Linear+RegressionModel-.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Importing Libraries"
      ],
      "metadata": {
        "id": "CDGSSstopLHX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split,cross_val_score\n",
        "from sklearn.linear_model import LinearRegression, Lasso,LassoCV\n",
        "import statsmodels.api as sm\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "Hdz8z-JcBCeL"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "ydSb8LZa_ctK"
      },
      "outputs": [],
      "source": [
        "data=pd.read_csv(\"HousingData.csv\") # loading datat"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Exploration"
      ],
      "metadata": {
        "id": "diCl8oPkpY9W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "id": "nO5MYEQKBSWE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "id": "KQdgFCv4EFI8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "id": "fxShtZqaBfRL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "null_columns = data.columns[data.isnull().any()]\n",
        "data[null_columns].isnull().sum()"
      ],
      "metadata": {
        "id": "CiFE443BCF9z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Handling Missing Values"
      ],
      "metadata": {
        "id": "FdJuyTlSpbQX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "medians = data.median()"
      ],
      "metadata": {
        "id": "CcJFimCfDDQf"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned = data.fillna(data.median()) #assiging median of each col to na values"
      ],
      "metadata": {
        "id": "JXgYRqOJE9qs"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned.head(9)"
      ],
      "metadata": {
        "id": "DIjplj9qFV-1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned.isnull().sum()"
      ],
      "metadata": {
        "id": "rl2bXdzVFvlz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Seperating  features X ( Predictors ) and Target variable Y"
      ],
      "metadata": {
        "id": "X3yPfeInGiSV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data_cleaned.drop('MEDV', axis = 1) # axis = 1 shows opertion perform on columns\n",
        "Y = data_cleaned['MEDV']"
      ],
      "metadata": {
        "id": "_7Ri6ozWG4qq"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Spliting data into Training and Testing Sets"
      ],
      "metadata": {
        "id": "1nBRbgXHHflU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "tnwIQiZKHimB"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()\n",
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "id": "CNg9JxqkJ0am"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bigger the Magnititude of Ceoficient, bigger is the influence of that feature in prediction. Intercept is the value of response variable when all other features are zero."
      ],
      "metadata": {
        "id": "T18Jon7Psm_j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Model Coefficients: \", model.coef_)\n",
        "print(\"Model Intercept: \", model.intercept_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_dHsJNSSK7o7",
        "outputId": "fee5d99e-32f6-416b-8041-087903a76204"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Coefficients:  [-1.12573993e-01  2.81793653e-02 -2.56559077e-02  3.27948215e+00\n",
            " -1.59783383e+01  4.71714026e+00 -1.81899639e-02 -1.50621917e+00\n",
            "  2.18216881e-01 -9.05444537e-03 -8.90009543e-01  1.20329382e-02\n",
            " -4.44166764e-01]\n",
            "Model Intercept:  28.16795337495841\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualize the importance of each feature. We can see NOX ( the higher the value of Nitrogen in envrionemnt, has biggest negative impact in pricing of house ) also RM and CHAS played role in the price"
      ],
      "metadata": {
        "id": "1CFl9yTisgG9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features = X.columns\n",
        "importances = model.coef_\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.barh(features,importances)\n",
        "plt.xlabel(\"Coeficient values\")\n",
        "plt.title(\"Feature Importance\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "yLkcxRwnMhMM",
        "outputId": "12ccaa09-a120-4ecd-e2a7-7d54cc6c1265"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "R2 indicates 65% of variance are explained by the features. While MSE indicate 24.9%, indicating the percntage of error. The Higher R2 value and lower MSE value, indicates better model fit. Our model performs moderately well."
      ],
      "metadata": {
        "id": "N3DeQkInuN80"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = model.predict(X_test)\n",
        "mse = mean_squared_error(Y_test,Y_pred)\n",
        "r2 = r2_score(Y_test,Y_pred)\n",
        "print(\"Mean Squared Error: \",mse)\n",
        "print(\"R2 : \",r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kWA7BgBaMqV2",
        "outputId": "8ec60a8d-dedf-4f56-c27f-8fde1b77da95"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error:  24.999384790103406\n",
            "R2 :  0.6591013893903506\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizing Actual vs Predicted value by model. If blue dots are closely align to red line, indicates better model fit. we see some of data points are far away but most of them are align on or closer to the line"
      ],
      "metadata": {
        "id": "UH2zLE_KurA7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.scatter(Y_test,Y_pred,color='blue')\n",
        "plt.plot([Y_test.min(), Y_test.max()], [Y_test.min(), Y_test.max()], color='red')\n",
        "plt.xlabel('Actual Values')\n",
        "plt.ylabel('Predicted Values')\n",
        "plt.title('Actual vs. Predicted Values')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "vc88ZUEUagJu",
        "outputId": "0b378a24-b77a-46a4-ebed-3133054ed2d3"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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3P+vaa6+1ChQokKZ09PTp062KFStaISEh1u23325t2rQpTQnw5ORka8KECVblypWtkJAQ68Ybb7Q+++wzq3v37lblypVT9Q8nSoDbxcfHW4UKFbIA64MPPkhz/4svvmjdcsstVvHixa1ChQpZtWrVssaPH29dvHgx5Rh76ej0SpxfCbD69u2b6TH2EuBLly5N9/6ff/7Zio6OtkqWLGmFhIRYlStXtjp37mytWLEi1XGrV6+26tevbwUHB1tVq1a15syZY40ZMybLEuCWlfP31N19zMp9991nAdawYcPS3Ld//37r3nvvtYoXL24VK1bMuu+++6yDBw+muU7SKwGelJRkDR8+3CpVqpRVuHBhq3Xr1taOHTvS/Z2dPn3aGjlypFW9enUrODjYKlWqlHXbbbdZ06ZNS7leli1bZrVq1coqU6aMFRwcbFWqVMl6/PHHrbi4OJder4iIO9gs64pxdhERERERkXxMa5JEREREREQcKCSJiIiIiIg4UEgSERERERFxoJAkIiIiIiLiQCFJRERERETEgUKSiIiIiIiIgzy/mWxycjIHDx6kaNGi2Gw2X3dHRERERER8xLIsTp8+TYUKFQgIyHi8KM+HpIMHDxIREeHrboiIiIiIiJ/Yt28f4eHhGd6f50NS0aJFAfOLCAsL83FvJDsSExP55ptvaNWqFUFBQb7ujuQDuubE23TNiTfpehNv86drLj4+noiIiJSMkJE8H5LsU+zCwsIUknKpxMREChcuTFhYmM//w5L8QdeceJuuOfEmXW/ibf54zWW1DEeFG0RERERERBwoJImIiIiIiDhQSBIREREREXGgkCQiIiIiIuJAIUlERERERMSBQpKIiIiIiIgDhSQREREREREHCkkiIiIiIiIOFJJEREREREQcKCSJiIiIiIg4UEgSERERERFxoJAkIiIiIiLiQCFJRERERETEgUKSiIiIiIiIA5+GpLFjx2Kz2VJ91apVK+X+Cxcu0LdvX0qWLEloaCgdO3bk8OHDPuyxiIiIiIjkdT4fSbruuuuIi4tL+Vq3bl3KfU899RTLly9n6dKlrF69moMHDxIdHe3D3oqIiIiISF5XwOcdKFCAcuXKpWk/deoUb7/9NgsXLqRZs2YAzJs3j9q1a7Nx40ZuvfVWb3dVRERERERccekStkWLKH74MNx1l6974zSfh6Tt27dToUIFChYsSGRkJBMnTqRSpUps3ryZxMREWrRokXJsrVq1qFSpEhs2bMgwJCUkJJCQkJByOz4+HoDExEQSExM9+2LEI+zvm94/8RZdc+JtuubEm3S9iVdcvIjtgw8InDqVAjt3UvuGG0js29fXvXL6uvdpSGrYsCHz58/nmmuuIS4ujnHjxhEVFcXvv//OoUOHCA4Opnjx4qkeU7ZsWQ4dOpThOSdOnMi4cePStH/zzTcULlzY3S9BvCg2NtbXXZB8RteceJuuOfEmXW/iCQEJCVSOjaX6J59Q+NgxABKKFuX4ddex4euvIcC3q33OnTvn1HE+DUlt2rRJ+blu3bo0bNiQypUr8+GHH1KoUKFsnXPkyJEMHjw45XZ8fDwRERG0atWKsLCwHPdZvC8xMZHY2FhatmxJUFCQr7sj+YCuOfE2XXPiTbrexCNOnybgjTcImDkT25EjAFjly5M8eDBJ3buzbf16v7jm7LPMsuLz6XaOihcvTs2aNdmxYwctW7bk4sWLnDx5MtVo0uHDh9Ndw2QXEhJCSEhImvagoCCfvymSM3oPxdt0zYm36ZoTb9L1Jm7x77/w8svm68QJ01alCgwfjq1HDwILFiTovylu/nDNOfv8Pq9u5+jMmTPs3LmT8uXLU79+fYKCglixYkXK/Vu3bmXv3r1ERkb6sJciIiIiIvnc4cMwfDhUrgzjxpmAdM01MH8+bNsGTzwBBQv6upfZ5tORpCFDhtCuXTsqV67MwYMHGTNmDIGBgXTp0oVixYrRu3dvBg8eTIkSJQgLC6N///5ERkaqsp2IiIiIiC/s2wdTp8Jbb8GFC6atbl0YNQo6doTAQN/2z018GpL2799Ply5dOH78OKVLl6ZRo0Zs3LiR0qVLAzBjxgwCAgLo2LEjCQkJtG7dmtdee82XXRYRERERyX927oRJk+Ddd8FeIa5hQxg9Gtq2BZvNt/1zM5+GpMWLF2d6f8GCBZk9ezazZ8/2Uo9ERERERCTFH3/AxImwaBEkJ5u2Jk1MOGrWLM+FIzu/KtwgIiIiIiJ+4KefYPx4iIm53NamjZlWd/vtvuuXlygkiYiIiIiI8d138OKL8NVXl9s6doRnnoGbbvJdv7xMIUlEREREJD+zLFixwoSj1atNW2AgdOkCI0fCtdf6tn8+oJAkIiIiIpIfWRYsX26m1f3wg2kLCoIePUx572rVfNo9X1JIEhERERHJT5KSYNkyE45++820FSoEjz0GQ4ZAeLhv++cHFJJERERERPKDxERYsMBUq9u2zbQVLQp9+8JTT0GZMr7tnx9RSBIRERERycsuXIB582DyZNizx7RddRUMGgT9+5ufJRWFJBERERGRvOjMGXjjDZg+HeLiTFvZsvD00/DEE2YUSdKlkCQiIiIikpecPAmvvgozZ8Lx46YtIgKGDYPevc36I8mUQpKIiIiISF5w9KgJRq++CvHxpq16dRgxAh56CIKDfdq93EQhSUREREQkNzt4EKZNM1Przp0zbdddB6NGwX33QQH9ye8q/cZERERERHKj3btNMYZ33oGLF01b/fowejTccw8EBPi0e7mZQpKIiIiISG7y99+mjPeCBWbPI4CoKDNy1KoV2Gy+7V8eoJAkIiIiIpIbbNkCEyaYjWAty7S1amXCUePGPu1aXqOQJCIiIiLizzZuhPHj4bPPLre1b2/C0c03+65feZhCkoiIiIiIv7EsWLXKhKMVK0xbQAB07gzPPAN16vi0e3mdQpKIiIiIiL+wLPjySxOO1q83bQUKwMMPm1LeNWr4tn/5hEKSiIiIiIivJSfDxx+bcPTzz6YtJAQeeQSGDoXKlX3bv3xGIUlERERExFcuXYLFi01Bhr/+Mm1FisCTT8LgwVC+vG/7l08pJImIiIiIeFtCArz7rtnn6J9/TFuxYjBgAAwcCCVL+rZ/+ZxCkoiIiIiIt5w7B2+9BVOnwoEDpq1UKTNq1KePCUricwpJIiIiIiKeFh8Pr70GL70ER4+atgoVzHqjRx81U+zEbygkiYiIiIh4yvHj8PLL5uvkSdN29dWmUl337qY4g/gdhSQREREREXc7dAimT4fXX4ezZ01brVpmj6MuXUxZb/FbendERERERNxl716YMgXmzjXFGQBuuAFGjYLoaLMhrPg9hSQRERERkZzavh0mTYL33jNlvQEiI004uususNl82z9xiUKSiIiIiEh2/f672eNoyRKzISxAs2YwejQ0aaJwlEspJImIiIiIuGrTJhg/Hj755HJb27Zm5Cgy0mfdEvdQSBIRERERcdbatSYcff21uW2zQadOpiDDDTf4tGviPgpJIiIiIiKZsSyIjYUXXzQhCSAwELp1M6W8a9f2bf/E7RSSRERERETSk5wMy5ebcLRpk2kLDoaePWHYMKha1bf9E49RSBIRERERcZSUBB9+aAoy/P67aStUCB5/HIYMgYoVfds/8TiFJBERERERgIsX4YMPYOJE2LHDtIWFQd++MGgQlCnj0+6J9ygkiYiIiEj+dv48vPMOTJ4M+/aZthIl4KmnoF8/KF7cp90T71NIEhEREZH86cwZmDMHpk2Dw4dNW7lyZkrd449DaKhv+yc+o5AkIiIiIvnLiRPwyiswaxb8+69pq1QJhg+HXr2gYEHf9k98TiFJRERERPKHI0dgxgyYPRtOnzZtNWrAyJHw4IMQFOTb/onfUEgSERERkbxt/34zpe7NN836I4A6dcwGsPfdZ/Y8EnGgkCQiIiIiedM//5hiDPPmQWKiabv5Zhg9Gu6+GwICfNs/8VsKSSIiIiKSt/z1lynjvXCh2fMIoHFjE45atACbzbf9E7+nkCQiIiIiecPPP8P48RATA5Zl2u68E0aNgkaNfNs3yVU0xigiIiIiudv69dC2Ldx0E3z0kQlI994LP/4IX36pgORDSUmwbp35ed26ywN7/k4hSURERERyH8uCFSugWTO4/Xb44guzxqhrV/jtNzOa1KCBr3uZr8XEQJUqJr+C+V6limn3dwpJIiIiIpJ7WBZ89hncdptZX7RypSnd3bs3bN0KCxbA9df7upf5XkwMdOpkCgs6OnDAtPt7UFJIEhERERH/l5QES5fCjTdCu3awcaPZ9LV/f9i5E+bOherVfd1LwbxVAwdeXhbmyN42aJB/T71T4QYRERER8V+JibBoEUyYYEaKAEJDoU8fGDwYypb1bf8kjbVr044gObIs2LfPHNekide65RKFJBERERHxPwkJMH8+TJoEu3ebtuLFzRDFgAFQooQPOyeZiYtz73G+oJAkIiIiIv7j7Fl4802YNg0OHjRtZcqYUaMnn4SwMN/2T7JUvrx7j/MFhSQRERER8b1Tp2D2bJgxA44dM23h4TB0KDzyCBQu7Nv+idOiosxbd+BA+uuSbDZzf1SU9/vmLIUkEREREfGdY8dg1ix45RUTlACqVoURI+DhhyEkxLf9E5cFBpq3tFMnE4gc2W/PnGmO81eqbiciIiIi3hcXB0OGmI1zXnzRBKTateGDD0yBhkcfVUDKxaKjYdkyqFgxdXt4uGmPjvZNv5ylkSQRERER8Z7du2HKFHjnHVOcAUxZ79GjoUMHsyGseE1SkqkyFxdn1ghFRblvhCc6Gtq3hzVrID4ePv8cGjf27xEkO12FIiIiIuJ527ZBz55Qowa8/roJSLffDl98AZs3m7+oFZC8KibGDOQ1bQpdu5rvVaq4d6PXwEBo1Mj83KhR7ghIoJAkIiIiIp7066/wwANQq5Yp6X3pErRoAatWmSGMNm3SLlwRj4uJMWuGrtzP6MAB0+7OoJQbKSSJiIiIiPv98IOZa1WvHixZYsqctWsHGzdCbCzccYfCkY8kJZntptKrPGdvGzTIHJdfKSSJiIiIiHtYFqxeDa1aQcOG8OmnJgh17gxbtpjbDRv6upf53tq1aUeQHFkW7NtnjsuvFJJEREREJGcsC776yqz6b9LEjBQFBkKPHvDXX2YkqV49X/dS/hMX597jMpOUBOvWmZ/Xrcs9o1MKSSIiIiKSPcnJ8PHHcPPNZm3Rd99BcDA8+STs2AHz5sE11/i6l3KF8uXde1xG7IUh2rY1t9u2dX9hCE9RSBIRERER11y6BAsXQt26pird5s1QuDAMHgy7dsFrr5m/hsUvRUWZ/YoyWhJms0FEhDkuu3J7YQiFJBERERFxzsWLMHeuqVTXrRv88QeEhcGoUbBnD0yfDhUq+LqXkoXAQJg1y/x8ZVCy3545M/vluvNCYQiFJBERERHJ3Pnz8MorUK0aPPoo7NwJJUvCiy+acPTii1CqlK97KS6IjoZly6BixdTt4eGmPTo6++fOC4UhCvi6AyIiIiLip06fNhu/Tp8OR46YtvLlYehQeOwxKFLEt/2THImONlXa1641RRrKlzdT7HK64as3C0N4ikKSiIiIiKT277/w8svm68QJ01alCgwfbirWFSzoy96JGwUGmoKE7uStwhCepJAkIiIiIsbhw/DSS6bwwpkzpu2aa2DkSOjaFYKCfNs/yRXshSEOHEh/XZLNZu7PSWEIT/ObNUmTJk3CZrMxaNCglLYLFy7Qt29fSpYsSWhoKB07duTw4cO+66SIiIhIXrRvHwwYYEaLpkwxAaluXbO/0R9/QPfuCkjiNE8XhvAGvwhJP/74I2+88QZ169ZN1f7UU0+xfPlyli5dyurVqzl48CDROVlFJiIiIiKX7dxpCjFUq2YKM1y4AA0bwvLlsGULdO7s33/JOkhKglWrYNEi892fK6flB54sDOENPg9JZ86coVu3brz11ltcddVVKe2nTp3i7bff5qWXXqJZs2bUr1+fefPmsX79ejZu3OjDHouIiIjkbkX37iWwe3eoWdOU9E5MNAtT/u//YMMGuPvujDfR8UP2TUubNjWzAps2zT2bluZl0dGwezd8/rm5/fnnZhstfw9I4Adrkvr27Uvbtm1p0aIFL774Ykr75s2bSUxMpEWLFilttWrVolKlSmzYsIFbb7013fMlJCSQkJCQcjs+Ph6AxMREEhMTPfQqxJPs75veP/EWXXPibbrmxGt+/hnbhAk0+9//UpqS77yT5BEjsG67zTRcuuSjzmXP8uXw0ENm7UuhQpfb//3XtAO0a+ebvonRsGEisbHme3IyJCf7ri/O/jvr05C0ePFifvrpJ3788cc09x06dIjg4GCKFy+eqr1s2bIcOnQow3NOnDiRcePGpWn/5ptvKFy4cI77LL4TGxvr6y5IPqNrTrxN15x4Sok//6TmsmWU/emnlLaDkZFs69SJU9WqwcmT8MUXvutgDgQGwsKFmR+TS19anuMP/8adO3fOqeN8FpL27dvHwIEDiY2NpaAby0iOHDmSwYMHp9yOj48nIiKCVq1aERYW5rbnEe9JTEwkNjaWli1bEqRFo+IFuubE23TNiUdYFrYVKwiYNImANWtMU0AASZ07s+b227m1Vy9uz+XX27p10LZt1sd9/jk0auT5/kj6/OnfOPsss6z4LCRt3ryZI0eOcNNNN6W0JSUlsWbNGl599VW+/vprLl68yMmTJ1ONJh0+fJhy5cpleN6QkBBCQkLStAcFBfn8TZGc0Xso3qZrTrxN15y4hWWZOWjjx8MPP5i2oCDo0QPb8OFYlSpx+osv8sT1dugQnD/v3HG5/KXmCf5wzTn7/D4LSc2bN+e3335L1dazZ09q1arF8OHDiYiIICgoiBUrVtCxY0cAtm7dyt69e4mMjPRFl0VERET8V1KSKRs2fjzY/8YqVMhUrxsyBCIiTFseWvuWFzYtFf/ks5BUtGhRrr/++lRtRYoUoWTJkintvXv3ZvDgwZQoUYKwsDD69+9PZGRkhkUbRERERPKdxERYsAAmToRt20xb0aLQty889RSUKePb/nlQXti0VPyTz6vbZWbGjBkEBATQsWNHEhISaN26Na+99pqvuyUiIiLiexcuwLx5MHky7Nlj2q66CgYNgv79zc95nH3T0k6dTCByDEq5ZdNS8U9+FZJWrVqV6nbBggWZPXs2s2fP9k2HRERERPzNmTPwxhswfTrExZm2smXh6afhiSfMKFI+Yt+0dOBA2L//cnt4uAlIuWFPHvE/fhWSRERERCQDJ0/Cq6+av/yPHzdtEREwbBj07p16k6B8Jjoa2reHtWtNbixf3kyx0wiSZJdCkoiIiIg/O3rUBKNXXwV7+eLq1WHECLNbanCwT7vnLwIDoUkTX/dC8gqFJBERERF/dPAgTJtmptbZN8C87joYNQruuw8K6M84EU/Rf10iIiIi/mTXLlOMYd48uHjRtNWvD6NHwz33QECAb/snkg8oJImIiIj4g7//NmW8Fywwex4BNGpkwlGrVpfLtYmIxykkiYiIiPjSli0wYYIp0WavYd2qlZlW17ixT7smkl8pJImIiIj4wsaNMH48fPbZ5bb27U04uvlm3/VLRBSSRERERLzGsmDVKhOOVqwwbQEB0LkzPPMM1Knj0+6JiKGQJCIiIuJplgVffmnC0fr1pq1AAXj4YVPKu0YN3/ZPRFJRSBIRERHxlORk+PhjE45+/tm0hYTAI4/A0KFQubJv+yci6VJIEhEREXG3S5dg8WJTkOGvv0xbkSLw5JMweDCUL+/b/olIphSSRERERNwlIQHefdfsc/TPP6atWDEYMAAGDoSSJX3bPxFxikKSiIiISE6dOwdvvQVTp8KBA6atVCkzatSnjwlKIpJrKCSJiIiIZFd8PLz2Grz0Ehw9atoqVDDrjR591EyxE5FcRyFJREREUklKgrVrIS7OLJ2JioLAQF/3ys8cPw6zZsErr8DJk6bt6qtNpbru3U1xBhHJtRSSREREJEVMjFk6s3//5bbwcJMHoqN91y+/cegQTJ8Or78OZ8+atlq1zB5HXbqYst4ikuvpv2QREREBTEDq1Mls6ePowAHTvmxZPg5Ke/fClCkwd64pzgBwww0wahTce6+G2kTymABfd0BERER8LynJjCBdGZDgctugQea4fGX7dujdG6pVg9mzTUCKjITPPoOffjLpUQFJJM9RSBIRERHWrk09xe5KlgX79pnj8oXff4euXc1UunfeMfseNWsG334L330HbduCzebrXoqIh