUkw6T6VhHx6NqqsdcliEgKiyd4FZlZXUIL5DGzTpS5u7EizrmvgB7lHN8KDKlmnSKSwVZu2sk5/0j9rSLiMW9laPec/p1aeFyJiKSieILXOOB1oJ2ZPQUMAEYmsygR8Y/YUa7fnn40lw+o8qbplPXQnBWAgpeIlC+euxpnm9lioB9gwI0xG6KKiByUTJhaFBGpjnjuavwxod3rX3XOvQKUmNmI5JcmIpnKOafQJSK+VGXwAsY553aUNpxz2wlNP4qIVNv9s5fR/Y6D259LRCTdxbPGq7xwFs/zRESixI5yvfnrwbRuUs+jakREal48AWqhmf0N+Ge4fT2hfb1EROLml6nFcWd397oEEUlh8Uw13gAUATOA6UAhofAlIlKl9dv2+CZ0AXRs2YCOLVPvveNEJDVUOuJlZtnAK865wTVUj4hkkNjANXrQkfxiSBePqqkZc5ZtBGBQ1zyPKxGRVFRp8HLOBcwsaGaNyy6wFxGpSmzoWnrXcOJ704v0NuX9rwAFLxEpXzxrvHYBS81sNrC79KBz7pdJq0pE0pqfphZFRKojnuD17/CHiEilHp27kr+9sSzqmEKXiMh+8excPzX8Xo3tnXPLa6AmEUlDsaNcL//yJC0yFxGJEc/O9WcBHxF6v0bM7HgzeynZhYlI+ihvalGhS0TkQPFMNY4H8oE5AM65j8wsfd/BVkQSZvPOQgb/6e2oY36fWrzn/OO9LkFEUlg8wavYObcj5m6kYJLqEZE0cdwdrxJ0+9uX9D2cW8/s5l1BKaJV47pelyAiKSye4PWZmV0CZJtZZ+CXwAfJLUtEUlns1OIndw4nKyvzt4qIx2tLvwFgWPfWHlciIqko3p3rjwX2AU8DO4CbklmUiKSu8tZzKXTtN6NgDTMK1nhdhoikqApHvMysDjAaOBJYCvR3zpXUVGEiklqe/fBr7nxpadQxv6/nEhGprsqmGqcCxcB7wDDgaDTSJeJLsaNcM38+kK6tGntUjYhI+qoseB3jnOsOYGaTgYKaKUlEUol2oRcRSZzK1ngVl36hKUYR/9m+p0ihS0QkwSob8ephZt+HvzagbrhtgHPONUp6dSLiiZMmvsXWXfsi7TOOa83EC3p6WFH6uPfi3l6XICIprMLg5ZzLrslCRCQ1xI5yfTR+GDnZ8dwALQBN69fyugQRSWHx7OMlIj6hqcVD98LitQCM6NXO40pEJBXpf2NFhFmfrFfoSpAXlqzjhSXrvC5DRFKURrxEfC42cD1+dX96Hd7Mo2pERDKbgpeIj2mUS0SkZmmqUcSHdu8rUegSEfGARrxEfObH/3iXFZt2RtoDO7fkwcvyPaxIRMQ/FLxEfCR2lGvxuNOplaOdYxLpX5cqxIpIxRS8RHxCU4s1o24tBVkRqZjWeIlkuDnLNyp01aDpC1YzfcFqr8sQkRSlES+RDHblG7uBhZH2Q5fnM+DIlt4V5AOvf7oBgIv7dvC2EBFJSQpeIhlKo1wiIqlHU40iGaawOKDQJSKSojTiJZJBrnh0Hh+u+i7S7tgoi5d/O8zDikREpCwFL5EMETvKVXD7UD4tmOtRNSIiUh4FL5EMoKnF1DHlqv5elyAiKUxrvETSWMGqrQpdIiJpRCNeImkqNnDde3EvTj22lUfVSKnH5q4E4IqBnTyuRERSUdJHvMws28yWmNkr4XZHM1tgZl+a2Qwzq5XsGkQyTXmjXApdqeGd5Zt4Z/kmr8sQkRRVE1ONNwJflGlPBO51zh0JbAOuqoEaRDJCcSCoqUURkTSW1OBlZm2BM4BHwm0DTgZmhh8yFRiRzBpEMsXN0xbRc/xrkXabJnUVukRE0ow555J3cbOZwD1AQ+A3wEhgfni0CzNrB7zmnOtWznNHAaMA8vLyej89bXpCatq9axf1GzRIyLXSjV/7