PowerCo_EDA.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hlx9rxBMQCph"
      },
      "source": [
        "# Exploratory Data Analysis\n",
        "---\n",
        "Importing python packages. \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "max2P4HBSFNj"
      },
      "source": [
        "## Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eCbaIH_9Mcpn"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Show Plot in jupyter Note\n",
        "%matplotlib inline \n",
        "\n",
        "# Setting colour style\n",
        "sns.set(color_codes=True)\n",
        "sns.set_style('whitegrid')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jVIySBNVPvyB"
      },
      "source": [
        "## Loading Data \n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40RP0nnUYUeB"
      },
      "source": [
        "\n",
        "For this project we will be using the following data set:\n",
        "\n",
        "1.  client_data\n",
        "2.  Price_data "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "LusenzKfPkTD",
        "outputId": "4fbd10d2-a7bc-4707-973f-82473a3d06a6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>id</th>\n",
              "      <th>channel_sales</th>\n",
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              "      <th>cons_gas_12m</th>\n",
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              "      <th>date_activ</th>\n",
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              "      <th>forecast_price_energy_peak</th>\n",
              "      <th>forecast_price_pow_off_peak</th>\n",
              "      <th>has_gas</th>\n",
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              "      <th>margin_gross_pow_ele</th>\n",
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              "      <th>nb_prod_act</th>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                     id  ... churn\n",
              "0      24011ae4ebbe3035111d65fa7c15bc57  ...     1\n",
              "1      d29c2c54acc38ff3c0614d0a653813dd  ...     0\n",
              "2      764c75f661154dac3a6c254cd082ea7d  ...     0\n",
              "3      bba03439a292a1e166f80264c16191cb  ...     0\n",
              "4      149d57cf92fc41cf94415803a877cb4b  ...     0\n",
              "...                                 ...  ...   ...\n",
              "14601  18463073fb097fc0ac5d3e040f356987  ...     0\n",
              "14602  d0a6f71671571ed83b2645d23af6de00  ...     1\n",
              "14603  10e6828ddd62cbcf687cb74928c4c2d2  ...     1\n",
              "14604  1cf20fd6206d7678d5bcafd28c53b4db  ...     0\n",
              "14605  563dde550fd624d7352f3de77c0cdfcd  ...     0\n",
              "\n",
              "[14606 rows x 26 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# client data\n",
        "client_df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/PowerCo_Project/client_data.csv')\n",
        "\n",
        "client_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "MouO6J0yTnl3",
        "outputId": "079b2332-d8f6-4fce-848f-e657e889157a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    </div>\n",
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            ],
            "text/plain": [
              "                                 id  ... price_mid_peak_fix\n",
              "0  038af19179925da21a25619c5a24b745  ...                0.0\n",
              "1  038af19179925da21a25619c5a24b745  ...                0.0\n",
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              "4  038af19179925da21a25619c5a24b745  ...                0.0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Pricing data \n",
        "price_df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/PowerCo_Project/price_data.csv')\n",
        "\n",
        "price_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nA-XLw6PFzWm",
        "outputId": "422a833d-99c5-46c8-cc4a-fda74ebaedfc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                14606\n",
              "channel_sales                         8\n",
              "cons_12m                          11065\n",
              "cons_gas_12m                       2112\n",
              "cons_last_month                    4751\n",
              "date_activ                         1796\n",
              "date_end                            368\n",
              "date_modif_prod                    2129\n",
              "date_renewal                        386\n",
              "forecast_cons_12m                 13993\n",
              "forecast_cons_year                 4218\n",
              "forecast_discount_energy             12\n",
              "forecast_meter_rent_12m            3528\n",
              "forecast_price_energy_off_peak      516\n",
              "forecast_price_energy_peak          329\n",
              "forecast_price_pow_off_peak          41\n",
              "has_gas                               2\n",
              "imp_cons                           7752\n",
              "margin_gross_pow_ele               2391\n",
              "margin_net_pow_ele                 2391\n",
              "nb_prod_act                          10\n",
              "net_margin                        11965\n",
              "num_years_antig                      13\n",
              "origin_up                             6\n",
              "pow_max                             698\n",
              "churn                                 2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# Number of unique values in Client dataset \n",
        "\n",
        "client_df.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wF8TRgv5LJmK",
        "outputId": "72c019ff-9e37-45eb-b96c-e0ca43210e1d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                    16096\n",
              "price_date               12\n",
              "price_off_peak_var     1853\n",
              "price_peak_var         1189\n",
              "price_mid_peak_var      711\n",
              "price_off_peak_fix       66\n",
              "price_peak_fix           31\n",
              "price_mid_peak_fix       28\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Number of unique values in Price dataset\n",
        "\n",
        "price_df.nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YpwnTDPnVlh0"
      },
      "source": [
        "\n",
        "## Descriptive statistics of data\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BAhDp1s7VuTe",
        "outputId": "a9794e3d-a4fa-447c-c7fd-8ebceb75f484"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The client dataset has 14606 rows and 26 colunms\n",
            "(14606, 26)\n"
          ]
        }
      ],
      "source": [
        "# client Data Shape\n",
        "print(f\"The client dataset has {client_df.shape[0]} rows and {client_df.shape[1]} colunms\")\n",
        "\n",
        "print(client_df.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xF6lAY8gZKSF",
        "outputId": "9ba0c0a5-7ce0-4428-a054-5371b2363768"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 14606 entries, 0 to 14605\n",
            "Data columns (total 26 columns):\n",
            " #   Column                          Non-Null Count  Dtype  \n",
            "---  ------                          --------------  -----  \n",
            " 0   id                              14606 non-null  object \n",
            " 1   channel_sales                   14606 non-null  object \n",
            " 2   cons_12m                        14606 non-null  int64  \n",
            " 3   cons_gas_12m                    14606 non-null  int64  \n",
            " 4   cons_last_month                 14606 non-null  int64  \n",
            " 5   date_activ                      14606 non-null  object \n",
            " 6   date_end                        14606 non-null  object \n",
            " 7   date_modif_prod                 14606 non-null  object \n",
            " 8   date_renewal                    14606 non-null  object \n",
            " 9   forecast_cons_12m               14606 non-null  float64\n",
            " 10  forecast_cons_year              14606 non-null  int64  \n",
            " 11  forecast_discount_energy        14606 non-null  float64\n",
            " 12  forecast_meter_rent_12m         14606 non-null  float64\n",
            " 13  forecast_price_energy_off_peak  14606 non-null  float64\n",
            " 14  forecast_price_energy_peak      14606 non-null  float64\n",
            " 15  forecast_price_pow_off_peak     14606 non-null  float64\n",
            " 16  has_gas                         14606 non-null  object \n",
            " 17  imp_cons                        14606 non-null  float64\n",
            " 18  margin_gross_pow_ele            14606 non-null  float64\n",
            " 19  margin_net_pow_ele              14606 non-null  float64\n",
            " 20  nb_prod_act                     14606 non-null  int64  \n",
            " 21  net_margin                      14606 non-null  float64\n",
            " 22  num_years_antig                 14606 non-null  int64  \n",
            " 23  origin_up                       14606 non-null  object \n",
            " 24  pow_max                         14606 non-null  float64\n",
            " 25  churn                           14606 non-null  int64  \n",
            "dtypes: float64(11), int64(7), object(8)\n",
            "memory usage: 2.9+ MB\n",
            "\n",
            "\n",
            "Missing Values\n",
            "================================================================\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                0\n",
              "channel_sales                     0\n",
              "cons_12m                          0\n",
              "cons_gas_12m                      0\n",
              "cons_last_month                   0\n",
              "date_activ                        0\n",
              "date_end                          0\n",
              "date_modif_prod                   0\n",
              "date_renewal                      0\n",
              "forecast_cons_12m                 0\n",
              "forecast_cons_year                0\n",
              "forecast_discount_energy          0\n",
              "forecast_meter_rent_12m           0\n",
              "forecast_price_energy_off_peak    0\n",
              "forecast_price_energy_peak        0\n",