2i6nYiIiBAX597jcq1Nm2D8ePjkk8ttbduaaXWRkT7rloh4l0KSiIiIUL68e4/LddauNeHo66/NbZvNTKV75hmz9khE8hWFJBERESEqylSxO3Ag/XVJNpu5PyrK+33zGMuC2Fh48cXL8wgDA6FbN1PKu3Zt3/ZPRHxGa5JERESEwEBT5hvSLrWx3545M4/UKEhONtPpbrkFWrc2ASk4GB5/HLZtg3ffVUASyecUkkRERAQw5b2XLYOKFVO3h4fnkfLfSUmwaBHUq2fKdm/aBIUKmbJ9//wDc+ZA1aq+7qWI+AFNtxMREZEU0dHQvr0ZXImLM2uQoqJy+QjSxYvwwQcwcSLs2GHaihaFfv1MQCpTxqfdExH/o5AkIiIiqQQGQpMmvu6FG5w/b8p3T55s6pcDlChhglG/fnDVVT7tnoj4L4UkERERyVvOnDFT56ZNg8OHTVu5cjBkiFl3FBrq2/6JiN9TSBIREZG84cQJeOUVU4Hi339NW6VKMHw49OoFBQv6tn8ikmsoJImIiEjuduQIzJgBs2fD6dOmrUYNGDkSHnwQgoJ82z8RyXUUkkRERCR32r/fTKl7802z/gigTh2zAex99+XyahMi4ksKSSIiIpK7/POPKcYwbx4kJpq2m2+G0aPh7rshQDuciEjOKCSJiIhI7vDnn6aM96JFZs8jgMaNTThq0SLtLrgiItmkkCQiIiL+7eefYfx4iIkByzJtd94Jo0ZBo0a+7ZuI5EkKSSIiIuKf1q834eiLLy633XuvWXPUoIHv+iUieZ5CkoiIiPgPy4JvvzXhaOVK0xYQAA88YKrVXX+9b/snIvmCQpKIiIj4nmXB55+bcLRxo2kLCoKHH4YRI6B6dd/2T0TyFYUkERER8Z2kJLPWaPx4+OUX01awIDzyCAwdajaDdfF0a9dCXByULw9RUaoELiKuU0gSERER70tMNFXqJkyArVtNW2go9OkDgwdD2bIunzImBgYONNsn2YWHw6xZEB3tpn6LSL6gkCQiIiLek5AA8+fDpEmwe7dpK17cpJsBA6BEiWydNiYGOnW6XPzO7sAB075smYKSiDhPIUlEREQ87+xZePNNmDYNDh40bWXKmFGjJ5+EsLBsnzopyWSsKwMSmDabDQYNgvbtNfVORJyjkCQiIiKec+oUzJ4NM2bAsWOmLTzcrDd65BEoXDjHT7F2beopdleyLNi3zxzXpEmOn05E8gGFJBEREXG/Y8fMYqBXXjFBCaBqVVOp7uGHISTEbU8VF+fe40REFJJERETEfeLizJS6OXPg3DnTVrs2jBoF998PBdz/p0f58u49TkREIUlERCQTKintpN27YcoUeOcdU5wB4MYbYfRo6NDBbAjrIVFRZgbfgQPpr0uy2cz9UVEe64KI5DGe+xdLREQkl4uJgSpVoGlT6NrVfK9SxbTLf7Ztg549oUYNeP11E5Buuw2++AI2bzYl5TwYkMCE1lmzzM82W+r77LdnzlS4FRHnKSSJiIikw15S+sqCAPaS0vk+KP36KzzwANSqZUp6X7oELVrAypWwbh20aZM2sXhQdLQp812xYur28HCV/xYR12m6nYiIyH/sU+sOHICnnnKupDTks+l4P/wA48fDp59ebmvXzqw5atjQd/3CBKH27fPZ+yEiHqGQJCIighkZGjgw81LSdvaS0uPHw1tvpX5MeLiZ+pWnRi4sC9asMS84Nta02Wxw333wzDNQr55v++cgMFBlvkUk5xSSREQk37NPrUtv5CgzY8akbbNPx8sTU7wsC77+Gl58Eb77zrQFBsJDD5lS3tdc49v+iYh4iNYkiYhIvpaUZEaQXA1IGbGfZ9Agc+5cKTkZPv4Ybr7ZrC367jsIDoYnn4QdO2DePAUkEcnTNJIkIiL52tq1zk2xc4V9Ot7atb6d+uVy+fJLl2DJEpg4Ef74w7QVLgxPPAFPPw0VKnil3yIivqaQJCIi+VpcnGvH22zOjzq5em53Sm+NVYbrpS5ehPfeg0mTYOdO0xYWBv37myGxUqW81W0REb+g6XYiIpKvlS/v2vHh4TBunGfO7S5Oly8/fx5eeQWqVYNHHzUBqWRJswZpzx7zXQFJRPIhjSSJiEi+FhVlgs+BAxmPEJUuDTNmmD14oqJM21tvZfwYm82c036sN2W2xspevnzUgNN02PY6ATOmw5Ej5s7y5WHIEHj8cShSxLudFhHxMxpJEhGRfC0w0ExBg7R7n9ps5mvOHOjWzawvCgzM+jEAM2f6Zn+ezNZYXcW/PGeN5bsDlQkYOdwEpMqV4fXX4Z9/YPBgBSQRERSSREREiI42JbsrVkzdHh6ecSnv7DzGG9JbB1WGw0xiOHuozFjGUYITxJevCfPnw/btpjBDwYJe76uzkpJg1SpYtMh8z7VVA0Uk19B0OxEREUyoad/etWpw2XmMpzmugwpnH0OZyqO8RSEuAPALdRnPKPq835EmzX3YUSe5VIBCRMRNFJJERET+Exjoesnu7DwmJ7Iq6x0VBbeX20mPQ5N4mHcJJhGAjTTkRUbzBW0Jj7CxyIt9zq6MNvnNUxv2iohf0nQ7ERGRXCImBqpUgaZNoWtX871KFYdqdX/8QWD3B1l7pCaPMJdgEllJE5rzf0SygS9sd4PN5rP1Uq7IqgAF5PINe0XEr2kkSUREJBfIbFRlQsfNNGw4norffwyADTh0Yxue3D+KT47ennJseLgpKJEbRl+y2uQ3ow17Xd5AV0QkHQpJIiIifi6jUZXbWccoazxt+Aq+/6+xY0d45hnK3XQTy3JxYHB2I17H47R+SUTcRSFJRETEz6UeVbFowf8xivE0YTUASQSwkK5cM28kt/S4NuVx3l4v5U7ObsRrP07rl0TEnbQmSUREAJVZzglP/+7MaIlFOz5lI7cSSyuasJqLBPEmj1KTbTzM++wMuTarU+Ua9k1+r9yHys5mg4gIc5zWL4mIuykkiYhI1gUBJEMe/90lJVH3ryX8Qj0+pT0N+YHzFGQWA6jGTh7nTf6hGuD86Etu4MqGva6sXxIRcYZCkohIPmefpnTlH5n2aUoKShnz6O8uMdFs9nrttVz3wgPU5TdOE8okhlOF3QxiFvuJAFKPquQlzm7Ym531SyIimfFpSHr99depW7cuYWFhhIWFERkZyZdffply/4ULF+jbty8lS5YkNDSUjh07cvjwYR/2WEQkb9E0pezz2O/uwgV4/XWoUQN69oRt2+Cqq/jz/rFUYQ/P2CZxhLIph185qpLXREfD7t2wciUsXGi+79qVen2Rq+uXRESy4tOQFB4ezqRJk9i8eTObNm2iWbNmtG/fnj/++AOAp556iuXLl7N06VJWr17NwYMHidaqSxERt9E0pexz9+8u8Px5AmbMgKpVoU8f2LMHypaFKVNgzx6uXTyGtz4qkeWoSl5kL0DRpYv5fmUYdGX9koiIM3xa3a5du3apbo8fP57XX3+djRs3Eh4ezttvv83ChQtp1qwZAPPmzaN27dps3LiRW2+91RddFhHJUzRNKfvc9rs7eZKAWbNoOX06gadPm7aICBg2DHr3hkKFUg6Njob27XNvWW9Psa9f6tTJBCLH0b28PtImIp7hNyXAk5KSWLp0KWfPniUyMpLNmzeTmJhIixYtUo6pVasWlSpVYsOGDRmGpISEBBISElJux8fHA5CYmEhiYqJnX4R4hP190/sn3pKfrrly5VL9DZ7pcfng1+GSHP/ujh4l4OWXCXj9dQLj4wkEkqtVI3nYMKxu3SA42ByXzoNvv7w/LMnJ5iu/a9fOjKgNH27WhNmFh8OkSeZ+XcNGfvo3TvyDP11zzvbBZlnpzab2nt9++43IyEguXLhAaGgoCxcu5K677mLhwoX07NkzVeABuOWWW2jatCmTJ09O93xjx45l3LhxadoXLlxI4cKFPfIaREREnFXw+HGqf/IJlb/5hgL//T8uvlIltnXqxMHbb8fScIeIiMecO3eOrl27curUKcLCwjI8zucjSddccw1btmzh1KlTLFu2jO7du7N69epsn2/kyJEMHjw45XZ8fDwRERG0atUq01+E+K/ExERiY2Np2bIlQUFBvu6O5AP57Zpbvhweesj8nN40pfffN5/CS1ou/e527SJg2jQC3n0X28WLACTfdBPJI0dS4M47ObBiRb655sS38tu/ceJ7/nTN2WeZZcXnISk4OJjq1asDUL9+fX788UdmzZrF/fffz8WLFzl58iTFixdPOf7w4cOUK1cuw/OFhIQQEhKSpj0oKMjnb4rkjN5D8ba8eM0lJaVdz2Jf8D9wYOpCBBERZh1HXi4IkFNO/e7+/hsmToQFCy6XumvUCEaPJqBVKwJsNqz/pn/kxWtO/JeuN/E2f7jmnH3+HIek+Ph4vv32W6655hpq166d09ORnJxMQkIC9evXJygoiBUrVtCxY0cAtm7dyt69e4mMjMzx84iI5DcxMWn/mA8PNwveVRAg+zL83f22BTpPMAtl7MNMrVrBqFHQuLFP+ywiIplzOSR17tyZxo0b069fP86fP0+DBg3YvXs3lmWxePHilEDjjJEjR9KmTRsqVarE6dOnWbhwIatWreLrr7+mWLFi9O7dm8GDB1OiRAnCwsLo378/kZGRqmwnIuIi+6anV65CtW96ai8h3aSJT7qX69lLVAOwcSN0GA+ffXb5gPbtTTi6+WZfdE9ERFzk8j5Ja9asIeq/jQY+/vhjLMvi5MmTvPzyy7z44osunevIkSM8/PDDXHPNNTRv3pwff/yRr7/+mpYtWwIwY8YM7r77bjp27Ejjxo0pV64cMdr6XUTEJd7YMDYpCVatgkWLzPd8t/msZZldTlu0gMhIE5BsNnjgAfj1V/jkEwUkEZFcxOWRpFOnTlGiRAkAvvrqKzp27EjhwoVp27YtQ4cOdelcb7/9dqb3FyxYkNmzZzN79mxXuykiIv9xZdPT7IwkZTWNL0+zLPjySxg/HtavN20FCphqDiNGQM2avu2fiIhki8sjSREREWzYsIGzZ8/y1Vdf0apVKwBOnDhBwYIF3d5BERHJGU9uGGufxndlCLNP48uzg//JyfDRR1C/PrRtawJSSAj06QM7dsA77yggiYjkYi6HpEGDBtGtWzfCw8MpX748Tf772HHNmjXUqVPH3f0TEZEcKl/evcfZeWMan9+5dAk++ACuv96kwJ9/hiJFYMgQ2LULZs+GypV93UsREckhl6fb9enTh1tuuYV9+/bRsmVLAgJMzqpatarLa5JERMTzoqLM9LcDB9IPNDabuf+/5aZO8/Q0Pr+SkADvvguTJ8M//5i2YsVgwACTFEuW9G3/RETErbJVArxBgwbUrVuXXbt2Ua1aNQoUKEDbtm3d3TcREXGDwECzPqhTJxOI0tv0dOZM18t9e3Ian984dw7eegumTjUpE6BUKRg82EytK1bMt/0TERGPcHm63blz5+jduzeFCxfmuuuuY+/evQD079+fSZMmub2DIiKSc9HRpsx3xYqp28PDL5f/dpWnpvH5hfh4mDQJqlQxcwYPHIAKFWDGDNi9G0aOVEASEcnDXA5JI0eO5JdffmHVqlWpCjW0aNGCJUuWuLVzIiLiPtHR5u/7lSth4ULzfdeu7Fegs0/js49GXclmg4gI16fx+dTx4/Dcc2Zd0ciRcPQoXH01vPGGmWY3aJBZgyQiInmay9PtPvnkE5YsWcKtt96KzeH/jNdddx07d+50a+dERCStpCSzzicuzozSREU5P1Uu1aanOeSpaXw+cegQTJ8Or78OZ8+atlq14JlnoEsXU9ZbRETyDZdHko4ePUqZMmXStJ89ezZVaBIREfeLiTEzwJo2ha5dzfcqVXxXatsT0/i8au9e6NfP/BKnTTMBqV49WLoUfv/d7HekgCQiku+4HJIaNGjA559/nnLbHozmzp1LZGSk+3omIiKp+OueRO6exucV27dD795QrZop252QALfeCp99Zsp6d+qUS4bARETEE1z+eGzChAm0adOGP//8k0uXLjFr1iz+/PNP1q9fz+rVqz3RRxGRfC+rPYlsNrNcpn173/xt785pfB71++8wYQIsWWI2hAVo1gxGjTLDcpoRISIiZGMkqVGjRmzZsoVLly5Rp04dvvnmG8qUKcOGDRuoX7++J/ooIpLvubInkaRj0ya4916oUwcWLTIBqW1bWL8eVqwwQUkBSURE/pOtidbVqlXjrbfecndfREQkA/liTyJPWLMGxo+Hb74xt202M5XumWfghht82jUREfFfLock+75IGalUqVK2OyMiIunL03sSuZtlmVA0fvzlobXAQOjWDUaMgNq1fds/ERHxey6HpCpVqmRaxS4pKSlHHRIRkbTsexIdOJD+uiSbzdyfq/YkcrfkZPj0UxOONm0ybcHB0LMnDBsGVav6tn8iIpJruBySfv7551S3ExMT+fnnn3nppZcYP3682zomIiKXuXtPopzsteR3kpLgww9NQYbffzdthQrB44/DkCFp65OLiIhkweWQVK9evTRtDRo0oEKFCkydOpVov675KiKSe9n3JBo4MHURh/BwE5Cc/ec3Jib9c8ya5b9lu9MNdUkX4YMPYOJE2LHDHFi0qNn3aNAgSGdPPxEREWe4bYe8a665hh9//NFdpxMRkXRER5sy39kdBbLvtXTllD37Xkv+uAHslaGuIOd5uvg7PFNgMoWP7TONJUqYYNSvH1x1lc/6KiIieYPLISk+Pj7VbcuyiIuLY+zYsdSoUcNtHRMRkfRld08iV/ZagqyDmDem7DmGuiKc4QnmMIRplDt5GIALxctRcPQQM7UuNNS9Ty4iIvmWyyGpePHiaQo3WJZFREQEixcvdlvHREQk+9ILMM7utTR+PLz1VubT8bwxZc8e6opZJ+jPKwxkFiX5F4A9VGIKw/kmtBd/DyqYe9dTiYiIX3I5JK1cuTLV7YCAAEqXLk316tUpUMBts/dERCSbMgownTo59/gxY9K2OU7HA+9M2dv46RH67J9BX2YTxmkAtlGDiYxkAd1IJBj2m/CXnZE1X0kvwIqIiH9xOdXccccdnuiHiIi4QWZrjmbOzP557dPxBg68fDujY+xT9rI9urN/P0ybRsPX3+R2zgPwK3WYwDMs5T6SSX3i3LSBbmYjcBoNExHxH06FpE8//dTpE95zzz3Z7oyIuE+eKvEsTnFmzVFAgDkuOywr8+l69mP27YNVq6B5cxef4J9/YNIkmD8fEhMpAPzAzbzIaD7jbiwC0n1YbtlAN7MA+9BDsHChb/olIiJpORWSOnTo4NTJbDabNpMV8QO5scSz5Jwza47s/0RfudeSu3XubNY1OXW9/fmnKeO9aNHlDjZuTNLI0XR8pAUHDtpy/Qa6WQVYx+OCgrzXLxERSV/6H8tdITk52akvBSQR37N/Wn3lH8v29SIxMb7pl3ies9POBg3y/P6q//4LHTvC889nMnL188/morz+erPfUVIS3HmnSXurVxN4Z0tmvWwKBV1RLyhbG+j6kjMBFmDDBu/0R0REMudUSBKR3MGZT6sHDcr+dCvxb85OO2vfHnbvhrFjXTu/feQmPDxtaMnImDFQufIV4Xz9emjbFm66CT76yFyc994LP/4IX34JjRqlHGrfQPfKUBce7p97OmXE2QB76JBn+yEiIs7JVjm6s2fPsnr1avbu3cvFixdT3TdgwAC3dExEXOdsiefcVg0sr/DEOjHHc5YpY8LDgQPpB2XH6WkxMfDCC84/jz0UzZplvjtbKQ/+G8XsaLF6zLdErRkP9iqpAQHwwAMwcqQZTcpATjfQ9QfOBthy5TzbDxERcY7LIennn3/mrrvu4ty5c5w9e5YSJUpw7NgxChcuTJkyZRSSRHzI2U+rc1M1sLzCE+vE0jtnyZKXizQ4BiXH6Wn/+59ZM+SK8HDzWHtfly2DRx810+oyZ9GWzxnFeCLHbTRNQUHw8MMwYgRUr+7U82d3A11/ERWVdYAFiIz0br9ERCR9Lk+3e+qpp2jXrh0nTpygUKFCbNy4kT179lC/fn2mTZvmiT6KiJOc/bQ6t1QDyys8sU4so3PaQ0uJEqnb7dPT2re/XMbbGf36mYGfXbtSh7noaPjww4wfF0ASnVjKz9zIZ7Qjko2cpyD77+0HO3bA3LlOB6S8IDDw8ihcRuur7MeJiIjvuRyStmzZwtNPP01AQACBgYEkJCQQERHBlClTeOaZZzzRRxFxkv3T6ozWi9hsEBGRO6qB5RWeWCfmTKnvQoXg//7PlJV2DDlZTcm8UseOZgQnvT/emzRJe70VIJGHeI8/uI6ldOYGfuE0oUxhKFezi7X3vQKVKjnfgTwks/VV77/vmz6JiEj6XA5JQUFBBASYh5UpU4a9e/cCUKxYMfbt2+fe3omIS5z5tDq3VAPLK1xZJ+bOc+7fb97nLl1ShxxXplpmFagdr7dgEniMN9hGTd6jO7XYygmKM5YxVGYPw5nCYcrl+1HM6GhTNGPlytQBtl07X/dMREQcubwm6cYbb+THH3+kRo0a3HHHHTz33HMcO3aM999/n+szWXgrIt5h/7Q6vfUvjmtKxDs8sU4sJ+d0JaTYA3VmBSeiW59lS/c3KfPeNMolHwTgMGV4icG8zpOcJgzIXXsaeVp666uSk33SFRERyYDTISkpKYnAwEAmTJjA6dOnARg/fjwPP/wwTz75JDVq1OCdd97xWEdFxHl5oRpYXuGJdWI5OWdWBQTAXCeLF5vrKKOCE7MnnOKefbNhxgzqHjsGwKmwcJ6NH8pcHuE8hVOO1yimiIjkNk6HpIoVK9KjRw969epFgwYNADPd7quvvvJY50Qk+3J7NbC84r/8kClX14k5Uykto1Eb+xS5Tp3SVsCzW7TockGJTp1SH1OSYzy+fxaNH34FOGUaq1aFESMo9vDDNPk8hI81iikiIrmc02uS+vbty7Jly6hduzZRUVHMnz+fc+fOebJvIiK5WlISPPVU1sdNn+7aCEtO155lVEAgIsLs7XrffWmLQ5Qjjmk8zR4qM5oXKc4ptheoTfJ7H8DWraYeeEhIhmtuFJBERCQ3cTokPfvss+zYsYMVK1ZQtWpV+vXrR/ny5Xn00Uf5/vvvPdlHEZFcydlKcqVLu37uzCqlLVuWdSjJKszY+16Z3cymD7u4mqd5iSKc4yduJJqPuObS76yJ6AYFUk9KsI9iXlk0QkREJLdwuXBDkyZNaNKkCbNnz2bx4sXMnz+fyMhIateuTe/evRk8eLAn+iki4rcyKmzg6c19c7r2LLMpmWd+2sY7TORBPiCISwB8x228yGi+4k7AlqO+i4hI/pCUBOvWmZ/XrYPGjXPHh2culwC3Cw0N5ZFHHmHdunUsX76cQ4cOMXToUHf2TUTE78XEQJUq0LQpdO1qvlepYtq9sbmv20dtfv0VHniAtkNq0ZP5BHGJWFrQhJU0Yh1f0QZ7QMpp30VEJG+z/z+ybVtzu23by/+P9HfZDknnzp1j/vz53HHHHdxzzz2ULFmS8ePHu7NvIiJ+zV7Y4MopdQcOmPajR923uW9SEqxaZYoqrFrl2uazTvnhBzMsVa8eLFmCzbKILdiOW9lIK2JZTRMcw5E2JhYRkcxk9f9Ifw9KLoek9evX88gjj1C+fHn69u1LlSpVWLlyJdu2bWPEiBGe6KOIiN+5srCBI3vb00/DjBnm55xs7pvZaFWOWBasXg2tWkHDhvDpp6ZjnTvDli2cXvApP9gaamNiERFxiTP/jxw0yAMf+LmR0yFpypQpKZXtfvvtN6ZOncqhQ4d49913ady4sSf7KCLid7IqymBZsG8flCqVswIL7vgkLs0o1CULvvzSDAM1aQKxsSbtdO8Of/4JS5ZAvXo5Lg4hIiL5k7P/j1y71nt9cpXThRumTp3Kgw8+yNKlS7n++us92ScREY/IqMDClcc4s8DUlaIMXbpkr8BCVp/E2Wzmk7j27TM+l+NmsDaS6cAnlAgaT93En8wBwcHQuzcMG2aGp66gjYlFRMRVni5c5A1Oh6SDBw8SFBTkyb6IiHiMY1iwCw83+w3ZR0Tsxxw/bkZd2raFkiVTH2PnalGG7Gzu68oncemd2z4KFWBdoitLeIYJXMefkAhnKczBdk9QY87TUKFCpv3QxsQiIuIKbxQu8jSnQ5ICkojkVvawcOWIjH3K2rJl5rb9mEKF0j/GMShFRZmQdeBA+iM9Npu5P7PCBlmNbOXkk7ikJBgy4CK9rPcYwSSqsxOAU4TxCv2ZxSAKbSnFrrLgjkEhZ0bpREQkf3DH/yN9LdvV7UREcgNnFo8OHAgDBri2wDQw0IwwQfaKMjhTjCHbn8SdP88/T73C6gPVmMujVGcnxyjJKF6kMnt4lhc5Rim3zQf3WGEJERHJlXL6/0h/oJAkInmaM1PW9u83n3Zldkx6gSK7hQ2cLcZg/yQuK8eO/ffD6dMwZQpWlSrUeGUAEeznIOUZzHQqs4cJjOIUxVM9NqfzwXN7iVcREfGM3F78RyFJRPI0dy4KTe9c0dGwezesXAkLF5rvu3Zl/I+/K2VRAwPhpZey7te4gf+S/NxYqFwZhg/HduQIu6nME7xOVf5hBoM5R5F0H5uT+eB5ocSriIh4jv3/kZ9/bm5//nnm/4/0J06tSYqPj3f6hGFhYdnujIiIu7lzUWhG53KlsIGrxRhKl8742DIcZjAv0efgawS8cAaArdRkAs+wkK5cIuO1pO6YD57TwhIiIpL3BQZCo0bwxRfmuz9PsXPkVEgqXrw4toy2jL9Ckj4yFBE/ktXiUTBBJCjIjBR5eoGpq8UY0js+nH0MZSqP8haFuADAiUp1eebMKN78tyPJWZRicNd88LxQ4lVERCQ9ToWklStXpvy8e/duRowYQY8ePYiMjARgw4YNvPvuu0ycONEzvRQRySb74tFOnUw4SC8EHT1qSn3b9x5ylJNAkV7FN1eLMTgeX40dDGcy3XmXYBIB2EhDxjOK5oPuZs5g5z7MCg83ryen0x3yQolXERGR9DgVku64446Un59//nleeuklunTpktJ2zz33UKdOHd588026d+/u/l6KiOSAffHolfskOfr3X/O9RAk4d+5ye3YDRUb7Ms2Y4VpZ1KgoaFb2D3odnsADLCaQZABW0oTxjOJbmhMeYeOBMs71a/RoGDvWPdMd8kKJVxERkfS4XLhhw4YNNGjQIE17gwYN+OGHH9zSKRERd4uOhp07oVSp9O+3jyIVKgSffmraJk40XyVKuFZ8ILOKb507g/0zpixHrTZvJvC+aFYcvp5uLCSQZL6gDbezjmas5FtbC7DZmDkzbfWgjDRv7r754HmhxKuIiEh6XA5JERERvPXWW2na586dS0REhFs6JSLiCevXO5TLToe9HPjGjeb2yJHw4INm35/y5eGpp2DVqswDU1YV3ywLFi+GJUsyKYtaZh20aQMNGsDHHwNw4NaOtCmzmbZ8wXpuT3189OVRnYyWj9psEBHh/lGd3F7iVUREJD1OTbdzNGPGDDp27MiXX35Jw4YNAfjhhx/Yvn07H330kds7KCLiLs4WEJgwARYtSt129KgZFZk50wSAWbPSDwBZVXwDU/GtdGlTFjVlzVI5i6iE/yNw0nhYvdocGBBgdmcdOZKK117LZ+mscbKP0mS29srTozrR0dC+fcZ9ExERyW1cDkl33XUX27Zt4/XXX+fvv/8GoF27djzxxBMaSRIRv+auAgL795swkt5ISWab0l55XGAgNLnDguXLYcR4sE9ZDgqCHj1g+HCoVi3lMVmVGs9o7ZW7CjVkxpUy6CIiIv7O5ZAEZsrdhAkT3N0XERGPcqYcuCsGDTIjKI4jJkePOvfYY4eTYMkyGD8efvvNNBYsCI89BkOGmLlx2aBRHRERkZzLVkhau3Ytb7zxBv/88w9Lly6lYsWKvP/++1x99dU0atTI3X0UEXFKeiW3HcNBVlPSXAlOGW2UmtnmrwAFSKQbC+g5dSIc2mYaQ0Ohb1+z6KlsWec7kQGN6oiIiOSMy4UbPvroI1q3bk2hQoX46aefSEhIAODUqVMaXRIRn4mJgSpVTJGFrl3N9ypVTLujzAoNjBvn+vPa1zldvGimtC1blv5xIVzgCV5nOzWYT0/CDm2Dq64y9bj37IFJk9wSkERERCTnXA5JL774InPmzOGtt94iKCgopf3222/np59+cmvnRCT3SEoyld8WLcq6Apy7ZVZyu1On9IPS7t2wciUsXGi+79oFtWu7Pi2tfHkYNgwKFzYDQZ98kvr+IpxhMNP5h6q8Th+qsIejAWVInjTFhKMxY0yNcREREfEbLk+327p1K40bN07TXqxYMU6ePOmOPolILpPRxqkZVYBzp6xKbtts6a8dunJKWkwM3H+/81Pu7Bulfv45TJuW9v5inKQfrzKImZTiOAD7CGcKw2n+QW86dCnk9GsUERER73J5JKlcuXLs2LEjTfu6deuoWrWqWzolIrmHq6M47pZVyW3HtUMZySxopcdeUnvqVJgxI/V9pTjKi4xiD5V5kWcpxXG2U53ezKVJ+E6aftRPAUlERMTPuTyS9OijjzJw4EDeeecdbDYbBw8eZMOGDQwZMoRnn33WE30UET+V3VEcd3J276PMjnNmbyNH9pLae/denlZYgQMMYRqP8SZFOAfA71zHeEYR3PU+ej5agDdzQZW5rIpfiIiI5Acuh6QRI0aQnJxM8+bNOXfuHI0bNyYkJIQhQ4bQv39/T/RRRPyUK6M4nqq25uzeR/bj0gsBzgatfv2gY8fLwaF/f6jCLoYzmZ7MI4SLAGyiPi8ymk+5B4sA+pXIHdXmfDltUkRExJ+4HJJsNhujRo1i6NCh7NixgzNnznDttdcSGhrqif6J5Hp5+ZN5d4zi5FRWex/Z1w5FRWUcAh591Lnn6tjRIez8/TdPbpzIDBZQADOctJZGvMhovqEVYEt5nMN+sB6Vk2vNPm3yyt+hfdpkehvnioiI5FUur0nq1asXp0+fJjg4mGuvvZZbbrmF0NBQzp49S69evTzRR5Fcy9my1LmVq6M4nmDf+wgurxWys9+eORP+97+M106NGQMlS6Z9vON5IiJM6GDLFujcGa69lms3vUcBkviaVjRmNY1Zyze0xjEgBQZCnz5ueKFZyMm1ltW0STDTJr1ZsVBERMSXXA5J7777LufPn0/Tfv78ed577z2XzjVx4kRuvvlmihYtSpkyZejQoQNbt25NdcyFCxfo27cvJUuWJDQ0lI4dO3L48GFXuy3idb4uaOAN9lEcp8KFB2W299GyZWZNVFZrpxz77Mh++90+Gwns0A5uvBGWLjUPbN+elx/8gTv5mrWkrfoJMHgwBAfn4MU5IafXmjuKX4iIiOQlToek+Ph4Tp06hWVZnD59mvj4+JSvEydO8MUXX1CmTBmXnnz16tX07duXjRs3EhsbS2JiIq1ateLs2bMpxzz11FMsX76cpUuXsnr1ag4ePEi05nyIn8svn8xnNYpjWfDII/Dhh57fOymjvY+io50LAcePQ48eZn9XxzvuK7WSQ9e3oOnISPjsM/PCHngAfv0VPvmEAe/fzNChaae1BQbC0KEwZYoHXqwDd1xr/jBtUkRExJ84vSapePHi2Gw2bDYbNWvWTHO/zWZjnIvb1X/11Vepbs+fP58yZcqwefNmGjduzKlTp3j77bdZuHAhzZo1A2DevHnUrl2bjRs3cuutt7r0fCLe4g8FDbzFPopz5Vof+/6oY8ZcbvNUEYAr1+J07pw6tDj7x/28eeZ7oYIWZTZtYmuZyUTs3QBHgQIF4KGHYMQIuOLfwClT4MUX4bXXYOdOswapTx/PjyCBe641f5g2KSIi4k+cDkkrV67EsiyaNWvGRx99RAmHHeKDg4OpXLkyFSpUyFFnTp06BZBy7s2bN5OYmEiLFi1SjqlVqxaVKlViw4YN6YakhIQEEhISUm7Hx8cDkJiYSGJiYo76J75hf99y0/sXFweFnNgKJy4OctHLylC7dnDXXbBhAxw6ZILCxInmD3TH38O//5qcYX+Ms5KSLp+7XDmIjLwcgpYvh+HDzdQyu4oVYfLky89Rrpxz74fNSqZ98scMT5xEvRd/Mc8dFAK9e5L89NNQubI5MJ03zWaDvn1Tt3njvXXHtXbrrVC9Ohw8mHHxi4oVzXF54Xr1R7nx3znJvXS9ibf50zXnbB9sluXs9onGnj17qFSpEraMFiFkU3JyMvfccw8nT55k3bp1ACxcuJCePXumCj0At9xyC02bNmXy5MlpzjN27Nh0R7QWLlxI4cKF3dpnEckbbElJVFyzhpoffUTR/4ZlLhUsyO7WrdnRvj0JDh8KiYiISO517tw5unbtyqlTpwgLC8vwOJdLgH/77beEhoZy3333pWpfunQp586do3v37q73Fujbty+///57SkDKrpEjRzJ48OCU2/Hx8URERNCqVatMfxHivxITE4mNjaVly5YEBQX5ujtOSUqCOnWy/mT+119zVzlwZ0Zt1q2Dtm2zPtfnn0OjRlk/30MPpf0d2j+jueoqMzqVnit/x+mdK9hK4MGk9xl8aRpVrX8AOEkx3ir4JNe9eS0PDerI+W+CnOqrr7jzWkvv/Q0Ph0mTXBv5E9flxn/nJPfS9Sbe5k/XnH2WWVZcDkkTJ07kjTfeSNNepkwZHnvssWyFpH79+vHZZ5+xZs0awsPDU9rLlSvHxYsXOXnyJMWLF09pP3z4MOXKlUv3XCEhIYSEhKRpDwoK8vmbIjmTm97DoCATHDp1Mrcd/3i1/4E/aRIULOj9vmVXRvvo7NyZeh+dQ4cgnQKYaRw6ZH5PGbEXJDh3LuNjMrsPYPt22LjRrMWJjjbn7NIFgpPO8ShvMZSphGMSwVFK8RKDeY0+JNoKsyjsC86fD+L8+aAs++pL7rzWoqNNJcC8uq9XbpCb/p2T3E/Xm3ibP1xzzj6/yyXA9+7dy9VXX52mvXLlyuzdu9elc1mWRb9+/fj444/59ttv05y3fv36BAUFsWLFipS2rVu3snfvXiIjI13tuohXZVWWOjcVaXSlgpq7igBkVZDAWY5FG8oVjmdI0iR2U4VZDCKcAxygAoOYQRV2M4mRxFPM5b76mjuvtcBAEyq7dDHfFZBERCQ/cnkkqUyZMvz6669UqVIlVfsvv/xCyZIlXTpX3759WbhwIf/73/8oWrQohw4dAqBYsWIUKlSIYsWK0bt3bwYPHkyJEiUICwujf//+REZGqrKd5Ap55ZN5Vyqo2fdOOnAg4+lf4eFZ753krnLT5ctj6nvPmkXD6a8QxUkA/uFqJjGCd+nORdKOPtv76o19ntwhr1xrIiIi/sDlkNSlSxcGDBhA0aJFadzYbJ64evVqBg4cyAMPPODSuV5//XUAmlxRl3bevHn06NEDgBkzZhAQEEDHjh1JSEigdevWvPbaa652W8Rn7J/M52au7KNj3zupU6fLeyXZ2ad/zZyZ9R/vOR29sdngxvKHaLx8Otz9Opw9SzDwF7WYwDMsogtJTvwT6Exf/UVeuNZERET8gcsh6YUXXmD37t00b96cAgXMw5OTk3n44YeZMGGCS+dyprBewYIFmT17NrNnz3a1q5LPXbl3jj5Vzz5Xp9BltHdSeLgJHc5M/8pqRCozEexlmDWFJ4/OJeCl/6pj1qtH0jOjuXPwvew7GOjUOd9/P3dNixQRERH3cDkkBQcHs2TJEl544QV++eUXChUqRJ06dahs3z9ExA/ExKT/B7onNjLNy+xB88ABKF0ajh1zfgpdTqd/OY5IOas62xnBJB7mPYK4BIlwvOatlHxpNNx1F4E2GzMKZDzKZVlmbdU990B8vCq6iYiI5FcuhyS7mjVrUvOKXedF/EFGVdgOHEhdhU0yl17QTE9mU+hyOv3LPiL1xBNw9GjGx13H7zzDBO5nCYEkA7CCZkxgFCu3NWVZgo1oW+pzZjbKlZgIX3yR/X6LiIhI7uZUSBo8eDAvvPACRYoUSbUHUXpeeuklt3RMJDuyqsJms5mRgvbtNfUuMxkFzfS4MoUuO6Kj4dIluP/+tPc14EdGMZ4O/C+l7TPaMp5RbMRUwEzvPVeRAxEREcmMUyHp559/JjExMeXnjNjsHymL+IgrVdi0wD19mQVNu9KlYcYMU3LaXeEiozVkSUnw9NOpj41iDaMYT2u+ASAZGx/RkQk8wxZuTHVsRu+5ihyIiIhIRpwKSStXrkz3ZxF/40oVNkmfM/sTHT1qApK7QkZma8hKlLC3W7TiG0YxnsasBeASgSykKxMZyd/UzvQ59J6LiIiIs7K9JknEH7lrI9P8zNtBM6s1ZIMGJNOeTxnFeG5mEwAJBDOPnkxhGLuo6tTz6D0XERERZzkVkqJdWGwQExOT7c6I5JS7NjLNzzwZNK+cUnfbbRlP7bNZSdzPhzzy6gSu5XcAzlGIN3icaQzhIBWdek695yIiIuIqp0JSsWLFUn62LIuPP/6YYsWK0aBBAwA2b97MyZMnXQpTIp7gro1M8zNPBc30ptSVLp22al0QF3mQDxjJRGqwA5LgtK0or1r9mMEgjlImTX9KlIDjx/Wei4iIiHs4FZLmzZuX8vPw4cPp3Lkzc+bMIfC/vzqSkpLo06cPYWFhnumliAvcsZGpv/HmxrieCJoZTalzDEgFOU8v3mE4k6nEPgCOU4KZDCLp8X5MeuMqc2A6/XnzTfM9L73nIiIi4jsur0l65513WLduXUpAAggMDGTw4MHcdtttTJ061a0dFMmOvFTiedky6NMndaDw9Ma47gyaWVXLK8IZnmAOQ5hGOQ4DEEc5pjGEN3ics4Sy8n5o0DLr/rjjPU9KgnXrzM/r1kHjxrnzuhEREZHsczkkXbp0ib///ptrrrkmVfvff/9NcnKy2zomklN5ocTzsGGQ3ucO+/d7fmNcdwXNjKrlFecE/XmFgcyiJP8CsIdKTGY479CLBApis0FE+OXnzao/OX3P7VMCjx+HRYugbVsoWdKzgVRERET8j8shqWfPnvTu3ZudO3dyyy23APD9998zadIkevbs6fYOiuRXS5emH5DsLMvzG+O6I2heWQWvNEd4ihn0ZTZhnAZgGzWYyEgW0I1EgoH0p/Z5Mvg6TgksVOhyu73KnicDqYiIiPgXl0PStGnTKFeuHNOnTyfuv79+ypcvz9ChQ3n6yh0fRSRbkpLMFLus5IaNce1V8CqynyFM4zHepDDnAfiN6xnPKJZyHyVKBZJ47PLjvLmeKLMpgZZlApunA6mIiIj4D5dDUkBAAMOGDWPYsGHEx8cDqGCDiJutXQvHjmV9HPj/JqlRFf/hgyKTuO/sfIJJBOAHbmY8o1hOO7AFEB4OO3bA+vWeXUOWUQGMrDbQtazMA6k3C2uIiIiI52VrM9lLly6xatUqdu7cSdeuXQE4ePAgYWFhhIaGurWDIvmRK8HHbzdJ/fNPmDiRwEWL6JaUBMBqGjOeUcTSErClmlIXHOzZEbH0SpDbC2AkJDh3jvTel8zOq+l5IiIiuZPLIWnPnj3ceeed7N27l4SEBFq2bEnRokWZPHkyCQkJzJkzxxP9FMlXnA0+pUtnvl+RfYTjwAFTHa90aahY0cMjHT//DOPHm/Rgn792552sbjSKB+c08lqJbsfRne3bYezYtNPp7OuNxo517pxXvi8ZlTbXOiYREZHczeWQNHDgQBo0aMAvv/xCyZIlU9rvvfdeHn30Ubd2TiS/sm/omtkUMIDXXss47KQ3wmHnkZGO9etNOPrii8tt994LzzwDDRpwB7B7hHempWX22h3Z1xu99VbmG+iCCZi33Xb5ttYxiYiI5F0Brj5g7dq1jB49muDg4FTtVapU4cCBA27rmEh+Zt/Q1T4dLT1Dh5rRivTYRzgyCgn2EuIxMTnsqGXBihXQrBncfrsJSAEB0LUr/PabeYIGDVIOt1en69LFfPdUQMrstaf3EvbvB/tnPBn9zo8ehWrVLv/OXFnHJCIiIrmLyyEpOTmZpP/WFzjav38/RYsWdUunROTyhq7h4anbS5eGDz+EKVPSf1xWm7c6GjTIHO8yy4LPPjNDKy1awMqVEBQEvXvD1q2wYAFcf31Kf1atMvsOrVqVzedzkiuv/Uo1apjfd8WKGR9jn0YXE+P8ujF/L6whIiIiabkcklq1asXMmTNTbttsNs6cOcOYMWO466673Nk3kXwvOhp27zYZZOFC8z0uDu67L+PHZDXCYZetkY6kJLOB0403Qrt2sHEjFCwI/fqZ8nRz50L16imHx8RAlSrQtKkZXGra1NzO8QhWBpx97ekpX978vnfuNBvIpscevgYNgjJlnD+viIiI5C7Z2ifpzjvv5Nprr+XChQt07dqV7du3U6pUKRYtWuSJPorka65uoOrqyIVTxycmmpQ2caIZKQIIDYUnn4TBg6FcuTQP8UVRg+yM2thsZrTOXgBj/Xo4fjzj4+3hEjJfx3TleUVERCT3cDkkRURE8Msvv7BkyRJ++eUXzpw5Q+/evenWrRuFHLepFxGfcHXkItPjL1yA+fNh8mQzpAVQvDgMGGC+Mhhy8VVRA1dfu2MJcns/nA1aR46YdWOdOpnzOL7W9M4rIiIiuYdLISkxMZFatWrx2Wef0a1bN7p16+apfolINjlbGS/TkY6zZ+HNN2Hq1MupoXRpePppM3qUxQbSOd2cNbvsrz2zKnWO0itB7mzQKl/e9H3ZsvT3SfJUaXMRERHxPJdCUlBQEBcuXPBUX0TEDeyV8dKb6nalNCMdp07B7NkwYwYcO2baKlaEYcPgkUegcGGn+uCrogaOrz290R3LgnHjTJGGjEqQR0VlXrzhynAZHW1GxLxR2lxERES8w+Xpdn379mXy5MnMnTuXAgVcfriIeIG9Ml5GewVFRFwx0nHsmEkXr7xighJA1aowYgQ8/DCEhLj0/K6MxrhbRq/d2dGdwEAzuxDSlgPPaBqdq+vGRERExL+5nHJ+/PFHVqxYwTfffEOdOnUoUqRIqvtjPFW2SiQfSEpy34iE4wjHgQNmn5/Spc0oScp54+Jg2jSYMwfOnTMPrF0bRo2C+++HbH4Q4syUv4gIzxU1yOnoTrt2ZsunChVM0T47TaMTERHJH1z+C6h48eJ07NjRE30RyddiYtIf/Zg1K/t/lGc4wrF7t9lo6Z13ICHBtN14I4weDR06mA1hcyAw0GwYO3Vqxsc88IBnp6S5Y3Tnt99MlXNNoxMREclfXA5J8+bN80Q/RPI1r5XL3rbNlPH+4AO4dMm03XabCUd33pl2flk2JSWZzWMzs3ix6Yo/hw5NoxMREcmfnP64ODk5mcmTJ3P77bdz8803M2LECM6fP+/JvonkC1mVywZTLjspKQdP8uuvZuimVi1T0vvSJWjRwuxOu24dtGnjtoAEzm3q6vJGtiIiIiJe4nRIGj9+PM888wyhoaFUrFiRWbNm0bdvX0/2TSRfWLXKuXLZq1Zl4+Tffw/33AP16sGSJeZk7dqZOWSxsWaYxI3hyM5X1e1ERERE3MHpkPTee+/x2muv8fXXX/PJJ5+wfPlyFixYQHJysif7J5KnxcRA587OHdu5szk+K0mXLH6euZpDdVrCrbfC8uUmCHXuDFu2wKefQsOGOep3VnxZ3c4VSUkmfC5aZL7naLRORERE8gynQ9LevXu56667Um63aNECm83GwYMHPdIxkbwioz/E7euQ/v3XufP8+685PsOgZFl8N/pLNheJ4sanmlDu9//jEoEsLdydb2b+aUaS6tXL+Qtygr26XUaDVDabZ6vbOSMmBqpUgaZNoWtX871KFeeCqIiIiORtToekS5cuUbBgwVRtQUFBJCYmur1TInlFRn+I2/fxyWqz1/SkWZ+UnAwxMZyo3oDbx9/FLRe/I4FgXucJarCd+8/P585Btbz6x799U1dwfq8hb7IH1CunOdoLZSxf7pt+iYiIiH9wurqdZVn06NGDEIdNJS9cuMATTzyRaq8k7ZMkYmRWse6++7J3Tvv6pLVroUmjS2Z0aMIE+PNPrgLOUpg3eJxpDCGOCv89yASTQYPM3kHeCiY53dTVU7IqlGGzmT10MytfLiIiInmb0yGpe/fuadoefPBBt3ZGJK9wpmJddgVxkUIL34NHJsHOnQBcKhLGpLP9mcVAjlE63edMCVdNcvb8rsjppq6ekFXlPcvKujKfiIiI5G1OhyTtjyTiPGdKYLuqIOd5hLkMYwoRb/138pIl4amn+KRsX559tHiW5/BFNTl/22tIFfVEREQkKy5vJisiWXPnH+KhnOZJXudpplOWIwBY5ctjGzIEHn8cihSh1CrnzuXranL+QL8DERERyYpCkviFpCT/mpKVU678IW6zpT8F7yr+ZQAvM4CXKcEJAHZTmROPjeDGWT3AoZCKvZrcgQPpn8tmM/f7spqcv3D2dyUiIiL5l9PV7UQ8JS+WYna2BPaHH0LFiqnvu7HCYb69ZTh7bZUZyzhKcIKt1GRwifn8vGQ7N77xRKqABP5fTc6fOPO7mjTJu30SERER/6KQJD6VVSnm3BqUnA0t990Hu3fDypXwySv72HfvADb/W4WmP0wh1DrDmap1WTdgCXH/9ydTj3Tn3s5BGT6nvZrclaErPNy0+6qanD/K6nfVrp1v+iUiIiL+QdPtxGecKcU8aBA47GGcqzhbAjtw1w6afDAJ3nsP7PuONWwIo0YRevfdNMpoOCqD5/S3anL+OpUys9+Vtn8TERHJ3xSSxGecKcW8bx9s2OC9PrlbpqHljz/MHkeLF5sNYcGUgRs1Cpo3z3iuXhb8qZpcTEz6IXHWLP8Y2fKn35WIiIj4D4Uk8RlnK8AdOgSFC3u2L56U5g/xzZth/Hj4+OPLbW3amHB0++3e7p7HZLaZbqdO2Z8C6K8jUyIiIpJ3aE2S+IyzFeDKlfNsP7xm3ToThho0uByQoqNh0yb44os8FZCc2Ux30CBznCvyYpEPERER8T8KSeIzzlaAi4z0br/cyrIgNtYMJUVFwVdfQUAAdOsGv/8OH30E9ev7updu5+xUyrVrnT9nXi3yISIiIv5HIUl8Jk+XrbYs+PRTuPVWaNUKVq+GoCB49FHYtg0++ACuu87XvfQYZ6dSOnucp0amsispCVatgkWLzHdvPa+IiIh4h0KS+FSeK1udlARLlkC9eqZiww8/mD2NBgyAnTvhzTehWjVf99LjnJ1K6exxnhiZyi5N+RMREcn7VLhBfM4fy1a7LDERFiyAiRPNSBFAaCj07QtPPQVly/q2f15mn0p54ED6oz82m7k/Ksq587l7ZCq7PFWMQkRERPyLQpL4hVxbivnCBXjnHZgyBfbsMW1XXWXmhvXvDyVK+LZ/PmKfStmpkwlEjqEiO1Mp3T0ylR3O7uvVvn0uC/giIiKShqbbiWTHmTMwfTpcfbUZLdqzB8qUuRyWxozJtwHJzp1TKZ0t8uHsyFR2+NOUPxEREfEsjSSJuOLkSXj1VTMMcvy4aQsPh+HDSerRm7WbChH3WS6dMugB7ppK6e6Rqezwlyl/IiIi4nkKSSLOOHrU/BX+6qsQH2/aqlWDkSPhoYeI+SyYgbVTjzSEh5s/7PP7GhV3TaW0j0wNHJj29zxzpud/z/4w5U9ERES8QyFJJDMHDsC0aaYq3blzpu266+CZZ6BzZyhQQIv5vciXRT7cXYxCRERE/JdCkkh6du2CyZNh3jy4eNG01a8Po0aZv9IDzHI+Leb3Pl8V+fCHKX8iIiLiHSrcIOLo77+he3eoUQPeeMMEpEaN4Msv4ccf4d57UwISaDF/fpPn9vUSERGRdGkkSQRgyxaYMMH8pWsfImjVyowcNW6c4cO0mD//yRP7eomIiEimFJIkz0lKcuEP2I0bYfx4+Oyzy23t25twdPPNWT5XmTLO9cnZ4yR3yLX7eomIiIhTFJIkT4mJSb/6Waoqc5YFq1aZcLRihWmz2eD++01Bhjp1vN1tEfEglz44ERERQSFJ8pAsq8wttYgu+IUJRxs2mDsLFICHHoIRI6BmTZef88gR9x4nIu7l1AcnIiIiV1BIkjwhsypzWMl0JIYaXSZA4s+mLSQEeveGYcOgcuVsP6/2zhHxXyrPLyIi2aXqdpInpFdlLpBLPMj7/M71LOU+6iT+TFLBIvD006bE9+zZOQpIcHnvHHsJ6CvZbBARob1zRLwtq/L8YMrzJyV5tVsiIpJLKCRJnuBYPS6YBB7lTbZyDe/zMNfyFycpxvM8yyez9pjNYd00tGPfOwfSBiXtnSPiOyrPLyIiOaGQJHlC+fJQiHMMYBY7qcabPE41/uEopRjJBCqzhzE8T8maJd3+3No7R8T/qDy/iIjkhNYkSe4XH0/j9a+xN+AlSiUfBeAAFZjKUN7iUc5RxEx7C/fctDftnSPiX7ReUEREckIhSXKv48fNXLdXXiHg5ElKAf9wNZMZwXy6c5EQwHvT3rR3joj/sK8XPHAg/XVJNpu5X+sFRUQkPT6dbrdmzRratWtHhQoVsNlsfPLJJ6nutyyL5557jvLly1OoUCFatGjB9u3bfdNZ8R+HDsHQoabowgsvwMmTUKsWvPcev3y4jS/CH0sJSKBpbyL5kdYLiohITvg0JJ09e5Z69eoxe/bsdO+fMmUKL7/8MnPmzOH777+nSJEitG7dmgsXLni5p+IX9u6Ffv2gShVTfOHsWahXD5Yuhd9/h4ce4t77CrB7N6xcCQsXmu+7dikgieRHWi8oIiLZ5dPpdm3atKFNmzbp3mdZFjNnzmT06NG0b98egPfee4+yZcvyySef8MADD3izq+JL27dzwyuvUGD1arh0ybTdeiuMHg133ZXmY2JNexMRO60XFBGR7PDbNUm7du3i0KFDtGjRIqWtWLFiNGzYkA0bNmQYkhISEkhISEi5HR8fD0BiYiKJiYme7bS412+/ETh5MgWWLaNycjIAyU2bkjxiBFaTJiYc2UOTiBvZ/63Qvxl5x+23X/45Odl8+RNdc+JNut7E2/zpmnO2D34bkg4dOgRA2bJlU7WXLVs25b70TJw4kXHjxqVp/+abbyhcuLB7OykeUXz7dmouXUr5H35IaTvUoAHbOnXiRK1acP48fPmlD3so+UVsbKyvuyD5jK458SZdb+Jt/nDNnTt3zqnj/DYkZdfIkSMZPHhwyu34+HgiIiJo1aoVYWFhPuyZZMW2di22iZMI/D/zH5Bls5Hc4V4uDnma748epWXLlgQFBfm4l5IfJCYmEhsbq2tOvEbXnHiTrjfxNn+65uyzzLLityGpXLlyABw+fJjyDhtZHD58mBtuuCHDx4WEhBASEpKmPSgoyOdviqTDsuCbb2D8eLNoALhEIAvpykRrJGd+rM2sQ4kEBn6h91C8TteceJuuOfEmXW/ibf5wzTn7/D6tbpeZq6++mnLlyrFixYqUtvj4eL7//nsiIyN92LPcJykJVq2CRYvM96QkX/cIsyDgk0/gllvgzjth7VoSCOYNHqMm2+jOe/xNbQ4cgIce8nVnRURERCQ/8elI0pkzZ9ixY0fK7V27drFlyxZKlChBpUqVGDRoEC+++CI1atTg6quv5tlnn6VChQp06NDBd53OZWJiYOBA2L//clt4uNk/xCflb5OS4MMPYcIEU7YbsAoV4u3AxxlzZggHSV2r13ETyKQk0AdeIiIiIuJpPg1JmzZtomnTpim37WuJunfvzvz58xk2bBhnz57lscce4+TJkzRq1IivvvqKggUL+qrLuUpMDHTqlHa3+QMHTLtX9wm5eBE++AAmTgR7MC5aFPr1Y/3Ng3g0ukyGD7X3f8MGcLhcREREREQ8wqchqUmTJlhX/gXvwGaz8fzzz/P88897sVd5Q1KSGUFK79drWaZ69qBBZv8Qj+4Xcv48vPMOTJ4M+/aZthIlzJP36wdXXcXeRc6dKpOihiIiIiIibuO3hRskZ9auTT3F7kqWZTLL2rUe2nj19GmYMwemT4fDh01buXIwZAg8/jiEhqYc6lCXI1P/1fIQEREREfEohaQ8Ki7Ovcc57cQJeOUVmDnT/AxQqRIMHw69ekE6UyWjosw6qQMH0h/5stnMd9XrEBERERFvUEjKo5wdnXH2uCwdOQIzZsDs2WYUCaBGDRg5Erp1g+DgDB8aGGgKSXTqZAKRY1CyByT7cSIiIiIinua3JcAlZ+yjM44hw5HNBhER5rgc2b/frC+qUgUmTTIB6frrTb3xv/6Cnj0zDUh20dGmkETF1MXtCA+H99/PYR9FRERERFygkaQ8ypnRmZkzczA6888/JhTNnw+Jiabt5pth1Cho1w4CXM/f0dGmkMTatWYaYPnyJsQlJ8MXX2SznyIiIiIiLlJIysPsozPp7ZM0c2Y2y3//+acp471o0eVdaRs3NuGoZcuMh66cFBiYtpBEcnKOTikiIiIi4hKFpDwuo9EZl0eQfv4Zxo83my/Zh6VatzbhKMdz9kRERERE/IdCUj6Q3uiM09avN+HIcb5bhw4mHDVo4IbeiYiIiIj4F4UkScuy4NtvTThaudK0BQTAAw+YanXXX+/b/omIiIiIeJBCklxmWfD55yYcbdxo2oKC4OGHYcQIqF7dt/0TEREREfEChSQxBRg++ggmTIBffjFtBQvCI4/A0KFmM1gRERERkXxCISk/S0yEhQtNtbqtW01baCg8+SQMHgzlyvm2fyIiIiIiPqCQlB9duGD2N5o8GXbvNm3Fi8OAAearZEkfdk5ERERExLcUkvKTs2fhzTdh6lRTDxygdGl4+mkzehQW5tv+iYiIiIj4AYWk/ODUKZg9G2bMgGPHTFvFijBsmFl3VLiwb/snIiIiIuJHFJK8JCnJDRu6uurYMZg1C155xQQlgKpVTaW6hx+GkBAPd0BEREREJPdRSPKCmBgYOBD277/cFh5u8kt0tAeeMC4Opk2DOXPg3DnTVrs2PPOM2euogN52EREREZGM6K9lD4uJgU6dzBZEjg4cMO3LlrkxKO3eDVOmwDvvQEKCabvxRhg1Cu6912wIKyIiIiIimdJfzR6UlGRGkK4MSHC5bdAgc1yObNsGPXtCjRrw+usmIN12G3zxBWzeDB07KiCJiIiIiDhJI0ketHZt6il2V7Is2LfPHNekSTae4NdfzQawH354OXW1aGFGju64A2y27HTbrzmu7SpTxrQdOeLFdV4iIiIikucpJHmQvcq2u45L8f33MH48LF9+ua1dOxOOGjZ08WS5x/Lladd2OfLoOi8RERERyTc0B8uDypd343GWBatXQ8uWcOutJjHYbNC5M2zZAp9+mqcDEsBDD2U+Mmdf5xUT470+iYiIiEjeo5DkQVFRZnQjo1lvNhtERJjjMmRZ8OWX5qAmTeD//s/MKeveHf78E5YsgXr1PNF9v2Ffs5Xe2i5Hbl3nJSIiIiL5lkKSBwUGmulfkDYo2W/PnJnBOprkZDMk0qAB3HUXfPcdBAfDE0/A9u0wfz7UquXB3vuPDRucP9ZxnZeIiIiISHYoJHlYdLQp812xYur28PAMyn9fugQLFkCdOqYq3U8/QeHC8NRTsGuXqV539dVe678/OHTI9ce4vM5LREREROQ/KtzgBdHR0L795aps6VZiu3gR3nsPJk2CnTtNW1gY9O9vqhWULu2TvvuDcuUgPt61xzi7HkxERERE5EoKSV4SGJhBme/z52HuXLMJrL0qQcmSZuSob18oXtyLvfRPkZHw9dfOVTS32cwoXabrvEREREREMqGQ5CunT5upc9Onm41+wAx/DBkCjz0GoaG+7Z8fcRxxs9kyLuCQ5TovEREREREnaE2St/37L4wdC5Urw/DhJiBVrmwC0z//wODBCkgZeP/9tGu7HGW4zktERERExAUaSfKW+HizAexrr8GZM6atZk145hno2hWCgnzbv1ygXbvUa7vKlDHtR45ksM5LRERERCQbFJK8JSjIlO0+cwbq1oVRo0z1Ov1V75IM13aJiIiIiLiJQpK3FCoEM2ZA0aJw993OVSEQERERERGvU0jypq5dfd0DERERERHJggo3iIiIiIiIOFBIEhERERERcaCQJCIiIiIi4kAhSURERERExIFCkoiIiIiIiAOFJBEREREREQcKSSIiIiIiIg4UkkRERERERBwoJImIiIiIiDhQSBIREREREXGgkCQiIiIiIuJAIUlERERERMSBQpKIiIiIiIgDhSQREREREREHCkkiIiIiIiIOFJJEREREREQcKCSJiIiIiIg4UEgSERERERFxoJAkIiIiIiLiQCFJRERERETEgUKSiIiIiIiIA4UkERERERERBwpJIiIiIiIiDhSSREREREREHCgkiYiIiIiIOFBIEhERERERcaCQJCIiIiIi4kAhSURERERExIFCkoiIiIiIiAOFJBEREREREQcFfN0BERG7pCRYuxbi4qB8eYiKgsBAX/dKRERE8ptcMZI0e/ZsqlSpQsGCBWnYsCE//PCDr7skIm4WEwNVqkDTptC1q/lepYppFxEREfEmvw9JS5YsYfDgwYwZM4affvqJevXq0bp1a44cOeLrromIm8TEQKdOsH9/6vYDB0y7gpKIiIh4k9+HpJdeeolHH32Unj17cu211zJnzhwKFy7MO++84+uuiYgbJCXBwIFgWWnvs7cNGmSOExEREfEGv16TdPHiRTZv3szIkSNT2gICAmjRogUbNmxI9zEJCQkkJCSk3I6PjwcgMTGRxMREz3ZYPML+vun9y5vWrYPjx6FQoYyPOXYM1qyBRo280yddc+JtuubEm3S9ibf50zXnbB9slpXe57f+4eDBg1SsWJH169cTGRmZ0j5s2DBWr17N999/n+YxY8eOZdy4cWnaFy5cSOHChT3aXxERERER8V/nzp2ja9eunDp1irCwsAyP8+uRpOwYOXIkgwcPTrkdHx9PREQErVq1yvQXIf4rMTGR2NhYWrZsSVBQkK+7I262bh20bZv1cZ9/7t2RJF1z4k265sSbdL2Jt/nTNWefZZYVvw5JpUqVIjAwkMOHD6dqP3z4MOXKlUv3MSEhIYSEhKRpDwoK8vmbIjmj9zBvatwYSpY0RRrSG9e22SA83Bzn7XLguubE23TNiTfpehNv84drztnn9+vCDcHBwdSvX58VK1aktCUnJ7NixYpU0+9EJPcKDIRZs8zPNlvq++y3Z87UfkkiIiLiPX4dkgAGDx7MW2+9xbvvvstff/3Fk08+ydmzZ+nZs6evuyYibhIdDcuWQcWKqdvDw017dLRv+iUiIiL5k19PtwO4//77OXr0KM899xyHDh3ihhtu4KuvvqJs2bK+7pqIuFF0NLRvD2vXQlwclC8PUVEaQRIRERHv8/uQBNCvXz/69evn626IiIcFBkKTJr7uhYiIiOR3fj/dTkRERERExJsUkkRERERERBwoJImIiIiIiDhQSBIREREREXGgkCQiIiIiIuJAIUlERERERMSBQpKIiIiIiIgDhSQREREREREHCkkiIiIiIiIOFJJEREREREQcKCSJiIiIiIg4UEgSERERERFxoJAkIiIiIiLiQCFJRERERETEgUKSiIiIiIiIA4UkERERERERBwpJIiIiIiIiDhSSREREREREHCgkiYiIiIiIOFBIEhERERERcaCQJCIiIiIi4kAhSURERERExIFCkoiIiIiIiAOFJBEREREREQcKSSIiIiIiIg4UkkRERERERBwoJImIiIiIiDhQSBIREREREXGgkCQiIiIiIuJAIUlERERERMSBQpKIiIiIiIiDAr7ugHheUhKsXQtxcVC+PERFQWCgr3slIiIiIuKfFJLyuJgYGDgQ9u+/3BYeDrNmQXS07/olIiIiIuKvNN0uD4uJgU6dUgckgAMHTHtMjG/6JSIiIiLizxSS8qikJDOCZFlp77O3DRpkjhMRERERkcsUkvKotWvTjiA5sizYt88cJyIiIiIilykk5VFxce49TkREREQkv1BIyqPKl3fvcSIiIiIi+YVCUh4VFWWq2Nls6d9vs0FEhDlOREREREQuU0jKowIDTZlvSBuU7LdnztR+SSIiIiIiV1JIysOio2HZMqhYMXV7eLhp1z5JIiIiIiJpaTPZPC46Gtq3N1Xs4uLMGqSoKI0giYiIiIhkRCEpHwgMhCZNfN0LEREREZHcQdPtREREREREHCgkiYiIiIiIOFBIEhERERERcaCQJCIiIiIi4kAhSURERERExIFCkoiIiIiIiAOFJBEREREREQcKSSIiIiIiIg4UkkRERERERBwoJImIiIiIiDhQSBIREREREXGgkCQiIiIiIuJAIUlERERERMRBAV93wNMsywIgPj7exz2R7EpMTOTcuXPEx8cTFBTk6+5IPqBrTrxN15x4k6438TZ/uubsmcCeETKS50PS6dOnAYiIiPBxT0RERERExB+cPn2aYsWKZXi/zcoqRuVyycnJHDx4kKJFi2Kz2XzdHcmG+Ph4IiIi2LdvH2FhYb7ujuQDuubE23TNiTfpehNv86drzrIsTp8+TYUKFQgIyHjlUZ4fSQoICCA8PNzX3RA3CAsL8/l/WJK/6JoTb9M1J96k6028zV+uucxGkOxUuEFERERERMSBQpKIiIiIiIgDhSTxeyEhIYwZM4aQkBBfd0XyCV1z