Property_Acquisition_Cost_Predictor.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wTlg_pmgZeF8"
      },
      "source": [
        "Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "BwgtpKZjYhhe"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XxjOZCbXZh2S"
      },
      "source": [
        "Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "xBXv2ushY8JH",
        "outputId": "496cfea5-b505-4fe4-fac1-686a2b9d1061"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
            "0  13300000  7420         4          2        3      yes        no       no   \n",
            "1  12250000  8960         4          4        4      yes        no       no   \n",
            "2  12250000  9960         3          2        2      yes        no      yes   \n",
            "3  12215000  7500         4          2        2      yes        no      yes   \n",
            "4  11410000  7420         4          1        2      yes       yes      yes   \n",
            "\n",
            "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
            "0              no             yes        2      yes        furnished  \n",
            "1              no             yes        3       no        furnished  \n",
            "2              no              no        2      yes   semi-furnished  \n",
            "3              no             yes        3      yes        furnished  \n",
            "4              no             yes        2       no        furnished  \n",
            "(545, 13)\n",
            "price               0\n",
            "area                0\n",
            "bedrooms            0\n",
            "bathrooms           0\n",
            "stories             0\n",
            "mainroad            0\n",
            "guestroom           0\n",
            "basement            0\n",
            "hotwaterheating     0\n",
            "airconditioning     0\n",
            "parking             0\n",
            "prefarea            0\n",
            "furnishingstatus    0\n",
            "dtype: int64\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 545 entries, 0 to 544\n",
            "Data columns (total 13 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   price             545 non-null    int64 \n",
            " 1   area              545 non-null    int64 \n",
            " 2   bedrooms          545 non-null    int64 \n",
            " 3   bathrooms         545 non-null    int64 \n",
            " 4   stories           545 non-null    int64 \n",
            " 5   mainroad          545 non-null    object\n",
            " 6   guestroom         545 non-null    object\n",
            " 7   basement          545 non-null    object\n",
            " 8   hotwaterheating   545 non-null    object\n",
            " 9   airconditioning   545 non-null    object\n",
            " 10  parking           545 non-null    int64 \n",
            " 11  prefarea          545 non-null    object\n",
            " 12  furnishingstatus  545 non-null    object\n",
            "dtypes: int64(6), object(7)\n",
            "memory usage: 55.5+ KB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "dataframe = pd.read_csv('Housing.csv')\n",
        "\n",
        "# printing first 5 columns\n",
        "print(dataframe.head())\n",
        "\n",
        "# printing the dimensions of the dataframe\n",
        "print(dataframe.shape)\n",
        "\n",
        "# Checking for missing values\n",
        "print(dataframe.isnull().sum())\n",
        "\n",
        "#data info\n",
        "print(dataframe.info())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Analysis of the output coloumn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {},
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 2000x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(20,8))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.title('House Price Distribution Plot')\n",
        "sns.histplot(dataframe.price)\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "sns.boxplot(dataframe.price)\n",
        "plt.title('House Pricing Spread')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Bivariate Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.title('Mainroad vs Price')\n",
        "sns.boxplot(x=dataframe.mainroad, y=dataframe.price, hue=dataframe.mainroad, legend=False)\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.title('guestroom vs Price')\n",
        "sns.boxplot(x=dataframe.guestroom, y=dataframe.price, hue=dataframe.guestroom, legend=False)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Infrence - having a mainroad and having a guestroom supply have corelation with price."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.title('basement vs Price')\n",
        "sns.boxplot(x=dataframe.basement, y=dataframe.price, hue=dataframe.basement, legend=False)\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.title('hotwaterheating vs Price')\n",
        "sns.boxplot(x=dataframe.hotwaterheating, y=dataframe.price, hue=dataframe.hotwaterheating, legend=False)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Infrence - having a basement and having a hotwarer supply have slight corelation with price."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['price', 'area', 'bedrooms', 'bathrooms', 'stories', 'parking']\n"
          ]
        }
      ],
      "source": [
        "numerical_list = [x for x in dataframe.columns if dataframe[x].dtype in ('int64','float64')]\n",
        "print(numerical_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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UXabuU58QurGRqCdicZntVHgsS/qggPXwnE1fOE/0298UBVZ0SwtRps6aOZa0vzjVZUrHBdIFhS0Nob6Ex78h89VEcBTp/XvCF+/27yG1rD3tI6wZEBAHAADTKFgBL+XvXRZPtaA6orDCd32Dp+YeZfGZ2AB692ZSHau8NLisMoC3DW7dNPbioQAAuYHQPT2hKhPNqvFmM1TYSOevXjRnLMWNUcvT9alPjlJ/pR1nAZiFZjX4nl3R7bO905q+NpcxFbVohVZqLShsmT6aA76bn4+uw9Gxypr2qVi4Erd4l7VOBWMHAXEAADCNAsV3SX/vUXqqFQagVHsnERcVuuyKYqUfB8v9oPRIi8+UCKDz8avxl3iB803Pjkh5DgAoAQcrDVeZjMaCCZgDe1nG+n5a43VZ6u+05TwAk4AvbwDH8X5H2BDk9gOQMBAFEKn+/vi+is+NGRqOiiPZm7BMSQQExAEAwHQvu1KFuGIC1FETrMgAFBe3695SvDrvq7lZScQWLuyHGigWJq/3VeUcgGMLFv+ztCZnzfqiAmN+YdDc8fuB80KFevY1tqTdA1BOREXCWSbsgcmzZ78vmHkpqdapqatMZCGsezPRpMnkzJ2f7z/dvdko/2ncsEYwMBjv+2mbkonHrmBb43PS35f2UQFb4fYXMa/Km4tZgn5hH9G8+eQsacvvq17YR2rJ8rQPD6SE3BddOE8qU5+1lnNInz1NzriWii/MQxSQhetsxM0lbKrD4TikD3p1DRxeqPPrb7muJ2ZZtDTtI6wZEBAHAADTveyi0jt9IjzEY4Pe27aEK7G3dklgio4WFLrkmyn2iO16ntzjRwp8zNfKzb/ev7dIVc4e4XrHNtKB9/O9z1kNLjdi/vHzYB/jl06XXl7q1AEAChAVSfM40t1dRSoT1dZJ2h1MV2VSJf7TuGGNoQG+nz5cCDayrUEhDtKgvr4idWiqEV4AVouXhfZVzvIV1i0OgMBi9+AA0Z5dxWrcthWyv1KL36Opy1Sz1JWYS1iTbZbtqxYtIzdkPuG0dXrXKygLCIgDAIDhXnassC6qfu6rfaZMJerrz1Nkl5xg+UFvv5BlwX5WaRZOOUr6mM+6NFxVzp81uTUvwO4/j9+TTp0cqt7Oti0xn8MTJKsmhgCUg0wduVs3hbep7i5S7StTOzQ5Ds4oefFAuKULb1/alnpaKG5Y44HvZxblkLttc+i17G7fQmp5R9pHCCxEs6qw0nVoqqmv2r41vI2yZeCydmvOBRhC9/eTDsuOlXnSFpknVWyMH2FdplqG61VpzhiMap9tHda0TzkXW7ti5u6d1pyLSoOAOAAAGIpMvjh1jAfAoGI8SkUdVCuW8u0OCXrnCLFoKeljzoHsEeyT7QsWkbpi3tAkkwP6U6ZGf45lE0MAyoEaLOEfm7LKhP2nac68SIW47E8b3LDGAt/PobYWey1D0QVSAB7iQyjXjW+jvB9YR6rzpJHWZarxviq2fdrUVw0Olrgmkc1SLhAQBwCAKlSM621bwxXZWbVi7AQqk5HgTaSvd1j6bCkf87j9EftUpo5U81CURALjpexhLJoYAlAWslZEkf6xKbcpTSpevbh8RfoqGNywxlPK19MS309euom9li1Un8J33wDQf+W30biMpCXLrWujIOU2UsqyyCJLI7TPAOi3EwMBcQAAqDLFOJ07m+fjHapWjJpA+eryXdvzbtp9L0l98AUiLiATtGjhAPq4lvjCmHGB7Kh9YcdY6kbZookhADb4xyrS8SoYExTifI7iFhVs75fqSpwf3m8BfKMeey2TXcB33xAQcMshwbS5V5K+eDF/R1Mzqcmt9gTbgDlthBcJC20xs/D2nK2kBaB9BkC/bW9A/H//7/9NjzzyCB04cIA2bNhAc+bMoQcffJDmzZtHb3rTm9I+PAAAqI5V45bxoROsUl7gXNnbGTeOdFaNLu/V0EBu92bSJ4/nAh5q0mRS668n/cphotOnSPf2hB4KB930qRPDn+RhYghA+X0I4xQ3aXsyDpTIPjEhLbShkZyr1pPmgluFiwpXrbe+X9LZjCO9N+T88Pas9VetozmdO2ZhgPfbcB4Y+O4bBOZVObQv0jj0cnHxxAWLZb8tbRQMIWPUtBlEEycV9d1yj1PBMYz7QV4kDF88XGVVP4n2mV/7Ifaa5P1pH2SNYJRY4Utf+hLddddd9IY3vIFOnTpFg9mboEmTJklQfCQ8/PDDNHfuXGpqaqJ169bRr3/969jn8/svXryYmpub6fLLL6cPfOAD1NMTHuABAADTV439CZbc7ARgj+7CYLgPb1dZexN+vRp/iQTW/WC4qEm5sMlzG8h97lfk/vwpohPHyVm2gpxp06XoDAc/nFVrJUgkj9s7ic6czj+GmEle5HFbODEEoBywJ6qae6UobIoUN3OvTN8ztaE6VDB67+7QhUTebjvKyUSfn327Zb8NyPi1am1oW+PtVo1fw/HdB4mAeVUAx5GFu/C+fJfstw1evNLnznoLd/ybbY4sw+E+evmKoXuc539N7rMb5DFv5/1J2GJmbriRMtfcIL9l0TBgK2lN+9y/xxNwBO4n+bFst6h9OnxNxF2TyLKqTYX4F77wBfryl79Mt9xyC33qU5/KbV+zZg391V/91bDf59vf/rYE1llpzsFwDnbfdNNNtGvXLpo+PX8ywHzjG9+gD33oQ/TYY4/RNddcQ7t376Y//dM/JaUUPfDAA2X7+wAAIEm1T9B3POff2ZdVl0eo2IrU59mbWsUr8xHKcnf7Fm/Q5hX9AnUDTZtOTsfqEXmIFvqlS1E9VpEODshE3aqbNwDGCLehSNsiDmRyG6MU4f5g+kyiCROL+yNeTDNBvYiimqXPz6njMk6EWqZYcn44GyO2rdXV2aPogv+pUYTOBy30c0ehunxgaxTIaNm5jWjefHKWtBEN9ovVl+65KNud9pUVbys5W0yL4fYZX5janvZpwjVpC0YFxNkmZeXKlUXbGxsb6fz588N+Hw5iv/Od76Tbb79dHnNg/Ac/+IEEvDnwXcgvf/lLuvbaa+mtb32rPGZl+a233krPPPPMmP4eAABIO72ucILF6g/xEV+1NtSjzX9urhBWX2/OEsDlwEYYEyaSu6WL9PGY1GhWm4/wb9QcAN+yqSjIbttEHYAxodTY9ifRny1rJ/dYQZCCFVuXzTFjwo/gXjz9/ZFjiiimbTk/pW7WLbqZh/+peSDglm2DcfUOLGqjsDUKwPc6i5aSe+K4Z8rB1wVfCz09sj2JRV0UICbSSqGoZuE1uW1LkX2MiNAsERpYFxBnn/Curi7xDQ/yxBNP0NKlS4f1Hn19fbRx40a6++67c9scx6Ebb7xRPMnDYFX4//k//0dsVdauXUv79++nH/7wh/Qnf/InkZ/T29srPz5nzpwZ1vEBAJKh1tvoqNU+/JzlHeEebdNmkJ7cKqpMVo1woMy/SVCTpsjnuZueLbph4JuK4Ep+OdSTmKjXNrXePsEI07V7LoZ6Rurx43MWUKliYXBvJG2U/VVlXIjy/bTEQ5xKFYA1oUBsUsC3uqJgDK3NItOJgsynHJInF1ELSXM7Y4u5CrZPUeofPUKqsckLAvf3kWZLSrajtEgAJOc4TiFO9sDXpN62OTwze9sWUm2dVp0PawLibHPy7ne/W7y7OYWXA9Tf/OY36f7776evfOUrw3qPY8eOiff4jBkz8rbz4507d4a+hpXh/LrrrrtOPndgYID+/M//nD784Q9Hfg4f03333TfCvxAAkBQ2tNHRqH3EG3zyFNLdXfmDLCtmeBKyfWvxSrQfCD+gvaKchcHvrO94JKNRB2KiXtPY0D6NIyJYmTaab/z2hHu6Cu2d6QfELQzujaiNavZYj/4O+cbNKgxta0mCQnGVBWPo6OBCdHGF5alztT1BJmQ+BdAl+m5dsfYpooAL54l++5tiUUBLC1Gmzpr+UoLAce1z+Qpr2ifX9Iq1dyp17w2GjVHO9O94xzvo05/+NH30ox+lCxcuSKCaC20+9NBD9Ja3vKVin/vUU0/RJz/5SfrHf/xHev755+l73/ueWKx84hOfiHwNK9BPnz6d+3nppZcqdnwAgJGDNhqNYiuSgkFWAt0RkxDeLvu56CanmCahnsREvaZB+0yW2CJiKaMGivsjH+lzBgYobWwsSjeSNho2puR9h4Ppf4dJYXJbSxoUiqscGENrd7xJDAszn5Lqu0fSPuNEAbK9354ip4p0fPu0Kcuq1H3uAO6Da1IhzrztbW+THw6Inzt3LrQIZhxTp06lTCZDr7zySt52fjxz5szQ13zsYx8TexQOyDPt7e3iWf6ud72LPvKRj4jlSpivOf8AAMwEbXSEHorjWjz9w9nTpK6YV+StKKpyeXH+ZEQCROPGl189iYl6TYP2mXARsVMnIgsepl5ErEo8XW0rSjeiNgrv7KG2xmPomqtJNTbnFcHSW55Pv62lAHyrKwPG0FECsYXVmU9J9d0jaZ+8CBOrBLZpkWYAc4kcMsdsIMWii5Brkv8ParSoJtuVLFy4kMaNGyc/zJ49e6i+vl6KXZaioaGBVq9eTU8++STdcsstss11XXl85513hr6Gg++FQW8OqjNsoQIAALZ4KGauvp7cHd3F3m2zLvWCVuNaROVVGBDS5U6NxkQdgPIwOBjvmZr2DUYVeboiuBcBFjA9Bge9MTSkCBZvtyrYBoCJoK/KAVujIXSJvlv391fOqgMLykM0oH36cO2V2GuS6+ukeoS1g1EB8T/90z+lP/uzP5OAeJBnnnlGPMTZ2mS4XuRvf/vbac2aNVIk88EHHxTF9+233y77b7vtNrr00kvF34m5+eab6YEHHqCVK1fSunXraO/evaIa5+1+YBwAAGrKQ/GlF4nmzSdnSVveqrO7azvRxElER1/JL+Cxo5vUshVEjU2hk+RyqycxUQegPOiGRi/ld3IrOfMW5CuwXzxAtCxdT0bpj/g4oo6PrUpSPD4wDHhciFvA5EJhFqAbm0jv2Bo5tqql7biWAUgTiC2K5u6qYxU5fX2eBQP3V6xKbbLM1qixidxtm0PnIXL/s7yjcp/N90txWXIWBYGlfU6fSTRhYtG5oDOnrWufHAyPLKrZuTq146o1jAqIb9q0ia699tqi7VdffXWkujuMN7/5zXT06FG655576PDhw9TZ2UlPPPFErtDmwYMH8xTh7FmulJLfL7/8Mk2bNk2C4X/3d39Xpr8MAADMgdPz1OJl5J447tX05gkHKxB6eshZvIw0FzbO7C2anIk1SkwgutzqSdssCgCoBEq7EqDTyvFSLLNBOuLMj9Yp3v40j4/Tgbmgb4hCXM2bb5ena5WSW8A8eoQUB7/9m1geU6bNsKbPZq/0uLGVLPJSB8BEILbIR1+8QHrLJnILzgVnffIc3BakzkXcPKmSfXdjEzlXrRe/8KIsuavWW7OgnGufy9rJPVZgIdPUTM5lc6xqn6qvL95KhxexLFu40vw3VyAmYFRAnIPSZ8+eLdrOxQgGR5guwgH0qCB6odK8rq6OPv7xj8sPAADUesfPVbyptye/mjqrE5a2k3Zd+b+z/nrSp06Ru+nXuXS9uEmy23NRBu+gwsQpw0ANiwIAxgYbv7HaSysO0LlEg4onXERNTVKgSPaneXx8DBEFfYXl6SrYwVixx3pQs9imp4eIF5L5ovXH7sZm0r29XptL+yCBtVRqTlltQBU9dD0ULgzI9mNHZLsUwLXk+sjNk7ifLkC2a7lzqtzn798bniW3fy+pjtVWXZOcURU6hvZckP/bck2WLJppWVFNffFCxELm2BfvjAqIv+pVrxIbk29+85s5qxIOhPO26667Lu3DAwCAmuj4eeqXV02dA+C+h293V5GHr7vpWQmKR02S9flzpLd2FXmc6fZOUi3jx3isAIAx4WfEhbRRau9M77j849A6XgWDWi7VcRN74TzRb39TPA60tBBl6qy5iZXgSXdxW1NtnTzypnpswF4qO6esLqCKztLXG2odw8h2XjyxpN+WLFgWDOzdVTxPWr7C218p+npJXTE3MkvOpu9B9/dJAcnIMZT3W3IuShbNtKiopq7w4p1RAfFPf/rTEhRfvHgxXX/99bLtZz/7GZ05c4Z++tOfpn14AABgdMc/XPWPVFO/cI7Uta8mxcEy//lLlhM9/wzRxYve+2UnI2ydIh7EIZNkVoZzMDzU42xrl3iclUMpnjZQVoHqRZG7a5u0b7GLy17DnA0ivsaLl6d7eAOD0p8oTldvbM7zXdZbnreroFSVwjepfDMf5p0t25e22dFfKie6re02oK0BK4ESuOBc7NxGtHgZOUvb8tvozm3kLO+w5lyULPJrWRFgqaEUWf+hrWKfiyy5IIrcvTvkbxYxhN8+FW/fSWrhUrIFyVqZNkPqehX5qZ8+5e0nS+ir7OKdUQHxZcuW0ZYtW+iLX/wibd68mZqbm6UAJluftLa2pn14AABgbMc/IvXP4ABl1l4navCiytVrr6PBX/88LyjOKXw6YpJsg8cZlFWg6n2NFy0Lbe9OW2f6vsYN9ZS5+nopElTUH119vWfvAqqjLkXId+iwus4SH3jj2xqwEyiBh+jrJWfh0ug2atO5KFWs0aJijiwUih3DKrgwjyy5gjF04RIRVBV9D+12jaEsJtPLV3jnotBbnjOwq/ze2qTFO6MC4szs2bPpk5/8ZNqHAQAAVdPxj1T9ozkdLaqa+vYtpFatI/2LQK0F3h81SQ7zMAtWS+/vJ33u7KgV1Wkrs6GsAtWO5ra9bUt0e09ZfZQ7vihlllXqqOpEvsPuzeGZQtu2kGrrsOI7lPPAKsNIhfgyK84DMAwogXNw0US9dVN4X9XdRap9pT1tlOfTbBUTsljC23m/TfUf9O4d4X33np2kFi2t3HXBWXJxWJQlpzN1pEtkNNrSPuX+M2pe1b3ZrvvP+sou3qUeEGdFeFtbm1z0/P84VqxYkdhxAQBA1XT8I1T/SLX0uVeSzqrAczQ1k5rcKp7DeXoEJxM9SS70MAv6kQdXtEehqDZCmQ1lFah2WPkU195TvtlS/f3xyiy+IbJICFONKL5ZjVO4sTerLSpDVojzuWD1Fi8+8Xh78SI5i5al3taApUAJnDf/dU+dILVgcZENgT6wlxyLFKgcTOP5dPg8e5U9wTbGdaWPjsoc0IXCoHLSgPZZlGUVOYba0z5x/5nc4l3qAfHOzk46fPgwTZ8+Xf6vlBIvpUJ4OxfYBACAmmekHf9IFeV+kPvQy8XFYxYszv88Lop26kTkJFk8zPg5Qb/xMC+8ESqqjVFmQ1kFaoFhtvfUlFlQF1c3pfrBsEyiGlWIi91YSFvTXGC6uRnXMkgeKIGH6O8PF21ki8jbNqdjcYn83bbXyMlkxJoiOnOgggXI0T5zYAwNgPvPxBbvUg+IHzhwgKZNm5b7PwAA2M6IO/6Rqn84ALV319DEz7c4mTyFqM4bFpy118jzqGkcKZ4oX3p56CRZPM7aO3MTSVbcBG8yRruiLZXE586XyutB9Q4r7BJdGYeyClSJvU8khe09S24Rq62DUlcXnz1Nas3VoUU1bVEXVzV8vQetsgpUl0WZRLUKF0d7IaK46Av7SHHhagDSmlMePUKqsWmoffb0kDNthhnjVELoxkbS+3aHWojpFw8QLWu3J+AG8rJ7YrOcKukhDqX+EBhDhz+vsuz+U1Vw8S71gPicOXPkd39/P9133330sY99jObNm5f2YQEAQPV0/CNVF3BQORAMF7UM3whMmkx657a8wBm/XpWwKFEt40l1rvYKaJZhRZutUnSBUsNX77ibnvXSziu8Mp4Lbvb1k7PuOjkWPyBvq3IDGG7vEwW395gUcb4RTBPd3x9bVJPbIgIUZsOZQs6a9aTPn8vf0dTsbedMIrKAwUFyYux/NDJdgXHYU7BPcDWpOfNCFeJq3nzZbxNGz10SnoeU2l/JMQxK/YIx9MRxPitZy5RBIl68W7zMrjG0oZGcq9aT3rOrOJvlqvVW3n8qbg8VaBOpB8R96uvr6Z//+Z8lIA4AAGD4Hf9I1QXBAJhvccJqmbFYnbBSnIMfUkAzjhIr2n4RkSj1jhwvTwwquDIeeoNQEJAvt3LDWHUxKEnump00mZy58/OLVnLhm47VqX6X3N6dVWtDPcR5e9oBcWpqik5VZsuUSqYqg7KgnEx0WE15+61Ju9++NbyA7Y5uUsva0z5CYOkYpS+cF/Vl/g7tbc/U2TPfUOQJQKLmmEvbyBaMsSY0AFXinqLUfpODfVVFNnOF2B6FM8uyCnG2/dO9vTJftAm9d3d4dqdSpHgBBdRWQJy55ZZb6PHHH6cPfOADaR8KAABUFSNSFwTTsGbMJpowkWhcixfQYL/wggDZiCxK+DOnz5T3LFSj0pnTpVe0+3rFJiVSvaMcogoqsyNvELITEOfaV0tNi3IGrKHQqXJKXbMpF77R3FbYdiTkBsPfn6p6N5ixUoDYMKUdsAfDsriK3pm1wLLhRt914wvYwv4HpNU+eYIXNgbY1D55XOZFgRiFuOy3BRTty6Hr6/PqIQWR7bw/lSOzD8W2RqwQzxXVHBRfcdU6xa58Fm6fJ49HZnfa1D6tCogvXLiQ/uZv/oZ+8Ytf0OrVq6mlpSVv/3vf+97Ujg0AAExn2OqCxqboNKygLUmQYVqUiFp9WbsoPoveu31lyZsuLqocqlT3/Y6XLK+sp16JGwSHV+XHX1K2j4NCp/opdc3S8hWp30jJDUaIjYNiG4dUj4xIlSi4WGo/MAA/iBRVuNWmIJPBBWyBpWgdPwZY1D41KePH68RA0b4cTvO4vHpI+fcunRCnJIVSnkI8pqimNaAAsJ0B8X/6p3+iSZMm0caNG+UnCCvyEBAHANQySdpm6P17S9uSBBlmuqBvHxFVqb1UgJfVObGFbRyHVCUnRGO4QRjV9weFTtVT8ppNO9jANozbu6NtHJavSPf4WKnIHtO80BVSVNOagoxVjsmFWxMtYHvwABEv3HJR6uxYoF2X9P49pBYtTfsIgY0oFT0vs8yWSpGOL+Kc9nidJCgaX1wPacVKcgYGhvruujpS4/IFmpXC7blIiu8jBrLXJAuMWCVtEywwwVxC4O9f79kZXQDYpsU7mwLiBw4cyP3fX62W1HQAAKhxxmKbMeJA7DBsSXSwsrXk1GqvuF2lA7wDJewRKm2fMMobhFF/f1DoVD98zcZVgk/Z8kMsR2JsHNK2JOFJf2xRTaQqGw9fQ25M4VbHFtsb9utftMy7jnPp3gOS7s3bNf8fgDTaZ9yirS3tkxkYjB1viIOhtsD3ClOnh87ZbSwaz/N4/qFM/dB9D2/jzNAKK8S5ILXM0VgAoLOffe6cFJHkQL01wEIvh2KLtTh7J1iw1WZA3FeJ//3f/z3t2bMnZ6Py/ve/n97xjnekfWgAAFARcrYZQa8w7UpqmO7tIbrYQ9QQHugebiA2GDTnYLc8njefnCVt+QoZDnpPmEjONTd4kzJ+jSZyf/sb8QB32joqG+DlvzNNxcoobhDGZHsChU7101Afn9aY8neofRsHvn45IOcvnLVcQsQ3e56IPFVYqR6pYF+xMuWjA8MKBK++2vOr54vJv8ZmXkpO69TUF4USo67OC2pMmer5hfvngWt09Fy0K90bmANfh3GLthYtvGu2jtm1LTSLw92zk9Ti5amPh0khFocrVpLb3+/9zf654ClDfYNVdn1yT8TzI7aSLLS7alsxPEHQWJThPF5wwUj+PciVqFXuMe+3RSmu+DuIyRi0KSDO7TCuADAtbbOmr7IqIH7PPffQAw88QO95z3to/fr1sm3Dhg1SZPPgwYPiLw4AADUHq6rPnCJH1Cn93qR03DjSp06R3rE1F0woDHQPNxBbFDT3FZlctET8FLJFS3p6yJkyRW7o9b49RT56vCLNabdOx+rKBXgTUKzEKepzNwihiwwR3uVjUcVDoVP1cEptrCdp5+p0J61ZmyHNyrfggQwMeNtTThGXFOE4FQzvt+RmsFrxC7Pq7q5ij+K2TiMWXRLzapa2Npj/Bw+6RrQ1YCn19eSsWhuaJcTb0160TTrgpmKyOOT/NsEB2O1bi4PAFtnoMJrFQlHWctvZWq6jcgsEfP/FizPHiq9JmjrNngVlKW6avT/t7c3fkanzMgZtmUugALC9AfEvfelL9OUvf5luvfXW3Lbf//3fpxUrVkiQHAFxAEBtqhJYXbdOAlT6+LGczUJhkcsixfEwArE8XBYFdztWk+bJRkTREs2WKby9SFFUR3TF3LIEeKOC0qMKSI+A4Sjq+bcoe4drQzMGVXyl/15QedTAQHw6etop2MqJLmoodxdO0kdUdAixCwrL2q25Aapqj2JevA3JOnJ3dpNa2k5W4Le1UyeKAxscZEq5rQE7Ydsp4jlXRLFXm2ypOENS2mPYvmwWhy3nwuUMMc7OCuu3s/VFuNikNcrkuOBjRRdKlGfVwtYobJmS/R44s08WsSz5DoS6Os+2aG+IUp/r3fB+mwoAQyGeCEZdVf39/bRmzZqi7atXr6aBtG8oAQCgzOSCs2yVkg08q0mTSa2/nvQrh6UIFx2gvCKXeYrjYQZiC4PTqrmF9LbwAkuyf3mH3DSE2kBwUDukCF8wwO0sayeXB+ztWwrU7V6At1RQesQB6WEyEmsT+T3czxujKr5Sfy9IiGrxge/vyxao9PwpvTT69JXXPKGPXVBI/IjAqFSXi5eFqrqcxXapLiWoFrHYbFVgA5gDF3V9YV+4Td4L+0gtbSNr8BeHIxYHIhePaxDFVincb4f4qTs8z+c5QvpThNpfmNcuKbbyCfkeeFFCs4WmLbBFzLYtKACcLQDMfTaL1PIWSvi6aJ0ChXitBsT/5E/+RFTibJsS5NFHH6W3ve1tqR0XAABU0jc8zn+Y1eG8Mpw37HFqHwegOWjN6a7sM8cDJt+IO86QLyQHVsOCcW6JoiXuoBeE58lhSKE0tlpxAgHbqAB35vrf8VRJgQBvaFCaFTuTJktRmTy/9PGXUFkZa8HPKMpgezKiADwwC76+4/xZTUhHj7qh4u0qk/TR5B9CCcED7zdFBTPiAsaWoNmWJ+bmTPaTBWjXW8gOU3Tt30NqmSVK+QBoM2ZkMUngM8wmjxesLBOdxS4OLFlOtsD9su7eHB185JpBZAeyMB9XGLqiH67I3bktXKm/a7tVC1YoAJyvEFdNXEust/g88XaL7GOsCoj7RTV//OMf09VXXy2Pn3nmGfEPv+222+iuu+7KPa8waA4AAFVFNjjLk684VQIH2WRiFgweNzRIoDwvAO17fD//a5nMOVetjw7EllLrsdpvUiu5PAmMCNZL0bCoALevuma/8cKCkoVB6RglemFhUFOVvLA9sZyGRmlvmosxFS5qxbXDxMgGKqNSUPOX2xKnVPswpf0Mt4CxlXABMA6IR11jvN8CpOBXbNq9PTfzDNqMQTYhPT3RmQtNTdYEVrgNxqqiLWqjXMgxNvjI/uKWwAvvceKkii7Ms21mzDWpLbomqybjMgkyjufxHzWvYt95UHsB8e7ublq1apX8f9++ffJ76tSp8sP7fJQlE2sAQA2THdQl8OxPvkJUphJMY4VVFrVshQSaiwLQgQC62KsoRYptOMLUy04mXNF66qS3n1OytCbn2hs8L/M580hduSCnlBD1yPatkrrGBe/UFXPlZj+niM1O3kJV1wWTmZwSvXBBIBtQl89g9VI51GVjLfgZA2xP7Ebv3R2+qOW3w5ThxS1q6ySH23r2+tROhtz9u0nNX5TuwXE7mT6TaMLEImUWnTltwILCyOyWbIWVbLYr3LRSpA+9LDerjm9LxG2Nt+/bTXTlQmsCj2gzZqEPHiBaspwcDqL41yVbqXDmwqKlZNPiAFTRQ4UkeX6uWLjR2Jyvlt/yvOy35VwQW5bwvVOEXzNbl1QMx5F7qtCCnuzlblFmkdQ7YB//VetIFfZVzz9jVb0Dhr//yOuiktekZRlnRgXE/+M//iPtQwAAgGTwg6+++jtGKa0WL/UC2JOnkDOplQa3bopWdGTtVYaC0Y3kLG0nffG8BOdkcsfPY0XruXN5r1czZpPetY3c/XvEikWfOCFpWTmammW7u3mjBMF11/PkHs9XqQeLgIau5hcEnfMWBILw3xv2GWNRl5XB2iQO2J5YSqWseMqpPpq/iFwuzhT0EO+5KNvTVh9JhgXXHThWoFTj/uayOWYEzUz/jk1RXUZ6iNujcHMWLvGu5YKimrw97baWKGgzZrXPRcs8lWHhdbnILo9/qKKHUPX1lLn6+tB+m7db5afO33tcdk8Frwt57zivaIuuSR4TMmuvC+2reLuuS9fiL0mQcZZcxplRAXEAALCGbHBW1NollNKMc/3vkKpvIGKf7TgCAXZ5ePY0qUy9V5mbJ1hTp5HD3mOFhb84AL20XZRCXGhHblQvXiR306+HCmNmiw6pjtWk9+8tPtZClXqY6rowKB20gxmucnyU6jJYmwArUzwzdV474/6DpTWDyrOw4Md8o8X7U1Z8RBYiHD9e+pDU26bp37EpHuIRqb3WeIhn6uSmjTjTgf9gX8XU2EyaF6RssglBmzEGzfM/vv7C9nHfy4pMsoRS191Av1XXhQTV+L4iF3wc9K4VVkzzfrIDERK9eCBSIU5L2yp2LsQLmu/L2OO/4HvggLjW7CZtCVp7fVLYLt7e0kK2UPKaXLLcmutCVzjjDAFxAABIgVxw9ugRb7U3SinNHf7J496gx4oqtjJhdXeBPUkOP8DO3uRckIODFKwO50GUVWunTxEtayN94cJQsNlXp794QCYcYltw4TyplvGkWD3y7C/FtiUX8F7eEa+wyarUw1TXRUHp7PEWnZ+48zEGdRmsTUDZqaAVT3nQXtHd/gKFOF//kgGSrgpM9/eR3hdRiJC3L1+Rfvs0/jtOGRQF89CuF9gIUbfJAk9UcdtaBG3GHEopfS1SApe87mSMtCj4GFKwT3bx9uZAhmiNI/dKc6/0Fi6DNDWTmtwaWzR6zPB8hz83bCHVsu8hd08bIpBgQZZN2WaMilGIW0VfZTPOEBAHAICUPK8kODtzthSo1OfO5r/I9/ie3OqpvVmtffxoLggeZk8iN9ynTngB7lmXkru1K09hnSu8ub1bAhQ6qMZmj7zgwOv7CtbXk7NqnSjMfV9BFaHqzuEOxqqu/aA0B8LEzmHN1UN2Ln6Qv9RnBFQ+I/UUg7UJKCsVtuIZO1zwMEIhzgE6lW4KqtQIiEsX5v1pY/x3XFl/xWEXBePrirMO/GusZbxst8YqRDmeQpwLFQavZSeTVeJapBCvkjZjFSHXpW1ontvOmEV0+Zwi32x66UVvP1mCH+SNui4sWigRDTb/3RGBWK4DUbHrghXgsQup9nwPjH5hX+jiOm9XS5aTTeiXX4qu/cALBLbQX9mMs5oNiD/88MP02c9+lg4fPkwdHR30hS98gdauXRv5/FOnTtFHPvIR+t73vkcnTpygOXPm0IMPPkhveMMbEj1uAIBdnle54GzQIy7KTzwQBC+0J8kFuzc96xXe3BpeNEjgIHtAqcZqbEnN8i1K2MONleF+xXMOznMxsBmzyFl7LenBAXKueRXpo0dkUC5csRdleUH6UmGghlPo9fatpI8cDv37hqsuq6SnGAAjzvZobBpSOPf0kDNtRvrqZtMV4nyjyTeDfn+TRfo0ttswIF24GuyWUu0L+WaNA+BFPvU9nrrNlht6Voiz1UBk6rs9CvFqaDM2Idelq4sWRZWjUh4BkkUsA5e2iWCkcLxxuIh7sG6OLddF1NhLFsHtYO+uaCvIto7KL6SGYNtCaq4eScg16XARSVsW1z0pi1f7oadgXjUw4NV+KCUcqyXqK5txVpMB8W9/+9t011130SOPPELr1q2TwPZNN91Eu3btounTpxc9v6+vj37v935P9n33u9+lSy+9lF588UWaNGlSKscPALDA86p7MymefLP6kQNT7NXXvpL09i3R/tkFQXB+nOFU9NmXeR6tfX2Uue41MmCWKrxJ/QP5N/GsmOG0wPkLiRqbRK3Jlc3FT5wHGg5g79xG7rEjnnJ9UiupSZNJ8edxMF9UgQOehyrfCAeD4WGBmmwAX/6m7ARH/q+U+KVLEL6EuqzSnmK2kZq6tFZgZVFB4IemFc85UlOI800Vt1E/GMKPeUKdskKc2zrbaoSpYNw9O+XmyARMtltKvS/kBRc35hqzRYnKmU68CIDUd+PbjFVwW+QLku/6gwElLlAn/nZkDRxc0lHjDds7LWuXoLk1Vlfd4eIZDkjyPYpNgVj31AlR3YptZNC67cBecioZiPUXSqdM9e6l/L5yXIu3qGzRQqrmcx5nv2aTb3amzsvIjsjulP1kCQ2VzTiryYD4Aw88QO985zvp9ttvl8ccGP/BD35Ajz32GH3oQx8qej5vZ1X4L3/5S6rPrjDMnTs38eMGAFjiecXB3ivmku56ntzjBcqpq9YTDbrR/tkBj27BdUlNnOQNitlaIzyBi0UG2KahY2kZL5NfmRSzOn3VWvGzU4HUPX4sqe9sq7JvD7kHD5Cz9hrv5opXrwOWJ2ryFNJZVWJkoCasAGc2gOP46YMl1GVsM1NJTzGbgNK+ioORJdGegpcVSEGVCT+WAJ1OXxHEKpjuEMVeW6dRiiBj7ZYq7K847GuMP6sg5ZsLOad9jSUGUt+rp81YBQdSNBHXkQltn9aEVUQ4EjvesDDAloD44KDYLEYFgaXgpi2w7VdMZm6l5yGqudmr81Q4bky1a9xQfE8bpxAPZlPXPNnszlMh/fYUi+ZVVPmMs5oLiLPae+PGjXT33XfntvEK8I033kgbNmwIfc2//uu/0vr16+nd7343/cu//AtNmzaN3vrWt9IHP/hByrB3bwi9vb3y43PmzJkK/DUAgNFiRBuN8LSKVIBnO/mSBchY0e1PYLkoDvuPB1VXfuqQ70NeMNGV1eW6em9VddLkoWA4f/aVCyN99HTLeFGO8wTaufo60ju2FXmU+5YnfiAwNlBTGNwvOG8l1WUV9hSzRT2dRkDXiPZZM8HIUrCvc4TKhG8uUlbviiKooN5BTqXW3eVl0qR2dFVCBfrCkbVR5aV8hymj+ebNlkU1P/U9Yvy05jyAijPSMbSoWGBwu0XXJcabITgD1Fm9LrR+h8P1O8o4hzYdzVmnu3dEW022jey6GFH7jLNMsa19Zu00w4qsuzu6vcxlsgXOOIOVThIZZzUXED927BgNDg7SjBkz8rbz4507d4a+Zv/+/fTTn/6U3va2t9EPf/hD2rt3L/1//9//R/39/fTxj3889DX3338/3XfffRX5GwAAY8eINhrhacUB6kgFOCuk494zq+j2K8Pr3h6igX7xLHXYniFTJ0FyZ816oqZG0qdOkbvp1566gV+7tN0bPPj1yzu8ycaEiaSuXCATDjV9pqSrhU4K2ZZl2QpSa6+V1/NreNLiF8IsUn37g1YcYR5ogfMWqy6rsKeYNerpFAK6RrTPcpHgwsyYVCYZXVCrgHsaZYRCPLVU5VqhAn3hiNooL9KyMpqKrzGVabbHO5vTmLmuRsjNvBTBWtae9hGCGmGk7XNM+2sIGW8K5rd5Ig2Lxhvd2EiKRTWBwK2P9OeVLCRpGEq75J49TWrN1UXFVvWW58kZYRsZVfuEZYooxIkzkUOU+myzKfttAf12YhlnsXEXW3BdV/zDH330UVq9ejW9+c1vlgKbbLUSBSvQT58+nft56aWXEj1mAEA8RrTRrOdVEaUKYbCFQNjrOKB91XrxOlRNTZ418NkzpE+dJDVunKRR6bOnyT30MunTJ4l6euV5zvrrSS1aKkoQRiZ4fAz8m5VsmzeS++wGotOnZBLGgW62TWH7FqlizZky/Nk8SeneTO6Gp8l97lfea06e8FZss9k0fEMhQS3Gn9TFUaBOHZEXWNT5LZOn2HDU06Icr3ZSCOga0T7LRUILM6OHFeHcrxRMnvmx3O2me8ursyo1SQHloqQ82eXfrFJj9VrqCwpVQAX6whG1UV5wibvGeL8N8Ng9b37otSzbLQq2gcpSU2Nogsh40tAggU/n2tdItqNz3WvkMW+3arzx++Xz54hYXMPzWf7Nj4P7bWBggDJXX+8FoIPnoqfH2871nirYPtkyRT6bC98GilLLdouQv/7FA96i8pr1uXtREV+9eMAik5AAnF0WnE/wY1BWak4hPnXqVLE5eeWVV/K28+OZM2eGvmbWrFniHR60R1m6dCkdPnxYLFgaQlYiGhsb5QcAYCYmtNEoz6vhBNCKXsfe3hwM37N7yHect61ZL2psLj6X+9yp00nNX0TucxtyN+Cs/FYzZ3v+32E2J1ue9wLewVTSrKLc4clgf583OZ48mejU8fxCmIVe4H7A309niiqEwWnkp06M2gus0p5i1WGHUZ0BXRPaZ7UUeykLfBexY2tR2+c04NQlYL5K7cTxgE/ioOef2TpFVGog+b5wxG3U5GssKVhRycrKqGvZIi9YUFlGNYaGWPkQix4sQvH9/tXXk8tt1LcTk1o42cCnVW00qw6PtHhqtsoyhUrYXalKtU+/KDXfyxW2T/bNtqUotS/PmHtlscVTUzOpya3WTCV8VGMj6RA/dbV8hZ2LAxWi5gLiHLxmlfeTTz5Jt9xyS04Bzo/vvPPO0Ndce+219I1vfEOeJxWniWj37t0SKA8LhgMAwFg8r3RdXckAmgQ4Aq/TrFzhavCnjpNauMR7Hk/c6zKk2laQ6uv3fIG5D+PXsCiUX3/6FKmJk4kc5U18J7cScRC6MKC9YtXQqjynenMq1vjxniqCJ4mc9n3iqLyf7xMefI88L3Ank/93RAVquII9p79NnzlqL7DReIq5PRelsBJbzUhKJB9jVBEl4+0wLAnoGkzuGj96hBSrN3ybhJ4ecqbNMMJr3t27U26suGBtrh9SSrarhUvTPTj2z+yJ8J/mPssgWyKTawlU0l9xOLj7d0vw2+Hr37/GnIxsV/MtCbpxu5eCV8Wp79wf2BRgAmahX9hHNG8+OUva8u0gXthHaslysgWef9OFC9FtdNzIAp9VDS9E47owwO5Kk8vWk5Hj5yKyBbmP5AWACPsY3m9N++Q5Ot/3h9U72LZF6h2A8lBzAXHmrrvuore//e20Zs0aWrt2LT344IN0/vx5uv3222X/bbfdRpdeeqn4OzF/8Rd/QV/84hfpfe97H73nPe+hPXv20Cc/+Ul673vfm/JfAgCoRc8rce0dhprPfx0HYRQX0LhirkxQ2SLF/fUv5DlSFf3FA5662y/U6RfT5AkFKwwGB0SxJlXjJ7WGB7R5Muz7trFvL79v91CxTTmebNq3fF5QER5QhYuaorcn/++ocKBmJJ5i+vw5UcEXKUC4kFJYGprxdhhjJxGlvQ2wsqjg/BH7+qfN4AA5C5eQG3Ld86KU5qr1aeL7T/Ox5VS1vBB30WubhvgkSi2B4KJHf1+udoMptQQq5a9Y8nMHB7ysJA4Gc1G2XMr3RXL4Zj7taywptJYU97Axhm9eoRAHaflmq8XLJIhSNAawAtUmKx+2HOQ2WtRXefYUbEloC7gu8s9FrHd1Bc+FfA88fh4rngN546c934PUqsIYKqC+TnLUZECcPcCPHj1K99xzj9iedHZ20hNPPJErtHnw4MGcEpy5/PLL6Uc/+hF94AMfoBUrVkiwnIPjH/zgB1P8KwAA1UaUejBsOxe/dFhxwNsydaRZ6V3fUBR8DC3omLU50adPyeTNK2w5FAyXfSGTulwg/EDW4oQD5Bw4l4F2wCvIySlpnNrtv58PW0qxulyC7fNzqmG/oKavCnfaOohC/g4TYGV4niVMcLV9axepztXFSnFL1NNpq0urmVI+83xeUz2PmTpy9+0KVx/tM0C9ywpxzkIJgZVqJijE+TvWF84T/fY3IenULXKObW4rmv/+qNoYruvtJwtQDrm7thEtWe7dZ/htzXXJ3bWd1GKLFJdVkFVhC5qDKLt3hF+Xe3ZKjRkr2mfQnqK+IVv3gNMplfeYg+EW2VPIdRGnQG3rsOa64Iy5ovueQBYtLWuv2Lng8VEycVmUw4s0WaU+X4uycDNCu5aqH0O3bQ5V6ss1ubyDrEHq61zt2fkEsydnXkpO69TayFA2hJoMiDNsjxJlkfLUU08VbVu/fj396le/SuDIAAC1SFHgmgPHy1aQM2myN6HhApi8qnvwgBQJ0Xt3F6lx1YqVHIEtHWjzbU6WLJebGZ40+MFvL9AdPanzld3OlQvFj80/Nglq843qjFmkHIdc9kJtneLZreTZp1z0JieHf5uzT9EHX5D9UYG/0KC+qI9XJqqsZJuUYDAr7xhZJc837gUBcZvU02mpS6se033mWSHO6qOBwfxc08FB2W6CQlwIuRnM258imtXge3ZF3yxzhonVbSer2goLMgX31ziilF+0TBZf89SnAwPkLFpmj1LesLHfdhSrouOuy1KF3msK7QXFTxWrcSWj0pK+KrdYGaNA5evGJmLPRUU/2PW8onmxiu/F/PGzuVm+A1Oy5JIaQ+OV+vaMoZrr65Ai3R2ilm/r5AoA9iyUVJiaDYgDAEBSFAWuAyrtwa2b8lXaXBhz987hqUnjAm3Hjw4NhIGbGZ7IBScRha/JeX3zKjNPvAoLyEybQbR4qRTh5HR3aqj3bqDYc5Hf48zpnPVKzj6FlafjxpmvnmXP8FHsh3oaxGK6zzyrj8Q7vC8/GCLZKc3e/pQxvXCQGhiIX0zjxULb4SDTjq7wopoWKS4jg0jsUcv7yQ6MGvstJ3ddnj9XVOyVixrbdF3yip3UrDA4IykpFCtQYzJKVak5cw2hBgbjz0Ul7Sk4+M0/IZYpNJUXaeyB53t59ayCCxO8fclyu9TyWzeFZ3B0d5FqZxEdKAfp3wUBAEC1UxC4jlVp79pBNGky0dFXSqtJSwXS/JXyYIpnKaWPv58L5+zaXmSLIl7kO7aFeoez3UqeB/kB8ixWIj5TUqW5gCV7n3PwPKu0yHmXJ62elWDg6PZDPQ0iqQafeR0dDCGVcop4NRQOKnUzbL2XoyJ3e0Sa83bL0pyZvbuKFwbYk9cmTM+csQwpUBy13aZir77atlAMwm10wWIjMpKSVKDqPTujM59sskxpbCC9e3t8FljFPl15tnEh16TmzDmbFmn4nziFONkDq+HjPcQhxCgXCIgDAMBYKQhcx6q0jx0hZ+78aNVj8L2CgTS/UGZgUBSv7mkzvDQ/njjxRKqU/2HW65vfo3DiN1y7leD/JfgREriP8z4PFvRMUj2rGxqGzlMBsp33J3Y0oGbgbAHOqJgwsWjSSmdOG+Azr72gR+TNVsrBkMHivigvE8aEYHM1LHqkCVuFxNzE2mQV4u7oDl8Y2NEtGQ/WYHrmjE2UKkRnUaE6hueukfNcroNjC1rHj70WXRecQRGbBVZJ+xjOHsI1OaQQT8nL3Tg4g4MtVlm8EqSpWbZjDC0fCIgDAMBYKQyG+IrpkCC2BMl8BUrY/kBQVtfVkbPuWs+7u9DDm9+HPbyXtnnFujjowK85fVIsTJSvBG1q8lLZs96RWpF4X3MBl8JVZw7cuazgjjhuNXmKN1k5dYIcTlubMctL8xscIH32DFFjU66IaKz3uR9Yz/69+vw5sSSQIDmfy+z7lLuoKaeHqmXt5PLq+o6tuaC8BOrbV3rnDIARIj7zfF1t7SpOteXrKm0VJN/0cipu+0pPUeIXVONivi+9SOqyy1M9PG6Xspiwcq2XbRIo+qk3/drbnzbcJ8UV121sItvRJ46HX2MHD5C61A6FmyzezFtImi3EAueBLptDqnWaGYs7SYFFJPPgvip4XbZc4hXzswlegNWa1Ktf6/kRB/uqLc9b1UZl3j2G/TVFqb+1gueCrzm3v4/UDb8XOgdybLom+Rb6wjlS175a6lkFCwDr558hh8iqDA7q0ZH9tucxDsoBAuIAADBWOPAaDJawSjvgIy5BMj/IPGUqKScjXuL8Or17R34Qjd9HimsSaQ4qnzzuvU/3lnAbk53bpHinTKKWryDFnmPdm8k9fqT4uc9tkKA28fuzapVVa3kBvOnkrFzrFQDdv7dY6TfrUklxdjpWy+SwUNHgF8uSG45g4KgwwD6uRQLpfC44UM/F6vKUq6MouhVZvKutg9ztW0kfOZy3PXPd7xD19XiLBLwIgWA4GCWyABRl+dHdlb5XLhfPvPwKcjnwEfQQv3hBtuu0b3rr6ymzaq23oFDQx/F2E+xIbCquO1qcK+aGX2NXzCVtwHeYBLxgrMY1k966qdgPv71TzoOydV5UuIiUeuaMXajmZu+7KPAoVlOnSYDYGgYHKcN9eci54O1R1jI1CRatjDgXXNhc5kAhHuK8na9Ja8aNgUHKrL3OGz8Lz8Xa67xaONagvH47aj5hUbddaRAQBwCAcgZLzpySVVvnmlfJaq5a2ub5aDsO6X17QtPJ9Yljeb7a/D4061L5P6u4Y9PH2MObK5RfMoGIA3ObniUdCIaHKbPdo0fEPiG0ojor0JUKD+5xyveKVd5z+PWFz8keO6uwcxQuDAT+dpoxq9jHfBRFt2KLd23tknNEgYC4bN+2Off+apTKcwByXrknj0f6/KXulcsBSj6m+gZPfjOYLeDEjzkNuJS3foXhTBhZ/Avrc7Z2keLFMUofFNeNgQuzxl1jBhRuTYRMnYwtoZYp2+zyUscikkEox/MoDkHmcxZ5FHM2j5wLHqu5r/L78sZm+84FFq3MOBdy7V0gYpEQj5f+NTmuxbprUlTRJfzUTZgPJlZfZ2vM3BhFNcuGJTNUAABIKFjS3xeqdGTvUPfs6RA7kjpSHDAO+GoHfcZD/ciD71GXIRp0c0HbyCJWvs2J/xouTrJ4WbFCnW0elq+gQQ4UXzGv2DalkZXUKtprjwODSpGzZn3OskWfOSOfn/c8fv3ObcMvMBpH3N/N3n+8EBBynJqVBiWCW5HK8xEq2EGN+/ytvpo03zAFb7BnXkpO61QDfP60Z5vUfzFfvcvHJZkR6cpMOC07tnBQ2gr2ACiuW+Iaq/MstHLU1WX9iS2RMsFLPQ8sIhmCb9MXEgQWxaVFhST5b+WMwLCsFdlu0bnAopUh54KvSVYCFx1UViFs0TUJP/UhUFQzORAQBwCAMqG5UOXWrvDVXFaHdayWIjVFN8s84SosNun7kPNEKcTrO8xqhQPZsr9wou+rtHduC7VdyVOoixJ8m1i66J3bi2/qL72MdE/E5Mz/nO1biSZO8o65p4dUy3hSV19P7vPPkLr0iuK/hVW0YSn1ww0klnqefy4Lj7Nw4aIg0B2rPB+Bgh3UNmK5U99AujtkIaytU3z701W0sAdShGUF32ipEoV4Kw0KB9UAfJFrohAbApo6LesMagf6xQOhCnHerpYsT/vwgKVIsLeneFFUttuyYMXwwh3/7SzSKOyrOHOR91sEFq0MOBdyzbHFWEH7FHV4s1XXpPipxxU3tcR+TcDcODEQEAcAgDLAwVPFKu0580hduWDILiEQaHaWd5DeFuI1zEEELvLjF5sM+JBzMFm/sH94ViscdA++h/983ha0XQkqzElL4U595JWh4504UYLh4YH9raIsD0M+h2/6IxRymbXXip93sW95wWLASD37Sj2Pz2Xc+YgKdMcpz0eiYAc1jiK3OyKtsduEtMZSwQ5tRuGgkBRZYssoFA6qCopu2oLbLcmmket07pWhN7CK7c3ILpBhZQgcUOPAL1vmFfaxLKQomCPVOrIwwPYLHHwc7Pd+cwFD3m7hdYnMJwPOBVulnD8XWKQZ9ALiPD+yqX3y4lRRJnXAgtCigDjmxsmBgDgAAJTzpo/VopxeN3O25zvH/rhcOJJ9RVlEx4P8nHlFA7wEzK9cSLRwCakZs71A9dprSHPK3LJ2UpzWKpN2vrFxpails/5V3v95G08SentyBSsZNXFyroilhLzOniY150pS02d6z2Xh6MmTcgyqdSo5V1/vBXlZuc2+25NbSR88UGSdIpOVaTNIF1id8HMkOT7C85wLD8r7Bl7n+Z0XLAaM1LMvzvuPfecK7FpCbWjCAt2lVt/HsDoPX/IaS2u8cI7Uta+WWgH+d6pdl/Tzz5iT1sg3+nwsvr8zPy7MnkgJ7mdoyXJyCs/f/j2kFi1N+/BAKfyUbu63+Rrz+7WWSzw/UEtSvmWc5eBFSLDN32/LDSwyrExCk/ubg0TtK73xyO9j2fNe5nhzyRo467KxkTS3yYLxkIvTW2VPkQXz0bTRXjHXkPFTvMVZJW4Juq6BnNXrSIsFW2AMZTvP1imk6+qtGUMZ/ZsXZdHS8W0Oud/mOl/7dkv2OCgPCIgDAEC5bvoaGijD1iDbtuSv5k6b4VXN7s63UxG/bl8dLSmtTUStU71ilzwB4uBzT4+8xg+cizqcFeinTpJi/20uVpYNqOeeM3U6qfmLyH1uQ241XY7BP7Y9O4dW4KdMJcUp7XX18p56x9bi1+zozvmZe4H3XvFEJ97+yqHg2YgPNkf5eRdsH6lnX6z3X1uHqNILPnF4ge4KVZ2Haq7G6O/32je37YIUbN4ui09pw4Fm9uIO3knwY64nYECKrFq0TPrHQhWM09Zpne9ytRLqdep7o4qPuAU4jhdsK5wDZOuI8I2sNSDDyhikj71iLrkcXMuzZLhADgfDLVJdSvCbfyLtnexC5qNHj5BqbPLORX8f6RPHyZk2HfPRpOAMYR4nQ9qnVeMn01AvzTNyDB3lfVfV9tsLl5BbaKXT10fOwiXeHB6UBQTEAQCgTDd9rAzngHOhOpr9tN3tW6J9RXkbZ8ydOkVq3LjQ9Cg/cC7Bcla2nDjuBbbDnhNmwcLH4B+b76EdtDXhAPnSdk8lzgOuBND6Sff1ikJTPMtZSe6nsZ07Sw5v50KdrC7JTlL0ubPR50o8wxuGCm4GVPKqvo4yrHiXQk9NxcUtSyhY4rz/VHsnOfz6AU9pIMqgMK91H3/CVYGq81DN1R6ab6r5RiakYJmfgp2+hzirS/qKi2pm2J8y3aPTnEWzo1sK/TpL2nKKID537s5uUkvbrFIEVSW+B6r8diO2W4BS3gJsyFjPC8uykGwLFcywAiODleCsfqb6Bm+M8lXR/JgzcXg/2YLyAo9h47VF9k7+fFRfOE/0298U3XPolhYR3Ng2H01FLe9bGoUhWcDpCxcSY9CVsTJ6DLWnqKao4eP6bcvU8pVsnwiIAwDAWAjc1KnG5tBiIGryFKJJk4cC0AF1tihSJEjlqQRyhS+LPNTqSHHA9/Sp8ArcbAvScyk5a6/1BgspxKJITZiYK17JAe2g13fehGPcONKnT3sB/v17Q4pvLiDF3qj79xYV81RZZbMMVFFqWD8Ivyvfm9wP5MugNv6SUSmqiwZILuKZHSD5tXrLJnILX3vVenKfHVLQhwW6K1J1Hqq52oNVsHEFy0xIweZjCJtUG1BUU1Qwi5eRe+K4F7z3/TN7esjhegU2qRernqL8H7IJvpZjPcRtupYrlGEFRkN2Uaq/eIwSlbRN7dQfr0PaojHjdUJoVoPvCbmf8B+3d1oVEE9PLZ9tf2FztOB+SywIWRwRZpmiWqd4+23rt08dDS8AbNF1UensagTEAQBgLARv6njgjnhOLhAcVGcHFddsN8LB3dYp4vXNAdeiwpTc8S9rp8H9e/LfP/ieO7Z6/9+RDayHFK8sCtAXFuoo8NzOvc+sS0sWotTjWjx1ScHzIgtZ+o87V4eudMcqqrs3k7N8Bblbu0IHSF5EiHqtPG/ZCtJbN8UGustedR6qudrD+IJlZk+qNWej9PSEZsZo9mJuarJOBVO1HDtqtQ1BzkM8qgiWRR7ilciwAmPJEooZo1JeFE0UHgt5MYDnuWHjoS3ZLHwqBgZCRTw5K0OLLBlSV8vHztHsQfsLU1GWKZz9TLagSPdciJ4bW5bN4lYwuxoBcQAAKMdN38njRA1NoXYgPNHxA7+5wPCpE17g+qUXSU+a7E0ABvq9YpwzZ3uWJ4uXkWLbkpMnckUyecLmrLuW9JFXvPfm7RywZUX5nHmkFi8lfeZMflBbimSy33hGipXwcfL+qMB3YYHLOP/vQmWzwwU8WVVdGKSeMjXeWzxq4h2jqKYJE8ndwr7s4QOkauuIVWNnlrUT3XBjyUB3WavOQzVXg+gSKZ4rzJhURxZsSn9SzcUzQy2luKgmt1NgPKKKNvgaSwTHIf3CvlD7H96uliwnW6hIhhWo0TEqWSSby/a+iimVsWJRRku6avnsHI2zhtkixb8m+X4qa7tnDZkMubu2hxZZZ5tQvi+2Bu0VlsfcmCqeXY2AOAAAjBLfqoOLW2gepE4cyy9K6duBsHcvB6wXLPaC3Rz05ok4+38tbfMKY/AkaNAl3X+BFBfK9JfA6xtIcaFNVvYUeYavJXIU6QsXsmmvWYXx+PHkXPtq7zj4c+obpGCm+9yvio5NCnoGJr1xge9Ijzv/c/1jG9dSrKruC1E+B21heLA7l11gCA5qMYrpogKeBTYzYsXAqrxssdEiBgYkjT1RoJqrPfg6i0nxTP2m0i9sONxCiElj+vkD1X+NJcXgoNj8uL29+dszdbI9zKahlil7hhUYQx+7kHRzU37A7dLLSbVOs6uPRV81BF8DRfaMATGPRQINUctzjSa+Tws5FxVVy+euSVVUg8O6a5LHUC6yHlKknrdbNYZibjyE1DyK6avGmF2NgDgAAJTLyyoYZGY42MrqbMchhwtGsgrgxPEhf7qBAdLsk9s6ldwXd5O6cpEXKOd0Tv7NarMzp0lNnETUOqV4hfilF6QCNe3bk59OxYHV+YvIfW7II1tUWSvXkrtlI6kr5uVsWorU5vwZjY3krFqbP9jw+8RZPxRMnAtV1XnFNnlQu3IhqekzPc9xtg0+esQrrjl5Sr4fWNyEPBigDysUGhP4L/neFQKqudpEMjwKg2A5T9KUff78woZR/s5pp4hzrQM+T+whnrsBGpQbIJ70p37+wAiusbj9FsDjPV+vUdYUvN8yypphBUaNGsdZiIP5nj2DrrfdpsCK6eNhkjQ2ST0dVkYXzZuvWi/7rQrExtxDVDT46F+TnKEQ9PiXjIVmu65Jvu8Uq5gotXxW/GUD/tzY1HuLJKmvj2+fY7yfR0AcAADK5WUVLGzJK/o8pzl+NBdsFjuVoD8dB4aXtpMeHCB12ZzsJMj1fON4UsB+rBwQb231FN5BhfiMWaQ4JZs1BUvbvEA6v4aD2/t2yw150PpEjlUpb/K7c3vxgMIBcP74A3tDBxsuwimTkRCGpWwOWMvkBrUixbsXuM7zA4tRVAcHwFIe5YU2MGmqsaGaqzE4yMUT97j9JlA4eebHXLjJlBugSJ9Ei26AQHWjlNS2CBuH3G1bSLV3pnZowGI461CKaPYVF9XMNHv7bcPU8TBh9N7d4fNmDsbxPNUSdEMj6d07oi1T2jor613Nc8imbAaHX1TTf5x6HZoEYbtQVsWH1CNRU6fZFQT27y3OnysSi7BwzZh7iwTQdXWjqkM2XCwcAQEAoAJeVkGFcndXcUD59CkJyOY67+Dzd28n56priDjgnE0LEt/w06fEF9zd3k3EKvGjr3ivZZUxp1+zL+n5c7JS7CvHRfGxeh25G58ptj6ZOEmC4aEDyqxLJSAVGVBm1bbW5BYEp4erbM4po0UJPozAddYPLE5RTePG54LlRfYpBe8dPBcmqLGhmqtBolShpnD8mJkFD7WGT2KtEKVwswS26YotUmeTEheYBYstUFRzCC7kHOyr+LFFfVUSvrzVhNJufN9dUdsSLRnBMicLvSYtCgJzzS2eN6CQpCDqcIhFqNIFgBEQBwCAGH/wUAVviFdVSYXykuXkcnDH96dzWAHgeFYoS5aJMoG4uCYHetlPbuYsoksvI33qFNHcK0k1NcnquCh56jLiUSqroSEDJS1cLJYkhZ7fcUFjDqrL+0R4dCnXJTX+kjEpm0UZPbmVBrduKh24DnqSxyiqpYDnlk3x/ub8Hpxu9arflaA+cdrw4IB8x1Blg3Ig2Q4GFwESBTZPoIM+hE5WmZ3yDQYHESnGJ1H2V8O4YDusZOJriRdbfIUbP+a+2RaFW4V9LgEYHZpczhxcvoIcXxnOYxRnNOzb7Vnv2dZX1Td49jF+X8WPbVPjor8aolRArZIe4uIbOYhr0i8kefBA6HxaBBKLlpI1sFgERbo9SvVF8BAHAIAK+4PzhHHZCnJ4wpi1Jskr1siPZ8wimtQqiu5C720O9PIcx7n6eqKBfq/jVlyE86iowKl1Kqm5V5LevzfcFoWV47xKzBYfPDnq6fWCMH19nk1LRABeXhMkLmjM+5qbPaU6fwYfZ12TpKdJIJ4/i33AOQA0/pJRB49KTir9YyzhSV4ULI+wcxFEOd9IunszuUXq9oBfOQCjQIq3chEgXsoJWqfU1UkRoNQL3/ACG1//fINVkI7LC0RpF2zSw/AQV6bWjUAfkq9wC0O226Fw0yV8Lnm/CdcysHCMWriEXA6GBy0H2EaPg+Fpj1GJwtYovPjq+UbnqGP//+x+W6iwL29VUepvrei58K9J5d1j+tRlrY5suiZLFtWs5MKEgf12TJFuq/rt+sq2TwTEAQAgzh+cLTs4SDzQT/r82Zynm5rcSmrmq0j39pDyvah5HweA6+pIzZztBZI5IH7qJKm6enJPHPOCyQ1KVnnVpZd7ViW88n3mvBQRcfwVXx706xvEc5uDvVKAUg7QHer4OaC1YLFXibpA3SFPPX0yv2L6uBZSa64mxaoDfh9Wmysi/cphoroGyqy9dsj/NGvpQjxRDnqeBxcGYhSSkcGjtg7PCsUv7FmoSHEyI/b3FqV49v3D0j/5mOXvKvR8P3Yk368cgFGguV3wfy6Gp2Dn9qcFB7/FG5V/AsFvvgFT2hwPce4P+BD9BbTGbDEhA9JCI+tGoA/Jwl9c3LVkSRiYlWz7Xw5V4nJtDxOyRZIGWRXpozN1XNI9W5yvYAwgnd1vCzweyoxxmNtr3Jf3pRfDFai8va3DnrMRU6+o8jWH/GsvfJdN1yTfP6OopofcO8R4pqd+b5F0+5w5m+iyK0g1Nuf1VfSbg2NunwiIAwBAlKceB4WlCOW2PCsUVk+oefPJ3fC0BHXV8hXk7ugm7Xt8B5+z6VlSrVO9wHewoKb/HA5oc1D46CvkLFrqWaFIUC1b5GecZzMiAd1TJ4ZSG/nmpnmcN0BcOO89/cxpCSrLMXGwfVKrpJiJ8kMC3Gu9ADe/z5ULvSA73yNx4J4D9hyUzirOiyxgAp7nQcuTMIVkbPBo+1YpOqp3dBcrUtasl0D6aPy947zGOYAfadNimU8iqGC6a1Thm9TTXbPBykIlOD/OBcpThBXgrBAPUQSJV6IJRZTgtQpGosTd2lU01jtcUNMmRReyKszDco//PCLHQ7LKl1cUqNu2FPdX7C1fUZsQs4i9h0iq5hDa51BRTba2DDbHQdfbbsJ8MCm4P+K/N+rewqL+SnH7ZOvZiLnVWNsnAuIAABAk6F3NQeGoIpSBApA8meSCcFL0MujHR5qcddd6AWIOmAeV1n5AmnGV3ESLhy4HtxubvMk5q8h5ctTXS2rpclKspDxxgtxNv87dWKtpMzxPNX4Oe5Cvv14U3/x5NH2WF0Rnlfi0GUT9faTmL5RJBU8uxGaElWtZdbaaPEWC5vz+hX7jkR7pYQrJmOARTZhIese28HPK1gkdq7wioaMgymtcJhNx2OSTCCqA9pTMYR7dhiichcLJsymTab+IUtj5E0XQuJr3LwS1AY/hete28HoCu7aTWrw87eWnxEBWhWHAN7s6xsMEYTMyvWNraEFrvofg7EqbzkpcvaKKw+0wm4EctLWzrn3yfbBkV/XlLw6I331zNqvFHuQewuDsyaRwOWtla1do7ICD5KpzNTmjjB3UdED84Ycfps9+9rN0+PBh6ujooC984Qu0du3akq/71re+Rbfeeiu96U1voscffzyRYwUAGETAhyquCGWwAGTu/wEVdV4weep0zyP83BlSyzs8ZRQPbHzDzJOe/n7Sx35LilPEWEV+6oQEpllF7duXSJB9ylRSLeNJrX8V6VcOkX5xv3iWFynY/SA5B5ivWu/NJ46+IoVKWOmgu7cUKd75uFnNLqvz/sQ4QOy5KFRIxgSHSr3PWCtFh3mN6740vQFBzcNthj26Q1RWnD1ijKKl8Dj4sQlBAN/jPNJDPF2P8/T9RasI7r9VwWP25rUENTjg1RPghZzgzfzAQLaegD2KS2RVmETWLqW/4LrkuZoEEQwZo5KkUIHKjy3qqxjFYy3fQ4R4iHOGq+y3jKh6RYnU4GDlb4jtnnXtk+d8kapoi9poNWRPJoTiBXZf4Bdit+qwNRsC4vl8+9vfprvuuoseeeQRWrduHT344IN000030a5du2j69OmRr3vhhRfor/7qr+j6669P9HgBAIb6yJWaDAb3u4OxKmruuH2P7rDURM1BclaEL20jxUGqgX75v/iHs1KSleOs6nb7vQGxscmzc9m9M//zOHgeFiTnzwl7fqHinQcW8TFX5KxaO+RJXiooFQyCxwWHSp3TSigtU/UGBDWPUmIFFKmy4tRjE+D+oyAF1YgAAGep8I1fCMaoYNCHlIbvzTJOfmFZfmyRLa94errRN/NWeX4iq8IgOPU+xtbLpiATg75KEEFP2D2L/3hZu02nI33bPSjEhzIuTZ4PJp09eejloriB5oxKE7Ink2KgP74A8MDY5hM1GRB/4IEH6J3vfCfdfvvt8pgD4z/4wQ/oscceow996EOhrxkcHKS3ve1tdN9999HPfvYzOnXqVMJHDQAwzUdOJiFBC5SQApA5uBgkq5/ZeiS4gskFQrgIJ6/ynj9H6soFEjiT13PBzbOnSbvsmTbO69BZGHD8aG6/KDXaOuWxW+BRTuPHE02a7Fm1+NuDQfnCY2cFzJx5RCeOFXmZisqdbVwmTQ73TJ99mbxf3uuC76816XNnvcBQWPAo+1wujBIMtPt/ZyWVlkZ4A4LahdvpvPme5VHQ8oPbTesUM3yDo4QkJgQAfJVLiGVK3v4UQR8yDMKKf5mQgZAwVWGflATIqjAvyJQt9J5LvW8Z7xWws+m6ZFR0EWybkDLbBcHwvHuCxI/IVrIK8RDfbE+0kP4cKDFKzfcMmA8mhnZJv7AvvOjtC/tIiXjNDnRDI+m9u0OFR/rFA2NevKu5gHhfXx9t3LiR7r777tw29vG78cYbacOGDZGv+5u/+RtRj99xxx0SEAcA2IvvI6f7+8i55gbuWLxgME9Y2Kt76jRSMy8lfeTQ0Gotp/JMnJS/gtncTM5V13iKMQ4MiFVKn/f6GbPI3fwcOavWeQqAujrvRqU+I+8thS45YM4q0+1biCZOygt851TdS5aT3rNz6NgnT5Gpk8OBdz7mwgA7B3B8e5TCQB17ne4K90xnn3S1tJ10d1e+B3pvjyduYEsW34d8xcr84FGUlUzQqiV7bJVSWqbqDQhqHlnw4v6B25Sv7mlulrZvU1rjaIm1TCEz4D6EaxxIauZA9sakoWHUNQ9qExVe0NU2+yRXF3k1K0fZ1Rcgq8L8QnUDg/YVqmN4zs3zcc62zI3X47yxxyI1ri5hUcj77VvSTINs8US5Dwx8J/zYFGu7pAlRRdOCxWQVg4PkcNFbnhv7WQQ8N+7pke3aBLFNkmNYjL3TWBdKai4gfuzYMVF7z5gxI287P965cyhoFOTnP/85/dM//RN1dXUN+3N6e3vlx+fMmTNjOGoAQLkZaxuVQOlAP7k7t3md8N5dxT7dbG8yeQopx/F8bhubPYuTOfOkeCWNG+9VCc/GBDT7i/LERgLgDeITLsF2VlcXqsLnzZdAMauvpbPn1Cn/wwuU32KFwqukBw+Qamryguhhgectz3uKcg5Qr17nfW5Qpc2fEaUWYV/ztg5S664b+nvYxzxwzH5w2y+Y5Qeg+WZLb9uaX1SUj58LkToOOetf5Q12HMCvOW9AUPNjaNx1y0FyUP2WKXwsFy+QPnrEK3osGTcDpE+dJD1tupfhU2PUVBtNCglcqGivZoviGsiqMKh9+oXqIgNujoVq3Kh6B/YsDkgbjMmCRRtNcPyU+6oCa0p+bGEwXP/mRaLlK8jxx04Wa/E8cd9uycC2Bm6LPDeOyjhjSx2L0DH2TmO1pqy5gPhIOXv2LP3Jn/wJffnLX6apU6cO+3X333+/2KsAAMxkrG1UcwGHLV1EkycXd8K+T/e24uKUEsjevDE/sL3lebkBpBcPkJ402VNW93CgXA1NPCe15gLKeZ7e2cC2Wrws99mRauur1ksAP3TA4MAzB853bg8NlkvK0UBf+MnwP7N7c2QxzqJjZiX9+Eu8APS5s+QeP1Ly+DktjO1aJPBegwEmUKtjaHaF6Fhx4RtJUTclCrZja7Hipq2TUsdXdkQpggxQL/J4IHUcuOhxkZdjC1GmruaCB6Nqoxz4DG0Dlnk1791VfC3zDZtlXs3IzDKoffIwZH37DKhxo8ZDmwKQDY3efcGeXaH3E8jiSHj8PH4M7ZNV0QuXkLu1q7jeVntnyayGWkMyzrZtKToXHABOf2acHErreHunMd4nKF1jeVJsmTJu3Dj67ne/S7fccktu+9vf/nbxBf+Xf/mXvOezKnzlypWUCSi83GyRDbZa4UKc8+fPH9bK3+WXX06nT5+mCRMmVOivAwAMl7G2UfbDHvzPfydnzXpyn8u3W5IVag5kh3TOMqnOeoSL6mLK1KEJdqbOCxZPbiU1c3ael6N2FNEL+4kum0OKJ+y8Elzf4KkSOaB+1bXk/uI/4j+b05DZA5w/I8/f2yXioI1fRIlXnU+d9F4zcbIXzOMAtJMh9+c/LbJSGdbfm51M++crc80NngKc3/3kCRr85X8O+73o1Em5icZNc+1SS2Ooe/68ZIJwYVzFNzF+m+YgKWeIcFCI21+Kx6d3bSNimyNWxvnHx3Od/XtILV6e7vGdO1u02Obj1VDoIIcX11LEPX+O9Nau6GNs7ySHVTyWtlHT20BS2HidgHRA+xzDeLhts9gQFqqi6fQpUss7rDkXIvxhQUuErRHm4aMH7XN0oH0OgfnEEPrECRrc4MURwgjGHEZDzSnEGxoaaPXq1fTkk0/mAuIc4ObHd955Z9HzlyxZQlu3bs3b9tGPflSU4w899JB0XmE0NjbKDwDATMbcRnlCwvBgzAQDzJxWyR1voDhmng1IXR2pWZd61iiS8uaIlQrHndmHW1b+OQ2K1VKNDaQP/dYb6HkfK8f5dazMYBuRpmbPpiRgLVL02Vl4UuvMnU86qMI+sNf7f6FlybIV4nlOrHrkycahl4nOnPZU5s9uyHtfKRYaUI8UrcyyKtzf4J+vYMGswP+H816ynxVlmIjXLDU1hmrX82Et0mtkfVsL02CTZnCAnEXLyOWFuWC6fH2DbGcrpzRRg4Pxyg8DfBLVwED8MdagamlEbTTXBooxog0keS1fOEfq2lcXLT7p558x4loGNrdPQ8eoJGHrwnkLSY/Leoj7mQssRmnl2j2115dHwpaGJ4+LUCXMMgXz8NGD8XN0yIJAjFe07LcEzCcCNFS2SHfNBcSZu+66SxTha9asobVr19KDDz5I58+fp9tvv13233bbbXTppZdKOktTUxO1tbXlvX7SpEnyu3A7AMAi/M6VC+yUKgqZtUTJ289B56XtXtCZC0+yApsV4ts2kz5xbCi4TpqcGbNIz7rM2xconOlNALzimDxRUr6HGHvY8me3TiF34zP5im530LMtydq88EQ3z/Il+Lds3VTwWfNJ798rwXId2FfSU9EPgmfPV1HBrGBxreBz497LX5AAwHTEf1V72RdB32B+zP7Xafuz+hlwUenyFfbuL4UUBorxMeX9qSexl7rxsOnGJK4NRPnypt0GEkL391Nm7XXe4klBW+Pturcn/WsZ2IfpY1TCqHHNkn2Zfy4ueNtt6sv7+2PvbTAPTwiMn3l3m3Fe0bSs3ZoxFPOJ5Ip012RA/M1vfjMdPXqU7rnnHjp8+DB1dnbSE088kSu0efDgQbFDAQCAkp3v6ZOk1qz3lDRcLPPKBUOBGt83OxsMzw3YXEzqqms8qxS/kJE7SPrsGVLL2ki52YkPb/fDzVzofslyzyucVRm8jydBfX2SsijF27hgJ1uc8Lb6Bina6ay71lOb+yll9Q15KuxCRbaav8hLw1vSRipboIPfl21Z6MA+UZ47rEC/4cYh5UycsxYH08a1DKnXWR2/vCOyuJYsMMTh7x/Gai+ne1arN2k1HzsIgbM6OLDAN9OD3JiV95i3l7rmK47ybv550hhUxLVc4m1P2a9f1deTs2otaQ7OBGlqlu28P3VKHYMJx5h2v8LDRGTRPrKDpmygLQQeZ9Nua8D2MSqris6NUeO8IEvqY1SCZLz5eOS54P2WoPneYtf26OBje6c1XXfqYPwU+E91WQTx6tcW2cforufIqugd5hPFcYSjR0j5he2zRUedaTPGPM+t2V6f7VHCLFKYp556Kva1X/3qV6nWWN3ZSYcOHYp9zqxZs2hjV1dixwRANXS++sIF8ROX1dmCQI37/K+9dPklbUPp9KzA5mI0HMzuzwYmuNM+fkz8xHV3eCFOLmrpLG2jwWd+LgHv4D7e5qxcS5oLuu3ZOaTy3rqp+L0uu4L0QL94ecuA0diYVYnv9V436zJyu4uLlWSuvp4Gf/UzsSzhYH3Qi4uDLKErs9m/Ve/oLjoOWrhYAuV+cUy/uJbuj3gvv0DdqRPDWu3lSQIH2IPvI56HK1YaX5Czmo8dhKHF0ii6qGbKpVr8dNwwRZwB6biaA7e9mciimrw/9fvBxqZ4dQpP0G3vV/hLilK42YLfliILxNqT+g4MHKNCxgBZuE17jErjXGzdVNxG2zutOhfKdcnlOXeEZYqTrakGEoDHzcj2aQ9cNDPTuZrckIxG3q57e9OfDyYF5hPF8LkomOPStOk0Vmo2IA7y4WD4S49/N/Y5l9/yh4kdDwDVgGbVCCueuYAHK7J5cjjQP2QNMn+Rt02RBMhlIimr+Q7pHVu94G7WBkBNmuwFVRYtJdV3ZVZN6npqPt6+ZLkM9KK0ZpUpBxIcRfrsWVIdq6VoZU6RfuVCWRWV954zb2gCe/CAp/bes5v08SNF6Y/893AwPEwN4m7bIkp3mXwUqB2DCu+8YAtbq8hnhatL9OzLyJk5O7dyy79F7R72Xv7CwMEXxH4mbrVXCgEVvF62Hzsi200uBFTNxw5KKLC5gC73B746d1yLp3pOe5GDF+fiFHFpqwMHB/P6N59cfydBinSJ7AMl4BzfX1nTr0DhJuj9e7xMK15cDgaYuIDtsva0Dw9YiTJ7DEgURS4XmV6y3MsWD/jyuru2S5FpaxgYiM3OkvsQkAzI4PBobIpWRZswn04Y/cI+onnzRXhHwazuF/ZJ3MAWdIXnuAiIAwBACDwg682bSJ86nuf3TU1NnvKTY+KzLiV3+1bP35v3c5oXB845QNZ7kZyrrx+yTeFJzcCAqLe5UCZx9XEuuDKuZSiIxsFiXv3m10gFzoxnkVKXIffsae/AOKg8+3Jv0uS/b109qZmzpJCnWKucOh4eWFreEV8Yjgfcgb5Qdbav8M5Lx9eaBvO8xgvejwMCIUV5it6LAwZKyd/Nwf+SgxoXAgpRasrn8naTCwFV87GDkRVE4uvZAAV2ThFXeBz8WOzjdPoFK+MKBxlyUx7aB5pidWRCvxIV9LYoGM5FsOILglnkTwwMIjsGhGHAGJAkbMOgpMh0Qe0Htu5atMyqoppsmRJZ/8LJyH6Luu8UQfvMUWq+nPp8Ojl4vsA2qi7HC4Jk6shhe1Wb5hN9lZ3jIiAOcpw7d5ZmZ33W44C1CrBmJfLU8Xz1BAeu/YGpqckraDF/oZfddvyoZ0vCAxhbkKxe763inj/nBcAlcF3neW5zwFt8CpVnRyIpYbw/4wW5RSHAyrJB+WzVMl6sSdxnN3h2LPy+BYpKViqqufOJDv/WK/S56dm8wVIKbGbVahLgZ0sUPg4OfvvKd0eJL3lUgEe2B/aJZ3kc/HkRRXkK32tEk+5ShX5MLgRUzccOqrRgWXaBjTNSgg2NH4udRcq3vIODXuGgwsm9cmS7THQNobDfMgb0K0bAC7soCAbMIzsGhC2KigDDnqtS8xxY/lMQZJRMFjW03wb8UxBpyZDakdnZPmP3W0bLeG8+nVVFW6WSzyJ9EfdLe3cVt8/lK+zqq/orO8dFQBzkcF23pK0KA2sVYMtKpFq4xBuECyeL7Fk1roXc5zZ4jznAPGUqKfYL5vT/kyfI3b3ds0fhSU5WBS22K7t3kHvimNieqOkzvaIhPAl3HFI8H/cfnz3jKUzZG0tsWZwhOxUalJRsOnUiF/SWFVIeOCe3yg05HxMHzQtXm6Mqyotdya7t5LTlF8QcUyE5PneVKDZXJQXuau7YQTTcRrl9i+osm+7q2yyZMJGPuteS9JaEj6XwENgyivsu7s8KPdi5rgDvT/cQzQf9ijkFweKysBI/IgBAVS0QJ4qOtysbyf0AqMBEzc4VCdXYKNnSodvJIsTmdHO0zakBdoK1MsdFQNyCYpnM2bNnEzkeAKoZVmuLGrGv11NiN48rKhgZDD5LUHvipOIA84xZpJa2eQVRfEuQ06eIWqeI55diNTYrxNkj/Mxp0vt2k2qdSrRoqWfJwoFxDq5zyuKRw54naVZ5zsVvJGB0+lSREty3KeFj4d9FU6uGRtK7d0Sr1ya3hnpx5c5LoU0A/44rkMkK+kBxzjF9J4HPjv3cYRTkTJVqPvaUibwOU8erGyAzqqDKWTI+svtBLJxNE6ZS06wSsswzclSgXzGDgUAWVkiROqtSnIF5SLaSW5zdZBsGLxAnCYtkYhfw0F+BpPFtRuP229Q+z54mteZqUo3N+R7iW563q302VHaOi4C4BcUymYm/83uJHA8A1ewZHlYwTc29kvSJY0M3ssGb3fo6Ilfnq7XZc4+9vYLBcLYWOXOa1OQppA//1vMF59Xv+gYveD59pvfe7JWrdb79ChdtW7XOK9DJr2PlZGOT51P64oFiJTjfgPu/g8dKWixTdIGyPPf3B4LpQS+uqPPCBebEUzeqQObCxaRi7FfG8p3wZ5pc4K6ai/OZSqnrMF1YEa6Jjh0tVjjzwpYpd9gcoA8N2KeMdqFSGyPoVwyhoT4yC0v856HUB2kSaZliIf19+RZnMl9vJqsoFVCzKeBmBGqEVio1TIRNiFX091Pm6utFDV54Lng7sUjIElSF57gIiAMArCeuerGvBJffHExm65MQz3BRa/OK7VXrRVVOEycNFdrkgXzmLC/gfeI4uXt2eh/AAetlK0RlThfODwXPA+pvffI4qfmLvCA4wxYMHLDm4pvz5nurpgElmqyS8m8uuhFzY17oMe6992CeF9ewqjr7heZ6e4YWAOrqpLho2AA1XJXvcD7b2AJ31Vycz8Lq4uVA6gxkLZNy32nLeG976gH77H1VZMA+5UODSq0soF9JHx77Yj3EO1ebsjwGgL1EFeaTBQIDLM6SAlZbZoEFK8HdtZ1o3nxylrTlqaJ5u2RfW4LmWmVbu2ItU2y6OlQF57gIiAMAQFz14lMnKLN8hbdCGxFYlsGKPcCvukaC3lJoU4pmkhcoZiW5pIApUlcuEOsU8QdnZTirVBgOVLGVSlOzBN71C/tF3Z2bDL1yiHQwyD6uRZ6rd3Z7fue+Ery3x/Ma5wnEtnDvMTn+EI/xnNexPwkeZlXn4RbIdDnozz9+4PzIYVHOs295kcp3GJ+txl9iZoG7ai7OZ2F18TGjXVLNzaTZLiF48Q8Metujbr4TRALzU6Z6C2r+RHJcixEBe83B+RibCc39XapHWD2gX0kXNTAQv7jDi+IAgBTJKm7ZnpDHQ7/mh9gVarsUuVyfI86GwBfigGSApZEIJDjLOkwV7bBC3CaBBNck4+zwBYtD58Z8rkB5QEAcAABiqhNzkIYH5sjAMqvH+TcHtzn4zQUxWU3OVir19RLo1Rywc13Sp056gfAZWbU4T8JZ0c02KX7Q55VDRPyekyZ7A2Bzs6cG5yB7oTJ92gxZLVcLlhD1XpRgnD55kvSBZ8WepcjuhWH1+ORWUjNmk5owMU+VLgNvwIuL/w5nzfpoH9QRVHXWF86TZpVvwQSHVe5cNMTpWJ2/ylvhitKgijD9WuCblijPQ1kHS7mUnh+wL9zOfZEJAfu6BnJWryMtPuz1OUUQF9R0ePFQ0toBqAJM76uA3fT05NuE8GOeY9qG4/glcIe28fjD1mcWAastYBracTyx1vIV5PhWRvX1pLnAJNfb4uAw2QHXG4vL9JZ6ZBahK2idiYA4GDHnzp2l2TNmxD5n1qxZtLGrK7FjAmBMxKQFclA6OBBFqsd9G5RsUU3iVK+eC6QLfcQ5wP7rX5C6Yl7O21uUKRfOe8pw/tm7yyu+6ThFwfigPYtiH/Gw/avWesH3TEYCTRJw52D2wQMyyZXBlVeXs4pMxcH3WZeSy1YtfFwNDTLw6G1bSR8/Em23Msx0Ss/yIjztS2ArmkKVL1I5gQ+uhbHhq4y2dhV7MnKV+rQD9g31Xh2lE8cDli6DYumiWqeQxvdbPdie8o2+CpgKq6HrG7w4sK+K5se83c8OtIKsLzMHVUyu+ZEQsNoyCNvHT8Z1yVm4hNyQ+arT3iniMVvQDY2kd++Mvnfm7GqyA11h60wExMGIcV23ZCHPy2/5w8SOB4BKVi+OS1djdbh74rgXWJ4zzwt8nzpJavoM0t3ZALBvBcB2BVm/Xufq68VnvHDFV49rITVzNrm7d5DKZMhlL/LJrVLsMk+lzcU0s4HtvIGS1d+sqOQ0SPYlZ1W6n1o1qdXzN+fB9dSJ8FVnVodPnTY08ASC4YV2K8QB9+FWdWbLi4L3Cr6n/H2FyrkKV5QGVUS1XAtcrLbwBpuDzgYgfUmYJ+OOblJL29M9uEGXNBcZDkG2WxWsAVVNtfRVwDK0t/B56mjEGGWXMlqswlrG52ckORkjLMSAxRw/ZmSdl0TJ1JG7axvRkuXkcCaHrxB3Xc9DfPFysgYWs8VYsKmozNRapK+y1pkIiAMArCcubVA1t4S/iNXXrKpmVWMQ9twb8Hy/5DkR6U5sFZKzM8namMhNSX+/BK7FzoAtT/bvDX0tNTbne5UGPyuoVg+qunft8KxYJk4KL/yVXWlVbR3RAw8HsBcs8hTuwx18SqWJ8+SvQDmHVE7gUw3XAgeX6dDLRYoWzTfdad9gDw6Qw56MhUFnLry7eJnn4W2CKirk/NGCxdEF0IB5WG7JUA19FbARRbrnQvTYlfYYlbSFWGNjdEaSZeNNJW0IwPCRxZio7RZ9D2pwgNSiZd5csGBxwFm0zPu/RTVJxrK/puivrB0dAuIAABCTNij7QhRfrA4X9WJEEEe8xXl1NyzwHCxseWCvF8h+6UXSHKyuayDKcKGfQa/o3YVz4a9dlq/q9N6rxGft3UXO3PnyONIG5tiR4sJfBQXvVH2jF8QvZxp5iHIOqZygKq4F7ZJ+YV+oApu3s/1RqrDKhoOT588VBQCosTHrp5ou3DdF9l1tHSkdFRgRsGQwv68CduIHeUNU0Xn7bYDr/PBCXQgyp7doAa/SNgRgmKB95tB8bxm1OCCLd83W2ITAgi25c4GAOAAAZJGJX2Dyx5NFvqllPzM9f5FX0PLgAU8dPXM20cULpK5c4NmU8OsnTvaCTfz/WZeKOo4TmvIsT7jgph+AYmsTfp+Bfs+2gCc9Eqzyhnvd20OZtdfS4IanifhYgqlSI/E6z9qSSHJV9vhiKSjCGWWvMmwFSVwaOS8gjBsfOeku/E6AvRh7LQwOegpsDgBy8M8PBraMzyqwB80IAHDqLR+LH6RrGZ9VH6UbAFCDg/nZLoV9V9rnDwzfkqFO5Su46uqyBWctSu01ua9KAX8ehcWBdBFVNAfYuH36Y1TzOFLuoF2t07cZYIEHT6T967Kxmai/L7pAdi1SYRsCMHzQPrOUan82tU9YsCV2LhAQBwCA4aYRTp9JmWtuIHfr5qLgsJq/iNxNvx4qlnnhAqnmZtJNzbLdDzJLAHj+QvFDU672lACsouPP3LmN9CuHht6Xn9u2gtTaa0lxQCvgI84Td7Fk8ANJpQLd/n6e+A+WUBvU1+cGnkjl+QgUJLFp5O2dpMbZkw4IahBWtEhVyJCJOi9+pV0UiRfhuC8KKVKkuEhRyjcYpRYMeL81iqCqhW/gNRFnFwW/LH5cxyo3u75BBIE9YMdgCDwG8U9k+7QL1dTszWkLLBlkTm1T+LHCNgRgmKB9ghBgwZbcuUBAHAAAhplGSBMmSuXrsOAwT2akaOWZM54NAW/v7yfFNijXvYbo3FkJjunTp7zJ+LYtxRW0l6+gwZPHc2pw9iHXPb1Ee3eTGyhKKYGsS68gxYMDHw9/fqmUdCfjraKyDcuxo/nB9ADynMam3MATqzwfgYIEaeSgZmFlLN9ERxVEkv3pHp+7Y6uxRTVViVTHUvuBIeiY7RbFwxEE9oAdg0lkF2xZAR30+Oe5WKY5/UXbJFGOtNEwrPNThyWDIUQIKnLjp0Xt0yeqpoxl4N45mXOBgDgAAAwzjTAuOMzFKrmYpuKAczbo7QfG2JaAFZru878mNXkKaf7hopsBODjtbttCasUq0s/9yvs8Vmfv30M0eTI5XEgzoBDnAJfTsTo3OIgVS4wtCduvyCoqK0Vn1hNNmUpu9+aYldYG7705kF8mBQnSyEGtItYjKKo5Otg6Ki4VkgsVg+ogKuBmCQgCB4Adg1mwJd/eXcVBpuUriJRFKlT4NQ8BSwazsHz89EFNmXxw71z5c4GAOAAADDfIG2NLwnYq+sxpCYgX2Y/wNvYZXntN1rJkkJz1r/L2800hF988ecIrsNnYlhPacfCcA+1F/t2sEL9yAWm2TclOmkRF2d5JtG0L6SOH84PcPIGob8jdiPsDSqmVVv6/LjXwRChIkDIOrIGLah5+mah9JTkcXM5e8zpTR/rAHlLZQrapYXhRTaSF1ggcSOLFnwIPVLnebAm4IQg8BOwYDEKTe2AvUVsnOdwe/THKyZC7f7dY/lnn11y4QOxvJ3vA2Gva+Nnszc1y4yfP11x7xk+/pszZ06TWXE2Kff2DReq3PI+aMqAiICAOAADDTRP0FSSZjKi3xSucby64cFhdPalJk4j6B0hxsTouwMk3IDzAL20X6xQJSnGgmANlu7aTPvpKvmUKB6h58A8chzyvcKX81AlSToY0e5kfL/bkpqVtwwpED2uldRQKEqSMA6vgBa55C8jl673AMkWK2bIPpAlFNUOQoEDKRTUZ7hdUxypyuH8cyN4ANTR4mTagCtDewgpbEQQVbvxYri9Lwkyi6CuYH2SzumQ+YFMQGHYMZo1RXOfmWLFvNm9PvfBzkkTV+wjutwhYMhg0fg7w4nFgMz8WD3FLxk9mcJAyV18vGdOF2Sy83aoxFCQGAuIAADDMILAEoqfNIDVn3pBqO5ORyaTuzvcWlwD36nVewUwO8Fw4P1QQ8+wZUouWkpp7pacG8G+YXzyQ7+fLXodhPt8cZAtLKeO07K1dXlr2+EtSUZAgZRxYBwdvOfAXkoItVippLwL5N/9RnowpF9Vk+PzpLZu8RYUs0gdjEa1KUNEZVKxwK1Xjolaor/fmAyFZXRJ0sikIDDsGc2ARhsljVBpE2cdYCCwZ0gYe/z66sYn01k2hliliK9q+0qaSJCAhEBAHqbK6s5MOHTpU8nmzZs2ijV1diRwTAIyztJ20pHqrIVuT3h5Sl83JG6wlOH1gX7iKu6GR9I7u4kD58hVSzC5PIT5jFilWdmuXnKuv8yZFmYw3Sc0W2cw9t0yFLiuiIEHKOKgQxtrwaNdLtT5xvMiSRLVOIW2AJ6l+YV9oUU3erpYsT/fYsIhWA2QXVZqavAC4n/LNj2XBJf1FlyTQdXXh8wH/cedqa27mYcdgEP4Y5WpPgeq3T7bSc5QRY1SS8PybJrdKBld+bZ5uUpYGxUHKcBsMHT9ts0wZyFuoKhxHxZYQgDKDgDhIFQ6Gv/T4d0s+7/Jb/jCR4wFA7D66N5O6Ym7RjS3fyKnWqeQGCmJGBaclUF4QDA+ucvNknPyAON84ctE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            "text/plain": [
