24_Churn_Analysis_TELCO.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Stefano0210/IULM_DDM2324_Notebooks/blob/main/24_Churn_Analysis_TELCO.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analisi del Customer Churn (Telco)"
      ],
      "metadata": {
        "id": "Luxa1cZ08ilq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "!wget \"https://github.com/Stefano0210/IULM_DDM2324_Notebooks/raw/main/data/Telco_customer_churn.xlsx\"\n",
        "# Ricaricare il dataset\n",
        "file_path = 'Telco_customer_churn.xlsx'\n",
        "churn_data = pd.read_excel(file_path)\n",
        "\n",
        "# Preparare i dati per le analisi\n",
        "# Convertire la colonna 'Churn Label' in formato numerico per facilitare l'analisi\n",
        "churn_data['Churn Numeric'] = churn_data['Churn Label'].apply(lambda x: 1 if x == 'Yes' else 0)\n",
        "#churn_data['Gender'] = churn_data['Gender'].apply(lambda x: 1 if x == 'Female' else 0)\n",
        "\n"
      ],
      "metadata": {
        "id": "yaXdJX2A8R7t",
        "outputId": "cc27a7d1-0760-4577-8e74-97b09f4aa775",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-04-09 15:00:56--  https://github.com/Stefano0210/IULM_DDM2324_Notebooks/raw/main/data/Telco_customer_churn.xlsx\n",
            "Resolving github.com (github.com)... 140.82.113.4\n",
            "Connecting to github.com (github.com)|140.82.113.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/Stefano0210/IULM_DDM2324_Notebooks/main/data/Telco_customer_churn.xlsx [following]\n",
            "--2024-04-09 15:00:57--  https://raw.githubusercontent.com/Stefano0210/IULM_DDM2324_Notebooks/main/data/Telco_customer_churn.xlsx\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1368250 (1.3M) [application/octet-stream]\n",
            "Saving to: ‘Telco_customer_churn.xlsx’\n",
            "\n",
            "Telco_customer_chur 100%[===================>]   1.30M  --.-KB/s    in 0.05s   \n",
            "\n",
            "2024-04-09 15:00:57 (24.1 MB/s) - ‘Telco_customer_churn.xlsx’ saved [1368250/1368250]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Proporzione di churn"
      ],
      "metadata": {
        "id": "TY8aazRF-yLg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calcolo delle proporzioni di churn e non churn\n",
        "churn_proportions = churn_data['Churn Label'].value_counts(normalize=True)\n",
        "\n",
        "# Creazione del grafico a torta\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.pie(churn_proportions, labels=churn_proportions.index, autopct='%1.1f%%', startangle=140, colors=['#ff9999','#66b3ff'])\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "id": "4wU_Gd8e9zdD",
        "outputId": "9e359ee3-a102-4de7-98a6-61e9b808889e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Distribuzione delle variabili numeriche"
      ],
      "metadata": {
        "id": "BHCgVrio-JfK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Identificazione delle variabili numeriche continue nel dataset\n",
        "continuous_vars = ['Tenure Months',\n",
        "       'Monthly Charges', 'CLTV']\n",
        "\n",
        "# Preparazione dei dati per sns.FacetGrid\n",
        "melted_data = pd.melt(churn_data, value_vars=continuous_vars)\n",
        "\n",
        "# Creazione dei grafici\n",
        "facet_grid = sns.FacetGrid(melted_data, col='variable', col_wrap=3, sharex=False, sharey=False)\n",
        "facet_grid = facet_grid.map(sns.histplot, 'value')\n",
        "\n",
        "\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "aCMTb-lE-IOB",
        "outputId": "6b0a0b68-584e-4cf1-9365-38d71ef71bc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x300 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "continuous_vars"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JfjRd42UO8J8",
        "outputId": "5ec4e236-c842-482e-879c-5ba85258f001"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Tenure Months', 'Monthly Charges', 'CLTV']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analisi delle variabili categoriche"
      ],
      "metadata": {
        "id": "v73KhXthBJgE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_vars = ['Gender',\n",
        "       'Senior Citizen', 'Partner', 'Dependents', 'Phone Service',\n",
        "       'Multiple Lines', 'Internet Service', 'Online Security',\n",
        "       'Online Backup', 'Device Protection', 'Tech Support', 'Streaming TV',\n",
        "       'Streaming Movies', 'Contract', 'Paperless Billing', 'Payment Method']\n",
        "\n",
        "# Preparazione dei dati per sns.FacetGrid\n",
        "melted_data_categorical = pd.melt(churn_data, value_vars=categorical_vars)\n",
        "# Creazione dei grafici per le variabili categoriche\n",
        "facet_grid_categorical = sns.FacetGrid(melted_data_categorical, col='variable', col_wrap=4, sharex=False, sharey=False)\n",
        "facet_grid_categorical = facet_grid_categorical.map(sns.countplot, 'value')\n",
        "\n",
        "# Ajusting for better visualization\n",
        "for ax in facet_grid_categorical.axes.flatten():\n",
        "    for label in ax.get_xticklabels():\n",
        "        label.set_rotation(45)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "CmeoFYMlBMVg",
        "outputId": "d2ee7f59-0c70-4d1b-f13c-ceb6dae9ace1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/axisgrid.py:718: UserWarning: Using the countplot function without specifying `order` is likely to produce an incorrect plot.\n",
            "  warnings.warn(warning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EDA: come le variabili impattano il churn"
      ],
      "metadata": {
        "id": "fSAIE1cw8sCo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "01YzFBkp8Qxc",
        "outputId": "25662b3c-4c29-4bf1-abbe-55e373c93635"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Calcolo delle proporzioni corrette per ogni categoria nelle variabili analizzate\n",
        "\n",
        "# Funzione per calcolare le proporzioni di churn\n",
        "def calculate_churn_proportions(df, column_name):\n",
        "    churn_counts = df.groupby([column_name, 'Churn Label']).size().unstack()\n",
        "    churn_proportions = churn_counts.div(churn_counts.sum(axis=1), axis=0)\n",
        "    return churn_proportions\n",
        "\n",
        "# Calcolare le proporzioni per le variabili selezionate\n",
        "prop_internet_service = calculate_churn_proportions(churn_data, 'Internet Service')\n",
        "prop_contract = calculate_churn_proportions(churn_data, 'Contract')\n",
        "prop_tech_support = calculate_churn_proportions(churn_data, 'Tech Support')\n",
        "\n",
        "# Grafici delle proporzioni\n",
        "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
        "\n",
        "# Churn vs Servizi Internet\n",
        "prop_internet_service.plot(kind='bar', stacked=True, ax=axes[0])\n",
        "axes[0].set_title('Churn vs Servizi Internet')\n",
        "axes[0].set_ylabel('Proporzione di Churn')\n",
        "axes[0].set_xlabel('Servizi Internet')\n",
        "\n",
        "# Churn vs Tipi di Contratto\n",
        "prop_contract.plot(kind='bar', stacked=True, ax=axes[1])\n",
        "axes[1].set_title('Churn vs Tipi di Contratto')\n",
        "axes[1].set_ylabel('Proporzione di Churn')\n",
        "axes[1].set_xlabel('Tipo di Contratto')\n",
        "\n",
        "# Churn vs Supporto Tecnico\n",
        "prop_tech_support.plot(kind='bar', stacked=True, ax=axes[2])\n",
        "axes[2].set_title('Churn vs Supporto Tecnico')\n",
        "axes[2].set_ylabel('Proporzione di Churn')\n",
        "axes[2].set_xlabel('Supporto Tecnico')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Distribuzione geografica\n",
        "\n",
        "Verrá preparato un dataset che poi verrá elaborato in qgis"
      ],
      "metadata": {
        "id": "KAEQOc0QFgi5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calcolo per ogni zip code il numero totale di clienti, il numero di clienti che hanno fatto churn e la proporzione di churn\n",
        "churn_by_zip = churn_data.groupby('Zip Code').agg(\n",
        "    Total_Customers=('CustomerID', 'count'),\n",
        "    Churned_Customers=('Churn Value', 'sum')\n",
        ")\n",
        "churn_by_zip['Churn_Proportion'] = churn_by_zip['Churned_Customers'] / churn_by_zip['Total_Customers']\n",
        "\n",
        "# Salvare il risultato in un file CSV\n",
        "output_file_path = 'churn_by_zip_code.xlsx'  # Sostituisci con il percorso desiderato\n",
        "churn_by_zip.to_excel(output_file_path)\n",
        "\n",
        "print(\"File salvato:\", output_file_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6bPvor-DNh-",
        "outputId": "8c2b73fe-14b9-40a1-cef3-0f3212ede396"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "File salvato: churn_by_zip_code.xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Si importa il file dopo il processing eseguito in qgis"
      ],
      "metadata": {
        "id": "2RNYAOHaKXwc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget \"https://github.com/Stefano0210/IULM_DDM2324_Notebooks/raw/main/data/los_angeles_area_zipcodes.csv\"\n",
        "la_area_zipcodes = pd.read_csv('los_angeles_area_zipcodes.csv')\n",
        "print(la_area_zipcodes.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hFmRO1VXJ6Ej",
        "outputId": "1b57a3de-eb00-4523-e9f7-035a4d9f47da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-04-09 15:14:53--  https://github.com/Stefano0210/IULM_DDM2324_Notebooks/raw/main/data/los_angeles_area_zipcodes.csv\n",
            "Resolving github.com (github.com)... 140.82.112.3\n",
            "Connecting to github.com (github.com)|140.82.112.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/Stefano0210/IULM_DDM2324_Notebooks/main/data/los_angeles_area_zipcodes.csv [following]\n",
            "--2024-04-09 15:14:53--  https://raw.githubusercontent.com/Stefano0210/IULM_DDM2324_Notebooks/main/data/los_angeles_area_zipcodes.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 55487 (54K) [text/plain]\n",