ngn9Dr31z34PnFyPerlW5fMyoe8Hw8t+TyzYC8CY/LqevL5ff+bg3777td+Qun0fcvLgRc65PuWdS9riejM7E9jknFtkZoOq+3zn3CRgEkCv3n1c/sBqX6JcBXPnkKhrpRu/9j3d+30oU4vp3veD5WW/W35VAED+wHxPXt+vP3Pwb9/92m9Iz74n867GAcDZZjYcqAM0Au4HmphZjnOuBGgLrE9iDSJpa9mG7zn//96LOqapxdT34GXeBC4RSQ9JC17OuVuAWwDCI16/cc791MyeBc4HpgOXAy8mqwaRdKWtIkREMpMX+3iNAaab2R+AJcBkD2oQSVm6azG9PfjfFQCMHtzZ40pEJBXVSPByzs0B5oS//grQWLxIjGDQcdy4WVHHFLrSz/yvtgAKXiJSPu1cL5IC7pu9jEfeXRlpd85ryPO/ONHDikREJBkUvEQ8Fju1OPeWU2lST2/oICKSiRS8RDyk9VwiIv6i4CXigdVbdnHm/e9EHVPoygwarRSRyih4idSw2FGuu8/twTk923pUjSTafT/p7XUJIpLCFLxEapCmFkVE/C2pb5ItIiHOOYUun7j3zWXc++Yyr8sQkRSlES+RJJv87krunb3/D3GLBrWZM+YUDyuSZPp47TavSxCRFKbgJZJEsaNc//ntEA5rVMejakRExGsKXiJJoqlFERGJpTVeIgn2zfY9Cl0iIlIujXiJJFCfu16jsDgYad96xrFc0q+DdwVJjcvTVLKIVELBSyRBYke5lt41HDPzqBrxysQLenpdgoikMAUvkUPknKP7HbOijmlqUUREyqM1XiKHYHrBmqjQlZNlCl0+N2HWZ0yY9ZnXZYhIitKIl8hBip1afONXg2nTtJ5H1UiqWLbhe69LEJEUpuAlchB016KIiBwMTTWKVMOWnYUKXSIictA04iUSp1P/8h827Ngbad8w5CiuHdTZw4pERCTdKHiJxEFbRUi8OrSo73UJIpLCFLxEqqCpRamO8ecc53UJIpLCtMZLpAIvf7ROoUtERBJKI14i5YgNXC/98iSOaNnAo2oknYx/8ZPQZ418iUg5FLxEYmiUSw7F6i27vS5BRFKYphpFwnYWFit0iYhIUmnESwT45dML+c8XGyPta07sxI2ndvWwIhERyUQKXuJ7saNcn9w5nKwsbRUhIiKJp+AlvqapRUm0rq0aeV2CiKQwBS/xpXkrt3DNlAWRdq2cLBaPG+ZhRZIpxg4/1usSRCSFKXiJ78SOcr14w4l0OqyhR9WIiIifKHiJr2hqUZJtzLNLAJh4QU+PKxGRVKTgJb6wp6iE/N+/EXVMoUuSYeP3hV6XICIpTMFLMt7kpft4/439oWv0oM78YshRHlYkIiJ+peAlGS12anHJ+GHkZmvfYBER8YaCl2QsrecSEZFUo+AlGWfxmu+47JF5UccUuqSm9GjX1OsSRCSFKXhJRokd5Xr2uoHs/GqJR9WIH918mt5qSkQqpsUukjHKm1o8unVjj6oRERE5kIKXpL2ikoDWc0nKuGnaIm6atsjrMkQkRWmqUdLahFmf8eS81ZH2z/p30Fu2iKe27ynyugQRSWFJC15mVgd4F6gdfp2ZzrlxZtYRmA40BxYBlzrn9C+VVFvsKNficadTKyfbo2pERESqlsypxn3Ayc65HsDxwOlm1g+YCNzrnDsS2AZclcQaJEOVN7Wo0CUiIqkuacHLhewKN3PDHw44GZgZPj4VGJGsGiTzfP7NDq3nEhGRtJXUNV5mlk1oOvFI4J/ASmC7c64k/JB1QJtk1iCZIzZwPXFNf3q2b+ZRNSLl63dEC69LEJEUZs655L+IWRPgeeB2YEp4mhEzawe85pzrVs5zRgGjAPLy8no/PW16QmrZvWsX9Rs0SMi10k069/3KN3ZHtR8dWj/u56