              "<Figure size 1500x1500 with 42 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.pairplot(dataframe)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Heatmap for numerical columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cor_matrix= dataframe[numerical_list].corr()\n",
        "plt.figure(figsize=(12, 8))  # Set the figure size\n",
        "sns.heatmap(cor_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n",
        "plt.title('Correlation Heatmap')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Data Pre-Processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "iRyhjqfeZopp"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Encoded Employee data : \n",
            "        price  area  bedrooms  bathrooms  stories  parking  mainroad_no  \\\n",
            "0    13300000  7420         4          2        3        2          0.0   \n",
            "1    12250000  8960         4          4        4        3          0.0   \n",
            "2    12250000  9960         3          2        2        2          0.0   \n",
            "3    12215000  7500         4          2        2        3          0.0   \n",
            "4    11410000  7420         4          1        2        2          0.0   \n",
            "..        ...   ...       ...        ...      ...      ...          ...   \n",
            "540   1820000  3000         2          1        1        2          0.0   \n",
            "541   1767150  2400         3          1        1        0          1.0   \n",
            "542   1750000  3620         2          1        1        0          0.0   \n",
            "543   1750000  2910         3          1        1        0          1.0   \n",
            "544   1750000  3850         3          1        2        0          0.0   \n",
            "\n",
            "     mainroad_yes  guestroom_no  guestroom_yes  ...  basement_yes  \\\n",
            "0             1.0           1.0            0.0  ...           0.0   \n",
            "1             1.0           1.0            0.0  ...           0.0   \n",
            "2             1.0           1.0            0.0  ...           1.0   \n",
            "3             1.0           1.0            0.0  ...           1.0   \n",
            "4             1.0           0.0            1.0  ...           1.0   \n",
            "..            ...           ...            ...  ...           ...   \n",
            "540           1.0           1.0            0.0  ...           1.0   \n",
            "541           0.0           1.0            0.0  ...           0.0   \n",
            "542           1.0           1.0            0.0  ...           0.0   \n",
            "543           0.0           1.0            0.0  ...           0.0   \n",
            "544           1.0           1.0            0.0  ...           0.0   \n",
            "\n",
            "     hotwaterheating_no  hotwaterheating_yes  airconditioning_no  \\\n",
            "0                   1.0                  0.0                 0.0   \n",
            "1                   1.0                  0.0                 0.0   \n",
            "2                   1.0                  0.0                 1.0   \n",
            "3                   1.0                  0.0                 0.0   \n",
            "4                   1.0                  0.0                 0.0   \n",
            "..                  ...                  ...                 ...   \n",
            "540                 1.0                  0.0                 1.0   \n",
            "541                 1.0                  0.0                 1.0   \n",
            "542                 1.0                  0.0                 1.0   \n",
            "543                 1.0                  0.0                 1.0   \n",
            "544                 1.0                  0.0                 1.0   \n",
            "\n",
            "     airconditioning_yes  prefarea_no  prefarea_yes  \\\n",
            "0                    1.0          0.0           1.0   \n",
            "1                    1.0          1.0           0.0   \n",
            "2                    0.0          0.0           1.0   \n",
            "3                    1.0          0.0           1.0   \n",
            "4                    1.0          1.0           0.0   \n",
            "..                   ...          ...           ...   \n",
            "540                  0.0          1.0           0.0   \n",
            "541                  0.0          1.0           0.0   \n",
            "542                  0.0          1.0           0.0   \n",
            "543                  0.0          1.0           0.0   \n",
            "544                  0.0          1.0           0.0   \n",
            "\n",
            "     furnishingstatus_furnished  furnishingstatus_semi-furnished  \\\n",
            "0                           1.0                              0.0   \n",
            "1                           1.0                              0.0   \n",
            "2                           0.0                              1.0   \n",
            "3                           1.0                              0.0   \n",
            "4                           1.0                              0.0   \n",
            "..                          ...                              ...   \n",
            "540                         0.0                              0.0   \n",
            "541                         0.0                              1.0   \n",
            "542                         0.0                              0.0   \n",
            "543                         1.0                              0.0   \n",
            "544                         0.0                              0.0   \n",
            "\n",
            "     furnishingstatus_unfurnished  \n",
            "0                             0.0  \n",
            "1                             0.0  \n",
            "2                             0.0  \n",
            "3                             0.0  \n",
            "4                             0.0  \n",
            "..                            ...  \n",
            "540                           1.0  \n",
            "541                           0.0  \n",
            "542                           1.0  \n",
            "543                           0.0  \n",
            "544                           1.0  \n",
            "\n",
            "[545 rows x 21 columns]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "#Method 1: by pandas library\n",
        "# Use pd.get_dummies() to one-hot encode the categorical columns\n",
        "#dataframe = pd.get_dummies(dataframe, columns=['furnishingstatus'], drop_first=True)\n",
        "\n",
        "#Method 2: by scikit learn library\n",
        "#Extract categorical columns from the dataframe\n",
        "#Here we extract the columns with object datatype as they are the categorical columns\n",
        "categorical_columns = dataframe.select_dtypes(include=['object']).columns.tolist()\n",
        "\n",
        "encoder = OneHotEncoder(sparse_output=False)\n",
        "\n",
        "# Apply one-hot encoding to the categorical columns\n",
        "one_hot_encoded = encoder.fit_transform(dataframe[categorical_columns])\n",
        "\n",
        "#Create a DataFrame with the one-hot encoded columns\n",
        "#We use get_feature_names_out() to get the column names for the encoded data\n",
        "one_hot_df = pd.DataFrame(one_hot_encoded, columns=encoder.get_feature_names_out(categorical_columns))\n",
        "\n",
        "# Concatenate the one-hot encoded dataframe with the original dataframe\n",
        "df_encoded = pd.concat([dataframe, one_hot_df], axis=1)\n",
        "\n",
        "# Drop the original categorical columns\n",
        "df_encoded = df_encoded.drop(categorical_columns, axis=1)\n",
        "print(f\"Encoded Employee data : \\n{df_encoded}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "First sepearate X & Y, then apply feature scalling on all X and then split in training and testing data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     area  bedrooms  bathrooms  stories  parking  mainroad_no  mainroad_yes  \\\n",
            "0    7420         4          2        3        2          0.0           1.0   \n",
            "1    8960         4          4        4        3          0.0           1.0   \n",
            "2    9960         3          2        2        2          0.0           1.0   \n",
            "3    7500         4          2        2        3          0.0           1.0   \n",
            "4    7420         4          1        2        2          0.0           1.0   \n",
            "..    ...       ...        ...      ...      ...          ...           ...   \n",
            "540  3000         2          1        1        2          0.0           1.0   \n",
            "541  2400         3          1        1        0          1.0           0.0   \n",
            "542  3620         2          1        1        0          0.0           1.0   \n",
            "543  2910         3          1        1        0          1.0           0.0   \n",
            "544  3850         3          1        2        0          0.0           1.0   \n",
            "\n",
            "     guestroom_no  guestroom_yes  basement_no  basement_yes  \\\n",
            "0             1.0            0.0          1.0           0.0   \n",
            "1             1.0            0.0          1.0           0.0   \n",
            "2             1.0            0.0          0.0           1.0   \n",
            "3             1.0            0.0          0.0           1.0   \n",
            "4             0.0            1.0          0.0           1.0   \n",
            "..            ...            ...          ...           ...   \n",
            "540           1.0            0.0          0.0           1.0   \n",
            "541           1.0            0.0          1.0           0.0   \n",
            "542           1.0            0.0          1.0           0.0   \n",
            "543           1.0            0.0          1.0           0.0   \n",
            "544           1.0            0.0          1.0           0.0   \n",
            "\n",
            "     hotwaterheating_no  hotwaterheating_yes  airconditioning_no  \\\n",
            "0                   1.0                  0.0                 0.0   \n",
            "1                   1.0                  0.0                 0.0   \n",
            "2                   1.0                  0.0                 1.0   \n",
            "3                   1.0                  0.0                 0.0   \n",