            "Saving to: ‘los_angeles_area_zipcodes.csv’\n",
            "\n",
            "los_angeles_area_zi 100%[===================>]  54.19K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2024-04-09 15:14:53 (5.06 MB/s) - ‘los_angeles_area_zipcodes.csv’ saved [55487/55487]\n",
            "\n",
            "   OBJECTID  ZIP_CODE      PO_NAME STATE  POPULATION  POP_SQMI  SQMI  \\\n",
            "0        33     90001  Los Angeles    CA       59907  16970.82  3.53   \n",
            "1        34     90002  Los Angeles    CA       53573  17281.61  3.10   \n",
            "2        35     90003  Los Angeles    CA       71967  20503.42  3.51   \n",
            "3        36     90004  Los Angeles    CA       63987  20508.65  3.12   \n",
            "4        37     90005  Los Angeles    CA       41844  26823.08  1.56   \n",
            "\n",
            "     SHAPE_Leng    SHAPE_Area  churn_by_zip_code — Sheet1_Total_Customers  \\\n",
            "0  44694.660695  9.828050e+07                                           5   \n",
            "1  44705.841112  8.656244e+07                                           5   \n",
            "2  52236.412174  9.791987e+07                                           5   \n",
            "3  43950.392481  8.689358e+07                                           5   \n",
            "4  43301.531459  4.352816e+07                                           5   \n",
            "\n",
            "   churn_by_zip_code — Sheet1_Churned_Customers  \\\n",
            "0                                             2   \n",
            "1                                             1   \n",
            "2                                             1   \n",
            "3                                             2   \n",
            "4                                             3   \n",
            "\n",
            "   churn_by_zip_code — Sheet1_Churn_Proportion  \n",
            "0                                          0.4  \n",
            "1                                          0.2  \n",
            "2                                          0.2  \n",
            "3                                          0.4  \n",
            "4                                          0.6  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Le ragioni per abbandonare l'azienda"
      ],
      "metadata": {
        "id": "_fZb5MmsKnAq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "overall_churn_reasons = churn_data['Churn Reason'].value_counts(normalize=True).head(10)  # Top 10 ragioni\n",
        "\n",
        "# Creare un grafico a barre per visualizzare le ragioni principali del churn\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.barplot(x=overall_churn_reasons.values, y=overall_churn_reasons.index, palette=\"vlag\")\n",
        "plt.title('Top 10 Ragioni di Churn')\n",
        "plt.xlabel('Percentuale del Totale Churn')\n",
        "plt.ylabel('Ragione del Churn')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "OxRzQ1JWKV1i",
        "outputId": "18f2593f-faee-4ae1-865c-4eb5f9e96adb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-10-d42668395b57>:5: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=overall_churn_reasons.values, y=overall_churn_reasons.index, palette=\"vlag\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Le ragioni per abbandonare l'azienda specifiche nell'area di LA"
      ],
      "metadata": {
        "id": "CsahiHy8LdIZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "la_churn_data = churn_data[churn_data['Zip Code'].isin(la_area_zipcodes['ZIP_CODE'])]\n",
        "\n",
        "overall_churn_reasons = la_churn_data['Churn Reason'].value_counts(normalize=True).head(10)  # Top 10 ragioni\n",
        "\n",
        "# Creare un grafico a barre per visualizzare le ragioni principali del churn\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.barplot(x=overall_churn_reasons.values, y=overall_churn_reasons.index, palette=\"vlag\")\n",
        "plt.title('Top 10 Ragioni di Churn')\n",
        "plt.xlabel('Percentuale del Totale Churn')\n",
        "plt.ylabel('Ragione del Churn')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "9wrF5jbsLjwm",
        "outputId": "9cfa77ab-5b1b-4543-d09d-d6589862429a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-6c960142c72d>:7: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=overall_churn_reasons.values, y=overall_churn_reasons.index, palette=\"vlag\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Valori mancanti"
      ],
      "metadata": {
        "id": "4QImVG44NRgr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "churn_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GqYQ6sztNS7B",
        "outputId": "52b89b68-49a5-468e-f100-1161828175ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID              0\n",
              "Count                   0\n",
              "Country                 0\n",
              "State                   0\n",
              "City                    0\n",
              "Zip Code                0\n",
              "Lat Long                0\n",
              "Latitude                0\n",
              "Longitude               0\n",
              "Gender                  0\n",
              "Senior Citizen          0\n",
              "Partner                 0\n",
              "Dependents              0\n",
              "Tenure Months           0\n",
              "Phone Service           0\n",
              "Multiple Lines          0\n",
              "Internet Service        0\n",
              "Online Security         0\n",
              "Online Backup           0\n",
              "Device Protection       0\n",
              "Tech Support            0\n",
              "Streaming TV            0\n",
              "Streaming Movies        0\n",
              "Contract                0\n",
              "Paperless Billing       0\n",
              "Payment Method          0\n",
              "Monthly Charges         0\n",
              "Total Charges           0\n",
              "Churn Label             0\n",
              "Churn Value             0\n",
              "Churn Score             0\n",
              "CLTV                    0\n",
              "Churn Reason         5174\n",
              "Churn Numeric           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Encoding delle Variabili Categoriche"
      ],
      "metadata": {
        "id": "QvC6PALBOmxC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "binary_vars = ['Gender','Senior Citizen', 'Partner', 'Dependents', 'Paperless Billing']\n",
        "categorical_vars = ['Phone Service',\n",
        "       'Multiple Lines', 'Internet Service', 'Online Security',\n",
        "       'Online Backup', 'Device Protection', 'Tech Support', 'Streaming TV',\n",
        "       'Streaming Movies', 'Contract',  'Payment Method']\n",
        "numerical_vars =['Tenure Months',\n",
        "       'Monthly Charges', 'CLTV',\n",
        "       'Churn Numeric']\n",
        "selected_vars =  binary_vars + categorical_vars + numerical_vars\n",
        "selected_vars"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bCwJrSV-OrFr",
        "outputId": "165658a7-5d8b-4690-9bd7-4858f51e3dc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Gender',\n",
              " 'Senior Citizen',\n",
              " 'Partner',\n",
              " 'Dependents',\n",
              " 'Paperless Billing',\n",
              " 'Phone Service',\n",
              " 'Multiple Lines',\n",
              " 'Internet Service',\n",
              " 'Online Security',\n",
              " 'Online Backup',\n",
              " 'Device Protection',\n",
              " 'Tech Support',\n",
              " 'Streaming TV',\n",
              " 'Streaming Movies',\n",
              " 'Contract',\n",
              " 'Payment Method',\n",
              " 'Tenure Months',\n",
              " 'Monthly Charges',\n",
              " 'CLTV',\n",
              " 'Churn Numeric']"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = churn_data[selected_vars]\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "mRJVIHdZPHu4",
        "outputId": "817c6d24-e79a-43b3-eaf9-1772882fa8b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Gender Senior Citizen Partner Dependents Paperless Billing Phone Service  \\\n",
              "0    Male             No      No         No               Yes           Yes   \n",
              "1  Female             No      No        Yes               Yes           Yes   \n",
              "2  Female             No      No        Yes               Yes           Yes   \n",
              "3  Female             No     Yes        Yes               Yes           Yes   \n",
              "4    Male             No      No        Yes               Yes           Yes   \n",
              "\n",
              "  Multiple Lines Internet Service Online Security Online Backup  \\\n",
              "0             No              DSL             Yes           Yes   \n",
              "1             No      Fiber optic              No            No   \n",
              "2            Yes      Fiber optic              No            No   \n",
              "3            Yes      Fiber optic              No            No   \n",
              "4            Yes      Fiber optic              No           Yes   \n",
              "\n",
              "  Device Protection Tech Support Streaming TV Streaming Movies  \\\n",
              "0                No           No           No               No   \n",
              "1                No           No           No               No   \n",
              "2               Yes           No          Yes              Yes   \n",
              "3               Yes          Yes          Yes              Yes   \n",
              "4               Yes           No          Yes              Yes   \n",