              "forecast_price_pow_off_peak       0\n",
              "has_gas                           0\n",
              "imp_cons                          0\n",
              "margin_gross_pow_ele              0\n",
              "margin_net_pow_ele                0\n",
              "nb_prod_act                       0\n",
              "net_margin                        0\n",
              "num_years_antig                   0\n",
              "origin_up                         0\n",
              "pow_max                           0\n",
              "churn                             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# client Descriptive Statistics \n",
        "client_df.info()\n",
        "\n",
        "print('\\n\\nMissing Values\\n================================================================')\n",
        "\n",
        "client_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_rF-KsLwrR_U",
        "outputId": "95727e55-c4c9-4ed6-f1e6-bf3f3283daaa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 193002 entries, 0 to 193001\n",
            "Data columns (total 8 columns):\n",
            " #   Column              Non-Null Count   Dtype  \n",
            "---  ------              --------------   -----  \n",
            " 0   id                  193002 non-null  object \n",
            " 1   price_date          193002 non-null  object \n",
            " 2   price_off_peak_var  193002 non-null  float64\n",
            " 3   price_peak_var      193002 non-null  float64\n",
            " 4   price_mid_peak_var  193002 non-null  float64\n",
            " 5   price_off_peak_fix  193002 non-null  float64\n",
            " 6   price_peak_fix      193002 non-null  float64\n",
            " 7   price_mid_peak_fix  193002 non-null  float64\n",
            "dtypes: float64(6), object(2)\n",
            "memory usage: 11.8+ MB\n",
            "\n",
            "\n",
            "Missing Values\n",
            "================================================================\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                    0\n",
              "price_date            0\n",
              "price_off_peak_var    0\n",
              "price_peak_var        0\n",
              "price_mid_peak_var    0\n",
              "price_off_peak_fix    0\n",
              "price_peak_fix        0\n",
              "price_mid_peak_fix    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Price Descriptive Statistics\n",
        "price_df.info()\n",
        "\n",
        "print('\\n\\nMissing Values\\n================================================================')\n",
        "\n",
        "price_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aE738-xgrYsr"
      },
      "source": [
        "## Statistical Summary\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "Y9hZAM5aooNr",
        "outputId": "96c81a30-5a2b-441e-bf33-d7b0399336f7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cons_12m</th>\n",
              "      <th>cons_gas_12m</th>\n",
              "      <th>cons_last_month</th>\n",
              "      <th>forecast_cons_12m</th>\n",
              "      <th>forecast_cons_year</th>\n",
              "      <th>forecast_discount_energy</th>\n",
              "      <th>forecast_meter_rent_12m</th>\n",
              "      <th>forecast_price_energy_off_peak</th>\n",
              "      <th>forecast_price_energy_peak</th>\n",
              "      <th>forecast_price_pow_off_peak</th>\n",
              "      <th>imp_cons</th>\n",
              "      <th>margin_gross_pow_ele</th>\n",
              "      <th>margin_net_pow_ele</th>\n",
              "      <th>nb_prod_act</th>\n",
              "      <th>net_margin</th>\n",
              "      <th>num_years_antig</th>\n",
              "      <th>pow_max</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.460600e+04</td>\n",
              "      <td>1.460600e+04</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.592203e+05</td>\n",
              "      <td>2.809238e+04</td>\n",
              "      <td>16090.269752</td>\n",
              "      <td>1868.614880</td>\n",
              "      <td>1399.762906</td>\n",
              "      <td>0.966726</td>\n",
              "      <td>63.086871</td>\n",
              "      <td>0.137283</td>\n",
              "      <td>0.050491</td>\n",
              "      <td>43.130056</td>\n",
              "      <td>152.786896</td>\n",
              "      <td>24.565121</td>\n",
              "      <td>24.562517</td>\n",
              "      <td>1.292346</td>\n",
              "      <td>189.264522</td>\n",
              "      <td>4.997809</td>\n",
              "      <td>18.135136</td>\n",
              "      <td>0.097152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.734653e+05</td>\n",
              "      <td>1.629731e+05</td>\n",
              "      <td>64364.196422</td>\n",
              "      <td>2387.571531</td>\n",
              "      <td>3247.786255</td>\n",
              "      <td>5.108289</td>\n",
              "      <td>66.165783</td>\n",
              "      <td>0.024623</td>\n",
              "      <td>0.049037</td>\n",
              "      <td>4.485988</td>\n",
              "      <td>341.369366</td>\n",
              "      <td>20.231172</td>\n",
              "      <td>20.230280</td>\n",
              "      <td>0.709774</td>\n",
              "      <td>311.798130</td>\n",
              "      <td>1.611749</td>\n",
              "      <td>13.534743</td>\n",
              "      <td>0.296175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.674750e+03</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>494.995000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.180000</td>\n",
              "      <td>0.116340</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.606701</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.280000</td>\n",
              "      <td>14.280000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>50.712500</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.411550e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>792.500000</td>\n",
              "      <td>1112.875000</td>\n",
              "      <td>314.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.795000</td>\n",
              "      <td>0.143166</td>\n",
              "      <td>0.084138</td>\n",
              "      <td>44.311378</td>\n",
              "      <td>37.395000</td>\n",
              "      <td>21.640000</td>\n",
              "      <td>21.640000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>112.530000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>13.856000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.076375e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3383.000000</td>\n",
              "      <td>2401.790000</td>\n",
              "      <td>1745.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>131.030000</td>\n",
              "      <td>0.146348</td>\n",
              "      <td>0.098837</td>\n",
              "      <td>44.311378</td>\n",
              "      <td>193.980000</td>\n",
              "      <td>29.880000</td>\n",
              "      <td>29.880000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>243.097500</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>19.172500</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.207104e+06</td>\n",
              "      <td>4.154590e+06</td>\n",
              "      <td>771203.000000</td>\n",
              "      <td>82902.830000</td>\n",
              "      <td>175375.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>599.310000</td>\n",
              "      <td>0.273963</td>\n",
              "      <td>0.195975</td>\n",
              "      <td>59.266378</td>\n",
              "      <td>15042.790000</td>\n",
              "      <td>374.640000</td>\n",
              "      <td>374.640000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>24570.650000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>320.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f9d9e479-3fcf-42d3-baf9-af67c4715301')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f9d9e479-3fcf-42d3-baf9-af67c4715301 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f9d9e479-3fcf-42d3-baf9-af67c4715301');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "           cons_12m  cons_gas_12m  ...       pow_max         churn\n",
              "count  1.460600e+04  1.460600e+04  ...  14606.000000  14606.000000\n",
              "mean   1.592203e+05  2.809238e+04  ...     18.135136      0.097152\n",
              "std    5.734653e+05  1.629731e+05  ...     13.534743      0.296175\n",
              "min    0.000000e+00  0.000000e+00  ...      3.300000      0.000000\n",
              "25%    5.674750e+03  0.000000e+00  ...     12.500000      0.000000\n",
              "50%    1.411550e+04  0.000000e+00  ...     13.856000      0.000000\n",
              "75%    4.076375e+04  0.000000e+00  ...     19.172500      0.000000\n",
              "max    6.207104e+06  4.154590e+06  ...    320.000000      1.000000\n",
              "\n",
              "[8 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Client Statistics Summary\n",
        "client_df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Xus25F41px8j",
        "outputId": "02881fb6-a4a2-44e9-ac98-364d5ab13f5b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3a8fafa2-5e44-418f-9790-882ac1aecaf3\">\n",
              "    <div 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>price_off_peak_var</th>\n",
              "      <th>price_peak_var</th>\n",
              "      <th>price_mid_peak_var</th>\n",
              "      <th>price_off_peak_fix</th>\n",
              "      <th>price_peak_fix</th>\n",
              "      <th>price_mid_peak_fix</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>193002.000000</td>\n",
              "      <td>193002.000000</td>\n",
              "      <td>193002.000000</td>\n",
              "      <td>193002.000000</td>\n",
              "      <td>193002.000000</td>\n",
              "      <td>193002.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.141027</td>\n",
              "      <td>0.054630</td>\n",
              "      <td>0.030496</td>\n",
              "      <td>43.334477</td>\n",
              "      <td>10.622875</td>\n",
              "      <td>6.409984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.025032</td>\n",