4m265sSbdL2Jt+XGay7PF24QERERERFxhUaSREREREREHCgkiYiIiIiIOFBIEhERERERcaCQJCIiIiIi4kAhSfzGmjVraNeuHRUqVMBms/HJJ5+kut+yLJ577jnKly9PoUKFaNGiBdu3b/dNZyXXmzhxIjfffDNFixalTJkydOjQga1bt6Y65sKFC/Tt25eSJUsSGhpKx44dOXz4sI96LLnd66+/Tt26dVM2U4yMjOTLL79MuV/Xm3jSpEmTsNlsDBo0KKVN15y409ixY7HZbKm+atWqlXJ/brveFJLEb5w9e5Z69eoxe/bsdO+fMmUKL7/8MnPmzOH777+nSJEitG7dmgsXLni5p5IXrF69mr59+7Jx40ZiY2NJTEykVatWnD17NuWYp556iuXLl7N06VJWr17NwYMHiY6O9mGvJTcLDw9n0qRJbN68mU2bNtGsWTPat2/PH3/8Aeh6E8/58ccfeeONN6hbt26qdl1z4m7XXXcdcXFxKV/r1q1LuS/XXW+WiB8CrI8//jjldnJyslWuXDlr6tSpKW0nT560QkJCrEWLFvmgh5LXHDlyxAKs1atXW5Zlrq+goCBr6dKlKcf89ddfFmBt2LDBV92UPOaqq66y5s6dq+tNPOb06dNWjRo1rNjYWOuOO+6wBg4caFmW/o0T9xszZoxVr169dO/LjdebRpIkV9i1axeHDh2iRYsWKW3FihWjYcOGbNiwwYc9k7zi1KlTAJQoUQKAzZs3k5iYmOqaq1WrFpUqVdI1JzmWlJTE4sWLOXv2LJGRkbrexGP69u1L27ZtU11boH/jxDO2b99OhQoVqFq1Kt26dWPv3r1A7rzeCvi6AyLOOHToEABly5ZN1V62bNmU+0SyKzk5mUGDBnH77bdz/fXXA+aaCw4Opnjx4qmO1TUnOfHbb78RGRnJhQsXCA0N5eOPP+baa69ly5Ytut7E7RYvXsxPP/3Ejz/+mOY+/Rsn7tawYUPmz5/PNddcQ1xcHOPGjSMqKorff/89V15vCkkiku/17duX33//PdXcaRFPuOaaa9iyZQunTp1i2bJldO/endWrV/u6W5IH7du3j4EDBxIbG0vBggV93R3JB9q0aZPyc926dWnYsCGVK1fmww8/pFChQj7sWfZoup3kCuXKlQNIUwXl8OHDKfeJZEe/fv347LPPWLlyJeHh4Snt5cqV4+LFi5w8eTLV8brmJCeCg4OpXr069evXZ+LEidSrV49Zs2bpehO327x5M0eOHOGmm26iQIECFChQgNWrV/Pyyy9ToEABypYtq2tOPKp48eLUrFmTHTt25Mp/4xSSJFe4+uqrKVeuHCtWrEhpi4+P5/vvvycyMtKHPZPcyrIs+vXrx8cff8y3337L1Vdfner++vXrExQUlOqa27p1K3v37tU1J26TnJxMQkKCrjdxu+bNm/Pbb7+xZcuWlK8GDRrQrVu3lJ91zYknnTlzhp07d1K+fPlc+W+cptuJ3zhz5gw7duxIub1r1y62bNlCiRIlqFSpEoMGDeLFF1+kRo0aXH311Tz77LNUqFCBDh06+K7Tkmv17duXhQsX8r///Y+iRYumzIkuVqwYhQoVolixYvTu3ZvBgwdTokQJwsLC6N+/P5GRkdx6660+7r3kRiNHjqRNmzZUqlSJ06dPs3DhQlatWsXXX3+t603crmjRoilrLO2KFClCyZIlU9p1zYk7DRkyhHbt2lG5cmUOHjzImDFjCAwMpEuXLrnz3zhfl9cTsVu5cqUFpPnq3r27ZVmmDPizzz5rlS1b1goJCbGaN29ubd261bedllwrvWsNsObNm5dyzPnz560+ffpYV111lVW4cGHr3nvvteLi4nzXacnVevXqZVWuXNkKDg62SpcubTVv3tz65ptvUu7X9Sae5lgC3LJ0zYl73X///Vb58uWt4OBgq2LFitb9999v7dixI+X+3Ha92SzLsnyUz0RERERERPyO1iSJiIiIiIg4UEgSERERERFxoJAkIiIiIiLiQCFJRERERETEgUKSiIiIiIiIA4UkERERERERBwpJIiIiIiIiDhSSREREREREHCgkiYhInmOz2fjkk088+hxNmjRh0KBBHn0OERHxDYUkERHJtg0bNhAYGEjbtm1dfmyVKlWYOXOm+zuVhXbt2nHnnXeme9/atWux2Wz8+uuvXu6ViIj4E4UkERHJtrfffpv+/fuzZs0aDh486OvuOKV3797Exsayf//+NPfNmzePBg0aULduXR/0TERE/IVCkoiIZMuZM2dYsmQJTz75JG3btmX+/Plpjlm+fDk333wzBQsWpFSpUtx7772Amaq2Z88ennrqKWw2GzabDYCxY8dyww03pDrHzJkzqVKlSsrtH3/8kZYtW1KqVCmKFSvGHXfcwU8//eR0v++++25Kly6dpr9nzpxh6dKl9O7dm+PHj9OlSxcqVqxI4cKFqVOnDosWLcr0vOlN8StevHiq59m3bx+dO3emePHilChRgvbt27N79+6U+1etWsUtt9xCkSJFKF68OLfffjt79uxx+rWJiIh7KCSJiEi2fPjhh9SqVYtrrrmGBx98kHfeeQfLslLu//zzz7n33nu56667+Pnnn1mxYgW33HILADExMYSHh/P8888TFxdHXFyc0897+vRpunfvzrp169i4cSM1atTgrrvu4vTp0049vkCBAjz88MPMnz8/VX+XLl1KUlISXbp04cKFC9SvX5/PP/+c33//nccee4yHHnqIH374wel+XikxMZHWrVtTtGhR1q5dy3fffUdoaCh33nknFy9e5NKlS3To0IE77riDX3/9lQ0bNvDYY4+lBEgREfGeAr7ugIiI5E5vv/02Dz74IAB33nknp06dYvXq1TRp0gSA8ePH88ADDzBu3LiUx9SrVw+AEiVKEBgYSNGiRSlXrpxLz9usWbNUt998802KFy/O6tWrufvuu506R69evZg6dWqq/s6bN4+OHTtSrFgxihUrxpAhQ1KO79+/P19//TUffvhhStBz1ZIlS0hOTmbu3LkpwWfevHkUL16cVatW0aBBA06dOsXdd99NtWrVAKhdu3a2nktERHJGI0kiIuKyrVu38sMPP9ClSxfAjM7cf//9vP322ynHbNmyhebNm7v9uQ8fPsyjjz5KjRo1KFasGGFhYZw5c4a9e/c6fY5atWpx22238c477wCwY8cO1q5dS+/evQFISkrihRdeoE6dOpQoUYLQ0FC+/vprl57jSr/88gs7duygaNGihIaGEhoaSokSJbhw4QI7d+6kRIkS9OjRg9atW9OuXTtmzZrl0gibiIi4j0aSRETEZW+//TaXLl2iQoUKKW2WZRESEsKrr75KsWLFKFSokMvnDQgISDUFDsw0NUfdu3fn+PHjzJo1i8qVKxMSEkJkZCQXL1506bl69+5N//79mT17NvPmzaNatWrccccdAEydOpVZs2Yxc+ZM6tSpQ5EiRRg0aFCmz2Gz2TLt+5kzZ6hfvz4LFixI89jSpUsDZmRpwIABfPXVVyxZsoTRo0cTGxvLrbfe6tJrExGRnNFIkoiIuOTSpUu89957TJ8+nS1btqR8/fLLL1SoUCGlwEHdunVZsWJFhucJDg4mKSkpVVvp0qU5dOhQqrCxZcuWVMd89913DBgwgLvuuovrrruOkJAQjh075vLr6Ny5MwEBASxcuJD33nuPXr16pUyD++6772jfvj0PPvgg9erVo2rVqmzbti3T85UuXTrVyM/27ds5d+5cyu2bbrqJ7du3U6ZMGapXr57qq1ixYinH3XjjjYwcOZL169dz/fXXs3DhQpdfm4iI5IxCkoiIuOSzzz7jxIkT9O7dm+uvvz7VV8eOHVOm3I0ZM4ZFixYxZswY/vrrL3777TcmT56ccp4qVaqwZs0aDhw4kBJymjRpwtGjR5kyZQo7d+5k9uzZfPnll6mev0aNGrz//vv89ddffP/993Tr1i1bo1ahoaHcf//9jBw5kri4OHr06JHqOWJjY1m/fj1//fUXjz/+OIcPH870fM2aNePVV1/l559/ZtOmTTzxxBMEBQWl3N+tWzdKlSpF+/btWbt2Lbt27WLVqlUMGDCA/fv3s2vXLkaOHMmGDRvYs2cP33zzDdu3b9e6JBERH1BIEhERl7z99tu0aNEi1eiHXceOHdm0aRO//vorTZo0YenSpXz66afccMMNNGvWLFV1uOeff57du3dTrVq1lOlmtWvX5rXXXmP27NnUq1ePH374IVUBBfvznzhxgptuuomHHnqIAQMGUKZMmWy9lt69e3PixAlat26daurg6NGjuemmm2jdujVNmjShXLlydOjQIdNzTZ8+nYiICKKioujatStDhgyhcOHCKfcXLlyYNWvWUKlSJaKjo6lduza9e/fmwoULhIWFUbhwYf7++286duxIzZo1eeyxx+jbty+PP/54tl6biIhkn826cgK1iIiIiIhIPqaRJBEREREREQcKSSIiIiIiIg4UkkRERERERP6//ToWAAAAABjkb713EGXRSBIAAMBIEgAAwEgSAADASBIAAMBIEgAAwEgSAADASBIAAMBIEgAAwAQcDCUezHV6NQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Each blue point represents a prediction from the model, with the x-axis showing the predicted value and the y-axis showing the corresponding residual (error).\n",
        "Residual= Yactual− Ypredicted\n",
        "​"
      ],
      "metadata": {
        "id": "4frSP7oavCXw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "residuals = Y_test -  Y_pred\n",
        "plt.scatter(Y_pred,residuals,color = \"blue\")\n",
        "plt.hlines(y=0, xmin=Y_pred.min(), xmax=Y_pred.max(), colors='red')\n",
        "plt.xlabel(\"Predicted Value\")\n",
        "plt.ylabel(\"Residuals\")\n",
        "plt.title(\"Residual Plot\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "HIgxdA0XhKHO",
        "outputId": "4e1b2b1c-a362-46a1-8c71-e166711a4fbb"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets fit another model and consider onl the most important features to see if model improves"
      ],
      "metadata": {
        "id": "U3wumNzmvdRK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X2 = data_cleaned[['RM','NOX','CHAS','LSTAT','DIS','PTRATIO','RAD']]\n",
        "Y2 = data_cleaned['MEDV']"
      ],
      "metadata": {
        "id": "3T4s6t65TCyF"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X2_train,X2_test,Y2_train,Y2_test = train_test_split(X2,Y2,test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "QG3VNrN_Mofm"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = LinearRegression()\n",
        "model2.fit(X2_train,Y2_train)"
      ],
      "metadata": {
        "id": "p84JdFdqT2iY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Coeficients: \",model2.coef_)\n",
        "print(\"Intercept: \",model2.intercept_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lM04Ix09UHm3",
        "outputId": "00aafb77-cc4c-40ac-e2d0-17f2323c50b5"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coeficients:  [ 4.70386543e+00 -2.05620584e+01  3.92290983e+00 -5.19518227e-01\n",
            " -1.15566279e+00 -9.74538920e-01 -8.47490910e-03]\n",
            "Intercept:  33.05335876919537\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "New Model with lesser feature did not improve our model as we got approximately same values for MSE and R2 as we had in previous model"
      ],
      "metadata": {
        "id": "0Rs2Suf1wezZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y2_pred = model2.predict(X2_test)\n",
        "mse2 = mean_squared_error(Y2_test,Y2_pred)\n",
        "r22 = r2_score(Y2_test,Y2_pred)\n",
        "print(\"MSE : \", mse2)\n",