              "\n",
              "         Contract             Payment Method  Tenure Months  Monthly Charges  \\\n",
              "0  Month-to-month               Mailed check              2            53.85   \n",
              "1  Month-to-month           Electronic check              2            70.70   \n",
              "2  Month-to-month           Electronic check              8            99.65   \n",
              "3  Month-to-month           Electronic check             28           104.80   \n",
              "4  Month-to-month  Bank transfer (automatic)             49           103.70   \n",
              "\n",
              "   CLTV  Churn Numeric  \n",
              "0  3239              1  \n",
              "1  2701              1  \n",
              "2  5372              1  \n",
              "3  5003              1  \n",
              "4  5340              1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1532b23a-92e5-4e0a-8c07-403b46bc4570\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Senior Citizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Paperless Billing</th>\n",
              "      <th>Phone Service</th>\n",
              "      <th>Multiple Lines</th>\n",
              "      <th>Internet Service</th>\n",
              "      <th>Online Security</th>\n",
              "      <th>Online Backup</th>\n",
              "      <th>Device Protection</th>\n",
              "      <th>Tech Support</th>\n",
              "      <th>Streaming TV</th>\n",
              "      <th>Streaming Movies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>Payment Method</th>\n",
              "      <th>Tenure Months</th>\n",
              "      <th>Monthly Charges</th>\n",
              "      <th>CLTV</th>\n",
              "      <th>Churn Numeric</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>2</td>\n",
              "      <td>53.85</td>\n",
              "      <td>3239</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>2</td>\n",
              "      <td>70.70</td>\n",
              "      <td>2701</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>8</td>\n",
              "      <td>99.65</td>\n",
              "      <td>5372</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>28</td>\n",
              "      <td>104.80</td>\n",
              "      <td>5003</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>49</td>\n",
              "      <td>103.70</td>\n",
              "      <td>5340</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 7043,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Senior Citizen\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Partner\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Paperless Billing\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Phone Service\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Multiple Lines\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Internet Service\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"DSL\",\n          \"Fiber optic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Online Security\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Online Backup\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Device Protection\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tech Support\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Streaming TV\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Streaming Movies\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Contract\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Month-to-month\",\n          \"Two year\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment Method\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Electronic check\",\n          \"Credit card (automatic)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tenure Months\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 0,\n        \"max\": 72,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          10,\n          64\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monthly Charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.090047097678493,\n        \"min\": 18.25,\n        \"max\": 118.75,\n        \"num_unique_values\": 1585,\n        \"samples\": [\n          89.35,\n          106.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CLTV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1183,\n        \"min\": 2003,\n        \"max\": 6500,\n        \"num_unique_values\": 3438,\n        \"samples\": [\n          2809,\n          2308\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Churn Numeric\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Il codice prepara i dati per un'analisi di churn convertendo le variabili categoriche in numeriche, necessario per utilizzare questi dati in modelli di machine learning."
      ],
      "metadata": {
        "id": "LqZcGQzzY0yn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "binary_data = data[binary_vars].copy()\n",
        "\n",
        "# Assuming 'binary_vars' is a list of binary variable column names\n",
        "for col in binary_vars:\n",
        "    print(col)\n",
        "    encoder = LabelEncoder()\n",
        "    binary_data[col] = encoder.fit_transform(binary_data[col].astype(str))\n",
        "\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "# Initialize OneHotEncoder\n",
        "encoder = OneHotEncoder(sparse = False)\n",
        "\n",
        "# Apply OneHotEncoder to categorical variables\n",
        "encoded_categorical_data = encoder.fit_transform(data[categorical_vars])\n",
        "\n",
        "# Creating column names for the encoded data\n",
        "columns = []\n",
        "for i, col in enumerate(categorical_vars):\n",
        "    print(col)\n",
        "    labels = [f\"{col}_{label}\" for label in encoder.categories_[i]]\n",
        "    columns.extend(labels)\n",
        "\n",
        "# Create a DataFrame with the encoded data\n",
        "encoded_categorical_df = pd.DataFrame(encoded_categorical_data, columns=columns)\n",
        "\n",
        "## -----\n",
        "\n",
        "data_dropped = data.drop(binary_vars + categorical_vars, axis=1)\n",
        "\n",
        "# Concatenate the encoded categorical data with the rest of the DataFrame\n",
        "encoded_data = pd.concat([data_dropped, binary_data, encoded_categorical_df], axis=1)\n",
        "\n",
        "print(encoded_data)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eA5165A0Pdyn",
        "outputId": "1b897f1f-ada9-40c3-bbf9-048935c2445d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gender\n",
            "Senior Citizen\n",
            "Partner\n",
            "Dependents\n",
            "Paperless Billing\n",
            "Phone Service\n",
            "Multiple Lines\n",
            "Internet Service\n",
            "Online Security\n",
            "Online Backup\n",
            "Device Protection\n",
            "Tech Support\n",
            "Streaming TV\n",
            "Streaming Movies\n",
            "Contract\n",
            "Payment Method\n",
            "      Tenure Months  Monthly Charges  CLTV  Churn Numeric  Gender  \\\n",
            "0                 2            53.85  3239              1       1   \n",
            "1                 2            70.70  2701              1       0   \n",
            "2                 8            99.65  5372              1       0   \n",
            "3                28           104.80  5003              1       0   \n",
            "4                49           103.70  5340              1       1   \n",
            "...             ...              ...   ...            ...     ...   \n",
            "7038             72            21.15  5306              0       0   \n",
            "7039             24            84.80  2140              0       1   \n",
            "7040             72           103.20  5560              0       0   \n",
            "7041             11            29.60  2793              0       0   \n",
            "7042             66           105.65  5097              0       1   \n",
            "\n",
            "      Senior Citizen  Partner  Dependents  Paperless Billing  \\\n",
            "0                  0        0           0                  1   \n",
            "1                  0        0           1                  1   \n",
            "2                  0        0           1                  1   \n",
            "3                  0        1           1                  1   \n",
            "4                  0        0           1                  1   \n",
            "...              ...      ...         ...                ...   \n",
            "7038               0        0           0                  1   \n",
            "7039               0        1           1                  1   \n",
            "7040               0        1           1                  1   \n",
            "7041               0        1           1                  1   \n",
            "7042               0        0           0                  1   \n",
            "\n",
            "      Phone Service_No  ...  Streaming Movies_No  \\\n",
            "0                  0.0  ...                  1.0   \n",
            "1                  0.0  ...                  1.0   \n",
            "2                  0.0  ...                  0.0   \n",
            "3                  0.0  ...                  0.0   \n",
            "4                  0.0  ...                  0.0   \n",
            "...                ...  ...                  ...   \n",
            "7038               0.0  ...                  0.0   \n",
            "7039               0.0  ...                  0.0   \n",
            "7040               0.0  ...                  0.0   \n",
            "7041               1.0  ...                  1.0   \n",
            "7042               0.0  ...                  0.0   \n",
            "\n",