Zzvw+VX/vu136D+u7Hvvu135C6fR9y8uBFzrk+5Z2rkbsanXPbzey/QH+giZnlhEe92gLrK3jOJGASQK/efVz+wEEJqaVg7hwSda10k459LwkEOX78a1HHqju1mI79ThS/9t2v/Qb13Y9992u/IT37nrQ1XmbWMjzShZnVBU4FvgD+C5wfftjlwIvJqkHS2z/eWh4Vun7cq63Wc0nKG/14AaMfL/C6DBFJUckc8WoFTA2v88oCnnHOvWJmnwPTzewPwBJgchJrkDQVu56r4Pah1Kulbeck9RUWB7wuQURSWNL+kjnnPgF6lnP8KyA/Wa8r6U93LYqISKbSWwZJytj4faFCl4iIZDTN3UhKuO3fH/PCknWR9gu/OJEj8xp6WJGIiEjiKXiJ5zTKJZnkpC6HeV2CiKQwBS/xTDDoOG7crKhjCl2S7q4Y2MnrEkQkhSl4iSfeWb6R659cGGmPGX4Ml/bv6GFFIiIiyafgJTUudmrxw9tPp24tvcG1ZIaRk+cBMOWq/h5XIiKpSMFLapTWc4mIiJ9pOwmpEVt37YsKXZ0Pa6jQJSIivqMRL0m6u1/5lGkL1kTaz1w3kGNaN/awIhEREW8oeElSaWpRRERkPwUvSQrnHN3v0FYR4j+nd2vldQkiksIUvCThPvp6Gz97+INI+/azunFR/uEeViRScy7u28HrEkQkhSl4SUKd+8C7/G/jzki74Pah1KulXzPxj71FAQBtkSIi5dJfREkYrecSgeueKAC0j5eIlE/bScgh21lYHBW6+nRoptAlIiJSDo14ySF5bO5K/vrGskh75s8H0rWVtooQEREpj4KXHDRNLYqIiFSPphql2pxzCl0iIiIHQSNeUi2frd/BRQ/OjbR/N+xoLvvRER5WJJJaRvRs63UJIpLCFLwkbpc+/AFLvt4Wac//f6fRoE6uhxWJpJ4Rvdp5XYKIpDAFL4mLphZF4rNtdxEATevX8rgSEUlFWuMlldqzryQqdB3TupFCl0glbp6+iJunL/K6DBFJURrxkgo9PX81f3z1s/3tUT/iuHZNPaxIREQkvSl4SblipxaX3jUcM/OoGhERkcyg4CUH0HouERGR5FDwkogVG3fy4wfejbRvGHIU1w7q7GFFIiIimUXBSwAYNXUBH3y5JdJ+/5ZTaVxPd2WJVNdF+Yd7XYKIpDAFL9HUokgCDeve2usSRCSFaTsJHyssDkSFrsOb11foEjlEG3bsZcOOvV6XISIpSiNePvXvRWu544VPIu0pV/WjT4fmHlYkkhlumfkRAFOu6u9xJSKSihS8fEhbRYiIiHhDwctntJ5LRETEOwpePrFq8y6ufGN3pH31iZ246dSuHlYkIiLiPwpePnDztEXM/vzbSPvdsafQrH5tDysSERHxJwWvDKepRZGaNXLAEV6XICIpTMErQxWVBOh15+uRdvMGtfnzAP24RZJtUNc8r0sQkRSmfbwy0KxP1keFWk4DCAAAD8dJREFUrkmX5/POmFM8rEjEP1Zt3sWqzbu8LkNEUpSGQDJM7NTiJ3cOJytLW0WI1JQ7X1oKaB8vESmfglcG0XouERGR1KbglQG27NrHoIlvRdqX9u/ImOHHeFiRiIiIlCdpwcvM2gGPA3mAAyY55+43s2bADKADsBq40Dm3LVl1ZLqn56/mj69+Fmm/f+tpNK6b62FFIiIiUpFkjniVAL92zi02s4bAIjObDYwE3nbOTTCzscBYYEwS68hYmloUERFJL0kLXs65DcCG8Nc7zewLoA1wDjAo/LCpwBwUvKolEHT0GDcr0j6zRxsmnH+8hxWJSKlrB3X2ugQRSWE1ssbLzDoAPYEFQF44lAF8S2gqUuL0ydptXDLpg0h7xuiBHNumsYcViUhZ/Tu18LoEEUlh5pxL7guYNQDeAe52