        "print(\"R2:,\",r22)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u5ja9KZsUoak",
        "outputId": "e904b12a-cd4a-4f93-fc82-47b6b8445444"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE :  25.27856882066974\n",
            "R2:, 0.655294357780431\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can try Lasso Regression with a penalty value of alpha ( Tuning regularization parameter). we keep trying different values for alpha till we get a value that does not improve model futher"
      ],
      "metadata": {
        "id": "dSQWg4TywqVq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lasso_model = Lasso(alpha = 0.001)\n",
        "lasso_model.fit(X_train,Y_train)"
      ],
      "metadata": {
        "id": "VpK4mR3TRryW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_lasso_pred = lasso_model.predict(X_test)"
      ],
      "metadata": {
        "id": "Q7kKLgVySAoz"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mse_lasso = mean_squared_error(Y_test,Y_lasso_pred)\n",
        "r2_lasso = r2_score(Y_test,Y_lasso_pred)\n",
        "print(\"Mean Squared Error: \", mse_lasso)\n",
        "print(\"R2: \",r2_lasso)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BcOkfbj8TvlK",
        "outputId": "00ad7045-8d93-49c3-eb66-f0f2261e5042"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error:  24.998863232828917\n",
            "R2:  0.659108501491377\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Rather then manually trying different Alpha values, we can use cross validation to chose the optimal value for Alpha"
      ],
      "metadata": {
        "id": "vXxso2u_w8eF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "alphas = np.logspace(-3,1,50)"
      ],
      "metadata": {
        "id": "1KKNwsn8XEsH"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lasso_cv_model = LassoCV(alphas=alphas, cv=5)\n",
        "lasso_cv_model.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        },
        "id": "OP3zfuDLXOiU",
        "outputId": "b73a0b33-2af5-4a0b-cfb0-8947019d97ce"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LassoCV(alphas=array([1.00000000e-03, 1.20679264e-03, 1.45634848e-03, 1.75751062e-03,\n",
              "       2.12095089e-03, 2.55954792e-03, 3.08884360e-03, 3.72759372e-03,\n",
              "       4.49843267e-03, 5.42867544e-03, 6.55128557e-03, 7.90604321e-03,\n",
              "       9.54095476e-03, 1.15139540e-02, 1.38949549e-02, 1.67683294e-02,\n",
              "       2.02358965e-02, 2.44205309e-02, 2.94705170e-02, 3.55648031e-02,\n",
              "       4.29193426e-02, 5.17947468e-0...\n",
              "       9.10298178e-02, 1.09854114e-01, 1.32571137e-01, 1.59985872e-01,\n",
              "       1.93069773e-01, 2.32995181e-01, 2.81176870e-01, 3.39322177e-01,\n",
              "       4.09491506e-01, 4.94171336e-01, 5.96362332e-01, 7.19685673e-01,\n",
              "       8.68511374e-01, 1.04811313e+00, 1.26485522e+00, 1.52641797e+00,\n",
              "       1.84206997e+00, 2.22299648e+00, 2.68269580e+00, 3.23745754e+00,\n",
              "       3.90693994e+00, 4.71486636e+00, 5.68986603e+00, 6.86648845e+00,\n",
              "       8.28642773e+00, 1.00000000e+01]),\n",
              "        cv=5)"
            ],
            "text/html": [
              "<style>#sk-container-id-13 {color: black;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LassoCV(alphas=array([1.00000000e-03, 1.20679264e-03, 1.45634848e-03, 1.75751062e-03,\n",
              "       2.12095089e-03, 2.55954792e-03, 3.08884360e-03, 3.72759372e-03,\n",
              "       4.49843267e-03, 5.42867544e-03, 6.55128557e-03, 7.90604321e-03,\n",
              "       9.54095476e-03, 1.15139540e-02, 1.38949549e-02, 1.67683294e-02,\n",
              "       2.02358965e-02, 2.44205309e-02, 2.94705170e-02, 3.55648031e-02,\n",
              "       4.29193426e-02, 5.17947468e-0...\n",
              "       9.10298178e-02, 1.09854114e-01, 1.32571137e-01, 1.59985872e-01,\n",
              "       1.93069773e-01, 2.32995181e-01, 2.81176870e-01, 3.39322177e-01,\n",
              "       4.09491506e-01, 4.94171336e-01, 5.96362332e-01, 7.19685673e-01,\n",
              "       8.68511374e-01, 1.04811313e+00, 1.26485522e+00, 1.52641797e+00,\n",
              "       1.84206997e+00, 2.22299648e+00, 2.68269580e+00, 3.23745754e+00,\n",
              "       3.90693994e+00, 4.71486636e+00, 5.68986603e+00, 6.86648845e+00,\n",
              "       8.28642773e+00, 1.00000000e+01]),\n",
              "        cv=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LassoCV</label><div class=\"sk-toggleable__content\"><pre>LassoCV(alphas=array([1.00000000e-03, 1.20679264e-03, 1.45634848e-03, 1.75751062e-03,\n",
              "       2.12095089e-03, 2.55954792e-03, 3.08884360e-03, 3.72759372e-03,\n",
              "       4.49843267e-03, 5.42867544e-03, 6.55128557e-03, 7.90604321e-03,\n",
              "       9.54095476e-03, 1.15139540e-02, 1.38949549e-02, 1.67683294e-02,\n",
              "       2.02358965e-02, 2.44205309e-02, 2.94705170e-02, 3.55648031e-02,\n",
              "       4.29193426e-02, 5.17947468e-0...\n",
              "       9.10298178e-02, 1.09854114e-01, 1.32571137e-01, 1.59985872e-01,\n",
              "       1.93069773e-01, 2.32995181e-01, 2.81176870e-01, 3.39322177e-01,\n",
              "       4.09491506e-01, 4.94171336e-01, 5.96362332e-01, 7.19685673e-01,\n",
              "       8.68511374e-01, 1.04811313e+00, 1.26485522e+00, 1.52641797e+00,\n",
              "       1.84206997e+00, 2.22299648e+00, 2.68269580e+00, 3.23745754e+00,\n",
              "       3.90693994e+00, 4.71486636e+00, 5.68986603e+00, 6.86648845e+00,\n",
              "       8.28642773e+00, 1.00000000e+01]),\n",
              "        cv=5)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_alpha_cv = lasso_cv_model.alpha_\n",
        "print(\"Best Value for alpha: \", best_alpha_cv)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v2L45PrxXZ8G",
        "outputId": "c0e57c2c-c789-4181-9a44-eb15f5a21ca6"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Value for alpha:  0.001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see even, Lasso model did not improve the result. But overall the reuslt and performance is average. One way to improve, would be trying a bigger data set or using non linear models like Decision Tree or Random Forest"
      ],
      "metadata": {
        "id": "qps90R5Yx54E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred_lasso_cv = lasso_cv_model.predict(X_test)\n",
        "mse_lasso_cv = mean_squared_error(Y_test,Y_pred_lasso_cv)\n",
        "r2_lasso_cv =  r2_score(Y_test,Y_pred_lasso_cv)\n",
        "print(\"Means square error: \", mse_lasso_cv)\n",
        "print(\"R squared: \",r2_lasso_cv)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JR1a9Rx2Yy4Q",
        "outputId": "8b5f89f1-7b21-4381-f286-9886a3088609"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Means square error:  24.998863232828917\n",
            "R squared:  0.659108501491377\n"
          ]
        }
      ]
    }
  ]
}