Predict_the_price_of_an_Airbnb_listing.ipynb

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  "metadata": {
    "colab": {
      "name": "Predict the price of an Airbnb listing.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMm55xbYa/ssuZe1YDwhJPB",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Azariagmt/jupyter-notebooks/blob/master/Predict_the_price_of_an_Airbnb_listing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zc4bImZZ7Smo"
      },
      "source": [
        "**Objective:** build a machine learning model that will automatically predict the price for lodging or homestays."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FIztLsrT7Pm5"
      },
      "source": [
        "import pandas as pd\n",
        "airbnb_data = pd.read_csv(\"https://raw.githubusercontent.com/dphi-official/Datasets/master/airbnb%20_data/airbnb_listing_train.csv\", index_col='id')"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "9fhfYSE67foi",
        "outputId": "21aad941-c4fa-4636-ffd9-bdcf0804da43"
      },
      "source": [
        "print(airbnb_data.shape)\n",
        "airbnb_data.head()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(12901, 15)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>room_type</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>last_review</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11602914</th>\n",
              "      <td>Luxury home 100m2+garden in center!</td>\n",
              "      <td>3123809</td>\n",
              "      <td>Marjolein</td>\n",
              "      <td>NaN</td>\n",
              "      <td>De Pijp - Rivierenbuurt</td>\n",
              "      <td>52.35368</td>\n",
              "      <td>4.90177</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>15-02-2020</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13289321</th>\n",
              "      <td>East is where your home is!</td>\n",
              "      <td>10259430</td>\n",
              "      <td>Ana</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Oud-Oost</td>\n",
              "      <td>52.35728</td>\n",
              "      <td>4.92052</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "      <td>19-06-2019</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40779315</th>\n",
              "      <td>Amsterdam Central - LUXURY house in BUSY STREET</td>\n",
              "      <td>224969266</td>\n",
              "      <td>Mark</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Centrum-West</td>\n",
              "      <td>52.37582</td>\n",
              "      <td>4.89850</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>16-03-2020</td>\n",
              "      <td>1.65</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7820311</th>\n",
              "      <td>great appartment down town</td>\n",
              "      <td>693472</td>\n",
              "      <td>Ayden</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Westerpark</td>\n",
              "      <td>52.37792</td>\n",
              "      <td>4.87300</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>3</td>\n",
              "      <td>42</td>\n",
              "      <td>17-02-2020</td>\n",
              "      <td>0.72</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27346603</th>\n",
              "      <td>Private room with canal view in peaceful area</td>\n",
              "      <td>41888346</td>\n",
              "      <td>Alissa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Westerpark</td>\n",
              "      <td>52.38051</td>\n",