            "      Streaming Movies_No internet service  Streaming Movies_Yes  \\\n",
            "0                                      0.0                   0.0   \n",
            "1                                      0.0                   0.0   \n",
            "2                                      0.0                   1.0   \n",
            "3                                      0.0                   1.0   \n",
            "4                                      0.0                   1.0   \n",
            "...                                    ...                   ...   \n",
            "7038                                   1.0                   0.0   \n",
            "7039                                   0.0                   1.0   \n",
            "7040                                   0.0                   1.0   \n",
            "7041                                   0.0                   0.0   \n",
            "7042                                   0.0                   1.0   \n",
            "\n",
            "      Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
            "0                         1.0                0.0                0.0   \n",
            "1                         1.0                0.0                0.0   \n",
            "2                         1.0                0.0                0.0   \n",
            "3                         1.0                0.0                0.0   \n",
            "4                         1.0                0.0                0.0   \n",
            "...                       ...                ...                ...   \n",
            "7038                      0.0                0.0                1.0   \n",
            "7039                      0.0                1.0                0.0   \n",
            "7040                      0.0                1.0                0.0   \n",
            "7041                      1.0                0.0                0.0   \n",
            "7042                      0.0                0.0                1.0   \n",
            "\n",
            "      Payment Method_Bank transfer (automatic)  \\\n",
            "0                                          0.0   \n",
            "1                                          0.0   \n",
            "2                                          0.0   \n",
            "3                                          0.0   \n",
            "4                                          1.0   \n",
            "...                                        ...   \n",
            "7038                                       1.0   \n",
            "7039                                       0.0   \n",
            "7040                                       0.0   \n",
            "7041                                       0.0   \n",
            "7042                                       1.0   \n",
            "\n",
            "      Payment Method_Credit card (automatic)  Payment Method_Electronic check  \\\n",
            "0                                        0.0                              0.0   \n",
            "1                                        0.0                              1.0   \n",
            "2                                        0.0                              1.0   \n",
            "3                                        0.0                              1.0   \n",
            "4                                        0.0                              0.0   \n",
            "...                                      ...                              ...   \n",
            "7038                                     0.0                              0.0   \n",
            "7039                                     0.0                              0.0   \n",
            "7040                                     1.0                              0.0   \n",
            "7041                                     0.0                              1.0   \n",
            "7042                                     0.0                              0.0   \n",
            "\n",
            "      Payment Method_Mailed check  \n",
            "0                             1.0  \n",
            "1                             0.0  \n",
            "2                             0.0  \n",
            "3                             0.0  \n",
            "4                             0.0  \n",
            "...                           ...  \n",
            "7038                          0.0  \n",
            "7039                          1.0  \n",
            "7040                          0.0  \n",
            "7041                          0.0  \n",
            "7042                          0.0  \n",
            "\n",
            "[7043 rows x 42 columns]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target = encoded_data['Churn Numeric']\n",
        "all_columns = list(encoded_data.columns)\n",
        "all_columns.remove('Churn Numeric')"
      ],
      "metadata": {
        "id": "EAvWCYheQBWv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Selezione delle variabili con RFE"
      ],
      "metadata": {
        "id": "7nb1qAXBqRvb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from sklearn.feature_selection import RFE\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "X = encoded_data[all_columns] # Features\n",
        "y = encoded_data['Churn Numeric'] # Target variable\n",
        "\n",
        "# Feature extraction\n",
        "model = LogisticRegression()\n",
        "rfe = RFE(model, n_features_to_select=8)\n",
        "fit = rfe.fit(X, y)\n",
        "print(\"Num Features: %s\" % (fit.n_features_))\n",
        "print(\"Selected Features: %s\" % (fit.support_))\n",
        "print(\"Feature Ranking: %s\" % (fit.ranking_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7D5Z25J4QKFR",
        "outputId": "65807a07-d482-4a4a-b4ad-5621453d6365"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num Features: 8\n",
            "Selected Features: [False False False False False False  True False False False False False\n",
            " False False  True False  True False False  True False False False False\n",
            " False  True False False False False False False False False  True False\n",
            "  True False False  True False]\n",
            "Feature Ranking: [27 33 34 28 26 24  1  4 31  2 18 29 17  3  1 13  1 10  8  1 15 14 32 22\n",
            " 30  1  9 11 23 20  7 25 21 12  1 16  1  6  5  1 19]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yOS2foh6Qm6l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "\n",
        "selected_features_rfe = list(fit.support_)\n",
        "\n",
        "final_features_rfe = []\n",
        "for status, var in zip(selected_features_rfe, all_columns):\n",
        "    if status == True:\n",
        "        final_features_rfe.append(var)\n",
        "\n",
        "final_features_rfe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6QtEi-jsQurA",
        "outputId": "dcd83904-239e-4c85-9a88-eb11a18c0e13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Dependents',\n",
              " 'Internet Service_Fiber optic',\n",
              " 'Online Security_No',\n",
              " 'Online Backup_No',\n",
              " 'Tech Support_No',\n",
              " 'Contract_Month-to-month',\n",
              " 'Contract_Two year',\n",
              " 'Payment Method_Electronic check']"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Regressione Logistica su dataset RFE\n",
        "\n",
        "Si inizia definendo due funzioni di utilitá"
      ],
      "metadata": {
        "id": "eaKmXpRsT-CM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, accuracy_score\n",
        "\n",
        "def calcolo_regressione_logistica(X,y):\n",
        "  X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=0)\n",
        "\n",
        "  lr_model = LogisticRegression()\n",
        "\n",
        "  # fit the model with data\n",
        "  lr_model.fit(X_train,y_train)\n",
        "  y_pred=lr_model.predict(X_test)\n",
        "\n",
        "  y_pred_proba = lr_model.predict_proba(X_test)[::,1]\n",
        "\n",
        "  return lr_model,y_test,y_pred, y_pred_proba\n",
        "\n",
        "def calcolo_metriche_regressione(y_test, y_pred):\n",
        "\n",
        "  # Calcolo della matrice di confusione\n",
        "  conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "  # Calcolo di precisione e richiamo\n",
        "  precision = precision_score(y_test, y_pred)\n",
        "  recall = recall_score(y_test, y_pred)\n",
        "  f1score = f1_score(y_test, y_pred)\n",
        "  accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "  return conf_matrix, precision, recall, f1score, accuracy"
      ],
      "metadata": {
        "id": "XOISsUwV_iX8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Regressione logistica su tutte le variabili"
      ],
      "metadata": {
        "id": "UeFZS-peeiAe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = encoded_data[all_columns]\n",
        "y = encoded_data['Churn Numeric']\n",
        "\n",
        "lr_model,y_test,y_pred, _ = calcolo_regressione_logistica(X, y)\n",
        "\n",
        "conf_matrix, precision, recall, f1score, accuracy = calcolo_metriche_regressione(y_test,y_pred)\n",
        "\n",
        "print(\"Accuracy: \",accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qPXeRgLnehdD",
        "outputId": "5606e1f1-9237-4a6c-8f7b-833e40fbb2cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.8086314593980692\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calcolo su variabili RFE"
      ],
      "metadata": {
        "id": "Z-5R8VxMBLfv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = encoded_data[final_features_rfe]\n",
        "y = encoded_data['Churn Numeric']\n",
        "\n",
        "\n",
        "lr_model,y_test,y_pred, _ = calcolo_regressione_logistica(X, y)\n",
        "\n",