zv3bzLY755qUOb/NOde0nOeNAkYB5OXl9X562vSE1LN71y7qN2iQkGvVtH99XMiH3wYi7UdOq0eWxb9VRDr3/VD4td/g37572e+vvw/9N9q+UbYnr+/Xnzn4t+9+7Tekbt+HnDx4kXOuT3nn/n97dx5kVXmncfz7yBI2N8QQjAjEMTqMG4stCFooQoka1KlEcTJEExJ0KpNCUzXRMI6ilUy0TEUnsQZjJIIRIQZcGA0WjIYgilAsbUAWV0SEAI5ro1Nsv/njHpqWaRSw73nv7fN8qijOe7fz/Kqrmh/nPfd9y3rFS1IrYDowOSIezh7eKKlLRGyQ1AXY1Nh7I+Ie4B6A3n36Rs3AQU2SaeG8OTTVZ+Wp4f1crVocxNJxw/b7M6q19s+rqHVDcWtPWfd/TpgPwMTz06zjVdSfORS39qLWDdVZe9lWrpckYAKwMiJ+0eCpGcAV2fEVwGPlytAcvPfR1k80XTcOP/GAmi4zMzNLr5xXvAYAI4Flkmqzx8YCtwIPSRoFvAFcWsYMVW3aorWMe2xZ/Xju9efSsf0XEiYyMzOzz6Oc32qcB+ztBqTB5Tpvc9H3lpn877ad9WMvFWFmZlb9vHJ9hdm5Mzi5wVIR5/b8Ende3idhIjMzM2sqbrwqyMr17/ON8fPqx5NHn8EpXf/fFz7NrIKNGXJ86ghmVsHceFWIsdNrmVH7Vv24dtwwWrYo23cfzKxMeh3TMXUEM6tgbrwqgLf+MWs+lq59B3ADZmaNc+OV0Acfb+OMf59VP77u/J6M7N8jYSIz+7z+Y/ZqACaOSrOOl5lVNjdeicyoXcfY6S/Uj+f8aDCdDm6TMJGZmZmVmxuvBM66dTbvbNlaP/bUopmZWTG48cpRRHDSjbuXihh43JHc/a2ahInMzMwsT268cvLqpg+56Fdz68cTR/Wjb/cjEiYyMzOzvLnxysGc1Rv55wcW1Y+XjhtGKy8VYdYsXX9+z9QRzKyCufEqs4brcw076Shuv7RX4kRmVk4ndDk0dQQzq2BuvMpk6/Yd9L75yfrx/d/tT+9uXtfHrLmb/+rbAPQ/tlPiJGZWidx4lcErGz/k4rt238/1/L8OpUObVgkTmVlefj3nZcCNl5k1zo1XE/vdc69z28wVAJzWoyP3fceLKJqZmVmJG68mEhFcctdcXtlUB8BPLjmZi3t3TZzKzMzMKokbrybw7patnHnr7PrxzGvPpmvHdgkTmZmZWSVy4/U5PfvyZq66fyEALVuIRf92Hi29VISZmZk1wo3X53DTo39h+uI3ARjZvwfXef0es8K7afhJqSOYWQVz43UAtm3fSa+bZ9aP7/tOP07r4VXozQx6HNkhdQQzq2BuvPbTa5vrGP7LP9ePnxs7lEPaeqkIMyuZs2ojAINO6Jw4iZlVIjde+2HKgjX89PEXATil62FMHj0gcSIzqzQTn30NcONlZo1z47UPIoLL7p7HivUfAKV7OL5x2jGJU5mZmVm1ceP1Gd7/aCsDfrZ7qYgnrhlEtyPaJ0xkZmZm1cqN16eY/+rbfG/igvrx0nHDaOWlIszMzOwAufHai58+vpwpC94AYERNN2742omJE5mZmVm1c+O1h207dtJr3O6lIu698nT6ebNbM9tHP/v6qakjmFkFc+PVwBv/s4UL7pxTP372x0M4tF3rdIHMrOp0ObRt6ghmVsHceGWmLVrLuMeWAdDzqEP4/dUDkZQ4lZlVm5nL1gMw7KSjEicxs0rkxguY/eKG+qbrhgv/jhGnd08byMyq1u8Xlu4NdeNlZo1x4wV0aNOKQ9q24oHvncFXvN2HmZmZlYkbL6D/sZ14buzQ1DHMzMysmfOiVGZmZmY5ceNlZmZmlhNPNZqZNaE7RvRJHcHMKpgbLzOzJnR4e6/9Z2Z756lGM7Mm9OiSN3l0yZupY5hZhXLjZWbWhB5duo5Hl65LHcPMKpQbLzMzM7OcuPEyMzMzy4kbLzMzM7OclK3xkvRbSZskLW/wWEdJsyW9nP19eLnOb2ZmZlZpynnFayJw3h6PXQ88FRHHAU9lYzOzZmP8yBrGj6xJHcPMKlTZGq+ImAu8s8fDFwGTsuNJwMXlOr+ZWQptW7egbesWqWOYWYXK+x6vzhGxITv+K9A55/ObmZXV1AVrmLpgTeoYZlahFBHl+3CpO/B4RJyYjd+LiMMaPP9uRDR6n5ek0cBogM6dO/d5cMrUJsm0pa6O9h06NMlnVZui1l7UuqG4taes+7aFHwNwXU3bJOcv6s8cilt7UeuGyq198DlnL46Ivo09l/eWQRsldYmIDZK6AJv29sKIuAe4B6B3n75RM3BQkwRYOG8OTfVZ1aaotRe1bihu7SnrPnj1fABqBvZPcv6i/syhuLUXtW6oztrznmqcAVyRHV8BPJbz+c3MzMySKedyElOA+cDxktZJGgXcCgyR9DJwbjY2MzMzK4SyTTVGxOV7eWpwuc5pZmZmVsnKenN9U5G0GXijiT6uE/B2E31WtSlq7UWtG4pbe1HrBtdexNqLWjdUbu3dIuLIxp6oisarKUlatLdvGjR3Ra29qHVDcWsvat3g2otYe1Hrhuqs3Xs1mpmZmeXEjZeZmZlZTorYeN2TOkBCRa29qHVDcWsvat3g2ouoqHVDFdZeuHu8zMzMzFIp4hUvMzMzsyQK03hJ+q2kTZKWp86SJ0ldJf1J0gpJL0oakzpTXiS1kbRQ0gtZ7TenzpQnSS0kLZX0eOoseZK0RtIySbWSFqXOkydJh0maJmmVpJWS0uxblCNJx2c/611/PpB0TepceZF0bfb7bbmkKZLapM6UB0ljsppfrLafd2GmGiWdBdQB9+/atLsIsj0xu0TEEkkHA4uBiyNiReJoZSdJQPuIqJPUCpgHjImI5xNHy4WkHwJ9gUMi4sLUefIiaQ3QNyIqcW2fspI0CXgmIu6V1BpoFxHvpc6VF0ktgLeA0yOiqdZ+rFiSvkzp91rPiPhY0kPAHyNiYtpk5SXpRGAqUANsBZ4Ero6IV5IG20eFueIVEXOBd1LnyFtEbIiIJdnxh8BK4MtpU+UjSuqyYavsTyH+pyHpaOAC4N7UWSwfkg4FzgImAETE1iI1XZnBwKtFaLoaaAm0ldQSaAesT5wnD38LLIiIjyJiO/Bn4O8TZ9pnhWm8DCR1B3oBC9ImyU823VYLbAJmR0RRar8T+BGwM3WQBAKYJWmxpNGpw+SoB7AZuC+bYr5XUvvUoXI2ApiSOkReIuIt4OfAWmAD8H5EzEqbKhfLgTMlHSGpHXA+0DVxpn3mxqsgJHUApgPXRMQHqfPkJSJ2RMSpwNFATXaJulmTdCGwKSIWp86SyMCI6A0MA76f3WZQBC2B3sD4iOgFbAGuTxspP9nU6nDgD6mz5EXS4cBFlJruo4D2kv4xbaryi4iVwG3ALErTjLXAjqSh9oMbrwLI7m+aDkyOiIdT50khm3L5E3Be6iw5GAAMz+51mgqcI+mBtJHyk10FICI2AY9Qug+kCNYB6xpc1Z1GqRErimHAkojYmDpIjs4FXo+IzRGxDXgYOCNxplxExISI6BMRZwHvAi+lzrSv3Hg1c9kN5hOAlRHxi9R58iTpSEmHZcdtgSHAqrSpyi8ifhwRR0dEd0pTL09HRLP/XzCApPbZl0jIptmGUpqWaPYi4q/Am5KOzx4aDDT7L9E0cDkFmmbMrAX6SWqX/a4fTOk+3mZP0hezv4+hdH/Xg2kT7buWqQPkRdIUYBDQSdI64KaImJA2VS4GACOBZdm9TgBjI+KPCTPlpQswKfum00HAQxFRqKUVCqgz8Ejp3yBaAg9GxJNpI+XqB8DkbNrtNeDbifPkImuyhwBXpc6Sp4hYIGkasATYDiylCldyP0DTJR0BbAO+X01fJCnMchJmZmZmqXmq0czMzCwnbrzMzMzMcuLGy8zMzCwnbrzMzMzMcuLGy8zMzCwnbrzMLHeS6vYYXynprhzP30/SAkm1klZKGpc9PkjSfi9AKWmipK9nx/dK6rkf7x0kycucmBVEYdbxMrPmT1LLbNPczzIJuDQiXsjWedu16OggoA547kAzRMR3D/S9Ztb8+YqXmVUUSd0lPS3pL5Keylam/sRVpWxcl/09SNIzkmYAK7LV65+Q9IKk5ZIua+Q0X6S0qfCu/TxXZJvIXw1cm10JO/NTzilJd0laLem/s8/b9Zo5kvpmx0MlzZe0RNIfsj1TkXSepFWSllBaddvMCsKNl5ml0DZrbmqzHRVuafDcr4BJEXEyMBn45T58Xm9gTER8ldJ+nOsj4pSIOJHSJrp7ugNYLekRSVdJahMRa4C7gTsi4tSIeOZTzncJpatkPYFv0cj+eJI6ATcA52abdi8CfiipDfAb4GtAH+BL+1CfmTUTbrzMLIWPs+bm1Ig4FbixwXP92b3v2u+AgfvweQsj4vXseBkwRNJtks6MiPf3fHFE3AL0BWYB/0DjzdmnOQuYkl0tWw883chr+lFqzJ7NmssrgG7ACZQ2Nn45SluHFGYDczNz42Vm1WM72e8sSQcBrRs8t2XXQUS8ROkK2DLgJ5IaNnU0eN2rETGe0sbCp2T7vu3POT+LgNkNGsyeETFqP95vZs2QGy8zqzTPASOy428Cu6b81lCamgMYDrRq7M2SjgI+iogHgNspNWF7vuYCZTtpA8cBO4D3gA+Bgxu8dG/nnAtcJqmFpC7A2Y1EeR4YIOlvsnO2l/RVYBXQXdKx2esub6wOM2ue/K1GM6s0PwDuk/QvwGbg29njvwEek/QCpanBLXt5/0nA7ZJ2AtuAf2rkNSOBOyR9ROmq1jcjYoek/wKmSbooy7G3cz4CnAOsANYC8/c8QURslnQlMEXSF7KHb4iIlySNBp7Izv8Mn2z2zKwZU+kWAzMzMzMrN081mpmZmeXEjZeZmZlZTtx4mZmZmeXEjZeZmZlZTtx4mZmZmeXEjZeZmZlZTtx4mZmZmeXEjZeZmZlZTv4P0XjQCbhJc+YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JQNu6vDdDfkb"
      },
      "source": [
        "We can evaluate the performance of the model based on some metrics which are shown below. \n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TblXZgqYf9Qf",
        "outputId": "c5373533-dd4f-4a8e-93e5-04372d9cfa83"
      },
      "source": [
        "print(\"R^2 Score: \", model.score(X_test, y_test))\n",
        "print(\"Metric Error Score: \", metrics.mean_absolute_error(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R^2 Score:  0.922804489108601\n",
            "Metric Error Score:  6.571229944692727\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "df5uHuaSdEvd"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}