              "      <td>0.049924</td>\n",
              "      <td>0.036298</td>\n",
              "      <td>5.410297</td>\n",
              "      <td>12.841895</td>\n",
              "      <td>7.773592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.125976</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.728885</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.146033</td>\n",
              "      <td>0.085483</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>44.266930</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.151635</td>\n",
              "      <td>0.101673</td>\n",
              "      <td>0.072558</td>\n",
              "      <td>44.444710</td>\n",
              "      <td>24.339581</td>\n",
              "      <td>16.226389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.280700</td>\n",
              "      <td>0.229788</td>\n",
              "      <td>0.114102</td>\n",
              "      <td>59.444710</td>\n",
              "      <td>36.490692</td>\n",
              "      <td>17.458221</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a8fafa2-5e44-418f-9790-882ac1aecaf3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3a8fafa2-5e44-418f-9790-882ac1aecaf3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3a8fafa2-5e44-418f-9790-882ac1aecaf3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       price_off_peak_var  price_peak_var  ...  price_peak_fix  price_mid_peak_fix\n",
              "count       193002.000000   193002.000000  ...   193002.000000       193002.000000\n",
              "mean             0.141027        0.054630  ...       10.622875            6.409984\n",
              "std              0.025032        0.049924  ...       12.841895            7.773592\n",
              "min              0.000000        0.000000  ...        0.000000            0.000000\n",
              "25%              0.125976        0.000000  ...        0.000000            0.000000\n",
              "50%              0.146033        0.085483  ...        0.000000            0.000000\n",
              "75%              0.151635        0.101673  ...       24.339581           16.226389\n",
              "max              0.280700        0.229788  ...       36.490692           17.458221\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "#Price Statistics Summary\n",
        "price_df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0uMZ0-RCYo-"
      },
      "source": [
        "### Relational Analysis and Data Visualization\n",
        "\n",
        "In this sub-section we will be looking the relationship that each variable such as consumption, tenurship, etc. have with churning rate and with the aid of visualiszation tools we can better understand the relationship that each variables have with the churning rate of of our customer. Below are function that can help you with data visualization. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 894
        },
        "id": "G_lkTNPqDgsX",
        "outputId": "b67804fa-0d47-4065-95b2-32ddc22f4129"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x936 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Corelation Analysis\n",
        "\n",
        "correlation = client_df[['cons_12m', 'cons_gas_12m', 'cons_last_month', 'forecast_cons_12m', 'forecast_discount_energy', 'forecast_price_energy_peak', 'forecast_price_pow_off_peak', 'churn']].corr()\n",
        "\n",
        "# Heat Mapping \n",
        "\n",
        "plt.figure(figsize=(15,13))\n",
        "sns.heatmap(correlation, xticklabels=correlation.columns.values, yticklabels=correlation.columns.values, annot = True, annot_kws={'size':10})\n",
        "\n",
        "# Axis ticks size\n",
        "plt.xticks(fontsize=10)\n",
        "plt.yticks(fontsize=10)\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2IJAzbCwDG9H"
      },
      "source": [
        "There seems to be a negative relationship between churn and consumtion of customer by -0.046 for eletricity consumption and - 0.038 for gas, and for forecast variables there our correlation shows a postive relationship between churn and forcast feature. Althought this is an important finding, the level of correlation is low and more analysis is needed."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_stacked_bars(dataframe, title_, size_=(18, 10), rot_=0, legend_=\"upper right\"):\n",
        "    \"\"\"\n",
        "    Plot stacked bars with annotations\n",
        "    \"\"\"\n",
        "    ax = dataframe.plot(\n",
        "        kind=\"bar\",\n",
        "        stacked=True,\n",
        "        figsize=size_,\n",
        "        rot=rot_,\n",
        "        title=title_\n",
        "    )\n",
        "\n",
        "    # Annotate bars\n",
        "    annotate_stacked_bars(ax, textsize=14)\n",
        "    # Rename legend\n",
        "    plt.legend([\"Retention\", \"Churn\"], loc=legend_)\n",
        "    # Labels\n",
        "    plt.ylabel(\"Company base (%)\")\n",
        "    plt.show()\n",
        "\n",
        "def annotate_stacked_bars(ax, pad=0.99, colour=\"white\", textsize=13):\n",
        "    \"\"\"\n",
        "    Add value annotations to the bars\n",
        "    \"\"\"\n",
        "\n",
        "    # Iterate over the plotted rectanges/bars\n",
        "    for p in ax.patches:\n",
        "        \n",
        "        # Calculate annotation\n",
        "        value = str(round(p.get_height(),1))\n",
        "        # If value is 0 do not annotate\n",
        "        if value == '0.0':\n",
        "            continue\n",
        "        ax.annotate(\n",
        "            value,\n",
        "            ((p.get_x()+ p.get_width()/2)*pad-0.05, (p.get_y()+p.get_height()/2)*pad),\n",
        "            color=colour,\n",
        "            size=textsize\n",
        "        )"
      ],
      "metadata": {
        "id": "_qiMnLflSwaB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "churn = client_df[['id', 'churn']]\n",
        "churn.columns = ['Companies', 'churn']\n",
        "churn_total = churn.groupby(churn['churn']).count()\n",
        "churn_percentage = churn_total / churn_total.sum() * 100"
      ],
      "metadata": {
        "id": "u7mhBOc4UE6_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_stacked_bars(churn_percentage.transpose(), \"Churning status\", (5, 5), legend_=\"lower right\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "dM9AHCZ1UN0O",
        "outputId": "adf7eda4-943b-4a77-bd2d-948bf4015dcc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There is approximatly 10% of our client customers are churn and 90% active.\n"
      ],
      "metadata": {
        "id": "XXa0V8jZUXlN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chacking the relationship between demand for our client service and rate \n",
        "# of churn of customers\n",
        "\n",
        "que_churn = sns.catplot(x= 'churn', y= 'cons_12m', hue='has_gas', data=client_df, height=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "vcKFTRI0Aln8",
        "outputId": "99f332c3-55f6-4da8-bbf9-f71926c2f5a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 777.8x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "h1yJ7x8cGWeU",
        "outputId": "349b63b2-5238-4bee-c4dd-18297a72056f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f56064b84d0>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 766.6x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Chacking the relaiontion the exist between categorical variables and churn\n",
        "\n",
        "# relationship between tenure of our client and the rate of churn. \n",
        "\n",
        "sns.catplot(x='num_years_antig', hue='churn', kind='count', data=client_df, height=10, estimator=np.count_nonzero)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZnZYByOSJv7g"
      },
      "source": [
        "From this bar chart above we can observe that there seems to be a relationship between the number of years a customer have spent with our client and the rate of churn. In the first two year there seems to be no churn amoung our client customer but as we enter the third year there was a sparp increase in the rate of churn and this increase contiune to the fourth year and gradualy decreasing when as our client customer enter the fiveth year. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1Io32CenJt8w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "outputId": "32723795-b586-47e3-aa07-928971e0ea5b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1486.6x720 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Relationship between the channel of sales and the churn rate of customers\n",
        "# and I will also be checking the relationship that exist between customers\n",
        "# that has both gas and eletricity, customers that doesn't.\n",
        "\n",
        "cs_churn = sns.catplot(y='channel_sales', hue='churn', col= 'has_gas', kind='count', data=client_df, height=10)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The graph above shows us that there is a relationship between the channel of sale a customer is useing, whether the customer also uses gas, and the churn rate. This observation shows us that depending on the channel of sales a customer is more or less likely to churn. \n"
      ],
      "metadata": {
        "id": "MvHHog3pB_4L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Relationship between the origin_up and churn rate\n",
        "\n",
        "orup_churn = sns.catplot(y='origin_up', hue='churn', kind='count', data=client_df, height=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "6rABcTVR19Ky",
        "outputId": "c3ddb87d-2486-4d2f-97dd-2f9d6ed5e3d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 766.6x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the Normalized Distribution \n",
        "\n",
        "def plot_distribution(dataframe, column, ax, bins_=50):\n",
        "    \"\"\"\n",
        "    Plot variable distirbution in a stacked histogram of churned or retained company\n",
        "    \"\"\"\n",
        "    # Create a temporal dataframe with the data to be plot\n",
        "    temp = pd.DataFrame({\"Active\": dataframe[dataframe[\"churn\"]==0][column],\n",
        "    \"Churn\":dataframe[dataframe[\"churn\"]==1][column]})\n",
        "    # Plot the histogram\n",
        "    temp[[\"Active\",\"Churn\"]].plot(kind='hist', bins=bins_, ax=ax, stacked=True)\n",