            "4                   1.0                  0.0                 0.0   \n",
            "..                  ...                  ...                 ...   \n",
            "540                 1.0                  0.0                 1.0   \n",
            "541                 1.0                  0.0                 1.0   \n",
            "542                 1.0                  0.0                 1.0   \n",
            "543                 1.0                  0.0                 1.0   \n",
            "544                 1.0                  0.0                 1.0   \n",
            "\n",
            "     airconditioning_yes  prefarea_no  prefarea_yes  \\\n",
            "0                    1.0          0.0           1.0   \n",
            "1                    1.0          1.0           0.0   \n",
            "2                    0.0          0.0           1.0   \n",
            "3                    1.0          0.0           1.0   \n",
            "4                    1.0          1.0           0.0   \n",
            "..                   ...          ...           ...   \n",
            "540                  0.0          1.0           0.0   \n",
            "541                  0.0          1.0           0.0   \n",
            "542                  0.0          1.0           0.0   \n",
            "543                  0.0          1.0           0.0   \n",
            "544                  0.0          1.0           0.0   \n",
            "\n",
            "     furnishingstatus_furnished  furnishingstatus_semi-furnished  \\\n",
            "0                           1.0                              0.0   \n",
            "1                           1.0                              0.0   \n",
            "2                           0.0                              1.0   \n",
            "3                           1.0                              0.0   \n",
            "4                           1.0                              0.0   \n",
            "..                          ...                              ...   \n",
            "540                         0.0                              0.0   \n",
            "541                         0.0                              1.0   \n",
            "542                         0.0                              0.0   \n",
            "543                         1.0                              0.0   \n",
            "544                         0.0                              0.0   \n",
            "\n",
            "     furnishingstatus_unfurnished  \n",
            "0                             0.0  \n",
            "1                             0.0  \n",
            "2                             0.0  \n",
            "3                             0.0  \n",
            "4                             0.0  \n",
            "..                            ...  \n",
            "540                           1.0  \n",
            "541                           0.0  \n",
            "542                           1.0  \n",
            "543                           0.0  \n",
            "544                           1.0  \n",
            "\n",
            "[545 rows x 20 columns]\n",
            "0      13300000\n",
            "1      12250000\n",
            "2      12250000\n",
            "3      12215000\n",
            "4      11410000\n",
            "         ...   \n",
            "540     1820000\n",
            "541     1767150\n",
            "542     1750000\n",
            "543     1750000\n",
            "544     1750000\n",
            "Name: price, Length: 545, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "X = df_encoded.drop(['price'],axis=1)\n",
        "Y = df_encoded[\"price\"]\n",
        "print(X)\n",
        "print(Y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vrSB8H7xhuMN"
      },
      "source": [
        "Feature Scaling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Standardisation (Standardize the whole dataset before splitting into a test and train.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "         area  bedrooms  bathrooms   stories   parking  mainroad_no  \\\n",
            "0    1.046726  1.403419   1.421812  1.378217  1.517692    -0.405623   \n",
            "1    1.757010  1.403419   5.405809  2.532024  2.679409    -0.405623   \n",
            "2    2.218232  0.047278   1.421812  0.224410  1.517692    -0.405623   \n",
            "3    1.083624  1.403419   1.421812  0.224410  2.679409    -0.405623   \n",
            "4    1.046726  1.403419  -0.570187  0.224410  1.517692    -0.405623   \n",
            "..        ...       ...        ...       ...       ...          ...   \n",
            "540 -0.991879 -1.308863  -0.570187 -0.929397  1.517692    -0.405623   \n",
            "541 -1.268613  0.047278  -0.570187 -0.929397 -0.805741     2.465344   \n",
            "542 -0.705921 -1.308863  -0.570187 -0.929397 -0.805741    -0.405623   \n",
            "543 -1.033389  0.047278  -0.570187 -0.929397 -0.805741     2.465344   \n",
            "544 -0.599839  0.047278  -0.570187  0.224410 -0.805741    -0.405623   \n",
            "\n",
            "     mainroad_yes  guestroom_no  guestroom_yes  basement_no  basement_yes  \\\n",
            "0        0.405623      0.465315      -0.465315     0.734539     -0.734539   \n",
            "1        0.405623      0.465315      -0.465315     0.734539     -0.734539   \n",
            "2        0.405623      0.465315      -0.465315    -1.361397      1.361397   \n",
            "3        0.405623      0.465315      -0.465315    -1.361397      1.361397   \n",
            "4        0.405623     -2.149083       2.149083    -1.361397      1.361397   \n",
            "..            ...           ...            ...          ...           ...   \n",
            "540      0.405623      0.465315      -0.465315    -1.361397      1.361397   \n",
            "541     -2.465344      0.465315      -0.465315     0.734539     -0.734539   \n",
            "542      0.405623      0.465315      -0.465315     0.734539     -0.734539   \n",
            "543     -2.465344      0.465315      -0.465315     0.734539     -0.734539   \n",
            "544      0.405623      0.465315      -0.465315     0.734539     -0.734539   \n",
            "\n",
            "     hotwaterheating_no  hotwaterheating_yes  airconditioning_no  \\\n",
            "0              0.219265            -0.219265           -1.472618   \n",
            "1              0.219265            -0.219265           -1.472618   \n",
            "2              0.219265            -0.219265            0.679063   \n",
            "3              0.219265            -0.219265           -1.472618   \n",
            "4              0.219265            -0.219265           -1.472618   \n",
            "..                  ...                  ...                 ...   \n",
            "540            0.219265            -0.219265            0.679063   \n",
            "541            0.219265            -0.219265            0.679063   \n",
            "542            0.219265            -0.219265            0.679063   \n",
            "543            0.219265            -0.219265            0.679063   \n",
            "544            0.219265            -0.219265            0.679063   \n",
            "\n",
            "     airconditioning_yes  prefarea_no  prefarea_yes  \\\n",
            "0               1.472618    -1.804941      1.804941   \n",
            "1               1.472618     0.554035     -0.554035   \n",
            "2              -0.679063    -1.804941      1.804941   \n",
            "3               1.472618    -1.804941      1.804941   \n",
            "4               1.472618     0.554035     -0.554035   \n",
            "..                   ...          ...           ...   \n",
            "540            -0.679063     0.554035     -0.554035   \n",
            "541            -0.679063     0.554035     -0.554035   \n",
            "542            -0.679063     0.554035     -0.554035   \n",
            "543            -0.679063     0.554035     -0.554035   \n",
            "544            -0.679063     0.554035     -0.554035   \n",
            "\n",
            "     furnishingstatus_furnished  furnishingstatus_semi-furnished  \\\n",
            "0                      1.700840                        -0.844888   \n",
            "1                      1.700840                        -0.844888   \n",
            "2                     -0.587945                         1.183588   \n",
            "3                      1.700840                        -0.844888   \n",
            "4                      1.700840                        -0.844888   \n",
            "..                          ...                              ...   \n",
            "540                   -0.587945                        -0.844888   \n",
            "541                   -0.587945                         1.183588   \n",
            "542                   -0.587945                        -0.844888   \n",
            "543                    1.700840                        -0.844888   \n",
            "544                   -0.587945                        -0.844888   \n",
            "\n",
            "     furnishingstatus_unfurnished  \n",
            "0                       -0.696429  \n",
            "1                       -0.696429  \n",
            "2                       -0.696429  \n",
            "3                       -0.696429  \n",
            "4                       -0.696429  \n",
            "..                            ...  \n",
            "540                      1.435896  \n",
            "541                     -0.696429  \n",
            "542                      1.435896  \n",
            "543                     -0.696429  \n",
            "544                      1.435896  \n",
            "\n",
            "[545 rows x 20 columns]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Initialize the scaler\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Fit and transform X\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Convert back to DataFrame (optional, for readability)\n",
        "import pandas as pd\n",
        "X_scaled = pd.DataFrame(X_scaled, columns=X.columns)\n",
        "\n",
        "print(X_scaled)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t12-tbxhhwmu"
      },
      "source": [
        "Splitting training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "pzNhfpxhZrzY",
        "outputId": "80a599d4-2f71-453b-8d1f-b53a1e0eba02"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_x, test_x, train_y, test_y = train_test_split(X_scaled,Y,test_size=0.2,random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VAft5hK3hqYY"
      },
      "source": [
        "Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yR4yS2ySZyEf",
        "outputId": "9a564255-bdc8-446f-a81f-4f4f5fdac163"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean Absolute Error (MAE): 970043.4039201643\n",
            "Mean Squared Error (MSE): 1754318687330.6682\n",
            "R² Score: 0.6529242642153175\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "\n",
        "# Initialize the model\n",
        "model = LinearRegression()\n",
        "\n",