              "      <td>4.87493</td>\n",
              "      <td>Private room</td>\n",
              "      <td>2</td>\n",
              "      <td>89</td>\n",
              "      <td>26-02-2020</td>\n",
              "      <td>4.02</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                     name  ...  price\n",
              "id                                                         ...       \n",
              "11602914              Luxury home 100m2+garden in center!  ...    220\n",
              "13289321                      East is where your home is!  ...    110\n",
              "40779315  Amsterdam Central - LUXURY house in BUSY STREET  ...    100\n",
              "7820311                        great appartment down town  ...    130\n",
              "27346603    Private room with canal view in peaceful area  ...     90\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K_MRzDgwA4W7"
      },
      "source": [
        "airbnb_data = airbnb_data[airbnb_data['price']< 2000]"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "ihf2o7dv7m2R",
        "outputId": "2c7ccde5-00aa-413f-ca0e-e82a7f2a960d"
      },
      "source": [
        "features = ['neighbourhood','room_type', 'minimum_nights', 'number_of_reviews', 'calculated_host_listings_count', 'availability_365']\n",
        "X = airbnb_data[features]\n",
        "y = airbnb_data['price']\n",
        "X.head()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>room_type</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11602914</th>\n",
              "      <td>De Pijp - Rivierenbuurt</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13289321</th>\n",
              "      <td>Oud-Oost</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40779315</th>\n",
              "      <td>Centrum-West</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7820311</th>\n",
              "      <td>Westerpark</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>3</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27346603</th>\n",
              "      <td>Westerpark</td>\n",
              "      <td>Private room</td>\n",
              "      <td>2</td>\n",
              "      <td>89</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    neighbourhood  ... availability_365\n",
              "id                                 ...                 \n",
              "11602914  De Pijp - Rivierenbuurt  ...                0\n",
              "13289321                 Oud-Oost  ...                0\n",
              "40779315             Centrum-West  ...                7\n",
              "7820311                Westerpark  ...                0\n",
              "27346603               Westerpark  ...               24\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "VcrPm-e58WzW",
        "outputId": "e6d678b8-aa3f-4c2b-b0cd-904f4cd821fe"
      },
      "source": [
        "airbnb_data.boxplot(column='price', by='room_type')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f34ee4205f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "7OmY-FCI8mQM",
        "outputId": "e8fe87d6-c33e-48d0-9f31-7c04ffb0b7a9"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "airbnb_data.boxplot(column='price', by='neighbourhood')\n",
        "plt.xticks(rotation = 90)\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qvJmtOBG-A5y"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3, random_state = 101)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ri6fWQIX82Y7",
        "outputId": "a21f0357-697d-4f18-848f-55d64b693827"