        "    # X-axis label\n",
        "    ax.set_xlabel(column)\n",
        "    # Change the x-axis to plain style\n",
        "    ax.ticklabel_format(style='plain', axis='x')"
      ],
      "metadata": {
        "id": "GB-79HMlX9Tu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(nrows=4, figsize=(18, 25))\n",
        "\n",
        "plot_distribution(client_df, 'cons_12m', axs[0])\n",
        "plot_distribution(client_df[client_df['has_gas'] == 't'], 'cons_gas_12m', axs[1])\n",
        "plot_distribution(client_df, 'cons_last_month', axs[2])\n",
        "plot_distribution(client_df, 'imp_cons', axs[3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9KDAwGrjmtN0",
        "outputId": "5b040087-272c-4394-d94c-00a7f55b1b25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x1800 with 4 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the histograms above, we can observe that the customer data is positivly stewed and it seems it is mostly stewed at the top and botton of which is mostly caused by outliers. In other to check for the present of outliers, we will be plotting a box plot, this will show us the present of outliers in the each features. "
      ],
      "metadata": {
        "id": "4lmQ3PZDp_Gr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(nrows=4, figsize=(18,25))\n",
        "\n",
        "sns.boxplot(data=client_df, x='cons_12m', ax = axs[0])\n",
        "sns.boxplot(data=client_df[client_df['has_gas'] == 't'], x='cons_gas_12m', ax=axs[1])\n",
        "sns.boxplot(data=client_df, x=\"cons_last_month\", ax=axs[2])\n",
        "sns.boxplot(data=client_df, x=\"imp_cons\", ax=axs[3])\n",
        "\n",
        "\n",
        "# Remove scientific notation\n",
        "for ax in axs:\n",
        "    ax.ticklabel_format(style='plain', axis='x')\n",
        "    # Set x-axis limit\n",
        "    axs[0].set_xlim(-200000, 2000000)\n",
        "    axs[1].set_xlim(-200000, 2000000)\n",
        "    axs[2].set_xlim(-20000, 100000)\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "q6p2iKa-rT68",
        "outputId": "17eb5231-d5dc-4937-d368-055bfd8bdcd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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H04ULF8bs2bM7JKDI9leq8LimT59etaxcLhczZ86MxsbGmDVrVv7xsmXLoqmpKaZPn97q9aUeeeSRigFFaX2qWbFiRbvaZeHChZHL5SoGFBGRHzsrVqwoW7ef/OQnrY43M3Xq1Favb25ujgsvvLA9hxHNzc3xpz/9Kf949uzZrepQbmyvWLEiZsyY0aqNS4+zWkARsaGPSvsxO9b2BhRZfV566aWYO3duq/pOmzat4vxcuHBhzJw5s+zYyvqutM0bGxuL5siCBQviiiuuKNrvVVddlf9/6bZCWfuX9uODDz6Y/3+5Y1qxYkX86U9/iscee6zivttaF7K+y+VyMX369FbHWq7cwvJfeumlOO+886qWUTq+MnfccUerulRq70KF46P0+LI2K9c/EcVju7Be5cZ8RMTNN99cNMezYy6noaGh6PGkSZNiypQprV5XWG5TU1PMnDkzv23GjBkVj3tjlbZvobvuuqvVc6Vt8MgjjxQde7atUhtUavP22Jz3lqt7tX6qNAY62rRp04rm16OPPtru95aOm/vuu6/o8dy5c/Pr6ooVK6qWU9q2hbJ2KFzDy+2j3Pw444wzis5bldq7Uj3K9XHh3J45c2Y88MADm9yGEeXbsbCM9qw3mcK1sNzxlq6DpT/wXnvtta32OXr06KLH5eZlykp/JvnJT37S5nsqtWO59WpT26NSv8+YMSP/3MKFC+Oll16q+LkqUzqPN2ed2pLcSdHNjRs3Lv75z39Gz54944PmnlG35xciYuPvpFjx2u9jm9r1MXTo0A6v47x58zp8n9CWwl+JSEFtbW0MGjQoFi1alL8adtxxx22RsCYlWT9kx5t9COqIu6sKryiWu6LcGQr7ccaMGZtUh829o6l0bJXWLWvz0juLyt0hFFH5anq5cssdb/b+SnczbIm5WXislcrNtPeuo3JXrNu6+6KwvQvddtttVcdH6d0PEf+6alY6trN6bcyYb89V1LZk5WZX8QqvYrf3qm5b2qpP6VXKzW2DSm3eHpvz3ojyda/UT5XGQEfLrr5mNuZOgHJ9V9hf1eZlaTmbchdk6T7aMzbaupuiPX1cOLfLrW0bezdFuXYcOXJkq/Wj2nqTqXQnW7WyCvusvevDh+luiraOuZxK7bixd31tbL3K3VVYeOdqpTFQOo8LFY7hrv6Z3Z0UwFYppYAiYkN9Fi5cWHQ17A9/+EM88cQTXVuxLaz0eLfEvkv/35kK+3FT67C5dzSVjq3SumVKP/RXu0umveVWU+mYtkRfFR5rW23Z3ruONrae1cZ4e8ZHpf4p168bW7/2HnM1WXlPPPFEq6vYHT23N7ZO7VGuDTZnTmzufCpX90r9VGkMdLTSH2w68jpntXlZWs6m3AVZuo/2tFFb86I9fVw4t8uV2RFtWG79aM85tdydbGy8rmrHcp8PC+9crTQGqo25zT3vdyRfnNnNbb/99tGzZ8/8d1Jsqh61fWPoFvpOCt9HQVf4MNxJccwxx2xVd1Icc8wxHb7v0nI6W2E/pngnRWGbZ7/LnKl0J8XGlFvteMt9J0h73repdcmOtVK5mY25k2JT61Dq6KOPbnN8lOufbL+l/Vru+Wrq6ura9bpqsnKPPvroVndSdPTc3pg6bU4bVGrz9tic90aUr3ulfqo0Bjpa6fchZd+90xGqzcvSckrbtj1K99GesdHWvGhPHxfO7Up3UmyucutHe86ppW3eEevA1qir2vHoo49u806KcmOg2h8J2Nh1aktyJwVd7vOfb9+vnEBHuuiiizqtrPZ8YOzRo0dMmDAhevTokX88ZsyYGDNmTPTs2bPN93fkh8XOMmLEiFbHmznrrLPKvmfPPfds9/4nTJiQ///G9HdHfsAv7MfsWDdWQ0NDXHrppRv9vl69euXrUDi2SuuWmThxYtH2iRMnxkEHHVT03CGHHJL/f+m2UhMmTGjVj2PHjs3/v9IxlavrpsrmTuGxttWWDQ0Nsdtuu7W578LxlfnCF75Q8fWl7V2ocHyUzuWszcr1T0TrsZ3Vq9KYP+qoo1o9V/rdE5lPfOITRY8POOCAOOWUU8q+Nit3zJgxrQLCSse9saq1b7kLHqVtUG2dLNcGldq8PTbnvRHl+69SP1UaAx2t9DsKzj///Ha/t3TcnHrqqUWPq83L0nI2ti3L7aNc+2633XZFjyu1d6V6lKtX4dzu0aNHfPWrX61ar7aUa8dy55dq602mtM1Lj7d0HRw8eHDR409/+tOt9tm3b9+ixx+2C5GlP5OMGDGizfdUasdy69Wmtkelfi/9vNLQ0FDxc1Wm2ncubcrc2lKEFHS59n4RHp2vrq4u+vfvv0X2XVtbGzU1NRUT5+z3mDvS7rvvHhEbkuKjjjoq/3hzlUueC4/ruOOOq1pWTU1NHHvssTF06ND4/Oc/n388cODA2GGHHeK4445r9fpSI0eOrPrDdXuT/bq6una1y5AhQ6KmpqZq6p6Nnbq6urJ1++Y3v9nqeDOlX74VsWHMtPeLdmtra4v+DN9RRx3Vqg7lxnZdXV2MGDGiVRuXHmfpB7FSNTU1rfoxO9ZRo0a16xiy+hx44IFx8MEHt6rvqFGjKs7PIUOGxLHHHlt2bGV9V9rmQ4cOLZojH//4x1t9MVrhnyAt96Vpmaz9S/ux8M9pljumurq6+NznPhfHH398xX23tS5kfVdTUxPHHXdcq2MtV25h+QceeGDVL2iMaD2+MqUf/qq1d6HC8VF6fFmbleufiOKxXVivcmM+IuLb3/520RzPjrmcG2+8sejxDTfc0OqHrNJyd9hhhzj22GPz20aMGNFhf4K02ofrcn/ar7QNRo4cWXTs2bZKbVCpzdtjc95bru7V+qnSGOhoo0aNKppfG/PnM0vHTemfID344IPz62pdXV3VckrbtlDWDoVreLl9lJsfP//5z4vOW239CdL29HHh3D722GPjlFNO2eQ2jCjfjoVltGe9yRSuheWOt3QdLP0TpOW+PLn0y5I/bH+CtPRnkvb8CdJK7VhuvdrU9qjU74UhypAhQ+LAAw+s+LkqUzqPN2ed2pKEFCRh22237eoqRET7rkb37t277PObc+Wv9AeenXfeOXbdddeIaJ3sV3vf9ttvX/R4u+22i969e+ePa5tttqm4n+yqa6ampiZ/BbempqbiD8B9+vTJ/yCSvWavvfaKPffcM38V8/DDD2+17wkTJsR+++0Xl156aWyzzTax8847Fx3D+eefH2PGjCl7RbNPnz75BTv74fuII47Ib991113z/dSvX7+I2PCBZOLEidGvX798Ujxx4sTo06dP1NTURK9evfJtMHbs2KipqYk99tij4lXDwYMHx5577hnDhg2LiRMn5utZW1sbffv2jYaGhhg2bFjsvffeMWbMmJg4cWKrNs7aar/99sun3WPGjCl6nD2X7b93794xbty4qKmpybfZHnvsEWPGjMlfGSrsq7Fjx0a/fv3i0ksvjb333jt22223/AeZrN8OPvjg/OsbGhqK6nrkkUdGRMROO+0Ue+yxR/Tt2zf23HPPmDhxYuy3334xceLE2HvvvVtdYRk7dmxceuml0aNHj2hoaMjXLSsru5pQ7ngzpVfhs6uD2dWbffbZJyI2jIePfvSjUVNTk786V+5KYlaHQYMGxbBhw6KhoaHVnG9oaMjXKRtjvXr1iokTJ+bHQm1tbYwePTr23HPP2GabbfLtWDgGzz///LL9mD03bNiwVvXL2ibbTzYHM4VXa3bccccYM2ZM/rnCOdG7d++YOHFiq7bNHmd9V67NS+dIxL/umCi8i6J0W6nC9s/6sfAuisJj6tGjR4wdOzY/VrK67rjjjhGx4YpR9qXNe+21V4wZM6boS7wK15cRI0bkPxhma0i5Y83KzT5MZnf1FLZ3NueyemSvKz2+UtnVsxEjRrTZ3oUK61qpzcr1T8S/xnZpvbLns37K5l3h2tfW1eLsbooDDjgg/1zpulhabjbGs/WvI2XtO3DgwPxVz8K5Vyprg8GDB+fX4m222Sb23HPPuPjii1v1e6lKbd4em/Pewrq3p58qjYGOVji/NlY2bkrvoijcnvVHW+UUtm3hGM3a4fzzz4+PfvSjVfdReDdFdj4tPG+1R3v6uHQd2pw2jCjfjlkZ2Wer9s67to43WwdLz/GZwrspss+t2Rz9sN1FkcnWlfbcRZGp1I6Fd1NsbntU6vehQ4fGNttskx+D1T5XZQrH4OauU1uKv+7RzV122WWxfPny/HdSZH/NY2P/useqv8+KfbfQd1Jk9YyILbb/7uD555+PQw89tKurwWbSj92Dfuwe9GP3oB+7B/3YfejLD7+u/pndnRQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBJqu7oCbFkjRoyIBQsWxKuvvtrVValqxIgRXV0FAAAAupiQopsbPnx4PP/888mHFMOHD+/qKgAAANDF/LoHAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJCE2q6uAJ1n/ep3Y9XfZ+X/HxH5x+15b8QuW6pqAAAAIKTYWgwdOrTo8bJlvSMiYuDAge3cwy6t9gEAAAAdSUixlTj77LO7ugoAAABQle+kAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAklDb2QXmcrmIiFi7dm1nF71VW7NmTVdXgQ6gH7sH/dg96MfuQT92D/qxe9CP3Ye+/HDLflbPfnbvbDW5Ti55+fLlMX/+/M4sEgAAANgIw4YNiwEDBnR6uZ0eUrS0tMTKlSujV69eUVNT05lFAwAAAFXkcrlYt25d9O/fP3r06PxviOj0kAIAAACgHF+cCQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSNGNLViwIE477bQ4/vjj47TTTovXX3+9q6u0VVm2bFmcffbZcfzxx0d9fX2MHz8+mpqaIiJi7733jvr6+jjppJPipJNOir/97W/59ziGtnoAACAASURBVD3++ONxwgknxIgRI+LCCy+MDz74YItuo23Dhw+PE044Id9fTz75ZEREvPjii3HiiSfG8ccfH1/72tdi6dKl+fd09jaqe/PNN/P9d9JJJ8Xw4cPj05/+dERU7t8I/ZiCm266KYYPHx577713zJ8/P/98tXNcStvYoFw/VjtPRjhXpqjSfExpHbXGtq1cP1Y7T0ak1cdsUG0NTWnebVJf5ui2zjzzzNxvfvObXC6Xy/3mN7/JnXnmmV1co63LsmXLck8//XT+8Y033pi77LLLcrlcLjds2LDcihUrWr1nxYoVuc9+9rO5BQsW5HK5XG7SpEm5n/70p1tsG+1zzDHH5P72t78VPbd+/frcsccem5szZ04ul8vlJk+enGtoaOiSbWy86667Lvfd7343l8uV799cTj+mYs6cOblFixa16qdq57iUtrFBuX6sdp7M5ZwrU1RpPqayjlpj26dSPxYqPE/mcun0Mf9SaQ1Nad5tal8KKbqpd955J3fooYfmmpubc7lcLtfc3Jw79NBDc0uXLu3imm29Hn300dxXv/rVXC5X+YPXtGnTcuecc07+8Z///OfcqFGjttg22qfcifmll17KfeELX8g/Xrp0ae6ggw7qkm1snDVr1uQOP/zw3Lx583K5XOUPXvoxLYX9VO0cl9I2Wqv2Q1HheTKXc65MWXtDCufKtFXqt9LzZLXX6sd0ZGtoSvNuU/uydnNuMSFdixcvjl122SV69uwZERE9e/aMnXfeORYvXhw77LBDF9du69PS0hK//OUvY/jw4fnnzjzzzFi/fn0cddRR8Y1vfCN69+4dixcvjkGDBuVfM2jQoFi8eHFExBbZRvtNnDgxcrlcHHrooXHxxRe3atcddtghWlpa4t133+30bdtvv/0WPvru5fHHH49ddtklPvGJT+SfK+3fbbfdVj8mrNo5LpfLJbPN+bb9yp0nI5wrP0xSWEetsR2j3HkyIo0+1o/lFa6hKc27Te1L30kBneDaa6+Nfv36xRlnnBEREU888UT8+te/jl/84hfx6quvxuTJk7u4hlTzi1/8In7729/Ggw8+GLlcLq655pqurhKb4cEHH4yTTz45/1j/QtcrPU9GOFd+mFhHu5fS82SEPk5duTX0w0xI0U3tuuuu8fbbb8f69esjImL9+vWxZMmS2HXXXbu4Zlufm266Kf7+97/HrbfeGj16bJhyWT/U1dXF6NGj44UXXsg/v2jRovx7Fy1alH/tlthG+2Tt1bt37zj99NPjhRdeaNWuTU1N0aNHj9h+++07fRvt9/bbb8ecOXOivr4+/1y5/s2e149pqnaOS2kb7VPuPBnhXPlhkso6ao3dfOXOkxHp9DGtla6hKc27Te1LIUU3teOOO8a+++4bv/vd7yIi4ne/+13su+++bj3tZD/84Q9j3rx5MXny5Ojdu3dERLz33nuxevXqiIhobm6Oxx57LPbdd9+IiDjyyCPjL3/5S/6b4e+///4YOXLkFttG21atWhXLly+PiIhcLhfTpk2LfffdN/bff/9YvXp1PPfccxGxoV1POOGEiIhO30b7PfTQQ/Fv//ZvMXDgwIio3L8R+jFl1c5xKW2jbeXOkxHOlR8mKa2j1tjNV3qejEirjylWbg1Nad5tal/W5HK53Ga2DYl67bXXoqGhId5///3Ydttt46abboqhQ4d2dbW2Gv/3f/8XX/ziF+NjH/tY9O3bNyIiBg8eHF//+tfjyiuvjJqammhubo6DDz44Jk2aFP3794+IiJkzZ8bNN98cLS0tse+++8aNN94Y/fr122LbqO6NN96Ib3zjG7F+/fpoaWmJPffcM77zne/EzjvvHC+88EJcddVVsWbNmthtt93i5ptvjo985CMREZ2+jfY5/vjj4/LLL4+jjjoqIqr3b4R+TMF1110X06dPj3feeScGDhwY22+/ffz+97+veo5LaRsblOvHW2+9tex5cvLkyTF37lznygSV68c77rgjqXXUGtu2SutqROvzZIRzZaoq/awxefLkpObdpvSlkAIAAABIgl/3AAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAOtwVV1wRxx9/fOyzzz7x61//umjbQw89FF/+8pfjkEMOiaOOOiq+//3vR3NzcxfVFABIiZACAOhw++yzT1x99dWx3377tdr2wQcfxKRJk+Lpp5+OqVOnxtNPPx3/8z//0wW1BABSI6QAgG5m8eLFMX78+DjiiCPi8MMPj2uuuSZaWlritttui2OOOSY+85nPxCWXXBLLly+PiIg333wz9t5773jooYfi6KOPjsMPPzxuv/32/P7+/Oc/5+98+OxnPxvf+9732qzDf/zHf8RnPvOZ6NOnT6ttp59+ehx22GHRu3fv2GWXXaK+vj5eeOGF/Pbhw4fHz372s6ivr4+DDjooJk2aFO+88058/etfj4MPPjjGjh0b7733Xge0FACQGiEFAHQj69evj3PPPTcGDRoUjz/+eMyePTtGjRoVv/71r+Ohhx6Ke++9N2bOnBmrVq2Ka665pui9zz//fDz66KMxZcqUmDx5crz22msREXH99dfHWWedFS+88ELMmDEjRo4c2aF1njNnTuy1115Fz02fPj3uueeeeOyxx+IPf/hDnH322XHxxRfH008/HS0tLXHfffd1aB0AgDQIKQCgG/nzn/8cS5YsiUsuuST69esXffr0icMOOywefvjhGDt2bOy+++7Rv3//uPjii2PatGlF3wUxfvz46Nu3b+yzzz6xzz77xF//+teIiKitrY2FCxdGU1NT9O/fPw466KAOq+8DDzwQ8+bNi6997WtFz59xxhnxkY98JHbZZZc47LDD4pOf/GTst99+0adPnxgxYkS88sorHVYHACAdQgoA6EYWL14cgwYNitra2qLnlyxZErvttlv+8W677RbNzc2xdOnS/HMf+chH8v/fZpttYtWqVRGx4U6K119/PUaOHBknn3xy/OEPf+iQus6cOTN++MMfxt133x077LBD0bbCuvTp06focd++ffN1AwC6l9q2XwIAfFjsuuuusXjx4mhubi4KKnbeeed466238o8XLVoUtbW1seOOO8Y//vGPqvv82Mc+Fj/84Q+jpaUlpk+fHt/85jfjmWeeiX79+m1yPWfPnh3f+c534q677oq99957k/cDAHQv7qQAgG7kk5/8ZOy0007xgx/8IFatWhVr1