        "# Train the model\n",
        "model.fit(train_x, train_y)\n",
        "\n",
        "# Make predictions\n",
        "predictions = model.predict(test_x)\n",
        "\n",
        "# Evaluate the model\n",
        "mae = mean_absolute_error(test_y, predictions)\n",
        "mse = mean_squared_error(test_y, predictions)\n",
        "r2 = r2_score(test_y, predictions)\n",
        "\n",
        "print(f\"Mean Absolute Error (MAE): {mae}\")\n",
        "print(f\"Mean Squared Error (MSE): {mse}\")\n",
        "print(f\"R² Score: {r2}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-B8V7VueS1Li"
      },
      "source": [
        "Ridge and Lasso Regularization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Ridge Regression Performance:\n",
            "Mean Absolute Error (MAE): 969804.6400385571\n",
            "Mean Squared Error (MSE): 1754634775432.648\n",
            "R² Score: 0.6528617291061839\n",
            "\n",
            "Lasso Regression Performance:\n",
            "Mean Absolute Error (MAE): 970043.4086546472\n",
            "Mean Squared Error (MSE): 1754318706811.3787\n",
            "R² Score: 0.6529242603612391\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import Ridge, Lasso\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "\n",
        "# Initialize models\n",
        "ridge = Ridge(alpha=1.0)  # You can tune alpha\n",
        "lasso = Lasso(alpha=0.01)  # You can tune alpha\n",
        "\n",
        "# Train models\n",
        "ridge.fit(train_x, train_y)\n",
        "lasso.fit(train_x, train_y)\n",
        "\n",
        "# Make predictions\n",
        "ridge_pred = ridge.predict(test_x)\n",
        "lasso_pred = lasso.predict(test_x)\n",
        "\n",
        "# Function to evaluate models\n",
        "def evaluate_model(name, y_true, y_pred):\n",
        "    mae = mean_absolute_error(y_true, y_pred)\n",
        "    mse = mean_squared_error(y_true, y_pred)\n",
        "    r2 = r2_score(y_true, y_pred)\n",
        "    print(f\"\\n{name} Performance:\")\n",
        "    print(f\"Mean Absolute Error (MAE): {mae}\")\n",
        "    print(f\"Mean Squared Error (MSE): {mse}\")\n",
        "    print(f\"R² Score: {r2}\")\n",
        "\n",
        "# Evaluate all models\n",
        "evaluate_model(\"Ridge Regression\", test_y, ridge_pred)\n",
        "evaluate_model(\"Lasso Regression\", test_y, lasso_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2x5tB3QYgBFI"
      },
      "source": [
        "Random Forest Regressor with RandomizedSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "izFjp2J_Z5Ve",
        "outputId": "1abe7be9-1f85-4720-bc19-2e08585e680b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s\n",
            "[CV] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.2s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s\n",
            "[CV] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.2s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s\n",
            "[CV] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.2s\n",
            "[CV] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.2s\n",
            "[CV] END bootstrap=True, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.2s\n",
            "[CV] END bootstrap=True, max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=True, max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=True, max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.3s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.2s\n",
            "[CV] END bootstrap=True, max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.2s\n",
            "[CV] END bootstrap=True, max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.3s\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.2s\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.2s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.4s\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.2s\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.2s\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.2s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.0s\n",
            "[CV] END bootstrap=True, max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.2s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.0s\n",
            "[CV] END bootstrap=True, max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.3s\n",
            "[CV] END bootstrap=True, max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.2s\n",
            "[CV] END bootstrap=True, max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.2s\n",
            "[CV] END bootstrap=False, max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=True, max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.3s\n",
            "[CV] END bootstrap=True, max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.2s\n",
            "[CV] END bootstrap=True, max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.2s\n",
            "[CV] END bootstrap=False, max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.0s\n",
            "[CV] END bootstrap=True, max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.2s\n",
            "[CV] END bootstrap=False, max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=True, max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.3s\n",
            "[CV] END bootstrap=True, max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.2s\n",
            "[CV] END bootstrap=False, max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=True, max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=True, max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=True, max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=True, max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=True, max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.2s\n",
            "[CV] END bootstrap=False, max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.0s\n",
            "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.0s\n",
            "[CV] END bootstrap=False, max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.2s\n",
            "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.0s\n",
            "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.0s\n",
            "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.0s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   0.3s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   0.3s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   0.3s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   0.3s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=10, max_features=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   0.4s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.0s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.0s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.0s\n",
            "[CV] END bootstrap=False, max_depth=30, max_features=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.0s\n",
            "[CV] END bootstrap=False, max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.0s\n",
            "[CV] END bootstrap=False, max_depth=30, max_features=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.0s\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.0s\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.0s\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.2s\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.0s\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.2s\n",
            "[CV] END bootstrap=False, max_depth=30, max_features=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=None, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.0s\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.2s\n",
            "[CV] END bootstrap=False, max_depth=30, max_features=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=False, max_depth=30, max_features=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.1s\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.1s\n",
            "\n",
            "Best Hyperparameters: {'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 10, 'bootstrap': False}\n",
            "\n",
            "Random Forest Performance with Best Hyperparameters:\n",
            "Mean Absolute Error (MAE): 1020346.0832473825\n",
            "Mean Squared Error (MSE): 1966842388596.8918\n",
            "R² Score: 0.6108784144382209\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "import numpy as np\n",
        "\n",
        "# Define the model\n",
        "rf = RandomForestRegressor(random_state=42)\n",
        "\n",
        "# Define hyperparameters to tune\n",
        "param_dist = {\n",
        "    \"n_estimators\": [50, 100, 200, 300],  # Number of trees\n",
        "    \"max_depth\": [None, 10, 20, 30],  # Tree depth\n",
        "    \"min_samples_split\": [2, 5, 10],  # Minimum samples required to split a node\n",
        "    \"min_samples_leaf\": [1, 2, 4],  # Minimum samples per leaf\n",
        "    \"max_features\": [\"sqrt\", \"log2\", None],  # Number of features considered per split\n",
        "    \"bootstrap\": [True, False],  # Bootstrap sampling\n",
        "}\n",
        "\n",
        "# Set up RandomizedSearchCV\n",
        "random_search = RandomizedSearchCV(\n",
        "    rf, param_distributions=param_dist, n_iter=20, cv=5, \n",
        "    scoring=\"r2\", verbose=2, random_state=42, n_jobs=-1\n",
        ")\n",
        "\n",
        "# Fit the model\n",
        "random_search.fit(train_x, train_y)\n",
        "\n",
        "# Get the best parameters\n",
        "best_params = random_search.best_params_\n",
        "print(\"\\nBest Hyperparameters:\", best_params)\n",
        "\n",
        "# Train final model with best parameters\n",
        "best_rf = RandomForestRegressor(**best_params, random_state=42)\n",
        "best_rf.fit(train_x, train_y)\n",
        "\n",
        "# Predictions\n",
        "rf_pred = best_rf.predict(test_x)\n",
        "\n",
        "# Evaluation\n",
        "mae = mean_absolute_error(test_y, rf_pred)\n",
        "mse = mean_squared_error(test_y, rf_pred)\n",
        "r2 = r2_score(test_y, rf_pred)\n",
        "\n",
        "print(\"\\nRandom Forest Performance with Best Hyperparameters:\")\n",
        "print(f\"Mean Absolute Error (MAE): {mae}\")\n",
        "print(f\"Mean Squared Error (MSE): {mse}\")\n",
        "print(f\"R² Score: {r2}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Polynomial Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 1033322.1443850703\n",
            "Mean Squared Error: 1899851265879.9915\n",
            "Root Mean Squared Error: 1378350.9226173107\n",
            "R^2 Score: 0.624131988817793\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "\n",
        "# Step 1: Create Polynomial Features\n",
        "poly = PolynomialFeatures(degree=2)  # You can change the degree based on your needs\n",
        "\n",
        "# Transform the training data to include polynomial features\n",
        "train_x_poly = poly.fit_transform(train_x)\n",
        "\n",
        "# Step 2: Train the Polynomial Regression model\n",
        "poly_reg_model = LinearRegression()\n",
        "poly_reg_model.fit(train_x_poly, train_y)\n",
        "\n",