      },
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "# Train the model\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "import numpy as np\n",
        "\n",
        "# Define preprocessing for numeric columns (scale them)\n",
        "numeric_features = [2,3,4,5]\n",
        "numeric_transformer = Pipeline(steps=[\n",
        "    ('scaler', StandardScaler())])\n",
        "\n",
        "# Define preprocessing for categorical features (encode them)\n",
        "categorical_features = [0,1]\n",
        "categorical_transformer = Pipeline(steps=[\n",
        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
        "\n",
        "# Combine preprocessing steps\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', numeric_transformer, numeric_features),\n",
        "        ('cat', categorical_transformer, categorical_features)])\n",
        "\n",
        "# Create preprocessing and training pipeline\n",
        "pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
        "                           ('regressor', GradientBoostingRegressor())])\n",
        "\n",
        "\n",
        "# fit the pipeline to train a linear regression model on the training set\n",
        "model = pipeline.fit(X_train, (y_train))\n",
        "print (model)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Pipeline(memory=None,\n",
            "         steps=[('preprocessor',\n",
            "                 ColumnTransformer(n_jobs=None, remainder='drop',\n",
            "                                   sparse_threshold=0.3,\n",
            "                                   transformer_weights=None,\n",
            "                                   transformers=[('num',\n",
            "                                                  Pipeline(memory=None,\n",
            "                                                           steps=[('scaler',\n",
            "                                                                   StandardScaler(copy=True,\n",
            "                                                                                  with_mean=True,\n",
            "                                                                                  with_std=True))],\n",
            "                                                           verbose=False),\n",
            "                                                  [2, 3, 4, 5]),\n",
            "                                                 ('cat',\n",
            "                                                  Pipeline(memory=None,\n",
            "                                                           steps=[('onehot',\n",
            "                                                                   OneHotEncoder(categories='...\n",
            "                                           learning_rate=0.1, loss='ls',\n",
            "                                           max_depth=3, max_features=None,\n",
            "                                           max_leaf_nodes=None,\n",
            "                                           min_impurity_decrease=0.0,\n",
            "                                           min_impurity_split=None,\n",
            "                                           min_samples_leaf=1,\n",
            "                                           min_samples_split=2,\n",
            "                                           min_weight_fraction_leaf=0.0,\n",
            "                                           n_estimators=100,\n",