qyJ559/Pr74xS/GlClT4o033oiVK1fGj370oxg5cmSrOy7K+d///d9oamqKHj16xLbbbhsRET16VP8IsXbt2lizZk3kcrlobm6ONWvWREtLS0REPPXUU/Htb387fvrTn8YnP/nJzT9oAKDbcCcFAHQjPXv2jDvuuCOuu+66OOaYYyIior6+PiZNmhRvv/12nHHGGbFmzZr43Oc+F1dccUW79vnkk0/GjTfeGKtXr45BgwbFj370o+jbt2/V9/znf/5nPPvssxERMXfu3Ljiiivi3nvvjcMPPzxuu+22WL58eZxzzjn51x966KHxs5/9bBOPGgDoLmpyuVyuqysBAAAA4Nc9AAAAgCT4dQ8AYKP99re/jauuuqrV84MGDYrf//73XVAjAKA78OseAAAAQBL8ugcAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQhNrOLrClpSVWrlwZvXr1ipqams4uHgAAAKggl8vFunXron///tGjR+ff19DpIcXKlStj/vz5nV0sAAAA0E7Dhg2LAQMGdHq5nR5S9OrVKyI2HHDv3r07u/it0rx582L//ffv6mqwmfRj96Afuwf92D3ox+5BP3YP+rH70JcffmvXro358+fnf3bvbJ0eUmS/4tG7d+/o06dPZxe/1dLW3YN+7B70Y/egH7sH/dg96MfuQT92H/qye+iqr2fwxZkAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEmq7ugJ0jrvvvjsaGxtbPb9s2bKIiBg4cGBnVylv6NChcfbZZ3dZ+QAAAKRBSLGVaGxsjHmv/C169t2+6Pn1q9+NiIh/LFvbFdXKlw8AAABCiq1Iz77bR789Pl/03Kq/z4qIaPV8Z8nKBwAAAN9JAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkobarK8CW9fjjj8eCBQu6uhpbpccffzwiIoYPH97FNQEAAPhwEFJ0czNmzIjly5fHgAEDuroqW50ZM2ZEhJACAACgvfy6BwAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQVsYdOmTYv6+vp48MEHY+LEiXHBBRfE6NGjY9y4cTF69OgYP358jBs3Lurr6+PLX/5yjB49On784x9HfX191NfXx9ixY+PEE0+M2267LV588cU45ZRTor6+Pr70pS9FfX19PProoxER0djYGKNHj47Ro0fHggULoqmpKcaPHx8nnnhiPPnkk/Gtb30rRo8eHd/61rdi7ty5rV47YcKEuPDCC2PChAmxbNmyorqfe+650djYGA0NDbFgwYJoaGiIZcuWRWNjY5x22mnx4osvxnnnnRf19fVx/vnn5+t54oknxp133pnfR7bfrLzx48fHKaecEl/+8pejvr4+XnrppXy7NTU15csp/H/h9okTJ8bo0aOjvr4+LrroojjxxBPjvPPOi4kTJxa9tpKmpqZ8vc8666yor6+P++67L0499dS48MILi+qbHXvW/hdddFFERFxwwQVRX18fp5xySqsyC99Xrv6Fz5W+NhsD1113XZx00knx5JNPttrHtddeG/X19XH99ddHU1NTvo/Hjx9f1I/l6pS164QJE/LtVfi4sbExLrzwwjj11FNjwYIFFduvoaEh5s6dG6eddlqbr6vUPtnYKjfGqr2/mmx8lGuHSvvLxns2p9pb/7bauT0aGxuLxl2l8bE5ZVXbZ1NTU37NGTNmTP49c+fOjZNOOik/N2+99daor6+Pn/zkJ2XLmD17dtTX18ef/vSnsuVX6pPM1KlT8+vlxrR79tzJJ5+cP45K47E72ZS50VnlZ2vruHHj8s9l54wFCxZUHSvtUbivSnUpfc3mHlN7tpeaMGFC1NfXx7e//e3N2s/GaGhoiPr6+pg0aVKH7ztTujakpqvnRkfalHG8KT7MbbYl6j5lypT858IPi7PPPjvq6+vjnHPO6eqqbLaeV1999dWdWeD69etjyZIlsfPOO0dtbW1nFr1VmjVrVqxduzb69OkT7yxbGb22H1q0fd17Gxa80uc7y7r3FsROA+vi2GOP7ZLyt6RZs2ZFRMQjjzwSEREvvvhiLF26NN57771obm6O999/P5qbm+O9996L999/PyIiWlpaorm5ORobG/P7+eCDDyIiYuXKlTFnzpz845aWloiIeO655+IrX/lKXH755dHU1BTNzc3x8ssvx5IlS+K5556LiIhnnnkmli5dGs3NzbFs2bKYM2dOrFq1qui1zzzzTCxbtiyWLl0aa9asiU996lMxYcKEiIhYvnx5vPLKK/Haa6/Fyy+/HK+99lqsWbMmfvOb38TSpUtjzpw5sXTp0oiIeP/99+PZZ5/N13P+/Pn5fWT7veeee+KZZ56J9957L9avX58/lmeffTZOOeWUiIi455574qmnnoo1a9bEvHnz8v//1Kc+ld/+9NNPR3Nzc0RsOEFl5RQeQzX33HNPvPDCCxERsXr16oiIePnll/PtVFjfp556Kl5++eV8OU1NTXH66afHHXfcEREb1rbSMgvfl7XZbrvtFoMGDSo6vtIyXnvttfwYeOuttyKXy8UzzzwTS5YsKSrjlltuiYiIN998M9asWRNz5szJj6lKbVDartnYKH38yiuvxOuvv54fI1/4whfKtt9TTz0Vc+bMiZUrV7b5urbap/Tf0rZpT58W7vvpp58u2w6V9peN92xOVXv94sWL47HHHqtYr42t8+WXXx5Lly7Nj7vSMV9tf+0tq9KYy8p79dVXIyJi3bp1cfrpp0dExEUXXRRr167Nz83rr78+IjZ8aM5eU+jCCy+MlpaWeOaZZ4rCjqz8Sn2SaWhoiIgN6+WaNWuqjpvS9eCpp56K9evX519XaTwWWrx4cQwaNKjqa1K2KXOjs8q/8847I2LDOSEbK9k4f/nll2PGjBkVx0p7FO7r4IMPLjsfC1/T1lhozzG1Z3up//qv/4qIiHfeeadozmzJvvvxj38cERFLliwpO087Quna0BE6cj529dzoSJsyjjdFR7ZZZ6+tW6K/r7jiiojYcC7ZUvOoo919990REbFixYrNrnNX/8zuTgrYgt55553I5XIdtr8VK1a0ei6Xy8V9990Xb7zxRv65hQsXFl0Nzn6QL7efhQsXxowZM4q2z5gxIx544IGiui9cuDByuVz+3xkzZuTLLK3XypUry9b/sccei8bGxpg5c2bF43vppZeiqakpZs2alS9n5syZkcvlYubMmfmrvpX2UXgM1RL1pqameOyxx6ruY/r06bFgwYJ8XRYuXFi0/d///d+LHk+bNq3oKnXh+7L6L1++vGjbzJkzW5VRbsw0NzcXtcG1115btD0Lw6q1QWm7Fvb79OnTix4XHuvChQtbXcEp3FfW/229Lqt7pfYp/be0bQrfX03p+Chsh0r1mTZtWr7dc7lcfv5Uev3y5csr1qvSeyppbGwsmr/Tp08vGvPV2qC9ZVUbczNmzGg1fsaMGRNz587Nz+UVK1bkP7BlSu+mmD17dn6taW5uLrpCXq1PMlOnTi16/Nhjj1UdN4XrQfZcoXLjsTvZ2HHWmeWfd955Ra8dN25c0ThfuHBhxbHSHqX7eu2111rVpfQ17RkLbbXpxrZ5FnxmsrsptmTfZUFfZkvcTVG6NqR2N0VXz42OtCnjeFN8mNtsS9R9ypQpRY8/DHdTnH322UWPP+x3U7iVoZt7991345///Gf885//jJbmnl1dnVZamldHY2NjXHbZZV1dlQ7X2NgYq1at6pSyfvWrX7V6bmPCkdIQo7m5udUCXWrdunXt3n9m/fr18YMf/KBVeYVuvPHGOPLII/N3VxS+tqWlJe6///7I5XJV95G97/777y+61bjQ/fffny+j2j5uueWWiq8rvGpbuN9x48aV3X9LS0v88Y9/jFdeeSW/reX/s3fvYVKVd57Af9Xd0M0tSiMgt0ZhBuIVjU5MZtGIchEiGTcq9j4TlcyOiaBxI7Q0ZJKJQ4wBjZcxgyQ6u9mYZ8fMxEQ3KipeYhJnNpdxjJfEiUm8YBB1IpgEUKGh9g+fU1NVXVXdQNP9gp/PP1B1Tr3nPe/lnK7vOVW1c2fNbZTL2uDHP/5xl+uWt0FxnbLQo3jdWr74xS/GqlWrSvazUp1rrZfVvVr7lCtvm+LX1/KNb3yjZH+K26FafbI7YjI33nhjnHbaaVXX/973vle1XtVeU012R0xxfXO5XLfaoLvbKl+vuMxKfb9ly5ZYuXJlyXM//elPSx7ff//9cckllxQeX3fddSXLr7nmmpg6dWph+9X6JHPLLbeUvL6rcVN8PKg2lsrH4/5kV8dZb25//fr1Jev+5je/6TTOixWPle4oL+ub3/xmp7o8+eSTnV7T1Vjoqk13tc2zOwkz//7v/75b5eyKn/3sZyWPy9uhJ5QfG1asWBG33nprj29nd/X13OhJ5WN9bx3T9uU22xt1v+2220oe/9M//VOce+65e1Tm3vbyyy+XPN6wYUMf1aRnuJMC6KQn7/4oV+1OgczmzZvj4YcfLryhyefzhfU7Ojriu9/9bjz88MNd1jGfz8d3v/vdqssffvjhbte3qzfwxbJtFu9DpqOjI5588smSZR0dHbu0jawNuqO8DcrbdVeU30VSaf+6Wq+47tVeX6y8bbq77+Xjo7gdqtWnvD2yx9XWf/LJJ6vWq9prqim+i6J8+121QXe3VWvMVRsL1e6IqqbSeC/efrU+6U65tfqv1lgqH4/7k10dZ329/UrjPLMrx9hKZb355pud6lK+TnfGQlf71FNt3td9t6fKjw2V7vLsS/t6+xbbnXG8O/blNtuX60517qTYzx144IFRX18fQ4YMiaeffaWvq9NJXUNTTJgwMr7whS/0dVV63LJly+Kpp57q62rsllwut9eCipaWlnjxxRerlj948OA48cQT4/777y+5opzP56OhoSGmTZtWuB2/Vh1zuVxMmzat6vKTTz654kckKtX3pZde6vYf0dk2Tz755MI+ZBoaGuKoo46K4cOHF5Y1NDTE6NGju72NrA3WrFnT5brlbVBcp13t45aWlpLHlfavq/Wyutd6fbHytil+fS0nn3xyyfgobodq9Slvj2zcVVv/qKOOip/+9KcV61XtNdWMGzeu0x+iWX26aoPubqt8veIyq42FQYMG7VJQ0dDQ0Gm8F2+/Wp90p9xa/Zd9ZKXSWCofj/uTXR1nfb39SuM8s6ufdy4vq6mpKTo6Okrq8uSTT5as052x0NU+9VSb93Xf7anyY8PgwYP7sDad7evtW6x8rO+tY9q+3Gb7ct2pzp0UsBcdfPDBvbKdefPmdXoue5PVHeV/IDY0NMT5559f8zX9+vXrdvmZ+vr6WLx4cc0/SJcuXRqtra1RV1dXqEu2fl1dXbS2tkZra2uXf9Q2NDTU/DK24m3UKqOtra3qevX1nT9ClW2zUvl1dXXxgQ98oGRZXV1dzW2Uy9rgve99b5frlrdBtXat9LhcW1tbyeNq7VdrvazutV5frLxtil9fS/n4KG6HavUp/wz9woULa67/gQ98oGq9qr2mmvI2Kx/ztdqgu9uqNeYq9fugQYOivb295Lljjjmm5PGMGTNKHme/eJMp/jx+rT7JnHfeeSWPuxo3xceDamOpvG33J7s6znpz+2PGjClZd+zYsTX7ovy7G7pSXtbZZ5/dqS7l63RnLHTVprva5pMmTSp5/O53v3u3ytkVRxxxRMnjo446qsfKzpQfG8q/B6Ov9fXc6Em7M453x77cZnuj7uVfBlvp7+zUlL/nGDVqVB/VpGcIKWAvOuigg3YpLOhKpasVuVwuzj333Bg3blzhuZaWljjttNMKj8vfhBSX09LS0unNxowZM+Kss84qqXtLS0vkcrnCvzNmzChss7xegwYNqlj/WbNmxYQJE6r+msvgwYNjypQp0dzcHKeeemphO9OnT49cLhfTp0+PoUOHRnNzc5e/CDNjxowYOnRo1eXNzc0xa9asmmXMnDkzDj300EJdyq9g3HHHHSWP58yZU9hm8T5kbTZ9+vQYMmRIybLp06d32kalMdPQ0FDSBuVfYjh79uwu26C8XYv7febMmSWPi/e1paUlDj300JKyi8vK+r+r9bK6V2uf8n/L26b49bWUj4/idqhWnzlz5hTaPZfLFeZPtfWHDBlStV7VXlPNhAkTSubvzJkzS8Z8rTbo7rZqjbkZM2Z0Gj/f+MY34thjjy3M5cGDB3f6stbi76OIiDjppJMKx5qGhoaS7xio1SeZs88+u+TxrFmzao6b4uNB9lyxSuNxf7Kr46w3t1/+HS+rV68uGectLS1Vx0p3lJc1ceLETnUpX6c7Y6GrNt3VNr/mmmtKHl999dW7Vc6uWLFiRcnjK6+8ssfKzpQfG6ZMmdLj29gTfT03etLujOPdsS+32d6oe/mFutS/jyLiCvgHjgAAIABJREFUP3/ZI3PTTTf1UU16hpAC9rLsCu38+fNj8uTJ0dLSEk1NTTF27NhoamqK8ePHx9ixYyPi7bsTmpqaSv6YHzZsWORyuRgxYkS0t7dHY2NjRPxn8JBd8W1ra4umpqZoamqKtra2aG1tjfHjx0cul4tFixbFhAkToqmpKSZMmBBLlizptO6kSZNi4sSJMWnSpE5Xl0ePHh2LFy+Oww8/PNra2uLwww8vXKkaOHBgtLe3F66cjRs3LpYuXRqNjY2Ry+Xi9NNPL5RRfDV00qRJMX78+GhsbCzclVF8Naa1tbWwneL/Fy+fPHlyNDU1RUTEH/3RH0Uul4sxY8bE5MmTu33FPat3dlKbN29eDBgwICZOnFhS32zfs/b/oz/6o4j4zzfzjY2NFa+6lbdZpf2rtG42Bk444YSoq6uLRYsWdSoju5vife97X7S2thb6ePz48SX9WKlOWbtOmjSp0F7FjxcvXhwTJ06MAQMGVL1yk5W1ZMmSGDhwYJfrVWufbGxVGmO1Xl9LNj4qtUO18rLxns2p7ta/q3bujra2tpJxV2187Mm2apVZ/NrikLG9vT3q6uoKc/PUU0+NiM53UWSyuykqXRmv1SeZ7G6K+fPn71K7Z8/179+/8Nz+fBdFZnfmRm9tPzu2Zue3iCicM9ra2mqOle4oLqtaXcrX2dN96s7yctndFNldFLtbzq7I7qbYG3dRZMqPDanp67nRk3ZnHO+OfbnN9kbds7sp9oW7KDLZ3RT7+l0UERG5/N78hrwKst9jP/LIIwt/7LP3LFu2LP7whz8UvpNi4PhTS5ZvfeHBiIhOz/eWrS88GIftx99JERE9tm+PPvpoHHfccT1SFn1HP+4f9OP+QT/uH/Tj/kE/7j/05b6vr9+zu5MCAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEhCQ19XgL1rxowZ8dxzz8WvfvWrvq7KO86MGTP6ugoAAAD7FCHFfu6UU06JRx99VEjRB0455ZS+rgIAAMA+xcc9AAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkNPR1Beg9O958Pba+8GCn5yKi0/O95e3tj+yTbQMAAJAWIcU7xIQJEyo+v2lT/4iIGDp0aG9Wp8jIqnUDAADgnUVI8Q5xwQUX9HUVAAAAoCbfSQEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJKGhtzeYz+cjImLbtm29vel3tLfeequvq0AP0I/7B/24f9CP+wf9uH/Qj/sH/bj/0Jf7tuy9evbevbfl8r285T/84Q/xzDPP9OYmAQAAgF0wadKkGDJkSK9vt9dDip07d8aWLVuiX79+kcvlenPTAAAAQA35fD62b98egwYNirq63v+GiF4PKQAAAAAq8cWZAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEASGvq6Auye5557LpYuXRqvv/56HHjggbFy5co45JBDStbZsWNHXHHFFfGDH/wgcrlcfOxjH4uzzz67y2X0nu7046pVq2LNmjVRV1cX/fr1i0svvTROPPHEiIhYunRp/Mu//EsMHTo0IiJOO+20WLBgQW/vxjted/rxS1/6UvzDP/xDjBgxIiIi3vOe98RnP/vZiIh44403YtmyZfGzn/0s6uvro729PaZNm9bbu0F0ry+XLFkSv/jFLwqPf/GLX8SqVavi1FNPrdnP9I6VK1fGfffdF+vXr48777wzJk2a1Gkd58f0dacfnR/T151+dH7cN3SnL50f07Zp06ZYsmRJrFu3Lvr37x/jx4+P5cuXR3Nzc8l6teZdr83JPPukc889N3/HHXfk8/l8/o477sife+65nda5/fbb83/xF3+R37FjR/61117Ln3jiifkXX3yxy2X0nu704/e///381q1b8/l8Pv/000/njzvuuPwbb7yRz+fz+fb29vzXv/713qswFXWnH2+44Yb8ihUrKr7+S1/6Uv6v/uqv8vl8Pv/cc8/l//RP/zS/efPmvVdhqupOXxZ7+umn8+9973vzb731Vj6fr93P9I6f/OQn+Zdeeik/bdq0/C9+8YuK6zg/pq87/ej8mL7u9KPz476hO31ZzPkxPZs2bcr/8Ic/LDxesWJFftmyZZ3WqzXvemtO+rjHPui1116Ln//853H66adHRMTpp58eP//5z2Pjxo0l661ZsybOPvvsqKuri+bm5pg+fXrce++9XS6jd3S3H0888cQYMGBARERMnjw58vl8vP76671eXyrrbj/Wcs8998Q555wTERGHHHJIHHnkkfH9739/r9SX6nanL2+77baYO3du9O/fv7eqSReOP/74GDVqVM11nB/T151+dH5MX3f6sRbnx3Tsal86P6bnwAMPjBNOOKHw+JhjjomXXnqp03q15l1vzUkhxT5ow4YNMXLkyKivr4+IiPr6+hgxYkRs2LCh03qjR48uPB41alS8/PLLXS6jd3S3H4vdcccd0dLSEgcffHDhua9+9asxd+7cWLhwYfz617/e6/Wm1K7049133x1z586Nv/iLv4jHHnus8PxLL70UY8aMKTw2H/vGrs7Jbdu2xZ133hlnnnlmyfPV+pl0OD/uf5wf923Oj/sX58f07dy5M2699dY45ZRTOi2rNe96a076TgrYR/z4xz+Ov/3bv43/9b/+V+G5Sy+9NIYPHx51dXVxxx13xF/+5V/GAw88UHiTRTpaW1vjwgsvjH79+sU///M/x8KFC2PNmjWFz0uz73nggQdi9OjRcdhhhxWe08/Q+5wf922Om/sf58f0fe5zn4uBAwfGRz7ykb6uSkXupNgHjRo1Kl555ZXYsWNHRLz9JV+vvvpqp1uwRo0aVXILz4YNGwpXGGoto3d0tx8jIh577LG47LLLYtWqVTFhwoTC8yNHjoy6uren8RlnnBFbt251haGXdbcfhw8fHv369YuIiP/yX/5LjBo1Kn75y19GRMTo0aNj/fr1hXXNx76xK3MyIuJb3/pWp6tEtfqZdDg/7j+cH/d9zo/7H+fHtK1cuTJeeOGFuP766wvHyWK15l1vzUkhxT5o2LBhcdhhh8Vdd90VERF33XVXHHbYYZ2+mfW0006Lb37zm7Fz587YuHFjPPDAAzFr1qwul9E7utuPTzzxRFx66aVxww03xBFHHFGy7JVXXin8/wc/+EHU1dXFyJEj937lKehuPxb31dNPPx3r16+PQw89NCLeno//+I//GBERzz//fDz55JOFb6in93S3LyMiXn755Xj00Udj7ty5Jc/X6mfS4fy4f3B+3D84P+5fnB/Tdu2118ZTTz0Vq1atqvp9IbXmXW/NyVw+n8/3eKnsdb/+9a9j6dKl8fvf/z7e9a53xcqVK2PChAlxwQUXxCWXXBJHHXVU7NixI5YvXx7//M//HBERF1xwQeGLTmoto/d0px/PPPPMWL9+fckfV1dddVVMnjw55s+fH6+99lrkcrkYPHhwLFmyJI455pg+3KN3pu70Y3t7e/zsZz8r/FTeJZdcEh/4wAciImLr1q2xdOnSePrpp6Ouri4uu+yymD59eh/v1TtTd/oyImL16tXxzDPPxHXXXVfy+lr9TO+44oorYu3atfHb3/42hg4dGgceeGDcfffdzo/7mO70o/Nj+rrTj86P+4bu9GWE82PKfvnLX8bpp58ehxxySDQ1NUVExNixY2PVqlXxZ3/2Z3HTTTfFyJEja8673pqTQgoAAAAgCT7uAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAD7sJUrV8Ypp5wSkydPjmeeeaZbr3n99ddj0aJFMWvWrPjgBz8Yf/d3f7eXa9k9DX1dAQAAAGD3nXrqqXHeeefFn//5n3f7NUuXLo33ve99ce2110ZExH/8x3/srertEndSAABJ+OEPfxjnnntuHHfccXHKKaeULHvttddi0aJFMXXq1DjuuOOitbU1Hn/88T6qKQCk5fjjj49Ro0Z1ev7xxx+Pc889Nz784Q/Hhz/84Xj44YcjIuL555+PZ555Js4///zCusOHD++t6tbkTgoAIAkDBw6MM888M04//fT4yle+UrJs69atcdRRR8XSpUtj2LBhcdttt8XHPvaxeOihh2LQoEF9VGMASNfvf//7+OxnPxs33XRTjBgxIl599dU466yz4q677opf/epXMXLkyPirv/qrePrpp+Oggw6KJUuWxB//8R/3dbXdSQEAqdqwYUNcfPHF8b73vS9OOOGEWL58eezcuTNuvPHGmDZtWrz//e+PJUuWxB/+8IeIiPjNb34TkydPjttvvz1OPvnkOOGEE2L16tWF8p544on48Ic/HO95z3viT//0T+MLX/hCl3W44447Ytq0aXHCCSfEqlWr4pRTTol/+Zd/KZR3zjnnxPHHHx9Tp06N5cuXx7Zt2yIiIp/Px5VXXhnvf//74z3veU/MnTu3y8/IHn300XHGGWfEuHHjOi0bN25cfPSjH40RI0ZEfX19nHPOObF9+/Z47rnnIiLi29/+drS2tsaVV14Zxx9/fJx66qnxb//2b/Htb387PvCBD8T73//+uP3227vX8ACwH3jsscfiN7/5TVxwwQXxZ3/2Z3HBBRdELpeLF154IXbu3BmPP/54fPjDH47bb789zj777FiwYEFfVzki3EkBAEnasWNHfPzjH4/3ve998dBDD0V9fX08+eST8e1vfztuv/32uOWWW6K5uTna29tj+fLlcfXVVxde++ijj8a9994bzz//fJx11lkxc+bMmDhxYnz+85+P8847L84444zYsmVL/PKXv6xZh1/96lfxN3/zN3HzzTfH0UcfHdddd1288sorheV1dXWxbNmyOPLII+Pll1+OCy64IP7hH/4h5s+fH4888kj867/+a9x3330xZMiQePbZZ2PIkCE91j5PP/10bN++PcaPH1947oknnoizzz47fvSjH8UNN9wQixYtimnTpsX9998fP/7xj+MTn/hEzJw5050XALwj5PP5mDx5cvyf//N/Ki4fNWpUHH/88RERMXPmzLjsssti48aN0dzc3JvV7MSdFACQoCeeeCJeffXVWLJkSQwcODAaGxvj+OOPjzvvvDPmz58f48aNi0GDBsWiRYtizZo10dHRUXjtxRdfHE1NTfHud7873v3ud8e///u/R0REQ0NDrFu3LjZu3BiDBg2KY445pmYd7r333pg2bVocf/zx0b9//7jkkksil8sVlh955JFxzDHHRENDQ4wdOzbOOeec+MlPflLY1pYtW+LZZ5+NfD4fEydOjBEjRvRI22zevDmWLFkSF198cUnwMXbs2DjzzDOjvr4+5syZExs2bIiLLroo+vfvH1OnTo3+/fvHunXreqQOAJC6Y489Nl544YX44Q9/WHjuiSeeiHw+H0ceeWQMHDiwcMHiJz/5SRxwwAExdOjQvqpugTspACBBGzZsiNGjR0dDQ+mp+tVXX40xY8YUHo8ZMyY6OjritddeKzx30EEHFf4/YMCA2Lp1a0REfP7zn48bbrghZs+eHWPHjo2LL744pk2bVrUOr776ahx88MElZR144IGFx88991ysWLEinnrqqXjjjTdix44dccQRR0RExPvf//748z//81i+fHmsX78+Zs6cGe3t7TF48ODdbJG3vfnmm3HhhRfGlClT4uMf/3jJsmHDhhX+39TUFBGlbdHY2BhbtmzZo+0DQIquuOKKWLt2bfz2t7+Nj370o3HggQfG3XffHTfeeGNcffXVceWVV8b27dtj3Lhx8eUvfznq6uriyiuvjGXLlsW2bdtiwIAB8Xd/93clFyP6ipACABI0atSo2LBhQ3R0dJQEFSNGjIj169cXHr/00kvR0NAQw4YNi5dffrlmmYccckhce+21sXPnzli7dm1ccskl8aMf/SgGDhxYcf0RI0YUvvMh4u2A4PXXXy88vvzyy+Pwww+Pa665JgYPHhz/+3//77jvvvsKy88777w477zz4rXXXotPfvKT8fd///fxyU9+cpfbIrNt27a46KKLYuTIkbF8+fLdLgcA9jef/vSn49Of/nSn548++uj4+te/XvE1Rx11VNx22217u2q7zMc9ACBBRx99dAwfPjyuueaa2Lp1a7z11lvx6KOPxumnnx5f+9rX4sUXX4wtW7bEddddF7Nnz+50x0Ul//f//t/YuHFj1NXVxbve9a6IePt7JaqZNWtWPPTQQ/Fv//ZvsW3btvjSl74U+Xy+sHzLli0xaNCgGDRoUPz617+OW2+9tbDsiSeeiMcffzy2b98eAwYMiP79+9fcVkTEzp0746233ort27dHPp+Pt956q/BFnNu3b49LLrkkGhsbY+XKlV2WBQDsm9xJAQAJqq+vjy9/+ctxxRVXFD6SMXfu3PjUpz4Vr7zySnzkIx+Jt956K6ZOnRqf+cxnulXmD37wg1ixYkW8+eabMXr06LjuuusKH4uo5I//+I/jM5/5TCxatCjeeOONOO+886K5uTn69+8fERHt7e3xmc98Jv7n//yfcdhhh8WcOXMKn3vdsmVLXHnllfGb3/ym8J0Q//2///ea9fvJT34S5513XuHx0UcfHe9973vj61//ejz22GPx3e9+N5qamuJP/uRPCuvcfPPNhS/9AgD2fbl88SURAIAqtmzZEn/yJ38S9913X8WfCQUA2FPulQQAqnrooYfijTfeiK1bt8bKlStj0qRJMXbs2L6uFgCwn/JxDwB4B/vOd74Tn/3sZzs9P3r06Lj77rvjwQcfjCVLlhR+ruzaa6/do2/+/uAHPxgvvfRSp+f/5m/+Jj70oQ/tdrkAwP7Bxz0AAACAJPi4BwAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJCEht7e4M6dO2PLli3Rr1+/yOVyvb15AAAAoIp8Ph/bt2+PQYMGRV1d79/X0OshxZYtW+KZZ57p7c0CAAAA3TRp0qQYMmRIr2+310OKfv36RcTbO9y/f//e3jx06amnnoojjzyyr6sBnRibpMrYJGXGJ6kyNknVtm3b4plnnim8d+9tvR5SZB/x6N+/fzQ2Nvb25qFbjE1SZWySKmOTlBmfpMrYJGV99fUMvjgTAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEhCQ19XAFJy8803x+OPPx633XZbyfObNm2KiIihQ4f2+DYnTJgQF1xwQY+XCwAAsK8RUkCRZ599Nl548aWobzqw5Pkdb74eEREvb9rWo9vLygUAAEBIAZ3UNx0YA8efWvLc1hcejIjo9PyeysoFAADAd1IAAAAAiRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUkCRTZs2xc6ON/u6Gsl56KGH4qGHHurragAAAPu5hr6uAKRk06ZNke/Y3tfVSM79998fERGnnHJKH9cEAADYn7mTAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASEJDX1cASN/27dvjF7/4RcydO7dP61FXVxc7d+6suGzQoEGxZcuW3S67X79+sX379prrHHDAAbFly5bo6OjY5dcOGzYsXnvttYiImDt3bvz85z+P9evXx/Dhw+OVV16Jbdu2RWNjY7S2tsbXvva1wvZ+//vfR3Nzc+G1EREHHXRQ/Pa3v41x48bFlClT4q677op58+bF+PHj4+qrr44RI0bE7373u4iIGDNmTBx33HHxzW9+MyIihg8fHv/xH/9RqHd9fX1cddVVccABB8SVV14Z+Xw+zjjjjPjiF78Yy5cvj/Xr18fq1atj3rx5cdddd8XFF18c119/fWzbti0WLlwY3/ve96K1tTW+8IUvxIoVKyKfz8fSpUsLdR49enR88pOfjK985Svx8Y9/PL7yla/ESSedFKtXr47zzz8//vEf/zEiIq666qo49NBDY+PGjfGJT3wifv/738eQIUNi586dhX4dNmxY7NixI15//fUYNGhQbN26NRoaGiKXy0VHR0fs3Lkzmpub44033oiPfvSjsXr16mhpaYnPfe5z8f/+3/+L1atXx4gRI2LAgAGxbt26yOfz0dzcHNdff31s2rQpLrvssti2bVscfPDBccABB8S4cePigQceKBl3Q4cOjU2bNsXIkSPjd7/7XQwaNKjQN2PHjo0dO3bEhg0bIpfLFfopq2M+n4/t27dHS0tLXHHFFTF06NBCnz777LOxbNmyWLZsWdx8883x4osvRl1dXezYsSPa29vj8MMPL/THmDFjYv78+fH5z38+duzY0WnsXXTRRXHaaafFxo0b46qrriq0e3t7e+Tz+bjqqqvinHPOiSuuuCK2bdsW8+fPj3/6p3+Kiy++OP72b/82tm3bFp/73OdiypQpERGxZs2aWL16dUREjBo1Ks4444xYvXp1jBs3Lv7bf/tv8cUvfjE+8pGPxHHHHVfYl/b29kLd2tvb46677or29vYYOnRoPPvss9HW1hbbt2+PE044IX70ox/F/Pnz48wzzyzsw2OPPRZ//dd/XXg8Y8aMuP/+++PdCINDAAAgAElEQVSiiy6Ku+66K9atWxcLFy6Mm2++ObZt2xbt7e0xderUTvPusccei8svvzza2trilltuiZdffjlGjRoVy5Yti1WrVkU+n49TTz01Vq9eHQ0NDVFfXx9jxoyJ//E//kfcdNNNhTpXk7XNRRddFCNHjozLL788li9fHlOmTCm0fzY/zj///Pjyl79cGJNZWyxbtiwuvvjiuPbaa6OjoyPq6+ujoaEhxo4dG5/97Gdj6NChhbKOP/74+NrXvlaYj8uWLYtvfOMb0d7eHs8//3xcfvnlMW7cuHjhhRfiqKOOiiuvvDK+//3vx9VXXx319fWF8TR16tTYuHFjtLe3x8svv1zY97Fjx8bMmTNj9erVhXH3l3/5l7F9+/Y45JBD4ktf+lLJ/j/77LOxePHikmPivHnz4txzz42IKNS7vb29MAfLx2d5G2d9lrVjueuvvz4efPDBmDFjRlxyySUly4rL/M53vhO33XZbzJs3L/r37x/Lly8vKbN4jBWPn/J5M2fOnLjmmmviwgsvjK9+9asxatSouPzyywt1zto3l8vFggUL4qtf/WqMHj06zj///FixYkWsWLEiXnzxxbj66qsLc+j111+PT33qU4W+K+7j8vbYuHFjfP7zn4/f/e538corrxTa75vf/GbccsstMX/+/Dj22GNj2bJlsWLFijj00EMrtmE21j71qU/FrbfeGgcffHA8+OCDhe00NzfH6aefHrfcckthG8Wy/czGRfHYzrabbWfRokWxY8eOmDdvXnzwgx+suF/lsvplZVVqj6997WuFPj3yyCNL9nHlypXxyCOPxEknnRSXXXZZSZ3nzp0bd955Z1x00UXx8MMPl8yXauOsXLX+qbSsq8df/vKX4+67745DDjkknn/++Zg7d2587GMfq7jdWseYSn3zr//6r1XnR3Eb5/P5kvaO6Dz3Nm7cGJ/61Kdi/fr1nY6xxX3R1NRUGIvFx/Hyttm0aVNhm9mcaG9vj0GDBsVf//VfR2NjY1x99dWF+lRSPk56SqU5U6vP91Rxn1U6d+1ueZla5Wbrjh07Nj70oQ/FjTfe2GP16Em5fD6f780NvvXWW/HUU0/FkUceGY2Njb25aejSOeecE1vf3B5DJpceZLe+8PaJfOD4U3t0e1tfeDAOmzAyvvCFL/RouT3t/PPPj40bN/Z1NehCQ0NDpwClO1paWuKII46Ie+65p6ScwYMHx5YtW6L4NFG8jVwuFxFvB0SbN2+OlpaWyOfz8eKLL3Yq/8UXX4xx48YVlpWfelpaWmLVqlVx4403FuqxJ7JQICJizpw5cc8993TaZmbOnDnx5JNPdqr33jRnzpxYsGBB4fHChQvjxRdfjMGDB8fmzZtL1m1oaIgZM2aUtEutUC6Xy8V3vvOduPHGG+Pee+8ttPvs2bMjn8/HvffeW+iz8u1kfTt48OC49dZbIyLiQx/6UEnbFbdt9pqmpqZCEJbtS3G5O3bsiNmzZ8eCBQs6Lc/ceeedhf+3trZW3L/ibZfX4/bbb++0flZO+dxoaWmJdevWdSqneHnWZsX9VC5rm1wuFwMHDowtW7YU2i5r/+z58jFZ3BbV5m62XlZWeT2zOTp79uz43ve+16nN7rzzzviv//W/lpSdtVW1uZbVs9K4K+6jiM59Xb5eVu/Zs2cX5mD5+Cxv46zPisdgseKwvLw+xWWuWbOm8HxTU1O8+eabJWUWj7Hi8VM+b+rr66Ojo6Ni/0VESfsWr5PN5ZaWlnjppZc69W9x3xX3cXl7lPdT1n7F7ZDVNTuOVmrD4mNM+XG9XLaNYtl+ZuOieGxn2y3eTmb27NkV96tc9rric0H564r3OTsGZvtYaVyUj/3snFU8X6qNs3LV+qfSsq4eV7rgUz6WM7WOMZnivine32rztfhcXdx35eOmeOyVH2OrXbSqNSez82zxnGhoaIjGxsbCXCyuTyXl46SnVJoztfp8TxX3WaVz1+6Wl6lVbqVjVqX1+/o9u497ADVt3LhRQLGP2J2AIiJi3bp1sXbt2k7lbN68udMfssXbyOfzkc/nC292161bV/ENS3bXQvZvpT+O161bFz/96U97JKDI6pZZs2ZNzT/I16xZ06sBRUTEfffdF5s2bYqIt68MZdsvDw4i3m7z++67r+S5WncN5fP5+Na3vhUPPvhgSbvff//98cADD5T0Wfl2Mps3b47HH3+8YtsVP85e8+abb8bjjz9esi/F6+Tz+XjggQfiscceq9rW3/rWtyLi7Sta1faveNvl9XjkkUdK1i0up3xuZAFFeTnFy7M6Z/1Urrht8vl8YVubN2+ORx55pND+2fPF27nvvvvipz/9aaEtqs3dtWvXxnPPPVcoq1w2R9euXVuxzRYsWNCp7I6Ojrjnnnvi3nvvrbjNbDvZesU+8YlPFP5fqa8zX//612Pjxo2FehcHLOXjs7iNi/ssG4PFrr/++pLHN9xwQ+H/xdsrDigi3h6fxWWWj7Fs/BSXkY2BrP2K23/t2rWxadOm+P73v9/pmJgpPi5W6t+s7x544IF49tlnK7bHxo0b44EHHih5XT6fjyuuuKLkuawf1q1bF/fcc0+nNiw/xnR1fTLrs0zxfnZ0dMT9999fMrbXrVsXzz33XMUxkYVTteZS8evWrVsXjz/+eKf2yO4wzBTv49KlS0uWXX311Z36JtuvfD4f9913X81xVq54XJTvR/my4vlaqW/Lx3Dmpptu6vRcrWNMVufyvilWPD/K27j4/88991ynuffII490+rsgO8aW90Wx7Dhe3jZr164t2WZxnYvnYlafSsr3odp6u6rScadWn++p8j4rP3ftSXmZauVWO2b1RD16mjspoEhv30mx+dd3x4CGHTFhwoQeLbcnvfTSS0IKekWluwj2Z+VX03ta+VW17Cpid0/73bniWr7+0KFDq+5LQ0NDNDU11ezjO++8s+pdFF0pvxK0u+WUlzlz5syKV9HK7zApf11E7eCwu+O92pX4PVHp7pHuyq6UdjVuZ8+eHffff3/Vemfjs7iNy/us/IpxrSvQN954Y83tFZdZ/IavuD7Zx4q609Zz5syJtWvX7nG/NDQ0xOjRo0uuLGftUe0OmlrK+7areVmrnOxuivKrtJVUu5MuU2sulY+lwYMHx5tvvlnSHuXBU1d25c7Cru6mKB5b5ftRvqy8L8sf16pT+V0ItY4xWZ276pvuzNeWlpZ47bXXSuZEpbpmx9iuPvq7q3OyUn0q3SVRvg89dTdFpePOiSeeWLXP99Su3PWwO+XVKrfWeClfv6/fs7uTAqjp9ddf7+sq8A7xTgooIiK++93vRkTstbs4ql1F7K7uXHEtX7/WvnR0dHSrj3c3WCjf3z0NKLIys34qV6ttOjo6uvzDvLvjvdqV+D3RE9enuhq3Dz/8cM16F19JzNq4vM925ZjQ1faKy6w0Njo6OrpdRsTb87cn+qWjo6PTleWsPR5++OFd7qvy9bual90ppzv7We1OuuIyqs2l8tdt3ry5Ynvsil3pm67GWfG4KK9P+bLyvix/vCtq9X1W5+6WWatv1q1b12lOVCp3V+u/K/OpvD6VlO9DtfV2VaXjTq0+31OV7nroyfJqPd+dY3IqfHEm9KG6hqaYkPh3UvTUdwRAV95pd1JMmzYtIqLkuzp60r54J0XE7n8Jbnb3wp6WU15m1k/lat2NsD/fSZHpatyefPLJ3b6TImvj8j4bPHhwt+vT1faKy6x2J0V3y4h4e/7urTspsvY4+eST+/ROiuI69sSdFNXmUvlYKr+TYtq0aXv9TopaisdF+X6UL9uTOynK1ZqnWZ27W2at+bord1Lsil2ZT+X1qaR8H6qtt6sqHXfK76SoNnZ3R3nb7mq7dlVerXJrjZc9rUdPcycFUFNra2tfV4FeUF9f39dViPb29r6uQq+pr68vzK22trYu16+r27XT9fz58zu9pqGhYZf+CFm6dGlceOGFu7R+rX2pq6uLJUuWVF0+f/78iNj9cbB48eKSxz0xnurq6qoeA2u1zeLFi2v2WX19fbfq19DQEG1tbV32f7V+HTt2bMXnFyxYUPImtLsOOeSQwv9r9fW8efOitbW1UO/y+hePz+I2Lm+T8u8aOPXU0o9czpgxo/D/4u3VsnTp0optv3jx4m6X0dDQEK2trXHppZd2uW5X6urqSsZLcXu0trZW7NsTTjihannlt6R3NS+rWbhwYeH/5ftZqU5tbW1dzv9qc6n8dUuXLu3UHmeddVbVso844oiSxyeddFLNvik/35WPs3LlY7l4P8qXFc/XSn1bPoYzlT5CUesYk9W51n4Wz49afdPW1tZpTixevLhTO2XH2Fp9kR3HI0rbZlfOPdXqWv787ozrSiodd2r1+Z4q77Pyc9eeller3FrjZU/r0dOEFEBNzc3N0dzc3NfVoBt2NwVvaWmJmTNndipn8ODBnd7IFG8jl8tFLpcrXNFpaWmJcePGVSw/l8sV/q305qilpSWOOeaYmD179m7tQ7nibcyZM6fmG7I5c+ZUrPfeNGvWrMJPmk2YMKGw/UpX9BoaGmLWrFklzw0aNKhq2blcLs4888w49dRTS9p9xowZMX369JI+K99OZvDgwTFlypSKbVd+hTXi7V9PmDJlSsm+FK+Ty+Vi+vTpceyxx1Zt6+yn64499tiq+1e87fJ6lP98WnE55XOj+ApctfGY1bnaT88Vt00ulytsa/DgwTF16tRC+2fPF29n1qxZccwxxxTaotrcnTlzZhx66KGFssplc3TmzJkV2yz7adViDQ0NMXv27E4/MZnJtpOtV6z4J0gr9XXm3HPPjebm5kK9TzvttJK2Kh6fxW1c3GfZGCz2yU9+suRx8U8sFm9vzpw5Jes1NTWVlFk+xrLxU1xGNgay9itu/5kzZ8bQoUPjpJNO6nRMzBQfFyv1b9Z306dPjwkTJlRsj+bm5pg+fXrJ63K5XHz6058ueS7rh5aWlpg9e3anNiw/xnQVUGV9linez+y7O4rHdktLSxx66KEVx8Ts2bO7nEvFr2tpaYkpU6Z0ao/zzz+/5DXF+7hixYqSZZdddlmnvsn2K5fLxaxZs2qOs3LF46J8P8qXFc/XSn1bPoYzlX6CtNYxJqtzed8UK54f5W1c/P9DDz2009ybOnVqp78LsmNseV8UK/4J0uK2mTlzZsk2i+tcPBez+lRSvg899ROklY47tfp8T5X32Z7+9GelsV6t3GrHrJ6oR08TUgBdGj58eF9XISJqX02u9aatO/r169flOgcccEDFPza789phw4YV/j937tyYOHFiNDU1xbhx46J///4REdHY2Fhy8j/ggAMil8uVvDYi4qCDDoqIt/8wPf300yPi7SuXWUI+YsSIaGxsjMbGxpgwYUKcffbZhdcW92W/fv2iqakp2traorW1NSZPnhyTJk2KRYsWRV1dXcmV9Hnz5sXAgQNj0aJFhfouWLAgDj/88Ghvb4+BAwcWrqQNGDAgxowZE01NTTFhwoRoa2uLww8/vPBvVub5558fTU1NhTpEvH3l5V3veldERAwZMqSkX4cNGxYHHnhgRLzd37lcLvr16xf9+/cvjI3m5uYYMGBA4Urx+PHjo7W1tbDNESNGxPjx4wsn5ubm5mhtbY22trbCfh188MExefLkwpuD4nGX/aEycuTIaGpqKumbsWPHxqhRoyLi7RP/8OHDS+qYjZOWlpZOV2Xa2tpi4MCB0d7eXnhzlF3Fyq7wZmNm4sSJsXTp0mhqaqo49rIroK2trSXt3traWnhuyZIlhf2dP39+oW8bGxsjl8uVXFksvpo3atSowuNx48YVxsq8efNK9qW4bosXLy5sP1ueLcuuCBdffYvofGUruyK4cOHCQv8tXLiwsA/VrgC1t7dHXV1dLFq0KA4++ODCPrS1tRXGe7Y/2U/hTZgwoVOdq8leu3DhwsK2srbL2nrp0qUxcODAuPDCC0vGZNYWWdtnx5b6+vpobGyMiRMnllxRP/zwwwvHh2w+tre3F+qZbX/8+PEREXHUUUdFxH9eOSseT1mZWZtk+z5x4sTCPmXjLuur4rsoMm1tbZ2OicVjIat38RwsH5/lbVzejuWyK9HFV4krbS+72jtv3rw466yzOpVZPMaKx0/5vLn00kujrq4uFixYEAMGDIgJEyaU1Dlr31wuV1hn4sSJsWTJksJxsfjq5ahRo2LAgAElfVerPVpbW2PSpEkxcuTIkvY777zzIuLtuZONo+w4WqkNs3WWLl0ahx9+eKcr+s3NzYUyi++iKN/PbFwUj+3iK9ptbW2FsZbdUdOduVS+D5VeV9yn5fuYvcE66aSTOtU5u0th4cKFneZLV3dRZGrtR/myrh5/8IMfjIj/nFO1voiy1jGmfD8XL15cc34Ut3F5e0d0Hjetra0xZsyYQtnFivuieCzWapvibRbXOZuLjY2NXd4dUanePaFS23Z37O6O4v3vyfIytcrN1h07dmzhzqvU7qKI8OseUKK3f91j6wsPxmGJfydFRMSyZcsiIpKv5/7u0UcfjeOOO66vqwGdGJukzPgkVcYmqerr9+zupAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAkiCkAAAAAJIgpAAAAACSIKQAAAAAktDQ1xWAlAwdOjTe+I/f93U1kjNjxoy+rgIAAPAOIKSAIkOHDo2XN23r62ok55RTTunrKgAAAO8APu4BAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQD/v717D66yvPMA/suFiyAdAlYMghd21pbWbaFBtBercu8KaC+rtCPbVQutl7VMSyuWWtp6K3Vb61S8MS7uth2d2a1Vi60LCm3Xe6AtFkvFBcWsCQWSUBAkkOTdPzo5SzAJ4ZKcJ8nnM8NMznne932e5/09OZcv7zkBACAJQgoAAAAgCUIKAAAAIAlCCgAAACAJQgoAAAAgCUIKAAAAIAlCCgAAACAJQgoAAAAgCUIKAAAAIAlCCgAAACAJQgoAAAAgCUIKAAAAIAlCCgAAACAJQgoAAAAgCUIKAAAAIAlCCgAAACAJQgoAAAAgCUIKAAAAIAlCCgAAACAJQgoAAAAgCUIKAAAAIAlCCgAAACAJQgoAAAAgCUIKAAAAIAlCCgAAACAJQgoAAAAgCUIKAAAAIAlCCgAAACAJxfkeAKSmYc/22L3pybfdFxFvu/9o9BUx5KgeEwAAoKsSUsB+RowYETt37owBAwY0u7+2tndERJSUlBzlHofEiBEjjvIxAQAAuiYhBexn1qxZsXr16igrK8v3UAAAAHoc30kBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkQUgBAAAAJEFIAQAAACRBSAEAAAAkobizO8yyLCIi9u7d29ldQ7vV1dXlewjQImuTVFmbpMz6JFXWJilqeq/e9N69sxVkndzzzp07Y/369Z3ZJQAAAHAITjvttBgwYECn99vpIUVjY2Ps2rUrevXqFQUFBZ3ZNQAAANCGLMti37590b9//ygs7PxviOj0kAIAAACgJb44EwAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEjCEYcU3/rWt2LKlCkxffr0mDFjRvzhD3/ItW3bti0uu+yymDx5ckyfPj3WrFnToW1wJF599dW4+OKLY/LkyXHxxRfHa6+9lu8h0Y3U1tbGrFmzYvLkyTFt2rS4+uqro6amJiIifv/738f06dNj8uTJcdlll0V1dXVuv45og7bccccd8a53vSvWr18fEdYn+VdXVxcLFiyISZMmxbRp0+L666+PiLaftzuiDVqycuXKuPDCC+OCCy6I6dOnx7JlyyLC+qTzLVy4MMaNG9fsOTyi89fiUVmn2RFasWJFtnfv3tzP48ePz7XNmzcvW7RoUZZlWVZeXp5NnDgxa2xs7LA2OBIzZ87MHn744SzLsuzhhx/OZs6cmecR0Z3U1tZmzz33XO72d77zney6667LGhoasgkTJmTl5eVZlmXZokWLsnnz5mVZlnVIG7Rl7dq12eWXX56dd9552csvv2x9koQbbrghu+mmm3Kv97Zu3ZplWdvP2x3RBgdqbGzMxowZk7388stZlmXZunXrslGjRmUNDQ3WJ52uvLw8q6yszD2HN+nstXg01ukRhxT7q6mpyd773vdmDQ0NWZZl2ahRo7Lq6upc+/nnn5+tWbOmw9rgcG3bti0rKyvL6uvrsyzLsvr6+qysrKzZWoOj6fHHH88++9nPZmvWrMnOP//83P3V1dXZqFGjsizLOqQNWlNXV5dddNFFWUVFRe4FjvVJvr355ptZWVlZ9uabbza7v63n7Y5og5Y0NjZmY8eOzVatWpVlWZa98MIL2aRJk6xP8mr/kKKz1+LRWqfFh37tRet+8pOfxLnnnhuFhYVRW1sbWZbFoEGDcu2lpaWxefPmGD58+FFve9/73nc0p0IPU1VVFUOGDImioqKIiCgqKorjjz8+qqqqmq03OBoaGxvjgQceiHHjxkVVVVUMHTo01zZo0KBobGyM7du3d0jbwIEDO2eSdDm33357TJ8+PYYNG5a7z/ok3yoqKmLgwIFxxx13xPPPPx/9+/ePL37xi9G3b99Wn7ezLDvqbV4L0JKCgoL4wQ9+EFdeeWX069cvdu3aFffee2+bryutTzpTZ6/Fo7VODxpSfPzjH4/KysoW25555pncAB577LH4+c9/Hj/5yU/a3TlAT3TDDTdEv3794pJLLonly5fnezgQv/vd72Lt2rUxd+7cfA8FmmloaIiKiop4z3veE9dee22sWbMmvvCFL8Ttt9+e76FB1NfXxz333BN33nlnlJWVxerVq2POnDnx3e9+N99Dgy7toCHFz372s4MeZPny5XHbbbfF/fffH8cdd1xERJSUlERERE1NTS41qaqqihNOOKFD2uBIlJaWxp///OdoaGiIoqKiaGhoiC1btkRpaWm+h0Y3s3Dhwti0aVPcfffdUVhYGKWlpc2C4JqamigsLIyBAwd2SBu0pLy8PDZs2BDjx4+PiIjNmzfH5ZdfHjNnzrQ+yavS0tIoLi6OqVOnRkTE+9///igpKYm+ffu2+rydZdlRb4OWrFu3LrZs2RJlZWUREVFWVhbHHHNM9OnTx/okCW29x+mItXi01ukR/3WPlStXxi233BL33Xdfs0tEIyKmTJkSDz74YERErFq1Kvbs2ROnn356h7XB4Ro8eHCMHDkyli5dGhERS5cujZEjR7p8jqPq+9//fqxduzYWLVoUvXv3joiI008/Pfbs2ROrVq2KiIgHH3wwpkyZ0mFt0JLZs2fHU089FStWrIgVK1bECSecEPfdd1987nOfsz7Jq0GDBsWZZ54ZTz/9dET89Vvjq6ur45RTTmn1ebut5/TDbYOWnHDCCbF58+bYuHFjRERs2LAhqqur4+STT7Y+SUJHrLfOWKcFWZZlRzLxs846K3r16tWs4/vvvz9KSkpi69at8ZWvfCUqKyujT58+8a1vfSs+8IEPRER0SBsciQ0bNsS8efNix44d8Y53vCMWLlwYI0aMyPew6CZeeeWVmDp1apxyyinRt2/fiIgYNmxYLFq0KH7729/GggULoq6uLk488cS49dZbc1eldUQbHMy4cePi7rvvjtNOO836JO8qKiria1/7Wmzfvj2Ki4tjzpw5cc4557T5vN0RbdCSRx99NBYvXhwFBQUREXHNNdfEhAkTrE863Y033hjLli2Lbdu2RUlJSQwcODAee+yxTl+LR2OdHnFIAQAAAHA0HPHHPQAAAACOBiEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUA9HD/+7//G+9617uivr4+30PpFDNnzoz/+I//yPcwAIAWCCkAgKNi3Lhx8cwzz+R7GM388Ic/jLlz5+Z7GABAOwkpAAAAgCQIKQAgz6qqquLqq6+Os846K84888z49re/HY2NjXHnnXfGeeedFx/84Afjq1/9auzcuTMi/v/jGT/72c/i3HPPjTPPPDPuuuuu3PFefPHF+MQnPhEf+MAH4kMf+lDccssthzSen/70p/Gxj30sRo8eHePHj48HH3ww11ZTUxOf//znYzr3//IAACAASURBVMyYMTF27Nj4zGc+E42NjfGVr3wlKisr4wtf+EKMHj06Fi9e3Orxm8b/05/+NM4555w444wz4oEHHogXX3wxpk2bFmPGjIlvf/vbue0P91z85je/iXvuuSd++ctfxujRo2P69Om5Y77xxhsxY8aMGD16dFx22WVRU1NzSOcIAOgYxfkeAAD0ZA0NDfH5z38+zjrrrFixYkUUFRXFH/7wh3jooYfiZz/7Wfz7v/97DBo0KK699tr49re/Hbfeemtu39WrV8fjjz8er732WnzqU5+KSZMmxd/8zd/ETTfdFP/4j/8YF154YezatSteeeWVQxrT4MGD45577onhw4dHeXl5zJo1K/7u7/4u3vve98aSJUtiyJAh8eyzz0ZExJo1a6KgoCBuvfXWWL16ddx4443xoQ99qF39rFmzJpYtWxbl5eVxxRVXxNlnnx33339/1NfXx4UXXhhTpkyJsWPHHva5+OhHPxqf//znY9OmTfEv//IvzfpeunRpLF68OEpLS2PWrFnxr//6rz4WAgAJcCUFAOTRiy++GFu2bImvfvWr0a9fv+jTp0+MGTMmfv7zn8c//dM/xfDhw6N///7xpS99KX7xi180+3LLq6++Ovr27Rvvfve7493vfnf86U9/ioiI4uLieP3116Ompib69+8fo0aNOqQxnXvuuXHSSSdFQUFBjB07Nj784Q/HqlWrcsfeunVrVFZWRq9evWLMmDFRUFBwWHO/6qqrok+fPvGRj3wk+vXrF1OnTo3BgwfHkCFDYsyYMfHHP/4xIuKIzkVrPvGJT8Spp54affv2jSlTpsS6desOaw4AwNElpACAPKqqqoqhQ4dGcXHzixu3bNkSJ554Yu72iSeeGPX19VFdXZ2777jjjsv9fMwxx8Tu3bsjIuKmm26K1157LT72sY/FJz/5yVi5cuUhjenXv/51XHTRRTF27NgYM2ZM/OY3v4na2tqIiLj88svj5JNPjssuuyzGjx8f99577yHPucngwYNzP/fp0+dtt5vmcyTnojXvfOc7D2l7AKBzCCkAII9KS0ujqqrqbX/+8/jjj4833ngjd7uysjKKi4ubvZFvzSmnnBLf//7349lnn41Zs2bFNddc0+434Xv37o1rrrkmLrvssnj66adj1apV8dGPfjSyLIuIiGOPPTbmzZsXTz75ZNx1112xZMmS3Ec/OsqRnIvDvcoDAMgPIQUA5NH73ve+eOc73xnf+973Yvfu3VFXVxerV6+OqVOnxr/9279FRUVF7Nq1K2677bb42Mc+9rYrLlryyCOPRE1NTRQWFsY73vGOiIgoLGzfU/7evXtj7969MWjQoCguLo5f//rX8fTTT+faV65cGZs2bYosy2LAgAFRVFSUCwKOO+64qKioOIyz0LYjOReDBw+ON954IxobG4/6uACAo09IAQB5VFRUFHfffXds2rQpzjvvvPjoRz8av/zlL+OTn/xkTJ8+PS655JIYP3589O7dO66//vp2HfO///u/4/zzz4/Ro0fHTTfdFLfddlv07du3Xfsee+yx8fWvfz3mzJkTZ5xxRixdujTGjRuXa9+0aVNceumlMXr06Lj44ovj05/+dJx11lkRETF79uy46667YsyYMXHfffcd+sloxZGciylTpkRExJlnnhkf//jHj9qYAICOUZA1Xb8JAAAAkEeupAAAAACScPAPcwIAXd6jjz4aCxYseNv9Q4cOjccee6zL9wcAdA8+7gEAAAAkwcc9AAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQUd3aHjY2NsWvXrujVq1cUFBR0dvcAAABAK7Isi3379kX//v2jsLDzr2vo9JBi165dsX79+s7uFgAAAGin0047LQYMGNDp/XZ6SNGrV6+I+OuEe/fu3dndH5G1a9fG6aefnu9h0IHUuGdQ5+5PjXsGde7+1Lj7U+OeQZ27lr1798b69etz7907W6eHFE0f8ejdu3f06dOns7s/Yl1xzBwaNe4Z1Ln7U+OeQZ27PzXu/tS4Z1DnridfX8/gizMBAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAkCCkAAACAJBTnewBdxeLFi2PNmjWxePHiiIgoKSk56D4jRoyIWbNmdfTQAAAAoFsQUrTTxo0bY1NFZe725tq9bW7fsGd7Rw8JAAAAuhUhxSEo6jsw93O/k8e3ue3uTU929HAAAACgW/GdFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQUAAAAQBKEFAAAAEAShBQAAABAEoQU7VRbWxuN9Xs6tc8VK1bEihUrOrVPAAAAyBchRTvV1tZG1skhxfLly2P58uWd2icAAADki5ACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASIKQAgAAAEiCkAIAAABIgpACAAAASEJxvgdA615++eXYt29fTJs2Ld9DOSL9+/ePXbt2RUlJSbz11lsxYMCA2Lp1axQUFERhYWE0NDTktu3du3f0798/amtrIyKiV69eMXTo0Kirq4vNmzfHkCFDYseOHfG1r30tfvzjH0d9fX1kWRZ1dXVRWVkZV1xxRSxZsiRKS0vj0ksvjZtvvjlKS0tjzpw5sWjRoti3b180NDTE5s2bIyJi2LBhsWDBgsiyLL773e/GlClTmo29pqYmvvWtb0VVVVXMnz8/fvSjH0V9fX1ERBQVFcXXv/71KCkpabb9N77xjXj99dfjhhtuiOHDh8dNN90UBQUFceWVV8add94ZWZbFVVddFffee29ce+21kWVZXH/99VFRURFf+cpX4pFHHsmNc8uWLTF//vx44IEH4tprr42SkpKoqanJHXP+/PnN7mtoaMiNq2lO+++3/+22HGzb1toPpY+uYv85HXhOW9r25ptvjizL3rY2OsvGjRvjuuuui+985ztx6qmnNhvbkdam6RizZ8/Ord/OnGN3XF8t6SnzzKd8nOOeUteeMk+A9vCYeHiKvvnNb36zMztseuN1/PHHR3Fx18lI/vM//zP21TdGYXHfiIjoNXBEm9vv+8ur8c6SY2PChAmH3eePf/zjw943Jfv27YuIiD179kR9fX3s3r0715ZlWbNtGxoaYs+ePbnbjY2N8Ze//CXefPPNiIjYtWtX1NfXR3l5eVRVVUVtbW3U1tbGzp07IyJi1apVsW/fvqitrY3y8vLYvXt31NbWxksvvRQbN26M2tra+Mtf/hINDQ3R0NAQtbW1UVdXF2vXro1nn3029u7dG+eee26u/yVLlkR5eXnU19fHCy+80KzP6urqqKurizPOOKPZ9qtWrYqIiBdeeCF27twZzz//fFRXV8cf//jH2LhxY+7nDRs25PpevXp1REQ8//zzsXXr1tw4m/qtrKzM9bVkyZLcMQ+8b/9xNc1p/232v92Wg23bWvuh9JFPVVVVMXTo0HZtu/+cDjynLW373HPPtbg2Osv8+fOjuro6XnrppTj//PObje1Ia9N0jJdeeim3fjtzjocyh0OpcWq6yu9RCg63zvk4xz2lrkd7nl35d5n2UeOeoafWuas+9uf7PbuPeyTqqquuyvcQktYUWhxo/9Bj/21ef/31Vo+1bNmyeOKJJyLLsvj973+fu4qjpqYmli9f3mafy5cvb3P7ZcuWtTiG119/PbIsi+XLl8d//dd/5e5vukrjwLlmWRZPPPFEbNy4MZ544olm/b/66qvN7jtwTk37Pfnkk7nbTWNuSU1NTZvbttZ+sP26ov3ntHz58mbntKXzcmBtOvscbNy4MSoqKiLir2vs1VdfzY3tSGuz/zGa1m9n1rk7rq+W9JR55lM+znFPqWtPmSdAe3hMPHyupGinQ72Soq56Xbz5l5r47W9/G08++eQh/3vllVc6Y1rEX6/WaAo3CgoKYs+ePbkrD9avX9/mvk0fNWlt+wOvFGlp/4Nt06SgoCDWrl0b27dvb7b/Sy+91Oy+lua0du3a2LFjRzQ2NkZBQUG89dZbraa5S5YsiQ0bNrS6bWvtB9svJe1N8/efU5Zl0djYGBHR6nnZv/77r43OMn/+/NixY0fudtPVFEejNvsfo0ln1vlQ59BV/8emK/0epeBw6pyPc9xT6toR8+yqv8u0nxr3DD2xzl35sT/f79ldSQHx/2FCQ0NDrFy5MiIifvWrX7Vrv0PZvrV+26O+vj73P9j773/gfQceu2m/pqs06uvrc2Nuya9+9as2t22t/WD7dUX7z2n/QKm183JgbTr7HDRdRdGk6eqdo1Gb/Y/RpDPr3B3XV0t6yjzzKR/nuKfUtafME6A9PCYevq5zKUMXU1jcN0aMGBK33HLLYe3f1b8ss6spKCiILMuiqKgozjvvvIiIOPfcc+OXv/zlQfc7lO1b67c9iouLY+jQoVFRUdHsKonhw4c3u+/AYzftV1lZGfX19VFcXJwbc0vOPffcWL58eavbttZ+sP26ov3nVFBQEBGRO6ctnZfHH3+8WW06+xw0rYUmJ510Um5sR1qb/Y/RpDPr3B3XV0t6yjzzKR/nuKfUtafME6A9PCYePldSJKrpzQUdr7i4OHcZU0FBQcyYMSMiImbMmHHQy5uKi4vb3L6oqOig+xcWtu/XsLCwML785S8366O4uDjmzp37tn73n1PTfk39FBYW5sbckhkzZrS5bWvtB9uvK9p/Tgee05bOy4G16exzMHfu3BZvH43a7H+MJp1Z5+64vlrSU+aZT/k4xz2lrj1lngDt4THx8AkpErVo0aJ8DyFpxx57bIv3N/1v94HbtBX6TJo0KSZMmBAFBQUxatSo3J8HGjRoUEycOLHNPidOnNjm9pMmTWpxDCeddFIUFBTExIkTY/Lkybn7WwpFjj322CgoKIgJEybEiBEjmv3FmIkTJ8app576tr8is/+cmvYbP3587nZbfwJp0KBBbW7bWvvB9uuK9p/TxIkTm53Tls7LgbXp7HMwYsSIGD58eET8dY01/QnSo1Gb/Y/RtH47s87dcX21pKfMM5/ycY57Sl17yjwB2sNj4uHzcY+E9erVK/fnO7uy/v37x65du6KkpCTeeuutGDBgQGzdujUKCgqisLAwGhoactv27t07+vfvn/v22169esXQoUOjrq4uNm/eHEOGDIkdO3bEtddeGz/+8Y+jvr4+9wWFlZWVccUVV8SSJUuitLQ0Lr300rj55pujtLQ05syZE4sWLYp9+/ZFQ0NDbN68OSIihg0bFjNmzMh9t8M555zTbOwzZsyIl19+OaqqqmLevHnxox/9KHe5e1FRUYv/m/7HP/4xXn/99Zg3b14MHz48NmzYEAUFBXHllVfGnXfeGVmWxVVXXRX33ntvru+XXnopKioq4ktf+lI88sgjuXFu2bIl5s2bFw888ECzKxaajnngfQ0NDblxNc1p/232v92Wg23bWvuh9NFV7D+nA89pS9tu3LgxsizL2zmYO3duXHfddW+7quJo1KbpGLNnz86t387UHddXS3rKPPMpH+e4p9S1p8wToD08Jh6eguxQvrnvKKirq4u1a9fG6aefHn369OnMro/IxRdfHLv37IuivgMjIqLfyePb3H73pidj5BF8J0VExHXXXRcRcUTH4NCsXr06ysrK8j0MOpg6d39q3DOoc/enxt2fGvcM6ty15Ps9u497AAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJKM73ALqKkpKSeGvrjk7tc+LEiZ3aHwAAAOSTkKKdSkpKYnPt3k7tc9y4cZ3aHwAAAOSTj3sAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkQUgAAAABJEFIAAAAASRBSAAAAAEkozvcAupKGPdtzP+/e9GQ7th3SwSMCAACA7kNI0U4jRoyInTt3Rn19fURElJSUHGSPITFixIiOHxgAAAB0E0KKdpo1a1asXr06ysrK8j0UAAAA6JZ8JwUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkAQhBQAAAJAEIQUAAACQBCEFAAAAkITizu4wy7KIiNi7d29nd31U1NXV5XsIdDA17