        "# Step 3: Transform the test data and make predictions\n",
        "test_x_poly = poly.transform(test_x)\n",
        "predictions = poly_reg_model.predict(test_x_poly)\n",
        "\n",
        "# Step 4: Evaluate the model\n",
        "mae = mean_absolute_error(test_y, predictions)\n",
        "mse = mean_squared_error(test_y, predictions)\n",
        "rmse = np.sqrt(mse)\n",
        "r2 = r2_score(test_y, predictions)\n",
        "\n",
        "# Output the evaluation metrics\n",
        "print(f\"Mean Absolute Error: {mae}\")\n",
        "print(f\"Mean Squared Error: {mse}\")\n",
        "print(f\"Root Mean Squared Error: {rmse}\")\n",
        "print(f\"R^2 Score: {r2}\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ljJzx0qfzby"
      },
      "source": [
        "XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SbBQivNQZ9IL",
        "outputId": "be4ba91d-a0b6-4a3e-fcd0-bf0b45f8f74b"
      },
      "outputs": [
        {
          "ename": "XGBoostError",
          "evalue": "\nXGBoost Library (libxgboost.dylib) could not be loaded.\nLikely causes:\n  * OpenMP runtime is not installed\n    - vcomp140.dll or libgomp-1.dll for Windows\n    - libomp.dylib for Mac OSX\n    - libgomp.so for Linux and other UNIX-like OSes\n    Mac OSX users: Run `brew install libomp` to install OpenMP runtime.\n\n  * You are running 32-bit Python on a 64-bit OS\n\nError message(s): [\"dlopen(/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/xgboost/lib/libxgboost.dylib, 0x0006): Library not loaded: @rpath/libomp.dylib\\n  Referenced from: <BBC4A126-D15A-3802-AD26-108872BA781A> /Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/xgboost/lib/libxgboost.dylib\\n  Reason: tried: '/opt/homebrew/opt/libomp/lib/libomp.dylib' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/opt/homebrew/opt/libomp/lib/libomp.dylib' (no such file), '/opt/homebrew/opt/libomp/lib/libomp.dylib' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/opt/homebrew/opt/libomp/lib/libomp.dylib' (no such file)\"]\n",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mXGBoostError\u001b[0m                              Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[107], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mxgboost\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m XGBRegressor\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m mean_absolute_error, mean_squared_error, r2_score\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Initialize XGBoost Regressor with default parameters\u001b[39;00m\n",
            "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/xgboost/__init__.py:6\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;124;03m\"\"\"XGBoost: eXtreme Gradient Boosting library.\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \n\u001b[1;32m      3\u001b[0m \u001b[38;5;124;03mContributors: https://github.com/dmlc/xgboost/blob/master/CONTRIBUTORS.md\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m tracker  \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m collective, dask\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m      9\u001b[0m     Booster,\n\u001b[1;32m     10\u001b[0m     DataIter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     15\u001b[0m     build_info,\n\u001b[1;32m     16\u001b[0m )\n",
            "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/xgboost/tracker.py:9\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01menum\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m IntEnum, unique\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Dict, Optional, Union\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _LIB, _check_call, make_jcargs\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mget_family\u001b[39m(addr: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mint\u001b[39m:\n\u001b[1;32m     13\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Get network family from address.\"\"\"\u001b[39;00m\n",
            "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/xgboost/core.py:269\u001b[0m\n\u001b[1;32m    265\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m lib\n\u001b[1;32m    268\u001b[0m \u001b[38;5;66;03m# load the XGBoost library globally\u001b[39;00m\n\u001b[0;32m--> 269\u001b[0m _LIB \u001b[38;5;241m=\u001b[39m \u001b[43m_load_lib\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    272\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_check_call\u001b[39m(ret: \u001b[38;5;28mint\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    273\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Check the return value of C API call\u001b[39;00m\n\u001b[1;32m    274\u001b[0m \n\u001b[1;32m    275\u001b[0m \u001b[38;5;124;03m    This function will raise exception when error occurs.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    281\u001b[0m \u001b[38;5;124;03m        return value from API calls\u001b[39;00m\n\u001b[1;32m    282\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n",
            "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/xgboost/core.py:222\u001b[0m, in \u001b[0;36m_load_lib\u001b[0;34m()\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m lib_success:\n\u001b[1;32m    221\u001b[0m         libname \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mbasename(lib_paths[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m--> 222\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m XGBoostError(\n\u001b[1;32m    223\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m    224\u001b[0m \u001b[38;5;124mXGBoost Library (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlibname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) could not be loaded.\u001b[39m\n\u001b[1;32m    225\u001b[0m \u001b[38;5;124mLikely causes:\u001b[39m\n\u001b[1;32m    226\u001b[0m \u001b[38;5;124m  * OpenMP runtime is not installed\u001b[39m\n\u001b[1;32m    227\u001b[0m \u001b[38;5;124m    - vcomp140.dll or libgomp-1.dll for Windows\u001b[39m\n\u001b[1;32m    228\u001b[0m \u001b[38;5;124m    - libomp.dylib for Mac OSX\u001b[39m\n\u001b[1;32m    229\u001b[0m \u001b[38;5;124m    - libgomp.so for Linux and other UNIX-like OSes\u001b[39m\n\u001b[1;32m    230\u001b[0m \u001b[38;5;124m    Mac OSX users: Run `brew install libomp` to install OpenMP runtime.\u001b[39m\n\u001b[1;32m    231\u001b[0m \n\u001b[1;32m    232\u001b[0m \u001b[38;5;124m  * You are running 32-bit Python on a 64-bit OS\u001b[39m\n\u001b[1;32m    233\u001b[0m \n\u001b[1;32m    234\u001b[0m \u001b[38;5;124mError message(s): \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mos_error_list\u001b[38;5;132;01m}\u001b[39;00m\n\u001b[1;32m    235\u001b[0m \u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m    236\u001b[0m         )\n\u001b[1;32m    237\u001b[0m     _register_log_callback(lib)\n\u001b[1;32m    239\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mparse\u001b[39m(ver: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[\u001b[38;5;28mint\u001b[39m, \u001b[38;5;28mint\u001b[39m, \u001b[38;5;28mint\u001b[39m]:\n",
            "\u001b[0;31mXGBoostError\u001b[0m: \nXGBoost Library (libxgboost.dylib) could not be loaded.\nLikely causes:\n  * OpenMP runtime is not installed\n    - vcomp140.dll or libgomp-1.dll for Windows\n    - libomp.dylib for Mac OSX\n    - libgomp.so for Linux and other UNIX-like OSes\n    Mac OSX users: Run `brew install libomp` to install OpenMP runtime.\n\n  * You are running 32-bit Python on a 64-bit OS\n\nError message(s): [\"dlopen(/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/xgboost/lib/libxgboost.dylib, 0x0006): Library not loaded: @rpath/libomp.dylib\\n  Referenced from: <BBC4A126-D15A-3802-AD26-108872BA781A> /Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/xgboost/lib/libxgboost.dylib\\n  Reason: tried: '/opt/homebrew/opt/libomp/lib/libomp.dylib' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/opt/homebrew/opt/libomp/lib/libomp.dylib' (no such file), '/opt/homebrew/opt/libomp/lib/libomp.dylib' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/opt/homebrew/opt/libomp/lib/libomp.dylib' (no such file)\"]\n"
          ]
        }
      ],
      "source": [
        "from xgboost import XGBRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "\n",
        "# Initialize XGBoost Regressor with default parameters\n",
        "xgb = XGBRegressor(objective=\"reg:squarederror\", random_state=42)\n",
        "\n",
        "# Train the model\n",
        "xgb.fit(train_x, train_y)\n",
        "\n",
        "# Make predictions\n",
        "xgb_pred = xgb.predict(test_x)\n",
        "\n",
        "# Evaluate the model\n",
        "mae = mean_absolute_error(test_y, xgb_pred)\n",
        "mse = mean_squared_error(test_y, xgb_pred)\n",
        "r2 = r2_score(test_y, xgb_pred)\n",
        "\n",
        "print(\"\\nXGBoost Performance:\")\n",
        "print(f\"Mean Absolute Error (MAE): {mae}\")\n",
        "print(f\"Mean Squared Error (MSE): {mse}\")\n",
        "print(f\"R² Score: {r2}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yJfSKAkxf2cY"
      },
      "source": [
        "Data Prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-Ay8OemaDLR",
        "outputId": "47ee85b5-4f7b-46a5-ad6b-623904f12df8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted Price: 6967549.995526315\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# Define the column names (make sure this matches your training data)\n",
        "feature_columns = [\n",
        "    'area', 'bedrooms', 'bathrooms', 'stories', 'parking', 'mainroad_no', \n",
        "    'mainroad_yes', 'guestroom_no', 'guestroom_yes', 'basement_no', \n",
        "    'basement_yes', 'hotwaterheating_no', 'hotwaterheating_yes', \n",
        "    'airconditioning_no', 'airconditioning_yes', 'prefarea_no', 'prefarea_yes', \n",
        "    'furnishingstatus_furnished', 'furnishingstatus_semi-furnished', \n",
        "    'furnishingstatus_unfurnished'\n",
        "]\n",
        "\n",
        "# Example input data (scaled and encoded as in your provided dataset)\n",
        "input_data = np.array([[7420, 4, 2, 3, 2, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0]])\n",
        "\n",
        "# Convert input data to DataFrame with the correct columns\n",
        "input_df = pd.DataFrame(input_data, columns=feature_columns)\n",
        "\n",
        "# Scale the input data using the same scaler used during training\n",
        "input_data_scaled = scaler.transform(input_df)\n",
        "\n",
        "# Predict using the trained model (e.g., RandomForest)\n",
        "predicted_price = best_rf.predict(input_data_scaled)\n",
        "\n",
        "# Output the predicted price\n",
        "print(f\"Predicted Price: {predicted_price[0]}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uUq1XGnPf78T"
      },
      "source": [
        "Saving the file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "id": "LA8FupxFaJy5"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "\n",
        "filename = 'Property_acquisition_cost_predictor.sav'\n",
        "pickle.dump(best_rf,open(filename,'wb'))\n",
        "loaded_model = pickle.load(open('Property_acquisition_cost_predictor.sav','rb'))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.13.1"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}