        "conf_matrix, precision, recall, f1score, accuracy = calcolo_metriche_regressione(y_test,y_pred)\n",
        "\n",
        "print(\"Accuracy: \",accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g9E1gY6zQ9T0",
        "outputId": "edea1cda-5164-4261-bd1b-64a2f34563f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.7910278250993753\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Precision, recall e matrice di confusione"
      ],
      "metadata": {
        "id": "T9__67fohitR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, accuracy_score\n",
        "\n",
        "\n",
        "# Visualizzazione della matrice di confusione\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='g')\n",
        "plt.xlabel('Predetti')\n",
        "plt.ylabel('Veri')\n",
        "plt.show()\n",
        "\n",
        "# Stampa di precisione e richiamo\n",
        "print(f'Precisione: {precision}')\n",
        "print(f'Richiamo: {recall}')\n",
        "print(f'F1 Score: {f1score}')\n",
        "print(f'accuracy: {accuracy}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "GGx7hd7wiarb",
        "outputId": "bf93e372-5616-4bd0-fb6f-a9d78147e9cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisione: 0.605080831408776\n",
            "Richiamo: 0.5708061002178649\n",
            "F1 Score: 0.5874439461883408\n",
            "accuracy: 0.7910278250993753\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Applicazione di SMOTE"
      ],
      "metadata": {
        "id": "eEfZLBdawGAN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "import pandas as pd\n",
        "import statsmodels.api as sm\n",
        "\n",
        "X = encoded_data[all_columns]\n",
        "y = encoded_data['Churn Numeric']\n",
        "\n",
        "# Apply SMOTE\n",
        "smote = SMOTE(random_state=42)\n",
        "X_res, y_res = smote.fit_resample(X, y)\n",
        "new_class_distribution = y_res.value_counts()\n",
        "new_class_distribution_percentage = y_res.value_counts(normalize=True) * 100\n",
        "new_class_distribution, new_class_distribution_percentage"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GL1EA3KCv5UN",
        "outputId": "d67217ec-1d13-4877-d2f2-5aa6a3f43035"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Churn Numeric\n",
              " 1    5174\n",
              " 0    5174\n",
              " Name: count, dtype: int64,\n",
              " Churn Numeric\n",
              " 1    50.0\n",
              " 0    50.0\n",
              " Name: proportion, dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr_model,y_test,y_pred, y_pred_proba = calcolo_regressione_logistica(X_res, y_res)\n",
        "\n",
        "conf_matrix, precision, recall, f1score, accuracy = calcolo_metriche_regressione(y_test,y_pred)\n",
        "\n",
        "print(f'Precisione: {precision}')\n",
        "print(f'Richiamo: {recall}')\n",
        "print(f'F1 Score: {f1score}')\n",
        "print(f'accuracy: {accuracy}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "55qqIZuswUF5",
        "outputId": "8bf7e702-0f08-463f-c347-5aac55bb511e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisione: 0.7780203784570596\n",
            "Richiamo: 0.8098484848484848\n",
            "F1 Score: 0.7936154417223459\n",
            "accuracy: 0.7850792423656745\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizzazione della matrice di confusione\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='g')\n",
        "plt.xlabel('Predetti')\n",
        "plt.ylabel('Veri')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "Im6ZQt01wp2d",
        "outputId": "77c57096-2225-44ef-ac3e-1a86270d9e1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# AUC"
      ],
      "metadata": {
        "id": "0l287ji59cx1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_proba)\n",
        "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "bJEv4edy---l",
        "outputId": "c4089244-f8cc-4bc0-8859-90d68fdad0ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "# Calcolo di FPR, TPR e soglie\n",
        "fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_proba)\n",
        "\n",
        "# Calcolo della distanza di ogni punto della curva ROC da (0,1)\n",
        "distances = np.sqrt((1 - tpr) ** 2 + fpr ** 2)\n",
        "\n",
        "# Trovare la soglia con la minima distanza\n",
        "optimal_idx = np.argmin(distances)\n",
        "optimal_threshold = thresholds[optimal_idx]\n",
        "\n",
        "print(f\"La soglia ottimale è: {optimal_threshold}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DTSuTfxQDKjJ",
        "outputId": "06ee54bc-1c08-413e-8769-129e209f134c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La soglia ottimale è: 0.5426712354294726\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_proba)\n",
        "roc_auc = metrics.roc_auc_score(y_test, y_pred_proba)\n",
        "\n",
        "# Calcolo della distanza di ogni punto della curva ROC da (0,1) e trovare la soglia ottimale\n",
        "distances = np.sqrt((1 - tpr) ** 2 + fpr ** 2)\n",
        "optimal_idx = np.argmin(distances)\n",
        "optimal_threshold = thresholds[optimal_idx]\n",
        "\n",
        "# Creare il grafico ROC\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "\n",
        "# Aggiungere un punto per la soglia ottimale\n",
        "plt.plot(fpr[optimal_idx], tpr[optimal_idx], marker='o', color='red', label='Optimal Threshold')\n",
        "\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "H81cAJDqDsyl",
        "outputId": "9d7c80df-f39b-4de6-e386-1cbb587fdd52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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3Autr53SBPbqWtwjK7QpjEUREbyEtLQuDB++Dv/92VRHk4mKNsLBeWLjQB+bmbFMozST99LZv344xY8Zg5cqVaNq0KRYvXgwfHx9ER0fD0dExz/lbt25FcHAw1q1bBy8vL8TExCAwMBAymQwLFy6U4B2QzigUwLFjQHw84OICtGoFGBvnPPe6mWDcIJWItCgy8h569NiFmJiHqpi/fy2sWeMHBwdLCTMjbZEJkbsDSvFr2rQpGjdujB9++AEAoFQqIZfLMWLECAQHB+c5f/jw4bh69SoOHz6sio0dOxanT5/G8ePH871HZmYmMjMzVccpKSmQy+VITk6GrS2XOS+Rdu0CRo0C7tx5EatUCViyBHhPwZlgRFQs4uKSUb3698jKUgAALC1NsGSJL/r3bwCZjCtEF7eUlBTY2dlp/ftbsq6xrKwsREZGol27di+SMTJCu3btEBERke81Xl5eiIyMVHWfxcbG4sCBA+jYsWOB95kzZw7s7OxUD7lcrt03Qtq1axfwySfqRRAA3L0DdOsGzOFMMCIqHnK5HYYObQQA8PR0wfnzgzBgQEMWQXpGsq6xpKQkKBQKODk5qcWdnJxw7Vo+XR4AevTogaSkJLRs2RJCCGRnZ2Pw4MGYOHFigfeZMGECxowZozrObRGiEkihyGkJyq+RMje0B8B7yCnhOf6HiLRMCKFW6MyZ0w6VK9th2LAmMDU1ljAz0hXJB0trIjw8HLNnz8by5ctx7tw57Nq1C/v378eMGTMKvMbMzAy2trZqDyqhjh3L2xL0qicAkuQsgohIq1JSMhEY+CtWrDirFjc3L4OgoOYsgvSYZC1CDg4OMDY2RmJiolo8MTERzs7O+V4zZcoU9O7dGwMGDAAA1K1bF2lpaRg4cCAmTZoEI6NSVdfRq+LjC3de/e9YBBGR1kRExKFnz124efMJtm+/jNat3VC7dgWp06JiIlnlYGpqCk9PT7WBz0qlEocPH0bz5s3zvebZs2d5ih3j/59JJOGYb9IWl0LuzlzY84iIXiM7W4lp08LRqtV63Lz5BABgYmKEGzceS5sYFStJp8+PGTMGffv2RaNGjdCkSRMsXrwYaWlp6NevHwCgT58+cHV1xZw5cwAAfn5+WLhwIRo0aICmTZvi+vXrmDJlCvz8/FQFEZVirVrlzA67e+fFmKCXyWQ5z7dqVeypEZF+iY19jF69diEi4kV3vJeXHJs3d0HVqvavuZL0jaSFUEBAAB48eICQkBAkJCSgfv36OHjwoGoA9e3bt9VagCZPngyZTIbJkyfj7t27qFChAvz8/DBr1iyp3gJp0/VdQGcBLMvnudzBi4sXv1hPiIhIQ0IIbNx4AcOH/4bU1CwAgLGxDCEh3pg4sRXKlOEQC0Mj6TpCUtDVOgT0Fl5dIPEigF8BJL90jlyeUwR17Vrs6RGRfnjyJAODBu3Djh2XVTF3d3ts2dIVzZpVkjAzKgxdfX9zXXCSVn67xNcF0KomYPo5UKZG3pWliYiKQCYDTp9+0RUWGFgfS5f6wsbGTMKsSGoshEhaJ0PUj7lCNBHpiJ2dOTZt6oKuXXdg+fKO6N79PalTohKAhRBJI7c77HHMixjXBiIiLYqOToKVlSkqVXrRjdKqVRXcujUKVlamEmZGJQlHhZE0cscECWXOcblaLIKISCuEEFi16iwaNFiFPn12Q6lUHwrLIohexkKIpJH1NOdXmdGL7jAiorf04EEa/P23Y/Dg/UhPz8aRI7ewenWk1GlRCcauMZKWlUvOhqlERG8pLOw6AgP3ICEhVRUbPNgTffrUkzArKulYCFHxi94JpN6VOgsi0hMZGdmYMOEQFi8+rYo5OFhi3bpO8POrKWFmVBqwEKLi9/JMMVMb6fIgolLv4sVE9Oy5Cxcv3lfFfHyqITTUH87O1hJmRqUFCyEqPvnNFOPYICIqov/+e4LGjdcgM1MBADAzM8bcue0xfHgTGBnJJM6OSgsOlqbiw5liRKRFVaqUVY3/qVvXEWfPDsTIkU1ZBJFG2CJExSN654stNGRGgH0NtgYR0VtbtMgHVarYYexYL5ib8yuNNMcWIdK9V7fRsK+RM1OMrUFEVEhpaVkYPHgfQkOj1OJWVqaYNOl9FkFUZPyTQ7rz6maqudgSREQaiIy8h549dyE6+iG2bLmIVq0qo1q1clKnRXqCLUKkG7mtQK8WQdxGg4gKSaFQ4rvvjqNZs7WIjn4IAFAqBS5duv+GK4kKjy1CpH357SjPzVSJSANxccno3Xs3jh79TxXz9HTB1q3dUKNGeQkzI33DQoi0p6CuMLYCEZEGduy4jEGD9uHJkwwAgEwGBAe3xLRpH8DU1Fji7EjfsBAi7civFQhgEUREhfb0aSZGjPgNGzZcUMXkclts2tQF3t5u0iVGeo2FEGnHy6tFA+wKIyKNZWYq8PvvN1THAQHvYcWKj2BvbyFhVqTvOFiatCN3N3kgpxWI0+OJSEMODpbYsMEftrZm2LjRHz/91I1FEOkcW4To7b28iaq1KwsgIiqU2NjHsLIygZPTiz3B2revhv/+G42yZc0lzIwMCVuE6O1xE1Ui0oAQAhs2RKFevZX44ou9EEKoPc8iiIoTCyF6Oy9vnQFwsUQieq3Hj9Px2We/IDBwD1JTs3DgwL9Yvz5K6rTIgLFrjIru1Zli3ESViF4jPPwWevfejTt3UlSxwMD66N79XQmzIkPHQoiKJr/p8mwNIqJ8ZGUpEBJyBHPnnkBuL5i9vTlWrfoY3bu/J21yZPBYCFHRvDpdnusFEVE+rl1LQs+eu3DuXLwq1rq1GzZu7IJKlWwlzIwoBwshKppXp8uzCCKiV8TGPkbDhquQnp4NADAxMcKsWW0wdqwXjIxkEmdHlIODpentcLo8ERXA3d0eXbvWBgDUrFkep04NwFdftWARRCUKW4RIcy+vG0RE9BrLlnVElSp2mDTpfVhamkidDlEebBGiwoveCayvrT5ImusGERGAjIxsBAUdxM6dl9XidnbmmDWrLYsgKrHeqhDKyMjQVh5UGuS3szxnihEZvIsXE9GkyRosXnwaAwfuQ1xcstQpERWaxoWQUqnEjBkz4OrqCmtra8TGxgIApkyZgrVr12o9QSpBcgdIy4xy1gziIGkig6ZUCixZcgqNG6/BxYv3AQDp6c9x9uw9iTMjKjyNC6GZM2ciNDQUc+fOhampqSpep04d/Pjjj1pNjkqI3C6xtP+f/mrlwk1ViQxcfPxTdOy4BaNHhyEzUwEAqFvXEWfPDkSXLrUlzo6o8DQuhDZu3IjVq1ejZ8+eMDY2VsXr1auHa9euveZKKrVyu8SEMueY44KIDNqePdfg4bESYWE3VLGgoGY4c+ZL1KnjKGFmRJrTeNbY3bt3Ub169TxxpVKJ58+fayUpKmFe7hKzr8FxQUQGKi0tC2PH/o5VqyJVMRcXa4SG+qNDh2oSZkZUdBoXQu+++y6OHTuGKlWqqMV//vlnNGjQQGuJUQmU2yVGRAYpJSUTv/zy4t8Af/9aWLPGDw4OlhJmRfR2NC6EQkJC0LdvX9y9exdKpRK7du1CdHQ0Nm7ciH379ukiRyIiKgFcXGzw449+6NFjF5Ys8UX//g0gk3FxRCrdZELkboFXeMeOHcM333yDCxcuIDU1FQ0bNkRISAg6dOigixy1KiUlBXZ2dkhOToatLfe5ea3onTnjgx7H5IwPsnYFBt2ROisiKiZxccmwsjJFuXIWavH799Pg6GglUVZkqHT1/V2klaVbtWqFP/74Q2tJUAn16rpBHCRNZDB27LiMQYP2oV07d+zY8Ylayw+LINInGs8ac3d3x8OHD/PEnzx5And3d60kRSXEq+sGcZA0kd5LSclEYOCvCAj4GU+eZODnn69g69aLUqdFpDMatwjdunULCoUiTzwzMxN373L/Kb2Q2yX26rpBRKTXIiLi0LPnLty8+UQVCwh4Dx07viNdUkQ6VuhCaO/evarfh4WFwc7OTnWsUChw+PBhuLm5aTU5kkD0TvW9xAB2iRHpuexsJWbN+gszZvwFhSJn2KiNjSmWLeuIXr08OCCa9FqhCyF/f38AgEwmQ9++fdWeMzExgZubGxYsWKDV5EgCJ0PUj9klRqTXYmMfo1evXYiIeDERwstLjs2bu6BqVXsJMyMqHoUuhJTKnFWFq1atir///hsODg46S4okEr1TfXA09xIj0mvXrz9Cw4ar8PRpFgDA2FiGkBBvTJzYCmXKvNWe3ESlhsZ/0m/evMkiSB+92iVWrhaLICI9V62aPdq2zZnk4u5uj+PHv0BIiDeLIDIoRZo+n5aWhqNHj+L27dvIyspSe27kyJFaSYyKUX7jgtgdRqT3ZDIZ1qzxQ5UqdpgxozVsbMykTomo2Gm8oOL58+fRsWNHPHv2DGlpaShXrhySkpJgaWkJR0dHxMbG6ipXreCCivlYX5tdYkR6LitLgZCQI2jVqjI++qiG1OkQaUxX398at38GBQXBz88Pjx8/hoWFBU6dOoX//vsPnp6emD9/vtYSo2LCcUFEei86OgnNm6/Fd9+dwBdf7EViYqrUKRGVGBoXQlFRURg7diyMjIxgbGyMzMxMyOVyzJ07FxMnTtRFjqQrHBdEpNeEEFi16iwaNFiFc+dy1gV7/DgdJ07ESZwZUcmh8RghExMTGBnl1E+Ojo64ffs2ateuDTs7O8TF8S9XqZC7YOLLLUEAxwUR6ZEHD9IwYMD/sHdvtCpWs2Z5bN3aDQ0bukiYGVHJonEh1KBBA/z9999455134O3tjZCQECQlJWHTpk2oU6eOLnIkbSmoAALYJUakR8LCriMwcA8SEl50gQ0Z0gjz53eApaWJhJkRlTwad43Nnj0bLi45/5uYNWsW7O3tMWTIEDx48ACrVq3SeoKkJbndYK8WQeVqsQgi0hMZGdkICjoIX98tqiLIwcESe/d+huXLP2IRRJQPjWeNlXYGOWssv+nxuStGswAi0hu3byfDw2MFkpMzAQC+vtWxfn1nODtbS5wZ0dsrMbPGCnLu3Dl8/PHH2no50qZXt83w25mziSqLICK9UrmyHVas+AhmZsZYutQXBw70YBFE9AYaFUJhYWEYN24cJk6cqFov6Nq1a/D390fjxo1V23BQCZP19MXv2Q1GpDfi458iJSVTLfb553Xx778jMGJEU26WSlQIhS6E1q5diw8//BChoaH47rvv0KxZM2zevBnNmzeHs7MzLl26hAMHDugyV3pb1q4sgoj0xJ491+DhsRIjR/6W5zm53E6CjIhKp0IXQkuWLMF3332HpKQk7NixA0lJSVi+fDkuXryIlStXonbt2rrMk4iIAKSlZWHw4H3w99+OpKRn2LDhAn755YrUaRGVWoWePn/jxg10794dANC1a1eUKVMG8+bNQ6VKlXSWHL2F3KnyWU+BtHipsyEiLYiMvIcePXYhJuahKubvXwve3m7SJUVUyhW6EEpPT4elpSWAnI36zMzMVNPoqQTKb70gUxtpciGit6JQKDF//klMnnwE2dk5YzEtLU2wZIkv+vdvwLFARG9BowUVf/zxR1hb58xAyM7ORmhoKBwcHNTO4e7zJcDL+4fJjAArl5wiiCtHE5U6cXHJ6N17N44e/U8V8/R0wdat3VCjRnkJMyPSD4VeR8jNze2N/+uQyWQa7z6/bNkyzJs3DwkJCahXrx6+//57NGnSpMDznzx5gkmTJmHXrl149OgRqlSpgsWLF6Njx46Fup/eryOU3/5h/a5Klw8RFVlMzEM0bfojnjzJAADIZEBwcEtMm/YBTE2NJc6OqHjp6vu70C1Ct27d0tpNc23fvh1jxozBypUr0bRpUyxevBg+Pj6Ijo6Go6NjnvOzsrLQvn17ODo64ueff4arqyv+++8/lC1bVuu5lVqvrhnEViCiUqt69XJo2tQVYWE3IJfbYtOmLhwPRKRlkq4s3bRpUzRu3Bg//PADAECpVEIul2PEiBEIDg7Oc/7KlSsxb948XLt2DSYmRVsqXu9bhFZVAlLv5vyeawYRlXrx8U8xbVo4vv22HeztLaROh0gyJX5laU1lZWUhMjIS7dq1e5GMkRHatWuHiIiIfK/Zu3cvmjdvjmHDhsHJyQl16tTB7NmzoVAoCrxPZmYmUlJS1B56K3rniyKIawYRlSrZ2UpMnx6OP/+8qRZ3cbHBqlV+LIKIdESyQigpKQkKhQJOTk5qcScnJyQkJOR7TWxsLH7++WcoFAocOHAAU6ZMwYIFCzBz5swC7zNnzhzY2dmpHnK5XKvvo8R4dWwQZ4gRlRqxsY/x/vvrMW3aUfTpsxuPHqVLnRKRwZCsECoKpVIJR0dHrF69Gp6enggICMCkSZOwcuXKAq+ZMGECkpOTVY+4uLhizLgYcWwQUakjhMDGjRdQv/5KRETcAQAkJKTiyJGbb7iSiLRFo+nz2uTg4ABjY2MkJiaqxRMTE+Hs7JzvNS4uLjAxMYGx8YvZErVr10ZCQgKysrJgamqa5xozMzOYmZlpN/mS5uXp8gDHBhGVAo8fp2Pw4P3YseOyKububo8tW7qiWTMuVEtUXIrUInTjxg1MnjwZn3/+Oe7fvw8A+O2333D58uU3XPmCqakpPD09cfjwYVVMqVTi8OHDaN68eb7XtGjRAtevX1fb3DUmJgYuLi75FkEGIb/p8iyCiEq08PBb8PBYqVYEBQbWR1TUIBZBRMVM40Lo6NGjqFu3Lk6fPo1du3YhNTUVAHDhwgVMnTpVo9caM2YM1qxZgw0bNuDq1asYMmQI0tLS0K9fPwBAnz59MGHCBNX5Q4YMwaNHjzBq1CjExMRg//79mD17NoYNG6bp29APrxZBALvEiEqwrCwFJkw4hDZtNuDOnZyJG2XLmmPHjk+wfn1n2Njoees1UQmkcddYcHAwZs6ciTFjxsDG5sWA3DZt2qimwRdWQEAAHjx4gJCQECQkJKB+/fo4ePCgagD17du3YWT0olaTy+UICwtDUFAQPDw84OrqilGjRmH8+PGavg398Oq4IHaJEZVod+6k4PvvzyB30ZIPPnDDxo3+3C2eSEIaryNkbW2NixcvomrVqrCxscGFCxfg7u6OW7duoVatWsjIyNBVrlqhV+sIcc0golJn7dpzGDJkP2bNaoOxY71gZMR9wogKQ/KVpXOVLVsW8fHxqFq1qlr8/PnzcHV11Vpi9AZcM4ioxEtKegZLSxNYWr5YAPaLLxrA29sN1auXkzAzIsql8Rihzz77DOPHj0dCQgJkMhmUSiVOnDiBcePGoU+fPrrIkfLzcrcY1wwiKnHCwq6jbt0V+Oqr39XiMpmMRRBRCaJxITR79mzUqlULcrkcqampePfdd/H+++/Dy8sLkydP1kWOlJ+spy9+zwHSRCVGRkY2goIOwtd3CxISUrF8+Vns3x8jdVpEVACNu8ZMTU2xZs0aTJkyBZcuXUJqaioaNGiAd955Rxf5UX7YLUZUIl28mIiePXfh4sX7qpivb3V4elaUMCsieh2NC6Hjx4+jZcuWqFy5MipXrqyLnOhN2C1GVKIolQLff38a48cfQmZmzt6HZmbGmDevPYYPbwKZjAOiiUoqjQuhNm3awNXVFZ9//jl69eqFd999Vxd50euwW4yoxIiPf4p+/fYgLOyGKla3riO2bu2GOnUcJcyMiApD4zFC9+7dw9ixY3H06FHUqVMH9evXx7x583Dnzh1d5EevYrcYUYkRHZ0ED4+VakVQUFAznDnzJYsgolJC40LIwcEBw4cPx4kTJ3Djxg10794dGzZsgJubG9q0aaOLHOll7BYjKjGqVy+Hd9+tAABwcbFGWFgvLFzoA3NzybZxJCINvdXu81WrVkVwcDC+/fZb1K1bF0ePHtVWXpSfVzdXZbcYkaSMjY2waVMX9O7tgX/+GYIOHapJnRIRaajIhdCJEycwdOhQuLi4oEePHqhTpw7279+vzdzoZdxclUhSCoUS3313HCdPxqnFK1e2w8aNXeDgYClRZkT0NjRuv50wYQK2bduGe/fuoX379liyZAk6d+4MS0v+I6Az3FyVSFJxccno3Xs3jh79D1WrlkVU1GDY2nKDVCJ9oHEh9Ndff+Grr77Cp59+CgcHB13kRK/i5qpEktmx4zIGDdqHJ09y9lG8desJfv/9Bj75hDNmifSBxoXQiRMndJEHvc7L0+VZBBEVi5SUTIwc+Rs2bLigisnltti0qQu8vd2kS4yItKpQhdDevXvx4YcfwsTEBHv37n3tuZ06ddJKYpQPTpcnKhYREXHo1Ws3YmMfq2IBAe9hxYqPYG9vIWFmRKRthSqE/P39kZCQAEdHR/j7+xd4nkwmg0Kh0FZuBKivG0REOpWdrcSsWX9hxoy/oFAIAICNjSmWLeuIXr08uEI0kR4qVCGkVCrz/T0VA64bRFRsbtx4hDlzjquKIC8vOTZv7oKqVe0lzoyIdEXj6fMbN25EZmZmnnhWVhY2btyolaTo/3HdIKJiVbOmA+bObQ9jYxmmT/8AR48Gsggi0nMyIYTQ5AJjY2PEx8fD0VF9+fiHDx/C0dGxxHeNpaSkwM7ODsnJybC1tZU6nYLlt25Qv6vS5UOkhx4/ToelpQnMzF40jgshcPnyA26RQVTC6Or7W+MWISFEvv3kd+7cgZ2dnVaSMnhcN4hI58LDb8HDYyUmT/5TLS6TyVgEERmQQk+fb9CgAWQyGWQyGdq2bYsyZV5cqlAocPPmTfj6+uokSYPDdYOIdCYrS4GpU4/gu+9OQAhg/vwI+PpWR9u27lKnRkQSKHQhlDtbLCoqCj4+PrC2tlY9Z2pqCjc3N3Tr1k3rCRqcV8cFsQgi0pro6CT06LEL587Fq2KtW7uhZk0uDktkqApdCE2dOhUA4ObmhoCAAJibm+ssKYPF/cSIdEIIgdWrIxEUFIb09GwAgImJEWbNaoOxY71gZMRp8USGSuOVpfv27auLPAjI2yXGcUFEb+3BgzQMGPA/7N0brYrVrFkeW7d2Q8OGLhJmRkQlQaEKoXLlyiEmJgYODg6wt7d/7aJijx490lpyBodbaRBpVXR0Ej74YAMSElJVsSFDGmH+/A6wtDSRMDMiKikKVQgtWrQINjY2qt9zdVUdeHkFaW6lQaQV7u72kMttkZCQCgcHS6xb1wl+fjWlTouIShCN1xEq7UrkOkJcM4hIZ/799yGCgw9j2bKOcHa2fvMFRFQilZh1hM6dO4eLFy+qjvfs2QN/f39MnDgRWVlZWkvMoHBsENFbUyoFli49jfPn49Xi77xTHr/88imLICLKl8aF0KBBgxATEwMAiI2NRUBAACwtLbFz5058/fXXWk/QIHBsENFbiY9/io4dt2DUqIPo0WMXnj17LnVKRFRKaFwIxcTEoH79+gCAnTt3wtvbG1u3bkVoaCh++eUXbeen/zg2iOit7NlzDR4eKxEWdgMAcO1aEn777V+JsyKi0kLj6fNCCNUO9IcOHcLHH38MAJDL5UhKStJudvru1bFB3F2eqNDS0rIwduzvWLUqUhVzcbFGaKg/OnSoJmFmRFSaaFwINWrUCDNnzkS7du1w9OhRrFixAgBw8+ZNODk5aT1BvcX9xIiKLDLyHnr02IWYmIeqmL9/LaxZ4wcHB0sJMyOi0kbjrrHFixfj3LlzGD58OCZNmoTq1asDAH7++Wd4eXlpPUG9xf3EiDSmUCjx7bfH0azZWlURZGlpgtWrP8auXZ+yCCIijWlt+nxGRgaMjY1hYlKyFykrMdPnV1V6MTaIRRBRoVy+fB/1669CdnZO97ynpwu2bu2GGjXKS5wZEemarr6/Ne4ayxUZGYmrV3PWunn33XfRsGFDrSVlUDhAmqjQ3nvPETNmtMbEiYcRHNwS06Z9AFNTY6nTIqJSTONC6P79+wgICMDRo0dRtmxZAMCTJ0/QunVrbNu2DRUqVNB2jkRkoJ4+zYSFhQnKlHnRi//VV15o184djRpVlDAzItIXGo8RGjFiBFJTU3H58mU8evQIjx49wqVLl5CSkoKRI0fqIkf98/KUeSLKV0REHOrXX4WZM/9SixsbG7EIIiKt0XiMkJ2dHQ4dOoTGjRurxc+cOYMOHTrgyZMn2sxP60rEGKH1tYFH13J+z+00iNRkZysxa9ZfmDHjLygUAkZGMhw71g9eXnKpUyMiCZWYMUJKpTLfAdEmJiaq9YXoDV5eSZpT5olUYmMfo1evXYiIuKOKNWtWCS4u3B6DiHRD466xNm3aYNSoUbh3754qdvfuXQQFBaFt27ZaTU7vcaA0EYCchVo3bryA+vVXqoogY2MZpk//AEePBqJqVXtpEyQivaVxi9APP/yATp06wc3NDXJ5TlN1XFwc6tSpg82bN2s9QSLSb48fp2PIkP3Yvv2yKububo8tW7qiWbNKEmZGRIZA40JILpfj3LlzOHz4sGr6fO3atdGuXTutJ0dE+i06Ognt229CXFyKKhYYWB9Ll/rCxsZMwsyIyFBoVAht374de/fuRVZWFtq2bYsRI0boKi8iMgBVqpRF2bLmiItLgb29OVat+hjdu78ndVpEZEAKPUZoxYoV+Pzzz3H27Fn8+++/GDZsGL766itd5qafOHWeSMXcvAy2bu2Gjh3fwT//DGERRETFrtCF0A8//ICpU6ciOjoaUVFR2LBhA5YvX67L3PQPd5snAyaEwOrVkbhy5YFavE4dR+zf3wOVKkm45Q0RGaxCF0KxsbHo27ev6rhHjx7Izs5GfHy8ThLTS69utMqp82QgHjxIg7//dgwatA89evyCzMxsqVMiIgKgQSGUmZkJKyurFxcaGcHU1BTp6ek6SUzvRO98sYgiwI1WyWCEhV2Hh8dK7N0bDQC4cCER+/bFSJwVEVEOjQZLT5kyBZaWlqrjrKwszJo1C3Z2dqrYwoULtZedPnm5NahcLRZBpPcyMrIRHHwIS5acVsUcHCyxbl0n+PnVlDAzIqIXCl0Ivf/++4iOjlaLeXl5ITY2VnUsk8m0l5k+ebU1iF1ipOcuXkxEjx67cOnSfVXMx6caQkP94ezMVaKJqOQodCEUHh6uwzT02KsDpNkaRHpMqRT4/vvTGD/+EDIzFQAAMzNjzJ3bHsOHN4GREf+zREQli8YLKpKGOECaDMjFi4kYM+Z3KJU5eznXreuIrVu7oU4dR4kzIyLKn8Z7jZEGOECaDEy9es6YOLElACAoqBnOnPmSRRARlWhsEdIlDpAmPffs2XOYm5dR6/IKCfFGhw7V0KpVFQkzIyIqHLYI6QoHSJOei4y8hwYNVmHBgpNqcRMTYxZBRFRqsBDSFbYGkZ5SKJT47rvjaNZsLWJiHmLSpD9x7hwXViWi0qlIhdCxY8fQq1cvNG/eHHfv5uybtWnTJhw/flyryZVabA0iPRUXl4y2bTciOPgwsrOVAAAPDydYW5tKnBkRUdFoXAj98ssv8PHxgYWFBc6fP4/MzEwAQHJyMmbPnq31BEsltgaRHtqx4zI8PFbi6NH/AAAyGTBhQkucPNkfNWqUlzg7IqKi0bgQmjlzJlauXIk1a9bAxMREFW/RogXOnTun1eRKraynL37P1iAq5VJSMhEY+CsCAn7GkycZAAC53BZHjvTF7NltYWpqLHGGRERFp/GssejoaLz//vt54nZ2dnjy5Ik2ctIf1q5sDaJSLTo6CR07bkVs7GNVLCDgPaxc+THKljWXMDMiIu3QuEXI2dkZ169fzxM/fvw43N3dtZJUqRa9E0i9K3UWRFpRqZItypTJ+WfCxsYUGzf646efurEIIiK9oXEh9OWXX2LUqFE4ffo0ZDIZ7t27hy1btmDcuHEYMmRIkZJYtmwZ3NzcYG5ujqZNm+LMmTOFum7btm2QyWTw9/cv0n217tXtNExtpMuFSAusrEyxdWtXfPCBGy5cGIzevetxT0Ei0isad40FBwdDqVSibdu2ePbsGd5//32YmZlh3LhxGDFihMYJbN++HWPGjMHKlSvRtGlTLF68GD4+PoiOjoajY8Er0t66dQvjxo1Dq1atNL6nznA7DSrFhBDYtOkftGghR7Vq5VRxT8+K+PPPPiyAiEgvyYQQoigXZmVl4fr160hNTcW7774La+ui7SjdtGlTNG7cGD/88AMAQKlUQi6XY8SIEQgODs73GoVCgffffx9ffPEFjh07hidPnuDXX38t1P1SUlJgZ2eH5ORk2NraFinnAq2q9KJbjNtpUCny+HE6Bg/ejx07LqNpU1ccO9YPJiYcBE1EJYeuvr+LvKCiqakp3n33XTRp0qTIRVBWVhYiIyPRrl27FwkZGaFdu3aIiIgo8LpvvvkGjo6O6N+//xvvkZmZiZSUFLWHTrw8NoiDpKkUCQ+/BQ+Pldix4zIA4PTpu9i3L0birIiIiofGXWOtW7d+bRP5n3/+WejXSkpKgkKhgJOTk1rcyckJ165dy/ea48ePY+3atYiKiirUPebMmYPp06cXOqci4dggKoWyshQICTmCuXNPILdd2N7eHKtX+6FLl9rSJkdEVEw0LoTq16+vdvz8+XNERUXh0qVL6Nu3r7byytfTp0/Ru3dvrFmzBg4ODoW6ZsKECRgzZozqOCUlBXK5XLuJcWwQlTLR0Uno0WOX2tYYrVu7YePGLqhUSctdxkREJZjGhdCiRYvyjU+bNg2pqakavZaDgwOMjY2RmJioFk9MTISzs3Oe82/cuIFbt27Bz89PFVMqc5b5L1OmDKKjo1GtWjW1a8zMzGBmZqZRXhp5dTsNjg2iEkwIgdWrIxEUFIb09GwAgImJEWbNaoOxY73UdpEnIjIEWtt0tVevXli3bp1G15iamsLT0xOHDx9WxZRKJQ4fPozmzZvnOb9WrVq4ePEioqKiVI9OnTqhdevWiIqK0n5LT2FwOw0qRc6fT8DgwftVRVDNmuVx6tQAfPVVCxZBRGSQNG4RKkhERATMzTVfZG3MmDHo27cvGjVqhCZNmmDx4sVIS0tDv379AAB9+vSBq6sr5syZA3Nzc9SpU0ft+rJlywJAnnix4XYaVIo0bOiCMWOaYeHCUxgypBHmz+8AS0uTN19IRKSnNC6EunbtqnYshEB8fDzOnj2LKVOmaJxAQEAAHjx4gJCQECQkJKB+/fo4ePCgagD17du3YWSktYYr3eFMMSqBMjOzYWpqrDbBYfbstvD1rY727au95koiIsOg8TpCuS01uYyMjFChQgW0adMGHTp00GpyuqDVdQheni1m7QoMuvP2CRJpycWLiejRYxeGDGmEoUMbS50OEdFb0dU6Qhq1CCkUCvTr1w9169aFvb291pIotV4eH8Qp81RCKJUC339/GuPHH0JmpgJjx/6ODz5ww7vvVpA6NSKiEkejPidjY2N06NCBu8zn4vggKmHi45+iY8ctGD06DJmZCgDAO++Ue8NVRESGS+PBN3Xq1EFsbKwucim9OD6ISoA9e67Bw2MlwsJuqGJBQc1w5syXbA0iIiqAxoXQzJkzMW7cOOzbtw/x8fHFs30FERUoLS0Lgwfvg7//diQlPQMAuLhYIyysFxYu9IG5udYmhxIR6Z1C/wv5zTffYOzYsejYsSMAoFOnTmozUYQQkMlkUCgU2s+SiPIVE/MQfn4/ISbmoSrm718La9b4wcHBUsLMiIhKh0IXQtOnT8fgwYNx5MgRXeZTery8ySqRRJycrJCVlfOfD0tLEyxZ4ov+/Ru8dj9AIiJ6odCFUO4se29vb50lU6pwxhiVAHZ25ti8uQvGjv0dGzd2QY0a5aVOiYioVNFojBD/l/kSzhgjCezceRlxcclqsRYtKiMioj+LICKiItBoFGWNGjXeWAw9evTorRIqdThjjIpBSkomRo78DRs2XMAHH7jh0KHeMDZ+8f8Y/ieFiKhoNCqEpk+fDjs7O13lQkT5iIiIQ69euxEb+xgAEB5+C/v2xaBz51oSZ0ZEVPppVAh99tlncHR01FUuRPSS7GwlZs36CzNm/AWFImeMno2NKZYt64hOnWpKnB0RkX4odCHEpnei4hMb+xi9eu1CRMSL/eu8vOTYvLkLqlbl9jZERNqi8awxItIdIQQ2bfoHw4cfwNOnWQAAY2MZQkK8MXFiK5Qpo/EaqERE9BqFLoSUSqUu8yAiAGfP3kPfvr+qjt3d7bFlS1c0a1ZJuqSIiPQY/3tJVII0buyKQYM8AQCBgfURFTWIRRARkQ5xEyIiCT1/rkCZMkZqY/AWLOiAjh3f4YBoIqJiwBYhIolERyehWbO12LDhglrcysqURRARUTFhIVQU3GeM3oIQAqtWnUWDBqtw7lw8Roz4DdevG9hCpEREJQS7xoqC+4xRET14kIYBA/6HvXujVTFXVxukpz+XMCsiIsPFQkhT0TuBR9deHHOfMSqksLDrCAzcg4SEVFVs8GBPLFjgA0tLEwkzIyIyXCyENPVya1C5WtxnjN4oIyMbEyYcwuLFp1UxBwdLrFvXCX5+HAtERCQlFkKa4q7zpIHr1x+ha9ftuHjxvirm61sd69d3hrOztYSZERERwEKo6LjrPBWCvb05Hj5MBwCYmRlj3rz2GD68CbesISIqIThrjEiHype3RGhoZ9Sr54SzZwdixIimLIKIiEoQtggRadH//heNxo1d1bq92revhsjIqjA25v87iIhKGv7LrAmuH0QFSEvLwuDB+9Cp0zZ88cWePJsUswgiIiqZ+K+zJrh+EOUjMvIeGjZcjVWrIgEAv/12Hfv2xUicFRERFQYLIU1wxhi9RKFQ4rvvjqNZs7WIiXkIALC0NMGaNX74+OMaEmdHRESFwTFCRcEZYwYvLi4ZvXvvxtGj/6linp4u2Lq1G2rUKC9hZkREpAkWQkQa2r79EgYP3o8nTzIAADIZEBzcEtOmfQBTU2OJsyMiIk2wECLSwKlTd/DZZ7+ojuVyW2za1AXe3m7SJUVEREXGMUJEGmjWrBJ69/YAAAQEvIcLFwazCCIiKsXYIkT0GkqlgJGR+gKIP/zQER999A4+/fQ9Lo5IRFTKsUWIqACxsY/RsuU67NhxWS1ua2uGgIA6LIKIiPQAW4QKi4spGgwhBDZt+gfDhx/A06dZuHp1H5o3rwS53E7q1IiISMvYIlRYXEzRIDx+nI7PPvsFffv+iqdPswAA5cpZqDZOJSIi/cIWocLiYop6Lzz8Fnr33o07d1JUscDA+li61Bc2NmYSZkZERLrCQkhTXExR72RlKRAScgRz555A7hZhZcuaY/Xqj9G9+3vSJkdERDrFQuhNonfmdIulxUudCelAbOxjdO++E+fOvfh8P/jADRs3+nNMEBGRAWAh9CYnQ4BH114cc3yQXrGwKIPbt5MBACYmRpg1qw3GjvXKM2WeiIj0EwdLv0nu2CCZEVCuFscH6RkXFxusXdsJtWo54NSpAfjqqxYsgoiIDAhbhArLygXod1XqLOgtHToUiwYNnFG+vKUq1qlTTXz4YXWYmHCfMCIiQ8MWITIIGRnZCAo6iPbtN2HQoH0QuaOi/x+LICIiw8RCiPTexYuJaNJkDRYvPg0A+OWXqzh48LrEWRERUUnAQoj0llIpsGTJKTRuvAYXL94HAJiZGWPpUl/4+laXODsiIioJOEaI9FJ8/FP067cHYWE3VLG6dR2xdWs31KnjKGFmRERUkrAQIr2zd280+vffi6SkZ6pYUFAzzJ7dFubm/CNPREQv8FuB9MqJE7fRufM21bGzszU2bPBHhw7VJMyKiIhKKo4RIr3i5SVHly61AACdO9fExYtDWAQREVGB2CL0OtE7gdS7UmdBryGEgEz2YgFEmUyGNWv80KlTTfTtW0/tOSIiolexReh1Toa8+D231ihx4uKS0abNRuzbF6MWL1/eEoGB9VkEERHRG7FF6HVyt9cAuLVGCbNjx2UMGrQPT55k4PLl+/jnnyFwdraWOi0iIipl2CJUkJe7xaxdgRqfSJsPAQBSUjIRGPgrAgJ+xpMnGQAAc/MyuHfv6RuuJCIiyostQgVht1iJExERh549d+HmzSeqWEDAe1ix4iPY21tIlxgREZVaLIQKwm6xEiM7W4mZM//CzJl/QaHI2SPMxsYUy5Z1RK9eHhwLRERERcZC6E3YLSapW7eeoEePXxARcUcV8/KSY/PmLqha1V7CzIiISB9wjBCVaEZGMly58gAAYGwsw/TpH+Do0UAWQUREpBUshPLD9YNKjMqV7bBy5cdwd7fH8eNfICTEG2XK8I8tERFpB79R8sOB0pI5duw/pKRkqsU++6wOLl8eimbNKkmUFRER6SsWQvnhQOlil5WlQHDwIXh7h2LEiN/yPM/NUomISBdKRCG0bNkyuLm5wdzcHE2bNsWZM2cKPHfNmjVo1aoV7O3tYW9vj3bt2r32/LfCgdLFIjo6Cc2br8V3352AEMDGjRfw++83pE6LiIgMgOSF0Pbt2zFmzBhMnToV586dQ7169eDj44P79+/ne354eDg+//xzHDlyBBEREZDL5ejQoQPu3uWYntJGCIFVq86iQYNVOHcuHgBgYmKEuXPboV07d4mzIyIiQyATQggpE2jatCkaN26MH374AQCgVCohl8sxYsQIBAcHv/F6hUIBe3t7/PDDD+jTp0+e5zMzM5GZ+WLMSUpKCuRyOZKTk2Fra5v/i66qlDNY2toVGHQn/3PorTx4kIYBA/6HvXujVbGaNctj69ZuaNjQRcLMiIioJEpJSYGdnd3rv7+LQNIWoaysLERGRqJdu3aqmJGREdq1a4eIiIhCvcazZ8/w/PlzlCtXLt/n58yZAzs7O9VDLpe//gU5Y0znwsKuw8NjpVoRNGRII5w7N4hFEBERFStJC6GkpCQoFAo4OTmpxZ2cnJCQkFCo1xg/fjwqVqyoVky9bMKECUhOTlY94uLiXv+CnDGmU8eO/Qdf3y1ISEgFADg4WGLv3s+wfPlHsLQ0kTg7IiIyNKV6Ks63336Lbdu2ITw8HObm5vmeY2ZmBjMzs8K/KGeM6VTLlpXh61sdBw9eh69vdaxf35m7xhMRkWQkLYQcHBxgbGyMxMREtXhiYiKcnZ1fe+38+fPx7bff4tChQ/Dw8NB+cpwxphMymQzr13fG7t1XMXhwI+4TRkREkpK0a8zU1BSenp44fPiwKqZUKnH48GE0b968wOvmzp2LGTNm4ODBg2jUqFFxpEpFkJCQio8+2orDh2PV4s7O1hgypDGLICIikpzkXWNjxoxB37590ahRIzRp0gSLFy9GWloa+vXrBwDo06cPXF1dMWfOHADAd999h5CQEGzduhVubm6qsUTW1tawtmYXS0mxd280+vffi6SkZ7hwIQEXLgxG+fKWUqdFRESkRvJCKCAgAA8ePEBISAgSEhJQv359HDx4UDWA+vbt2zAyetFwtWLFCmRlZeGTT9S7raZOnYpp06YVZ+qUj7S0LIwd+ztWrYpUxZRKgVu3nrAQIiKiEkfydYSK2xvXIeAaQkUWGXkPPXvuQnT0Q1XM378W1qzxg4MDiyAiIio6Xa0jJHmLUInCNYSKRKFQYv78k5g8+Qiys5UAAEtLEyxZ4ov+/RtwLBAREZVYLIRexjWENHbnTgp6996N8PBbqpinpwu2bu2GGjXKS5cYERFRIUi+11iJwjWENJae/hx//53TiiaTARMmtMTJk/1ZBBERUanAQig/XEOo0N55pzyWLv0Qcrktjhzpi9mz28LU1FjqtIiIiAqFhRBp5MyZu3j27LlarF+/+rhyZRi8vd2kSYqIiKiIWAjl4kDp18rOVmL69HB4ea3FuHG/qz0nk8lgbW0qUWZERERFx0IoFwdKFyg29jHef389pk07CoVCYMWKszhy5KbUaREREb01zhoDclqDHl17ccyB0gAAIQQ2bfoHw4cfwNOnWQAAY2MZQkK80apVFYmzIyIiensshAD11qBytThQGsDjx+kYMmQ/tm+/rIq5u9tjy5auaNaskoSZERERaQ8LIYDT5l9x9Ogt9O69G3FxKapYYGB9LF3qCxsbMwkzI5KOQqHA8+fP33wiERWZqamp2rZaxYGF0Ms4bR5Hj95C69YbkLvxir29OVat+hjdu78nbWJEEhFCICEhAU+ePJE6FSK9Z2RkhKpVq8LUtPgm4LAQIjUtW1bG++9XwdGj/6F1azds3NgFlSppb08XotImtwhydHSEpaUlt4wh0hGlUol79+4hPj4elStXLra/ayyESI2xsRE2beqCnTuvYPToZjAy4j/6ZLgUCoWqCCpfnqulE+lahQoVcO/ePWRnZ8PExKRY7snp8wbswYM0dOu2AydO3FaLy+V2GDOmOYsgMni5Y4IsLS0lzoTIMOR2iSkUimK7J1uEDFRY2HUEBu5BQkIqzp2Lx4ULg2Fry4HQRPlhdxhR8ZDi7xpbhAxMRkY2Ro8+CF/fLUhISAUApKZmISbmocSZERERFT+2CBmQixcT0aPHLly6dF8V8/WtjvXrO8PZ2VrCzIiIiKTBFiEDoFQKLFlyCo0br1EVQWZmxli61BcHDvRgEURE9JLo6Gg4Ozvj6dOnbz6ZCi0rKwtubm44e/as1KmoYSGk5+Ljn6Jjxy0YPToMmZk5g8/q1nXE2bMDMWJEU459INJTgYGBkMlkkMlkMDExQdWqVfH1118jIyMjz7n79u2Dt7c3bGxsYGlpicaNGyM0NDTf1/3ll1/wwQcfwM7ODtbW1vDw8MA333yDR48e6fgdFZ8JEyZgxIgRsLHR330nly1bBjc3N5ibm6Np06Y4c+bMG69ZvHgxatasCQsLC8jlcgQFBan9eXJzc1P9mXv5MWzYMAA5A6HHjRuH8ePH6+x9FQULIT3fdf7Ro3SEh99SHQcFNcOZM1+iTh1H6ZIiomLh6+uL+Ph4xMbGYtGiRVi1ahWmTp2qds7333+Pzp07o0WLFjh9+jT++ecffPbZZxg8eDDGjRundu6kSZMQEBCAxo0b47fffsOlS5ewYMECXLhwAZs2bSq295WVlaWz1759+zb27duHwMDAt3odXeb4trZv344xY8Zg6tSpOHfuHOrVqwcfHx/cv3+/wGu2bt2K4OBgTJ06FVevXsXatWuxfft2TJw4UXXO33//jfj4eNXjjz/+AAB0795ddU7Pnj1x/PhxXL58Oc89JCMMTHJysgAgkpOTcwLragkxHzmPdbWkTU5Hli49JZyd54uwsOtSp0JUqqSnp4srV66I9PR09Sc2eQqx0rX4H5s8C5173759RefOndViXbt2FQ0aNFAd3759W5iYmIgxY8bkuX7p0qUCgDh16pQQQojTp08LAGLx4sX53u/x48cF5hIXFyc+++wzYW9vLywtLYWnp6fqdfPLc9SoUcLb21t17O3tLYYNGyZGjRolypcvLz744APx+eefi08//VTtuqysLFG+fHmxYcMGIYQQCoVCzJ49W7i5uQlzc3Ph4eEhdu7cWWCeQggxb9480ahRI7VYUlKS+Oyzz0TFihWFhYWFqFOnjti6davaOfnlKIQQFy9eFL6+vsLKyko4OjqKXr16iQcPHqiu++2330SLFi2EnZ2dKFeunPjoo4/E9eu6/be6SZMmYtiwYapjhUIhKlasKObMmVPgNcOGDRNt2rRRi40ZM0a0aNGiwGtGjRolqlWrJpRKpVq8devWYvLkyfleU+DfOZHP97eWsEVIz/YZu3AhAZmZ2Wqx4cOb4MqVoejQoZpEWRHpmbSEnJbk4n6kJRQ55UuXLuHkyZNqWxf8/PPPeP78eZ6WHwAYNGgQrK2t8dNPPwEAtmzZAmtrawwdOjTf1y9btmy+8dTUVHh7e+Pu3bvYu3cvLly4gK+//hpKpVKj/Dds2ABTU1OcOHECK1euRM+ePfG///0PqampqnPCwsLw7NkzdOnSBQAwZ84cbNy4EStXrsTly5cRFBSEXr164ejRowXe59ixY2jUqJFaLCMjA56enti/fz8uXbqEgQMHonfv3nm6k17N8cmTJ2jTpg0aNGiAs2fP4uDBg0hMTMSnn36quiYtLQ1jxozB2bNncfjwYRgZGaFLly6v/fnMnj0b1tbWr33cvn0732uzsrIQGRmJdu3aqWJGRkZo164dIiIiCrynl5cXIiMjVe85NjYWBw4cQMeOHQu8z+bNm/HFF1/kGYLRpEkTHDt2rMB7FTfOGstVyvcZUyiUmD//JCZPPoJRo5pi/vwOqudkMhns7S0kzI5Iz1g5l4r77tu3D9bW1sjOzkZmZiaMjIzwww8/qJ6PiYmBnZ0dXFxc8lxramoKd3d3xMTEAAD+/fdfuLu7a7za79atW/HgwQP8/fffKFeuHACgevXqGr0GALzzzjuYO3eu6rhatWqwsrLC7t270bt3b9W9OnXqBBsbG2RmZmL27Nk4dOgQmjdvDgBwd3fH8ePHsWrVKnh7e+d7n//++y9PIeTq6qpWLI4YMQJhYWHYsWMHmjRpUmCOM2fORIMGDTB79mxVbN26dZDL5YiJiUGNGjXQrVs3tXutW7cOFSpUwJUrV1CnTp18cxw8eLBaMZWfihUr5htPSkqCQqGAk5OTWtzJyQnXrl0r8PV69OiBpKQktGzZEkIIZGdnY/DgwWpdYy/79ddf8eTJk3y7GCtWrIj//vvvtfkXJxZCeiAuLhm9e+/G0aM5f7AWLIiAv38ttGxZWeLMiPRUr5I166UgrVu3xooVK5CWloZFixahTJkyeb54C0vk7sSsoaioKDRo0EBVBBWVp6en2nGZMmXw6aefYsuWLejduzfS0tKwZ88ebNu2DQBw/fp1PHv2DO3bt1e7LisrCw0aNCjwPunp6TA3N1eLKRQKzJ49Gzt27MDdu3eRlZWFzMzMPCuOv5rjhQsXcOTIEVhb552Ze+PGDdSoUQP//vsvQkJCcPr0aSQlJalagm7fvl1gIVSuXLm3/nlqKjw8HLNnz8by5cvRtGlTXL9+HaNGjcKMGTMwZcqUPOevXbsWH374Yb4FmYWFBZ49e1YcaReKYRdCejBQeseOyxg0aB+ePMkZuS+TAcHBLdGkiavEmRGR1KysrFStL+vWrUO9evWwdu1a9O/fHwBQo0YNJCcn4969e3m+sLKysnDjxg20bt1ade7x48fx/PlzjVqFLCxe3xptZGSUp8jK3drk1ffyqp49e8Lb2xv379/HH3/8AQsLC/j6+gKAqsts//79cHVV//fQzKzgVfQdHBzw+PFjtdi8efOwZMkSLF68GHXr1oWVlRVGjx6dZ0D0qzmmpqbCz88P3333XZ775LbC+fn5oUqVKlizZg0qVqwIpVKJOnXqvHaw9ezZs9VamfJz5coVVK6c9z/DDg4OMDY2RmJiolo8MTERzs4FtzhOmTIFvXv3xoABAwAAdevWRVpaGgYOHIhJkybByOjFSJv//vsPhw4dwq5du/J9rUePHqFChQqvzb84GfYYoZMhL35vWrqmSaakZCIw8FcEBPysKoLkclscOdIXs2e3hampscQZElFJYmRkhIkTJ2Ly5MlIT08HAHTr1g0mJiZYsGBBnvNXrlyJtLQ0fP755wByukZSU1OxfPnyfF//yZMn+cY9PDwQFRVV4PT6ChUqID4+Xi0WFRVVqPfk5eUFuVyO7du3Y8uWLejevbuqSHv33XdhZmaG27dvo3r16moPuVxe4Gs2aNAAV65cUYudOHECnTt3Rq9evVCvXj21LsPXadiwIS5fvgw3N7c8OVhZWeHhw4eIjo7G5MmT0bZtW9SuXTtPEZafwYMHIyoq6rWPgrrGTE1N4enpicOHD6tiSqUShw8fVnUh5ufZs2dqxQ4AGBvnfM+8WsiuX78ejo6O+Oijj/J9rUuXLr22Va7YaXXodSmgNup8peuLGWPRr59JUJKcPHlbuLsvEcA01SMgYKd49OiZ1KkR6ZXXzWAp6fKbjfX8+XPh6uoq5s2bp4otWrRIGBkZiYkTJ4qrV6+K69eviwULFggzMzMxduxYteu//vprYWxsLL766itx8uRJcevWLXHo0CHxySefFDibLDMzU9SoUUO0atVKHD9+XNy4cUP8/PPP4uTJk0IIIQ4ePChkMpnYsGGDiImJESEhIcLW1jbPrLFRo0bl+/qTJk0S7777rihTpow4duxYnufKly8vQkNDxfXr10VkZKRYunSpCA0NLfDntnfvXuHo6Ciys7NVsaCgICGXy8WJEyfElStXxIABA4Stra3azze/HO/evSsqVKggPvnkE3HmzBlx/fp1cfDgQREYGCiys7OFQqEQ5cuXF7169RL//vuvOHz4sGjcuLEAIHbv3l1gjm9r27ZtwszMTISGhoorV66IgQMHirJly4qEhATVOb179xbBwcGq46lTpwobGxvx008/idjYWPH777+LatWq5Zm5p1AoROXKlcX48eMLvH+VKlXExo0b831OilljLITmI+fXUuLIkZvC2Hi6qgCysZktNm6MyjM9kYjenr4VQkIIMWfOHFGhQgWRmpqqiu3Zs0e0atVKWFlZCXNzc+Hp6SnWrVuX7+tu375dvP/++8LGxkZYWVkJDw8P8c0337x2+vytW7dEt27dhK2trbC0tBSNGjUSp0+fVj0fEhIinJychJ2dnQgKChLDhw8vdCF05coVAUBUqVIlz7+DSqVSLF68WNSsWVOYmJiIChUqCB8fH3H06NECc33+/LmoWLGiOHjwoCr28OFD0blzZ2FtbS0cHR3F5MmTRZ8+fd5YCAkhRExMjOjSpYsoW7assLCwELVq1RKjR49W5frHH3+I2rVrCzMzM+Hh4SHCw8N1XggJIcT3338vKleuLExNTUWTJk1Uyxm8/H769u2rOn7+/LmYNm2aqFatmjA3NxdyuVwMHTo0z+ceFhYmAIjo6Oh873vy5ElRtmxZ8exZ/v9xl6IQkglRxBFwpVRKSgrs7OyQnJwM25/ezRkjZO0KDLojdWqF8vy5Ai1brseZM3fh5SXH5s1dULWqvdRpEemljIwM3Lx5E1WrVs0zgJb017Jly7B3716EhYVJnYreCQgIQL169Qqcbfa6v3Nq39+2tlrLybAHS5dCJibG2LKlK7Zvv4Tx41uiTBnDHuZFRKRtgwYNwpMnT/D06VO93majuGVlZaFu3boICgqSOhU1/BYtwR4/TkfPnrsQGXlPLV69ejlMmvQ+iyAiIh0oU6YMJk2axCJIy0xNTTF58uQ3ziQsbmwRKqHCw2+hd+/duHMnBZGR93Du3CBYWmq2kBkRERG9HpsUSpisLAWCgw+hTZsNuHMnBQBw/34aLl8ueDM8IiIiKhq2CJUg0dFJ6NFjF86de7GmRuvWbti4sQsqVdLewDAiIiLKwUKoBBBCYPXqSAQFhSE9PWfDVBMTI8ya1QZjx3rByEj2hlcgIiKiomAhJLEHD9IwYMD/sHdvtCpWs2Z5bN3aDQ0b5t0IkYiIiLSHhZDE4uJScODAv6rjIUMaYf78DhwYTUREVAwMd7D0v7tLxIarDRu6YObM1nBwsMTevZ9h+fKPWAQR6SOFAggPB376KedXhULqjN7atGnTUL9+/VJxnw8++ACjR4/WSj6acHNzw+LFi9/qNQIDA+Hv7//ac6R6f/rAcAuh07Ne/L4YN1y9di0Jz5+r/wM4bpwXLl8eCj+/msWWBxEVo127ADc3oHVroEePnF/d3HLiOhQXF4cvvvgCFStWhKmpKapUqYJRo0bh4cOHGr+WTCbDr7/+qhYbN26c2uadUpg2bRpkMtlrH0SvY7iFUFbqi9+3mKHz2ymVAkuWnEL9+isxc+Zfas8ZGxvB0dFK5zkQkQR27QI++QS488o2Pnfv5sR1VAzFxsaiUaNG+Pfff/HTTz/h+vXrWLlypWqX8YJ2g9eEtbU1ypcvr4Vsi27cuHGIj49XPSpVqoRvvvlGLVZUz58/12KmVFIZbiGUy9oVqPGJTm8RH/8UHTtuwejRYcjMVGDmzGM4c0b6bjkiKgIhgLS0wj1SUoCRI3Ouye91AGDUqJzzCvN6GmwNOWzYMJiamuL333+Ht7c3KleujA8//BCHDh3C3bt3MWnSJNW5bm5umDFjBj7//HNYWVnB1dUVy5YtU3seALp06QKZTKY6frXLKrcLZ/bs2XByckLZsmXxzTffIDs7G1999RXKlSuHSpUqYf369Wq5jh8/HjVq1IClpSXc3d0xZcqUQhch1tbWcHZ2Vj2MjY1hY2OjFsulVCrx9ddfo1y5cnB2dsa0adPUXksmk2HFihXo1KkTrKysMGtWTs/Bnj170LBhQ5ibm8Pd3R3Tp09HdnbODF8hBKZNm4bKlSvDzMwMFStWxMiRI9Ve99mzZ/jiiy9gY2ODypUrY/Xq1WrPX7x4EW3atIGFhQXKly+PgQMHIjU1FQVJS0tDnz59YG1tDRcXFyxYsKBQPyvKHwshHduz5xo8PFYiLOyGKjZyZBN4eDhJmBURFdmzZ4C1deEednY