            "                                           n_iter_no_change=None,\n",
            "                                           presort='deprecated',\n",
            "                                           random_state=None, subsample=1.0,\n",
            "                                           tol=0.0001, validation_fraction=0.1,\n",
            "                                           verbose=0, warm_start=False))],\n",
            "         verbose=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "KSayjQul-hkF",
        "outputId": "c558e71a-76c6-4e94-fc60-1d9e6c055eca"
      },
      "source": [
        "from sklearn.metrics import *\n",
        "# Evaluate the model using the test data\n",
        "predictions = model.predict(X_test)\n",
        "mse = mean_squared_error(y_test, predictions)\n",
        "print(\"MSE:\", mse)\n",
        "rmse = np.sqrt(mse)\n",
        "print(\"RMSE:\", rmse)\n",
        "r2 = r2_score(y_test, predictions)\n",
        "print(\"R2:\", r2)\n",
        "\n",
        "# Plot predicted vs actual\n",
        "plt.scatter(y_test, predictions)\n",
        "plt.xlabel('Actual Labels')\n",
        "plt.ylabel('Predicted Labels')\n",
        "plt.title('Airbnb price Predictions')\n",
        "# overlay the regression line\n",
        "z = np.polyfit(y_test, predictions, 1)\n",
        "p = np.poly1d(z)\n",
        "plt.plot(y_test,p(y_test), color='magenta')\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE: 9005.956533034687\n",
            "RMSE: 94.8997182979733\n",
            "R2: 0.20347267648080702\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NY/yodovwGYYRA1GUwlpgN+Ajv98dW/zGiInZC5qYMmsJW1tcxvam5q1MmbUEwBSDYRSBTAnxfo7zIWwCXhaRJ/3+F4AXiiOeUW1Mm7M0qRASbG3ZybQ5S00pGEYRyDRSSGRCnY8LSU3wbGzSGFXP2uatOZUbhlFYMiXEu72jFxeRFbi0GDuBHara6NNvzwAGASuAM/0saQFuxq2+tgU4X1Vf7KgMRmXRr76OpgAF0K/egt4MoxhEiT46UETuF5FXROSNxCuHe4xV1ZGqmliBbTLwlKoeCDzl9wGOBw70r4lEWAfa6HxMGjeMutqaNmV1tTVMGjcs5AzDMApJlBnNv8M10DuAscAdwJ0duOepuBBX/Pv4lPI71PE8UC8ifTtwH6MCGT+qgetPG05DfR0CNNTXcf1pw82fYBhFIkr0UZ2qPiUioqorgWv8DOcfRjhXgSdERIFbVXU60EdV3/LH1wF9/HYDsDrl3DW+7C2MqmL8qAZTAoZRIqIohW0i0gV4XUS+hQtH3SPi9T+jqk0i8jHgSRF5LfWgqqpXGJERkYk48xIDBw7M5VSjCrA5DobRMaKYj76DS3PxbeBw4Dzgq1EurqpN/v0dXATTEcDbCbOQf3/HV28CBqSc3p+A+RCqOl1VG1W1sXfv3lHEMKqExByHpuatKLvmOMxeYNNqDCMqWZWCqv5LVT9U1TWqeoGqfgk4I9t5IrK7iOyZ2AaOBV4CHgYm+GoTgIf89sPAV/1Kb0cCm1LMTIaRlUxzHAzDiEbUhHjpnAl8N0udPsCDLtKUrsDdqvonEfkXMFNELgRW+msBPI4LR12GC0m9IE/ZjCrF5jgYRsfJVylItgqq+gYwIqD8PXal4U4tV+CSPOUxypBi2/c78xwH85UYxSLUfCQivUJe+xBBKRjVTSns+511joP5SoxikmmkMB8XUhqkALbHI47RWShFDqPEdTtbj9ryQRnFJFOai8HFFMToXJTKvt8Z5ziYr8QoJlFCUg0jZ8Ls+J3Bvl9s7FkaxcSUghELndW+XwrsWRrFJN/oI8PISGe175cCe5ZGMREXCRpwwKW4DkVVN8QiUQ40NjbqvHnzslc0DMMwkojI/JTM1W2IGn00ENjot+uBVYA5oiuccot9Lyd5ykkWwygmWaOPROQ3wIOq+rjfP55d6a6NCqXc1kIuJ3nKSZZqxBRyaYniaD4yoRAAVPWPwH/EJ5KRL7MXNDFm6tMMnvwYY6Y+nXFyU7nlCSonecpJlmrDJuqVniiO5rUichW7FtY5F1gbn0hGPuTauy232PdykqecZEmns/eibaJe6YmiFM4Brsalvlbgr77MKCNy/TOVW56gXOSJu2Est2eToCNmratmL+GeuavZqUqNCOeMHsB144d3SJY4voNyVsjVQpTU2RtU9Tu4BXMOU9VLyyHyyGhLrn+mcot9jypPMcwL5fZsEuRr1rpq9hLufH4VO32k4U5V7nx+FVfNXpKXHHF+BzZRr/RkVQoi8h8i8grwqt8fISK/il0yIydy/TOV21rIUeUphr2/3J5NgqiKP923dPfcVYHn3TN3dWB5NuL8DspVIVcTUcxHNwHjcIvgoKqLRORzsUpVBRR6+D1p3LA2pgXI/mcqtzxBUeQplnmh3J4NRDNrBZmYwtgZMkcpG3F+BzZRr/REmtGsqqv9YjkJdobVNbITR8hjtfyZytXeXwyiKP6gXnwYNZJfBvy4v4NyVMjVRBSlsFpE/gNQEanFrdn8arxidW7Cht9XzFzEZTMW5t2gV8OfKZ8RUWchiuLPpbd+zugB2SsFUM3fQTUQRSlcDNwMNABNwBPAN6PeQERqgHlAk6qeJCKDgXuBfXCzpr+iqttFpDtwB3A48B5wlqquyOGzVAxhf9zEcN4mS4VTLSOiMLIp/rBefF1tF7bv0IJEH1X7d1ByWnE5JQbFc/nQ3EfJCiJjVPW5bGUZzr8caAT28kphJjBLVe8VkV8Di1T1FhH5JnCoql4sImcDX1TVszJdu1JzH42Z+nRGW2+Chvo6npt8dBEkcnT2GPhqIN00Ca4XXw6OciNPNgHTgRuBd1PK5wDH5nfJTLmPosxo/nnEsqAb9wdOBH7r9wU4GrjfV7mdXSkzTvX7+OPHiORp9CxzgiIsgihmbLbNJO0clGvklJEDrwAX4DLNJbLN/RdtFcIJuJY0BkLNRyLyaVw6i96+t59gLyB7i+b4Ke7j7On39wGaVXWH31+DM0vh31cDqOoOEdnk66c+CkRkIjARYODAgRHFKB5Retvpw+8uIoGRIMV0ntpM0s5DNfiWOg2twEO4UcDzGeqdD3wXOCR+kTL5FLoBe/g6e6aUvw+cnu3CInIS8I6qzheRozoiZCqqOh03mKKxsTG/mLoACmE6ySWqKPWPGzbkL6bjzmaSGkYR2AjcAkwDmkPq7IvrSk8E9i6SXClkypL6F+AvIvJ7VV2Zx7XHAKeIyAnAbrgRxs1AvYh09aOF/jjnNf59ALBGRLriHsd7edw3ZwoVIppvb7scHHfVHOppGLGxGKcA7sxQ5zM4JXAiZbEWZpToo9+KyBmq2gwgIj2Be1V1XKaTVHUKMMWfcxTwXVU9V0Tuw4007gUm4AZP4CbHTQD+6Y8/rdm84AWiUKaTjvS2ow7543IGW5ihYXSQncAsnCkoU/zLROByoEz/WlGUwr4JhQCgqhtF5GMduOeVwL0ich2wALjNl98G/EFElgEbgLM7cI+cKJTpJO7edpx5/sNGK+CipSwiySgWFRMF9y7wS5wS2BJSZz/cKODrtDXClzFRlEKriAxU1VUAIrI/LltqZFT1WeBZv/0GcERAnY+AM3K5bqEoVGMed287bmdw+mjFFpsxik1Z/+ZexJmC7s1QZywwCTgOFzlUgUSxYH0f+LuI/EFE7sSlzp4Sr1jFpVBJuOIOByy2M9gWmzGKTdn85lqAu4AR7AoNPZz2CuGbwDJcN1mBp4HjqViFABFGCqr6JxE5DDjSF12qqu9mOqfSKKSjN85wwGI7gy0iySg2JfvNvYObfXUjsD2kTn+cKehrwO7xilNKMs1TOEhVX/MKAXattjbQm5NejF+84lEJsd3FdgZbRJJRbOp71LJxS0tgeUF5AacAHshQ51icEjiaiu7550qmkcIVwDeA/xdwTIltPl31EdWxVuzQVYtIMopNWLxhh+IQtwP34JTAKxnqfRu4jNhyClUKmeYpfMO/jy2eONVHro61Yo5oymH+hFFdbNrafpSQqTyQt4Cf4ZRAa0idITiH8ATABr5tyGQ+Oi3Tiao6q/DiVB/FTC+RT6hfJZjVolAxYY5VTl4my+dwCuDhDBc+AWcK+hxVZQrKh0zmo5P9+8dwOZCe9vtjgX/gpmlUNYVoaIrlWCvrUL+YqebPXmlkNVluw80OvhH4d4YLfRe38kv/2ETttGQyH10AICJPAAer6lt+vy/w+6JIV4YkFEFT81aEXRM2MjU0qefU+OR3DV6JFMuZWwkJ7+LqzVfCZzcc6SbLEVLPj1ccygGHZZj59XHcKOA8oHsxpOzcRJm8NiChEDxvA+WXnrQAXDV7CffMXR26EEl6jzPd9xXU0KSfk76QzpcOb+CB+U2xO3PLPbw0zt58uX92w6PAX2H8jQ2MfzzDd34KTgmMKZJcVUYUpfCUiMzB+e8BzgL+HJ9IpeGq2Uu48/lVyf2dqsn9hGKIsv7t2uatbXq8YWmxwSmRZ15bz/WnDY/cQ87Um850LJcRSSns73H25i20tkzZirM5TAPeDKnTBacA/hPoVxyxqp0ok9e+JSJfxLloAKar6oPxilV87pm7OrQ8oRSi9Cz3rqsNHBmEsbZ5a07J8MJ600DGnnbU8NJS2d/j7M1baG2ZsBL4CS4yKIyDcUrgHFzyfqPoRBkpgMv68YGq/llEeojInqr6QZyCFZuwxju1PKzHmaCutgYRso4mUsmlt5otBUCmnnbU8NJC9thzGXHE2Zu30NoSoMBTOIfwkxnqnY5zCo8uhlBGFLIqBRH5Bi7Zay9gKG6FtF8Dx8QrWnGpCTHz1KSsCBrU40w4mxOO48tmLMzpvoneapQGNJ/edOqxKCOSQvXYcx1xxN2b7yyhtWXLZuB/cUpgTUidbrhRwLeAPkWSy8iZKAnxLsG5dN4HUNXXcWGqnYpzRg/IWh6U8O6ms0ayYuqJPDf5aMaPasipZ1tfV8v4UQ2B6yNfOmMho/77iTZrJIddu199XcZjuVCo6+Sa2MzWFq4wluNahkSyuD1wM4JTFcJIXFK57bie0zbgR5hCKHOimI+2qep28T1mvypaURa/KSYJv0F69FHj/r3arSfw3OTwDB9BPd4gBLjmFLfgapgDe+OWlpz8AoXoaReqx57PiCOX3rxNRisiCvwJNwp4NkO9s3GzhA/LUMcoe6Iohb+IyPeAOhH5Ai5Z7CPxilUcghqW9BDUSfcvomXnrjDSSfcvAjI7XSWCztytdtcgLVNDmW7P3622S7LBrq+r5ZpTDmkjS0cbynT7+951tYjAZTMWMm3O0sjXjNNHUAxneFUrnQ+A3+KUwLqQOrvjFMAluDWFjU5DFKVwJW7doCXARcDjuJ9MRkRkN9zaC939fe5X1atFZDAuK/k+wHzgK34k0h24A5e1/D3gLFVdkfMnikiUhuXaR15OKoQELTuVax95ObCB2HXNsIQru9ja0pq8XzYHdiLMNb0Hv21H2/sUym6euE5HGt+xB/XmrudXtVGPhfIRxD0ZrepmQP8b+DHwmwx1GnH+gC8SPTzFiIW4OywZfQoiUgO8qqq/UdUzVPV0vx3FfLQNOFpVR+Csi8eJyJHADcBNqnoAsBG40Ne/ENjoy2/y9WIjis07KIVvpvIo8xjS73fFzEUZFQI4pVGKxUfyvefsBU08ML+pjUIQ4LCBezNtzlIGT36MMVOfbuMvyYW4J6OVzUIvcdCKG+d/ll3+gGG0UwirT9gCC9m1eMy/cOsimkIoKUH+xymzluT9Xwoio1JQ1Z3AUhHJeQazOj70u7X+lUi5fb8vvx0Y77dP9fv448eISGypq+JoWPI5N9s8BnBffJjiaGre2qEGNhP5PqOgRlWBfyzfUJAfc6Gc4WF0qhnQm3BmoN44BVCDmxH8911Vtu/Zyk/G/ptDvzOHQVc+xqArH+PYw/7K7NbC/6aMjlGMDkuU6KOewMsi8pSIPJx4Rbm4iNSIyELcukZP4mIWmlV1h6+yBhfiin9fDeCPb8KZmNKvOVFE5onIvPXr10cRI5AoDUt9XfDCHony2QuaGDP16WTPt+ALgUQkjt4C5N/4hjWeYWlBcqVQy6eGEbfSiZVXgAvYNQqoxxmAU9dKPBKXznIHoDD2e8/ysyNe5/3ddiSrdJqRUSejGB2WKErhB8BJwH/jFtxJvLKiqjtVdSQuV+ERwEF5ypl6zemq2qiqjb179877OlEalmtOOYTaLm0HK7VdhGtOOSRwGPfhRzuorWk/uNm9Ww1dYk7Xm+ufOF2hBSmUfBvfXBrPfH7M40c18KXDG5JzSGpE+NLhhZuHELfSKRg7cY37p9mlBA6hfbrKC4CX2GUK+ifON+A/YtwNTZTfmhGNYnRYMq2nsBtwMXAAzsl8W0oPPydUtVlEnsH9fOtFpKu/Vn8g8QtpAgYAa3zY6944h3MsBM1yHXtQb6bNWcplMxYmHTjTzhixKyuqQEurcumMhXQRaE3r+ra0anIU0ZyyKMjm7dH9DB0hzMSU7pgae1DvNkn4whyp+c4EzjTJL51+9XU5O85mL2hixgurk6a3narMeGE1jfv3yksxBN0/l3xU2a7VUWWVuOaH61r45qsH8LV/DKb2w5D+3L44h/BE3D8oAnvX1bb5vaaWd5Sqc9rHTDFStkiYz1hEZgAtwN+A44GVqvqdyBcW6Q20eIVQBzyBcx5PAB5Q1XtF5NfAYlX9lYhcAgxX1YtF5GzgNFU9M9M9Ghsbdd68eVFFykhQdE9dbQ3Xn+ZCVCfdt4iWdC1QIsJmX6eSCFeF9vMXwhrohvq6jHMwciGbIgL3fMOyxGaauDby2icCG7H6uloWXn1sTrLtXVfL5u072kSZZbt/puuG/YbyagAXw+opWxjweI/wOp/FKYETiDbuD2DUf6buMkQAABxvSURBVD8RGDzRs0ctC36Y/XlmYszUpwM7K4X8rVUbheh4iMh8VW0MOpYpluBgVR3uL3AbbqnrXOgL3O4jmLoAM1X1URF5BbhXRK4DFgC3+fq3AX8QkWXABtxUmIIT9kCzOXDKRSFANOd089YWJt23iN27dw10+gaRMBcU4kcXFB7buH+vdte99pGXA5/7FTPD54MEKYRM5alcNXtJm1DZoHPyDW/tUKjsDpwp6EZcoLZnAG0Vwt0jVvLbT73JtqGtBWtUm0Oi6cLKc6FTOe3LhLhTtmRSCslfhKruyDUQSFUXA6MCyt/A+RfSyz/CBb3FxuwFTVw+Y2Fy2dam5q1c7nMVVcqPt8HbDrOFsYJTZFEaygQJU05cw/30H/PsBU2h4b07VWOZkJY+dyKMfL73nH5D7wK/xCmBLSEX3A9+dPAr3HvoKjZ3b6tspDln8UKJc6KhpS2vPDINOEeIyPv+9QFwaGJbRN4vloCFZMqsxe3W8W715fnkFSoFTc1bY1NUPbp1KWqMfrZrht23Z0iUV1h56v2ijvfy+c4zOgFfxI19Ew7h3sA1tFUIY4E/4n6UCrwFf/rCunYKIV/5wojTsV4xTnsjSahSUNUaVd3Lv/ZU1a4p23sVU8hCETbTeGtLK2MPCo5kGntQbyaNG9YuCqmUdNSQFfZJXn9nc+gIJA5FFOWaiXkYqZErV598SLsor9oa4eqTD+nw/SD/RivRAHbdKZz6cj/++L+fZcUNJ/LclKPdPP0ZaSd8E1jGrqigp4HjaPMFFaNRjTMZoSU6rDxCHc2VQK6O5kGTHws91hAyzE04xGYvaOKah1/OyRxTTiTamWwpNcLI1TE4e0ET1z7yctI8FJSnKcwJmS53eqqMhPM/yO+RyR+S6X49e9TSvKUlPx/KO8DPcaag7SF1BuAcwhfg8gblQFXnYTJiIZOjuaqUwpApj7ULIwUCw0tTWTH1xHZlURq0ciPxOfKR/bwjB7ZJFpiJ9ESCCWq7CNPOGNFm+dBMGWVzjZLKFv0TdFyAc3P4bIALubgReCBDnWNxSuBo2vT8rYE3yoF8o486HV8ePbDNOsyp5YmU2enUiAT+kcce1DvwWpXApHHDuDTHxYCeeW3X7PFsDdu0OUvbKQRwju/USJywjKyJHnuupqwwf8gVMxcl55586fAGnnltffRGeTtudfIbcbOFw/g2cBkwKLxKZ4nZj1uxmeIsLVU1UgAXkpi+ZsJ144dnNC3V1da0631279qlokxJ3bt2YcdOjRTOGoQAb049MbS3nb76XNhdEteJQq4x7oMnP5bV35J13sBbwM04JRB2sSG4tNETgBz8vZ0hZr/gczGKfH3DYSOFFK4bPzzQVBDmU4DgtY9zyYZaDqSn2c6VfvV1XDV7SeDoKNF2Jnq+PbrVhM7iDouaCeod5jp7M4q/pN28gedwCiBTNq8TcKagzxHupY9ApYQ9ZyLutOVxX9/ITtUphTCirphWrbz74bZI5rJszy+oQQ8yq0y6bxF77OYm3iVmcDdkMSVk+w677ejCF19u4OK5Q2BKBiG/C3wHl4SlgHQkZr9cTCpxK7bOoDgrHVMKnlT7dqU5kItBR0caCeat3NCuMQvqHba0ajJyaadqcoSQqSFM91E0fFDHhH8N4hv/GhIu0Mdxo4DzgO4pje8vCt/45pu3ppx8EXFPRrPJbqWn6nwKUajEyKJKoUaE5def0KYsii8A2oYHt+s1j2yAvwDTcGsDhvDEgeu4+7OrGP+t9qkCwqKhevao5eqTDymYzTzXHn85+SKK4VO4fObCNtGAXQR+cuZIMx8VEPMp5IgNVeMjyNEdde5E6rKkrVuUc18ayMS5Qxk4JSRhXBf494QPuGSf+bxes7nNobmzNgC0i5oKMj1t3NISW6qPKJSTSSXfzLlRmbdyQ7vw8FYNHmEa8WBKIYB8J3gZ+TFp3LCsWWj7b6rj24sPZPwNDYwnpHE4BGcKOhvo5oo+zp5smdoKabmCgpyXmRrZQjs7cxkxlJtJJc6EbPfMXR1antNcEiNvTCkEYE7neBk8+bE2DeH4UQ1tZj+jMGblPlw0dyifWxG+kNJjw95i+hHLWdRvU5uQ2Wk/advYRu1p1/eoDU3QF1Q/X3L1ERQjh365EBYynW8otZE7phQCSPwxM8XbG/mTukYzwPiPN3Dq3xqYOHcI/T4I7v3u7KbU/JdwUuvfeGln+3yMmTK8hjX26T3tbO1OoXrmuYZdxm2yKSfC1gqpiW+5diONPJfl6Px0xj9cuTBwYw9+9MQnefW64xh/WAPsAdf8+ZA2CuHlj23i2ycv4MDvPs7sF5uo2SbwI/j66UNCE8SFNbaqBC6rmt7T3pRhMmIhe+bl5CMoN84ZPSCncqPw2EghA+ZbKAAKR73Rm4teGMqnV+0TWm3NuC1cvN98Xtqv/SggKDVGqrmpe1fXtwlrVJu3trRfOzug4xn2fdeIFHRGba4+gnIKSY2bhN8gKOuAURyqWilkc/ZVcn6jYpKaBmT3bTWcu2R/Jj4/hH03dw+sv7l2B7eOXs4fRq2kR7+uPDf5aPrTg5cmBy/TEdTYf/jRruXCE6vMha01XCPSLhdTy05tZ64Js90XOsVCrj6CapvlG5Z1wCgOsSkFERkA3AH0wZmRp6vqzSLSC5dZfhCwAjhTVTeKW9rtZlxSgS3A+ar6YlzyRel9pSaBM4IZ8t7u3LDyUD71ZK/QOov2a+bW0cv508fX0ZpmsDzxoL7J7bBUI+k96CmzFreLVGppVbbv2BmYpyosYCBd2cRluw/qfFx/2vDI9zFzk1FM4hwp7ACuUNUXRWRPYL6IPAmcDzylqlNFZDIwGbgSOB440L9GA7f491jItsKYzWxujygcvfxjXDR3KEesCVcCj45Yyy8OW8ZrH/sg6zVTFW9QD7q2Rti8bUcyYmnsQb1DF0va0tLKT88a2a6xDfsug8w1hQ63DOt8XH/a8MgTz8otJNXo3MSmFFT1LVzOSVT1AxF5FWgATgWO8tVuB57FKYVTgTvUTbF+XkTqRaSvv07BCetlNTVvDVwLoBrZc1tXvrxwIBfNHUqvrd0C62zq3sKto5dz56iVvL/bjsA6mUj9HtJ76vU9avnwox1Jk1BT81buymLOC2vUcw3pLFSuoUKYfqopJBXKJ89TtVIUn4KIDAJGAXOBPikN/TqceQmcwkidubLGl7VRCiIyEZgIMHDgwLxlCut9dRGqViEc8O4eXDR3CGe8FB7pMb/fRqaPXs6TB7zdzhSUD/U9ahkz9ek2DUCiBz1m6tPtQkkzfTNhazTnahYqpGO3EKafagpJrSanerkSe+4jEdkDl5Xmf1R1log0q2p9yvGNqtpTRB4Fpqrq3335U8CVqhqa3KgjuY/CcrhUy4S1Lq3whdf34+K5Qxj1Vs/QejOHr2b6EW+wbN8PCy5DbY2A0sY/kLo2Q67mu5+eVZj8OIXMNRR2rfq6Wnbv3rXTN/K5Uk55njozJct9JCK1uEUL71LVWb747YRZSET64la4BWjCrWSboL8vi4Ww3leuK5JVCntvreW8Bftz0dwh7LU9uEf9Xt02bh39BveMXMUH3XM3BUVFcCOE5i0t7Xr+qWszZDpf0/bPPXJghxrVVJNFWDcpHx9ToJ+ki7B5e1uzmPWGHeZULz1xRh8JcBvwqqr+JOXQw7g1q6b694dSyr8lIvfiHMyb4vInJEhXDAknc2fgE+/sycS5Q/niK+GNzNz+73Hr6Dd4Zug7aBEnjN501kimzFqS12zxutqa3JfUzEK2taIT5DOrNqjzsWX7jnZmsc4cYpoL5lQvPXGOFMYAXwGWiEii+/09nDKYKSIXAiuBM/2xx3HhqMtwIakXxCgbEGy/rERqWoXjlu7HRS8M4dB19aH17h6xkt8c8SZv9tocWqcYhGUjDaOhvi5WM0tUefLNv5Pu/B4csvRrIXvDleqsnTRuWLtAj9qa9rPPjfiIM/ro74QvXnhMQH0FLolLniBybZzKhZ5bapnw4iAmvjCEHi3BX+Hbe3zErUcsZ8ahq9ncvbw+Y66NX9y25KjyNFTIQjIV76wNsykaRaGqZzRXip3yk+v24qK5Qzn5tX6hdZ7b/11uPWI5fx38bofWES4G+aQPSe/5jj2od04mpEw95yjyFDIENO4Q00qeAT1tztLAiYmVIHtnoaqVQlhjkO7ILCZddwonvtaXi+cO5RPr9wqtd/thK7it8U1W9dxSROmCGTO0F88t3xC5fq6pyYN6vqnpR5qat3L5TGehDGo4svWcg+Sp6SK0tiqK8yV86fDCTWqLO8S0kp21lSx7Z6GqlUJY41RMhTCguY6bHxnFYWvDw0Kb9tzKraOXc9/wNWztVl6mIIAzGgfyz+UbiLqKc1Cj2KNbF15/p72vY8zQXpHMfK0K35u1OLBhzdZzDps0lzhjpyoPzG+icf9eBVUMcfV8K9lZW8mydxaqWimkNgbFcjIft3Q/fj378Ix1/jpoPb8evZx/7P9e2ZuCwD2/qAohQXqjOGbq04H1Vry3NXIvcUtI+osovc9UeYImzVWK+QUqewZ00Cp8QWnOjfioaqUAuxRDHPMTurd0YfJfDuKC+YMz1lve60O+deqLvBohV1A5kotCDZt1nKnh7mgK8/DZ68LsBU3tGvpKN2FU+gzo9CgvW3WtuFS9Upi9oIlJ9y0qyLU+9kF3vjZ/MBfPHZqx3j2HruK6o18pu6iguKmtEa4++ZDAY5nMBlF9EGHTCMLO36kaGJXTGUwYcZqn4uSah18mfanuVnXllfh5KpGqVwpB0Q5ROXxNTy6eO4QvLNsvY73LT1zIrEOaKsIUVGhqRGhVzdpbzWTySO/59uhWw+bt7RXEuaODc2Elzr9i5qJ2vc4gs1Alm18qnaD1MDKVG4Wn6pVCVJNA9x1d+OJLDVz0wlAGb9w9tN6tRyznd4evYN1eHxVKxIpmpyoNEcwX40c1MG/lhjYrbqVG/KT3fM/9zT/bRDyNGdor48Is40c1cFmIibBY6yoYRiVQ9UohzFTQ9/3d+Nq8wXzjX0NCz13e60NuPeINZh/SxPauubpaK4cuAl27CNvzzB4bZfLU7AVNPDC/KdmTzxTxM3tBEy+8ubFN2Qtvbgz0D6SSi1moUs0vlU7PHrXtnPyJcqM4VL1SmDRuGJfeu5BPr9qHi+YO4ag3PxZad86B67j1iDd4sf/G0DqdjdoaYdrpI7jm4ZfZ3oEhfLbonVwmXF3z8MuBE5yy2Z3NLFQ44kqjcfXJhwSmuQjzRRmFpzqVguJS9f0PjF/RwHja/5h3SCu3jn6D3x++gvV7bCu6iKUkKNdQmOklQV1tF7a2tFIjEhotkslUl0vET752ZzMLFYY402gEmRHP+tQA+46KSHUqhUeBb7Qtem3f97l19Bs8+om1tNRUdwhcUK6hTGGh5x05sI09PywnfqbonWJF/JhZqOPEmUYjFzOiEQ8FWDurAhkL/D9gLlz14BIGXfkYx134Nx78ZFOnVwhdskRAZQrrrKutaVNWV1vDT88a2c7BG1Y3k5kmn3NyYfaCJsZMfZrBkx9jzNSnmb0gtqU6Oj1xzuPItna6ET/VOVLYA2aPbeLaR14OdGp1Zvbaza34FdbrD5snlIvpJR8zTS7n5OqMrPisoWVGnKO6Sp842BmoSqUQdVGVzsimrS0svPrYjMsehpGL6SVT3TAnZdTr5+qMrOSsoeVInA77zjBxsNKpSvNRpa6jUAgSf65J44a1MyV1EWKPxEko5Ca/7GWi156LOWf8qAamnT6Chvo6BKfIpp0+IrSBt95nYRk/qoHrTxve5vlff9rwgijYuM2IRnbiXI7zf4GTgHdU9ZO+rBcwAxgErADOVNWNfunOm3Err20BzlfVF+OSrVJXWOsoqX+ueSs3BKYTmLdyQ6y950L12nMZtVRj7zPuldficthbhFjpiXOk8HvguLSyycBTqnog8JTfBzgeONC/JgK3xChXXmvtVjJBvbl75q4OrBtWXihK0Wuvtt5nIUZjpWT8qAaem3w0b049kecmH20KocjEuRznX0VkUFrxqcBRfvt24FngSl9+h1+S83kRqReRvqr6VhyyVVvWxTenntiuLOwZxP1sStFrr7bep/lQjI5QbEdzn5SGfh3Qx283AKld1DW+rJ1SEJGJuNEEAwcGJ0DLRkMHUzFXEnW1wYPBsElmcY+iSjWruJrmJ5gPxegIJXM0+1FBzt1SVZ2uqo2q2ti7d++87h1kTuisfOnw/oHx+eeMHhBYP6y8UMTppDQcYaOuzuxDMQpHsUcKbyfMQiLSF3jHlzcBqa1Rf18WC3EurFNsarsIrcDOgPTfY4b24oH5TYHx+YkJZ6npBM4ZPSBjptFCUU299lJgOZ6MjlBspfAwMAGY6t8fSin/lojcC4wGNsXlT+gMpOcmAtpMxKuvq+WaUw7Jalu+bvzwoigBo7hUmw/FKCxxhqTeg3Mq7ysia4CrccpgpohcCKwEzvTVH8eFoy7DhaReEJdcCa595OW4b1E0wnre2dYPiDts0SgdNhoz8iXO6KNzQg4dE1BXgUvikiWISk5vkXCSZ0vXkCnSJ5/UD6ZEDKPzU5UzmjsTmZKFZYrPzzXxWKXHvhuGEY2qVArl1JB1qxFqazoWBhoWapgp0ifXsEXLXmkY1UFVJsQrl4asi8CNp48AdjkF63vUouoS1+1dV4sING9poV99HZu37QhcSCZTqGGYbTnXSWQW+24Y1UFVKoVyacha1SmDqFP5g7K75htqmGvYYjXmDzKMaqQqzUd715XPIuC5KKhCTvzK9VrVlj/IMKqVqhspzF7QxObtO4p6TyF86nauPe1Chhrmuj4CWOy7YXR2qk4pTJuztM3iLMXgprNGAlT8LFOLfTeMzk/VKYWO+hMSvf76ulo2b9+RVcE01Ne1aUitp20YRjlTdUohzGFaX+fWLl7bvJW9Qxr8RPqIREOeOpkr6Jz0kYD1tA3DKHeqTimERd2kNvYQbfZueiNvM34Nw6h0RCt4wZnGxkadN29ezudZ420YRjUjIvNVtTHoWNWNFMDMOIZhGGFU5TwFwzAMIxhTCoZhGEYSUwqGYRhGElMKhmEYRhJTCoZhGEaSig5JFZH1uGU9o7Av8G6M4sRJJcsOlS1/JcsOlS1/JcsO5S3//qraO+hARSuFXBCReWFxueVOJcsOlS1/JcsOlS1/JcsOlSu/mY8MwzCMJKYUDMMwjCTVpBSml1qADlDJskNly1/JskNly1/JskOFyl81PgXDMAwjO9U0UjAMwzCyYErBMAzDSNLplYKIHCciS0VkmYhMLrU86YjIABF5RkReEZGXReQ7vryXiDwpIq/7956+XETkZ/7zLBaRw0r7CRwiUiMiC0TkUb8/WETmejlniEg3X97d7y/zxweVUm4vU72I3C8ir4nIqyLy6Up5/iJymf/dvCQi94jIbuX87EXkf0XkHRF5KaUs52ctIhN8/ddFZEIJZZ/mfzeLReRBEalPOTbFy75URMallJd1m4SqdtoXUAMsB4YA3YBFwMGllitNxr7AYX57T+DfwMHAjcBkXz4ZuMFvnwD8Ebcy6JHA3FJ/Bi/X5cDdwKN+fyZwtt/+NfB//PY3gV/77bOBGWUg++3A1/12N6C+Ep4/0AC8CdSlPPPzy/nZA58DDgNeSinL6VkDvYA3/HtPv92zRLIfC3T12zekyH6wb2+6A4N9O1RTEW1SqQWI+Uv8NDAnZX8KMKXUcmWR+SHgC8BSoK8v6wss9du3Auek1E/WK6HM/YGngKOBR/2f+N2UP0vyewDmAJ/22119PSmh7Hv7hlXSysv++XulsNo3jl39sx9X7s8eGJTWsOb0rIFzgFtTytvUK6bsace+CNzlt9u0NYlnXwltUmc3HyX+NAnW+LKyxA/nRwFzgT6q+pY/tA7o47fL8TP9FPgvoNXv7wM0q+oOv58qY1J+f3yTr18qBgPrgd9589dvRWR3KuD5q2oT8GNgFfAW7lnOp3KefYJcn3XZfAdpfA03soHKkz1JZ1cKFYOI7AE8AFyqqu+nHlPXpSjL2GEROQl4R1Xnl1qWPOmKMwncoqqjgM04E0aScn3+3vZ+Kk6x9QN2B44rqVAdpFyfdTZE5PvADuCuUsvSUTq7UmgCBqTs9/dlZYWI1OIUwl2qOssXvy0iff3xvsA7vrzcPtMY4BQRWQHcizMh3QzUi0hiuddUGZPy++N7A+8VU+A01gBrVHWu378fpyQq4fl/HnhTVderagswC/d9VMqzT5Drsy6n7wAROR84CTjXKzWoENmD6OxK4V/AgT4aoxvOufZwiWVqg4gIcBvwqqr+JOXQw0AiqmICzteQKP+qj8w4EtiUMvQuOqo6RVX7q+og3PN9WlXPBZ4BTvfV0uVPfK7Tff2S9QxVdR2wWkSG+aJjgFeojOe/CjhSRHr431FC9op49ink+qznAMeKSE8/WjrWlxUdETkOZzo9RVW3pBx6GDjbR3wNBg4EXqAC2qSSOzXifuEiGP6N8/h/v9TyBMj3GdxweTGw0L9OwNl6nwJeB/4M9PL1Bfil/zxLgMZSf4aUz3IUu6KPhuD+BMuA+4Duvnw3v7/MHx9SBnKPBOb572A2LqKlIp4/cC3wGvAS8AdctEvZPnvgHpz/owU3Srswn2eNs98v868LSij7MpyPIPHf/XVK/e972ZcCx6eUl3WbZGkuDMMwjCSd3XxkGIZh5IApBcMwDCOJKQXDMAwjiSkFwzAMI4kpBcMwDCOJKQWj0yIi40VEReSgCHUvFZEeHbjX+SLyi6jlGa6zQkT27eh9DSNfTCkYnZlzgL/792xcCuStFAyjs2BKweiU+FxSn8FNMDo7pbxGRH7s1x9YLCL/KSLfxuUOekZEnvH1Pkw553QR+b3fPtmvRbBARP4sIn3IAxG5RUTmiVsL4dq0w/8lIktE5AUROcDX7y0iD4jIv/xrTMA1z/Cfa5GI/DUfuQyja/YqhlGRnAr8SVX/LSLvicjh6pL2TcSlPx6pqjtEpJeqbhCRy4Gxqvpuluv+HThSVVVEvo5LcXBFHvJ939+3BnhKRA5V1cX+2CZVHS4iX8VloD0Jl0/qJlX9u4gMxKV1+ETaNX8IjFPVptTFXgwjF0wpGJ2Vc3ANKbhEfefg0kp/HpeKYAeAqm7I8br9gRk+cVs33FoM+XCmiEzE/Qf74hZlSSiFe1Leb/LbnwcOdimOANjLj4ZSeQ74vYjMxCXHM4ycMaVgdDpEpBcuW+twEVHcalcqIpNyuExq/pfdUrZ/DvxEVR8WkaOAa/KQbzDwXeBTqrrRm6ZS76EB211wI5SP0q61q6LqxSIyGjgRmO9HR+WQBdWoIMynYHRGTgf+oKr7q+ogVR2A69F/FngSuCiRWtorEIAPcMuhJnhbRD4hIl1wK2ol2JtdqY7zXRt4L9y6DZu8T+L4tONnpbz/028/AfxnooKIjEy/qIgMVdW5qvpD3MJBA9LrGEY2TCkYnZFzgAfTyh7w5b/FpZxeLCKLgC/749OBPyUczbiFdh4F/oHLjJngGuA+EZmPW84yCueLyJrEC7eGwQJcdtO7cWafVHqKyGLgO8BlvuzbQKN3jr8CXBxwn2neQf2Sl3tRRPkMI4llSTUMwzCS2EjBMAzDSGJKwTAMw0hiSsEwDMNIYkrBMAzDSGJKwTAMw0hiSsEwDMNIYkrBMAzDSPL/AWDWreBD8ELBAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HSB04Ndl_IRe"
      },
      "source": [
        "import joblib\n",
        "\n",
        "# Save the model as a pickle file\n",
        "filename = './models/predict-price.pkl'\n",
        "joblib.dump(model, filename)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}