5LT8FESKnpcjOrnCv9+xZoVJ89OgRwsLCMHTo0DzbGTg7O6Nnz57Yvn07Xt5ze968eahXrx7Onz+P4OBgjBo1Cn/88QcA4O+//wYArF+/HvHx8arj/Pz555+4d+8e/vrrLyxcuBBTp07Fxx9/DHt7e5w+fRqDBw/GoEGDcOelFjIbGxuEhobiypUrWLJkCdasWYNFixYV6r1qYsOGDbCyssLp06cxd+5cfPPNN6r3mGvatGno0qULLl68iC+++ALHjh1Dnz59MGrUKFy5cgWrVq1CaGioqkj65ZdfsGjRIqxatQr//vsvfv31V9StW1ftNRcsWIBGjRrh/PnzGDp0KIYMGYLo6JyZwmlpafDx8YG9vT3+/vtv7Ny5E4cOHcLw4cMLfB9fffUVjh49ij179uD3339HeHg4zp07p+WflgHR6l72pUBycrIAIJIXuQgxH0KsdNXJfVJTM8WgQf8TwDTVw9l5vggLu66T+xGR9qWnp4srV66I9PT0F8HUVCFySpjif6SmFirvU6dOCQBi9+7d+T6/cOFCAUAkJiYKIYSoUqWK8PX1VTsnICBAfPjhh6rj/F5v6tSpol69eqrjvn37iipVqgiFQqGK1axZU7Rq1Up1nJ2dLaysrMRPP/1UYP7z5s0Tnp6eBd7ndapUqSIWLVqUJ+7t7S1atmypFmvcuLEYP3686hiAGD16tNo5bdu2FbNnz1aLbdq0Sbi4uAghhFiwYIGoUaOGyMrKKjCfXr16qY6VSqVwdHQUK1asEEIIsXr1amFvby9SX/ps9+/fL4yMjERCQoIQIufn2rlzZyGEEE+fPhWmpqZix44dqvMfPnwoLCwsxKhRo/LNoTTJ9+/c/1N9fycna/WenD6vA5GR99Cjxy7ExLwYkNi5c038+GMnODhYSpgZEb01S0vgNd0Wav76C+jY8c3nHTgAvP9+4e6tAaFBV1rz5s3zHBdlttN7770HI6MXnQ1OTk6oU6eO6tjY2Bjly5fH/fsvtg3avn07li5dihs3biA1NRXZ2dmwtdX+avoeHh5qxy4uLmp5AECjRo3Uji9cuIATJ06oWoAAQKFQICMjA8+ePUP37t2xePFiuLu7w9fXFx07doSfnx/KlHnx9fryfWUyGZydnVX3vXr1KurVqwcrqxfjRFu0aAGlUono6Gg4Oan3Hty4cQNZWVlo2rSpKlauXDnUrMnJNkXFrjEt+/PPm2jWbK2qCLK0NMHq1R9j9+4AFkFE+kAmA6ysCvfo0AGoVCnnmoJeSy7POa8wr1fIGVDVq1eHTCbD1atX833+6tWrsLe3R4UKFYr6UyiQiYn68h8ymSzfmFKpBABERESgZ8+e6NixI/bt24fz589j0qRJyMrKKpbccvPI9XJBAgCpqamYPn06oqKiVI+LFy/i33//hbm5OeRyOaKjo7F8+XJYWFhg6NCheP/999XGOBXmviQdFkJa1qKFHO++m/OPi6enC86fH4Qvv/TkFE4iQ2RsDCxZkvP7V/8NyD1evDjnPC0qX7482rdvj+XLlyM9PV3tuYSEBGzZsgUBAQFq/y6dOnVK7bxTp06hdu3aqmMTExModLD20cmTJ1GlShVMmjQJjRo1wjvvvIP//vtP6/cpqoYNGyI6OhrVq1fP88ht+bKwsICfnx+WLl2K8PBwRERE4OLFi4V6/dq1a+PChQtIS0tTxU6cOAEjI6N8W3mqVasGExMTnD59WhV7/PgxYmJi3vKdGi4WQlpmZlYGW7d2xaRJrXDyZH/UqCHt1FIikljXrsDPPwOururxSpVy4l276uS2P/zwAzIzM+Hj44O//voLcXFxOHjwINq3bw9XV1e1rh4g58t37ty5iImJwbJly7Bz506MGjVK9bybmxsOHz6MhIQEPH78WGt5vvPOO7h9+za2bduGGzduYOnSpdi9e7fWXv9thYSEYOPGjZg+fTouX76Mq1evYtu2bZg8eTIAIDQ0FGvXrsWlS5cQGxuLzZs3w8LCAlWqVCnU6/fs2RPm5ubo27cvLl26hCNHjmDEiBHo3bt3nm4xIGeWXP/+/fHVV1/hzz//xKVLlxAYGKjWHUma4U/uLaSkZOLLL/fi8mX1Pub33nPEzJltYGqq3f/lEVEp1bUrcOsWcOQIsHVrzq83b+qsCAJyCoyzZ8/C3d0dn376KapVq4aBAweidevWiIiIQLly5dTOHzt2LM6ePYsGDRpg5syZWLhwIXx8fFTPL1iwAH/88QfkcjkaNGigtTw7deqEoKAgDB8+HPXr18fJkycxZcoUrb3+2/Lx8cG+ffvw+++/o3HjxmjWrBkWLVqkKnTKli2LNWvWoEWLFvDw8MChQ4fwv//9r9DrK1laWiIsLAyPHj1C48aN8cknn6Bt27b44YcfCrxm3rx5aNWqFfz8/NCuXTu0bNkSnp6eWnm/hkgmNBlNpwdSUlJgZ2eH5EUusFXE56wjNOjOmy98RUREHHr12o3Y2Mfw8HDCmTMDYGbGsedE+iQjIwM3b95E1apVYW5uLnU6OuPm5obRo0dziwaS3Ov+zqm+v5OTtTqYni1CGsrOVmL69HC0arUesbE5zcM3bz7GP/8kSpwZERERacpwmzDS4gEN/4MXG/sYvXrtQkTEixYkLy85Nm/ugqpV7bWcIBEREema4RZCuQqx4aoQAps2/YPhww/g6dOcKZ3GxjKEhHhj4sRWKFOGDWtEVHrdunVL6hSIJMNC6A0brj5+nI4hQ/Zj+/bLqpi7uz22bOmKZs0q6To7IiIi0iHDLoQKseHq1atJ2Lnziuo4MLA+li71hY2Nma6zI6ISwsDmlBBJRoq/a+zTeQMvLzkmTWqFsmXNsWPHJ1i/vjOLICIDkbsi8LNCbnZKRG8nd0VxYy0vMvo6ht0ilI+bNx+jcmU7GBu/qBGnTHkfgwZ5wtVV+3vfEFHJZWxsjLJly6r2hbK0tOQq8UQ6olQq8eDBA1haWqrt1aZrLIT+nxACq1dHIigoDFOnemP8+Jaq50xMjFkEERkoZ2dnAMizOScRaZ+RkREqV65crP/hYCEE4MGDNAwY8D/s3RsNAJg8+Qg6dKiGBg1cJM6MiKQmk8ng4uICR0dHtY00iUj7TE1Ni327EIMvhMLCriMwcA8SElJVsQEDGqBmTQcJsyKiksbY2LhYxy0QUfEoEYOlly1bBjc3N5ibm6Np06Y4c+bMa8/fuXMnatWqBXNzc9StWxcHDhzQ+J4Zz40xekcz+PpuURVBDg6W2Lv3M6xY8TEsLU2K9F6IiIio9JC8ENq+fTvGjBmDqVOn4ty5c6hXrx58fHwK7I8/efIkPv/8c/Tv3x/nz5+Hv78//P39cenSJY3u+8GKQCz5s67q2Ne3Oi5eHAI/v5pv9X6IiIio9JB809WmTZuicePGqp12lUol5HI5RowYgeDg4DznBwQEIC0tDfv27VPFmjVrhvr162PlypVvvF/upm1AMABzmJkZY9689hg+vAlngxAREZVQutp0VdIxQllZWYiMjMSECRNUMSMjI7Rr1w4RERH5XhMREYExY8aoxXx8fPDrr7/me35mZiYyMzNVx8nJybnP4N1qZli7+XO8+24FPH369K3eCxEREelOSkoKAO0vuihpIZSUlASFQgEnJye1uJOTE65du5bvNQkJCfmen5CQkO/5c+bMwfTp0/N5ZhGu3ACaN8/vOSIiIiqJHj58+P89O9qh97PGJkyYoNaC9OTJE1SpUgW3b9/W6g+SNJeSkgK5XI64uDitNnNS0fDzKDn4WZQc/CxKjuTkZFSuXBnlypXT6utKWgg5ODjA2NgYiYmJavHExETVImavcnZ21uh8MzMzmJnl3RLDzs6Of6hLCFtbW34WJQg/j5KDn0XJwc+i5ND2OkOSzhozNTWFp6cnDh8+rIoplUocPnwYzZs3z/ea5s2bq50PAH/88UeB5xMREREVRPKusTFjxqBv375o1KgRmjRpgsWLFyMtLQ39+vUDAPTp0weurq6YM2cOAGDUqFHw9vbGggUL8NFHH2Hbtm04e/YsVq9eLeXbICIiolJI8kIoICAADx48QEhICBISElC/fn0cPHhQNSD69u3bas1gXl5e2Lp1KyZPnoyJEyfinXfewa+//oo6deoU6n5mZmaYOnVqvt1lVLz4WZQs/DxKDn4WJQc/i5JDV5+F5OsIEREREUlF8pWliYiIiKTCQoiIiIgMFgshIiIiMlgshIiIiMhg6WUhtGzZMri5ucHc3BxNmzbFmTNnXnv+zp07UatWLZibm6Nu3bo4cOBAMWWq/zT5LNasWYNWrVrB3t4e9vb2aNeu3Rs/O9KMpn83cm3btg0ymQz+/v66TdCAaPpZPHnyBMOGDYOLiwvMzMxQo0YN/lulJZp+FosXL0bNmjVhYWEBuVyOoKAgZGRkFFO2+uuvv/6Cn58fKlasCJlMVuAeoi8LDw9Hw4YNYWZmhurVqyM0NFTzGws9s23bNmFqairWrVsnLl++LL788ktRtmxZkZiYmO/5J06cEMbGxmLu3LniypUrYvLkycLExERcvHixmDPXP5p+Fj169BDLli0T58+fF1evXhWBgYHCzs5O3Llzp5gz10+afh65bt68KVxdXUWrVq1E586diydZPafpZ5GZmSkaNWokOnbsKI4fPy5u3rwpwsPDRVRUVDFnrn80/Sy2bNkizMzMxJYtW8TNmzdFWFiYcHFxEUFBQcWcuf45cOCAmDRpkti1a5cAIHbv3v3a82NjY4WlpaUYM2aMuHLlivj++++FsbGxOHjwoEb31btCqEmTJmLYsGGqY4VCISpWrCjmzJmT7/mffvqp+Oijj9RiTZs2FYMGDdJpnoZA08/iVdnZ2cLGxkZs2LBBVykalKJ8HtnZ2cLLy0v8+OOPom/fviyEtETTz2LFihXC3d1dZGVlFVeKBkPTz2LYsGGiTZs2arExY8aIFi1a6DRPQ1OYQujrr78W7733nlosICBA+Pj4aHQvveoay8rKQmRkJNq1a6eKGRkZoV27doiIiMj3moiICLXzAcDHx6fA86lwivJZvOrZs2d4/vy51jfYM0RF/Ty++eYbODo6on///sWRpkEoymexd+9eNG/eHMOGDYOTkxPq1KmD2bNnQ6FQFFfaeqkon4WXlxciIyNV3WexsbE4cOAAOnbsWCw50wva+v6WfGVpbUpKSoJCoVCtSp3LyckJ165dy/eahISEfM9PSEjQWZ6GoCifxavGjx+PihUr5vmDTporyudx/PhxrF27FlFRUcWQoeEoymcRGxuLP//8Ez179sSBAwdw/fp1DB06FM+fP8fUqVOLI229VJTPokePHkhKSkLLli0hhEB2djYGDx6MiRMnFkfK9JKCvr9TUlKQnp4OCwuLQr2OXrUIkf749ttvsW3bNuzevRvm5uZSp2Nwnj59it69e2PNmjVwcHCQOh2Dp1Qq4ejoiNWrV8PT0xMBAQGYNGkSVq5cKXVqBic8PByzZ8/G8uXLce7cOezatQv79+/HjBkzpE6NikivWoQcHBxgbGyMxMREtXhiYiKcnZ3zvcbZ2Vmj86lwivJZ5Jo/fz6+/fZbHDp0CB4eHrpM02Bo+nncuHEDt27dgp+fnyqmVCoBAGXKlEF0dDSqVaum26T1VFH+bri4uMDExATGxsaqWO3atZGQkICsrCyYmprqNGd9VZTPYsqUKejduzcGDBgAAKhbty7S0tIwcOBATJo0SW1vTNKtgr6/bW1tC90aBOhZi5CpqSk8PT1x+PBhVUypVOLw4cNo3rx5vtc0b95c7XwA+OOPPwo8nwqnKJ8FAMydOxczZszAwYMH0ahRo+JI1SBo+nnUqlULFy9eRFRUlOrRqVMntG7dGlFRUZDL5cWZvl4pyt+NFi1a4Pr166piFABiYmLg4uLCIugtFOWzePbsWZ5iJ7dAFdy6s1hp7ftbs3HcJd+2bduEmZmZCA0NFVeuXBEDBw4UZcuWFQkJCUIIIXr37i2Cg4NV5584cUKUKVNGzJ8/X1y9elVMnTqV0+e1RNPP4ttvvxWmpqbi559/FvHx8arH06dPpXoLekXTz+NVnDWmPZp+Frdv3xY2NjZi+PDhIjo6Wuzbt084OjqKmTNnSvUW9Iamn8XUqVOFjY2N+Omnn0RsbKz4/fffRbVq1cSnn34q1VvQG0+fPhXnz58X58+fFwDEwoULxfnz58V///0nhBAiODhY9O7dW3V+7vT5r776Sly9elUsW7aM0+dzff/996Jy5crC1NRUNGnSRJw6dUr1nLe3t+jbt6/a+Tt27BA1atQQpqam4r333hP79+8v5oz1lyafRZUqVQSAPI+pU6cWf+J6StO/Gy9jIaRdmn4WJ0+eFE2bNhVmZmbC3d1dzJo1S2RnZxdz1vpJk8/i+fPnYtq0aaJatWrC3NxcyOVyMXToUPH48ePiT1zPHDlyJN/vgNyff9++fYW3t3eea+rXry9MTU2Fu7u7WL9+vcb3lQnBtjwiIiIyTHo1RoiIiIhIEyyEiIiIyGCxECIiIiKDxUKIiIiIDBYLISIiIjJYLISIiIjIYLEQIiIiIoPFQoiIiIgMFgshIlITGhqKsmXLSp1GkclkMvz666+vPScwMBD+/v7Fkg8RlWwshIj0UGBgIGQyWZ7H9evXpU4NoaGhqnyMjIxQqVIl9OvXD/fv39fK68fHx+PDDz8EANy6dQsymQxRUVFq5yxZsgShoaFauV9Bpk2bpnqfxsbGkMvlGDhwIB49eqTR67BoI9KtMlInQES64evri/Xr16vFKlSoIFE26mxtbREdHQ2lUokLFy6gX79+uHfvHsLCwt76tZ2dnd94jp2d3VvfpzDee+89HDp0CAqFAlevXsUXX3yB5ORkbN++vVjuT0RvxhYhIj1lZmYGZ2dntYexsTEWLlyIunXrwsrKCnK5HEOHDkVqamqBr3PhwgW0bt0aNjY2sLW1haenJ86ePat6/vjx42jVqhUsLCwgl8sxcuRIpKWlvTY3mUwGZ2dnVKxYER9++CFGjhyJQ4cOIT09HUqlEt988w0qVaoEMzMz1K9fHwcPHlRdm5WVheHDh8PFxQXm5uaoUqUK5syZo/bauV1jVatWBQA0aNAAMpkMH3zwAQD1VpbVq1ejYsWKUCqVajl27twZX3zxhep4z549aNiwIczNzeHu7o7p06cjOzv7te+zTJkycHZ2hqurK9q1a4fu3bvjjz/+UD2vUCjQv39/VK1aFRYWFqhZsyaWLFmien7atGnYsGED9uzZo2pdCg8PBwDExcXh008/RdmyZVGuXDl07twZt27dem0+RJQXCyEiA2NkZISlS5fi8uXL2LBhA/788098/fXXBZ7fs2dPVKpUCX///TciIyMRHBwMExMTAMCNGzfg6+uLbt264Z9//sH27dtx/PhxDB8+XKOcLCwsoFQqkZ2djSVLlmDBggWYP38+/vnnH/j4+KBTp074999/AQBLly7F3r17sWPHDkRHR2PLli1wc3PL93XPnDkDADh06BDi4+Oxa9euPOd0794dDx8+xJEjR1SxR48e4eDBg+jZsycA4NixY+jTpw9GjRqFK1euYNWqVQgNDcWsWbMK/R5v3bqFsLAwmJqaqmJKpRKVKlXCzp07ceXKFYSEhGDixInYsWMHAGDcuHH49NNP4evri/j4eMTHx8PLywvPnz+Hj48PbGxscOzYMZw4cQLW1tbw9fVFVlZWoXMiIgAa71dPRCVe3759hbGxsbCyslI9Pvnkk3zP3blzpyhfvrzqeP369cLOzk51bGNjI0JDQ/O9tn///mLgwIFqsWPHjgkjIyORnp6e7zWvvn5MTIyoUaOGaNSokRBCiIoVK4pZs2apXdO4cWMxdOhQIYQQI0aMEG3atBFKpTLf1wcgdu/eLYQQ4ubNmwKAOH/+vNo5ffv2FZ07d1Ydd+7cWXzxxReq41WrVomKFSsKhUIhhBCibdu2Yvbs2WqvsWnTJuHi4pJvDkIIMXXqVGFkZCSsrKyEubm5ACAAiIULFxZ4jRBCDBs2THTr1q3AXHPvXbNmTbWfQWZmprCwsBBhYWGvfX0iUscxQkR6qnXr1lixYoXq2MrKCkBO68icOXNw7do1pKSkIDs7GxkZGXj27BksLS3zvM6YMWMwYMAAbNq0SdW9U61aNQA53Wb//PMPtmzZojpfCAGlUombN2+idu3a+eaWnJwMa2trKJVKZGRkoGXLlvjxxx+RkpKCe/fuoUWLFmrnt2jRAhcuXACQ063Vvn171KxZE76+vvj444/RoUOHt/pZ9ezZE19++SWWL18OMzMzbNmyBZ999hmMjIxU7/PEiRNqLUAKheK1PzcAqFmzJvbu3YuMjAxs3rwZUVFRGDFihNo5y5Ytw7p163D79m2kp6cjKysL9evXf22+Fy5cwPXr12FjY6MWz8jIwI0bN4rwEyAyXCyEiPSUlZUVqlevrha7desWPv74YwwZMgSzZs1CuXLlcPz4cfTv3x9ZWVn5fqFPmzYNPXr0wP79+/Hbb79h6tSp2LZtG7p06YLU1FQMGjQII0eOzHNd5cqVC8zNxsYG586dg5GREVxcXGBhYQEASElJeeP7atiwIW7evInffvsNhw4dwqeffop27drh559/fuO1BfHz84MQAvv370fjxo1x7NgxLFq0SPV8amoqpk+fjq5du+a51tzcvMDXNTU1VX0G3377LT766CNMnz4dM2bMAABs27YN48aNw4IFC9C8eXPY2Nhg3rx5OH369GvzTU1Nhaenp1oBmqukDIgnKi1YCBEZkMjISCiVSixYsEDV2pE7HuV1atSogRo1aiAoKAiff/451q9fjy5duqBhw4a4cuVKnoLrTYyMjPK9xtbWFhUrVsSJEyfg7e2tip84cQJNmjRROy8gIAABAQH45JNP4Ovri0ePHqFcuXJqr5c7HkehULw2H3Nzc3Tt2hVbtmzB9evXUbNmTTRs2FD1fMOGDREdHa3x+3zV5MmT0aZNGwwZMkT1Pr28vDB06FDVOa+26JiamubJv2HDhti+fTscHR1ha2v7VjkRGToOliYyINWrV8fz58/x/fffIzY2Fps2bcLKlSsLPD89PR3Dhw9HeHg4/vvvP5w4cQJ///23qstr/PjxOHnyJIYPH46oqCj8+++/2LNnj8aDpV/21Vdf4bvvvsP27dsRHR2N4OBgREVFYdSoUQCAhQsX4qeffsK1a9cQExODnTt3wtnZOd9FIB0dHWFhYYGDBw8iMTERycnJBd63Z8+e2L9/P9atW6caJJ0rJCQEGzduxPTp03H58mVcvXoV27Ztw+TJkzV6b82bN4eHhwdmz54NAHjnnXdw9uxZhIWFISYmBlOmTMHff/+tdo2bmxv++ecfREdHIykpCc+fP0fPnj3h4OCAzp0749ixY7h58ybCw8MxcuRI3LlzR6OciAye1IOUiEj78htgm2vhwoXCxcVFWFhYCB8fH7Fx40YBQDx+/FgIoT6YOTMzU3z22WdCLpcLU1NTUbFiRTF8+HC1gdBnzpwR7du3F9bW1sLKykp4eHjkGez8slcHS79KoVCIadOmCVdXV2FiYiLq1asnfvvtN9Xzq1evFvXr1xdWVlbC1tZWtG3bVpw7d071PF4aLC2EEGvWrBFyuVwYGRkJb2/vAn8+CoVCuLi4CADixo0befI6ePCg8PLyEhYWFsLW1lY0adJErF69usD3MXXqVFGvXr088Z9++kmYmZmJ27dvi4yMDBEYGCjs7OxE2bJlxZAhQ0RwcLDadffv31f9fAGII0eOCCGEiI+PF3369BEODg7CzMxMuLu7iy+//FIkJycXmBMR5SUTQghpSzEiIiIiabBrjIiIiAwWCyEiIiIyWCyEiIiIyGCxECIiIiKDxUKIiIiIDBYLISIiIjJYLISIiIjIYLEQIiIiIoPFQoiIiIgMFgshIiIiMlgshIiIiMhg/R+29j8KwBPnTgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}