hnUuftT455Bnbs/Ne7+1LhnUOeuo+m9etN7985WkHVyzzt37oz169d3ZpcAAADAITjttNNiwIABnd5vp4cUjY2NsWvXrujVq1cUFBR0ZtcAAABAG7Isi3379kX//v2jsLDzvyGi00MKAAAAgJb44kwAAAAgCUIKAAAAIAlCCgAAACAJQgoAAAAgCUIKAAAAIAlCCgAAACAJQgoAAAAgCUIKAAAAIAlCinZ49dVX4+KLL47JkyfHxRdfHK+99lq+h0Q71NbWxqxZs2Ly5Mkxbdq0uPrqq6OmpiYiIn7/+9/H9OnTY/LkyXHZZZdFdXV1br/DbSO/7rjjjnjXu94V69evjwg17m7q6upiwYIFMWnSpJg2bVpcf/31EdH24/PhtpEfK1eujAsvvDAuuOCCmD59eixbtiwi1LirW7hwYYwbN67Z43NEx9RVzfOjpRq39RoswnN0V9Pa73GTA1+DRagxRyjjoGbOnJk9/PDDWZZl2cMPP5zNnDkzzyOiPWpra7Pnnnsud/s73/lOdt1112UNDQ3ZhAkTsvLy8izLsmzRokXZvHnzsizLDruN/Fq7dm12+eWXZ+edd1728ssvq3E3dMMNN2Q33XRT1tjYmGVZlm3dujXLsrYfnw+3jc7X2NiYjRkzJnv55ZezLMuydevWZaNGjcoaGhrUuIsrLy/PKisrc4/PTTqirmqeHy3VuLXXYFl2+M/DnqPzp7Xf4yx7+2uwLFNjjpyQ4iC2bduWlZWVZfX19VmWZVl9fX1WVlaWVVdX53lkHKrHH388++xnP5utWbMmO//883P3V1dXZ6NGjcqyLDvsNvKnrq4uu+iii7KKiorcE6Qady9vvvlmVlZWlr355pvN7m/r8flw28iPxsbGbOzYsdmqVauyLMuyF154IZs0aZIadyP7v4HpiLqqef619Aa2SdNrsCw7/Odhz9H5d2CNW3oNlmVqzJErzveVHKmrqqqKIUOGRFFRUUREFBUVxfHHHx9VVVUxaNCgPI+O9mpsbIwHHnggxo0bF1VVVTF06NBc26BBg6KxsTG2b99+2G0DBw7s1Pnw/26//faYPn16DBs2LHefGncvFRUVMXDgwLjjjjvi+eefj/79+8cXv/jF6Nu3b6uPz1mWHVabx/X8KCgoiB/84Adx5ZVXRr9+/WLXrl1x7733tvkcrMZdV0fUVc3Ttf9rsAjP0d1JS6/BItSYI+c7KegRbrjhhujXr19ccskl+R4KR9Hvfve7WLt2bXzmM5/J91DoQA0NDVFRURHvec974qGHHoq5c+fGP//zP8fu3bvzPTSOkvr6+rjnnnvizjvvjJUrV8Zdd90Vc+bMUWPoBrwG6568BqMjuZLiIEpLS+PPf/5zNDQ0RFFRUTQ0NMSWLVuitLQ030OjnRYuXBibNm2Ku+++OwoLC6O0tDQqKytz7TU1NVFYWBgDBw487Dbyo7y8PDZs2BDjx4+PiIjNmzfH5ZdfHjNnzlTjbqS0tDSKi4tj6tSpERHx/ve/P0pKSqJv376tPj5nWXZYbeTHunXrYsuWLVFWVhYREWVlZXHMMcdEnz591Lgbauu11eHWVc3TdOBrsIjwOqybaO012C233KLGHDFXUhzE4MGDY+TIkbF06dKIiFi6dGmMHDnSpYNdxPe///1Yu3ZtLFq0KHr37h0REaeffnrs2bMnVq1aFRERDz74YEyZMuWI2siP2bNnx1NPPRUrVqyIFStWxAknnBD33XdffO5zn1PjbmTQoEFx5plnxtNPPx0Rf/0G/+rq6jjllFNafXxu67Hb43p6TjjhhNi8eXNs3LgxIiI2bNgQ1dXVcfLJJ6txN3S4tVPzrqWl12ARXod1F629BvvIRz6ixhyxgizLsnwPInUbNmyIefPmxY4dO+Id73hHLFy4MEaMGJHvYXEQr7zySkydOjVOOeWU6Nu3b0REDBs2LBYtWhS//e1vY8GCBVFXVxcnnnhi3HrrrXHcccdFRBx2G/k3bty4uPvuu+O0005T426moqIivva1r8X27dujuLg45syZE+ecc06bj8+H20Z+PProo7F48eIoKCiIiIhrrrkmJkyYoMZd3I033hjLli2Lbdu2RUlJSQwcODAee+yxDqmrmudHSzX+wQ9+0OprsIjDfx72HJ0frf0e72//12ARasyREVIAAAAASfBxDwAAACAJQgoAAAAgCUIKAAAAIAlCCgAAACAJQgoAAAAgCUIKAOiGzj///Hj++efzPQwAgEPiT5ACAAAASXAlBQAAAJAEIQUAdEPjxo2LZ555Jn74wx/GNddcE3Pnzo3Ro0fHtGnT4tVXX4177rknPvjBD8Y555wTTz31VG6/mTNnxve+97341Kc+FR/4wAfiiiuuiO3btx+0v1WrVsWMGTNizJgxcc4558RDDz0UERE7d+6Mr371q3HWWWfFeeedF3feeWc0NjZGRMRDDz0Un/70p2PhwoVxxhlnxLhx4+LXv/517pgPPfRQjB8/PkaPHh3jxo2LRx999CifJQAgNUIKAOjmVq5cGRdccEGUl5fHyJEj4/LLL4/Gxsb4zW9+E1dddVV84xvfaLb9ww8/HDfffHM89dRTUVxcHDfe5J9FJgAAAzBJREFUeGObx3/jjTdi1qxZcckll8Szzz4bDz/8cIwcOTIiIm644YbYuXNnPPHEE/GjH/0oHnnkkfjpT3+a2/fFF1+MU089NZ577rn43Oc+F/Pnz48sy2L37t1x4403xuLFi+N3v/tdPPjgg7ljAgDdl5ACALq5MWPGxNlnnx3FxcUxZcqUqK2tjdmzZ0evXr3i7//+7+ONN96IHTt25La/4IIL4rTTTot+/frFF7/4xXj88cejoaGh1eMvXbo0PvShD8XUqVOjV69eUVJSEiNHjoyGhob4xS9+EV/+8pfj2GOPjWHDhsWll17a7IqIoUOHxkUXXRRFRUXx8Y9/PLZu3Rrbtm2LiIjCwsJ45ZVXYs+ePXH88cfH3/7t33bcSQIAkiCkAIBubvDgwbmf+/btGyUlJVFUVJS7HRGxe/fu3DalpaW5n4cOHRr79u2L2traVo9fVVUVJ5100tvur62tjX379sXQoUObHe/Pf/5z7vZxxx2X+/mYY47JjaVfv35x2223xYMPPhgf+chHYvbs2bFhw4Z2zxkA6JqEFABAM1VVVc1+bro6ojWlpaXx+uuvv+3+kpKS6NWrV1RWVjY73pAhQ9o1jrPPPjuWLFkSTz31VIwYMSKuv/76Q5gFANAVCSkAgGYeffTR+J//+Z9466234vbbb4/JkyfnrrxoybRp0+KZZ56JX/ziF1FfXx+1tbWxbt26KCoqiilTpsRtt90Wb775ZrzxxhuxZMmSmD59+kHHsG3btnjiiSdi9+7d0bt37+jXr18UFnrZAgDdnWd7AKCZCy64IObNmxcf/vCHY+/evTF//vw2tx86dGgsXrw4lixZEmPHjo0LL7ww/vSnP0VExPXXXx/HHHNMTJgwIT7zmc/E1KlT45Of/ORBx9DY2Bj3339/nH322TF27NgoLy+Pb37zm0djegBAwgqyLMvyPQgAIA0zZ86M6dOnxz/8wz/keygAQA/kSgoAAAAgCcX5HgAAkL5HH300FixY8Lb7hw4dGo899lgeRgQAdEc+7gEAAAAkwcc9AAAAgCQIKQAAAIAkCCkAAACAJAgpAAAAgCQIKQAAAIAk/B/+6Szphp5YKwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1296x1800 with 4 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is shows us that there is a stronge present of outliers in the dataset, this will affect our model performance. The next phase of this project will handle this problem of outliers.  "
      ],
      "metadata": {
        "id": "yMOvDpiovQE8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(nrows=7, figsize=(18, 25))\n",
        "\n",
        "plot_distribution(client_df, 'forecast_cons_12m', axs[0])\n",
        "plot_distribution(client_df, 'forecast_cons_year', axs[1])\n",
        "plot_distribution(client_df, 'forecast_discount_energy', axs[2])\n",
        "plot_distribution(client_df, 'forecast_meter_rent_12m', axs[3])\n",
        "plot_distribution(client_df, 'forecast_price_energy_off_peak', axs[4])\n",
        "plot_distribution(client_df, 'forecast_price_energy_peak', axs[5])\n",
        "plot_distribution(client_df, 'forecast_price_pow_off_peak', axs[6])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iO0c45XYomHe",
        "outputId": "e3cbafb5-46a5-4787-d1ea-c0597db7a75d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1296x1800 with 7 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can observe that our data are mostly positivly skewed and this can be seen from the distribution of our data, and high values with long tail in our graphs."
      ],
      "metadata": {
        "id": "eTNE36WW7wVe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Price Sencitivity Hypothesis Testing\n",
        "---\n",
        "\n",
        "In this section we will be defining what price sencitivity, how we are mearsuring it with our dataset, and explaining how changes in price of our services can affect the churning rate of our client. "
      ],
      "metadata": {
        "id": "dC47gXmv28E6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### What is Price Sensitivity?\n",
        "\n",
        "Price sensitivity can be define as the degree of responses of a feature to a change in the price. \n",
        "\n",
        "In testing our client hypothesis on whether churn has a significant relationship with change in price, we will be deriving the correlation between price and churn over time. "
      ],
      "metadata": {
        "id": "Ocdt_2go5cYb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Measuring rice Sensitivity to Churn Rate.\n",
        "\n",
        "To measure the price sensitivity of our data we will be measuring correlative relationship that exist between churn and price. Base off the result we get from this measurement we will be able to determine whether there exist a negative or positive relationship between churn and price. If the relationship between the churn and price is positive, we will not reject the null hypothesis stating that there exist a relationship between churn and price. Other wise, we will accept the Alternative Hypothesis that there exist no relatonship between churn and price. The following steps will be taken to clean, sort, and measure our data in other to test our client hypothesis:\n",
        "\n",
        "\n",
        "\n",
        "*   STEP 1: Convert the `price_date` column into date_time formate. This is will make it easy to sort and manupulate our data according to the date of each customer.\n",
        "\n",
        "*   STEP 2: Get the average price for a year, half the year, and three month( a quarter of a year).\n",
        "\n",
        "*   STEP 3: Change the names of all columns in the price dataset to reflect the new dataset, after this we will add the price dataset `price_off_peak_ver` with `price_off_peak_fix` and do the same for the ramaining 4 columns. \n",
        "\n",
        "*   STEP 4: Marge the a`client_churn` and `new_price`dataset into one dataframe using the `i.d` as the primary key. \n",
        "\n",
        "*   STEP 5: Perform a correlation on the new marge data to measure the relationship between churn and price. Following this we will create a heat map to visualize this relationship.\n",
        "\n",
        "*   STEP 6: Marge the `price_analysis` dataset with the `client_df` and dropping the churn column."
      ],
      "metadata": {
        "id": "O3euKa_d4keE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "STEP 1: CONVERTING THE STRICK DATASET TO A DATE_TIME FORMATE.\n",
        "\"\"\"\n",
        "client_df['date_activ'] = pd.to_datetime(client_df['date_activ'])\n",
        "client_df['date_end'] = pd.to_datetime(client_df['date_end'])\n",
        "client_df['date_modif_prod'] = pd.to_datetime(client_df['date_modif_prod'])\n",
        "client_df['date_renewal'] = pd.to_datetime(client_df['date_renewal'])\n",
        "price_df['price_date'] = pd.to_datetime(price_df['price_date'])\n",
        "\n",
        "client_df.info()\n",
        "price_df.info()\n",
        "\n"
      ],
      "metadata": {
        "id": "ammkQKtVuW3P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7896534-880f-45d8-fbe8-f28162a4bfa7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 14606 entries, 0 to 14605\n",
            "Data columns (total 26 columns):\n",
            " #   Column                          Non-Null Count  Dtype         \n",
            "---  ------                          --------------  -----         \n",
            " 0   id                              14606 non-null  object        \n",
            " 1   channel_sales                   14606 non-null  object        \n",
            " 2   cons_12m                        14606 non-null  int64         \n",
            " 3   cons_gas_12m                    14606 non-null  int64         \n",
            " 4   cons_last_month                 14606 non-null  int64         \n",
            " 5   date_activ                      14606 non-null  datetime64[ns]\n",
            " 6   date_end                        14606 non-null  datetime64[ns]\n",
            " 7   date_modif_prod                 14606 non-null  datetime64[ns]\n",
            " 8   date_renewal                    14606 non-null  datetime64[ns]\n",
            " 9   forecast_cons_12m               14606 non-null  float64       \n",
            " 10  forecast_cons_year              14606 non-null  int64         \n",
            " 11  forecast_discount_energy        14606 non-null  float64       \n",
            " 12  forecast_meter_rent_12m         14606 non-null  float64       \n",
            " 13  forecast_price_energy_off_peak  14606 non-null  float64       \n",
            " 14  forecast_price_energy_peak      14606 non-null  float64       \n",
            " 15  forecast_price_pow_off_peak     14606 non-null  float64       \n",
            " 16  has_gas                         14606 non-null  object        \n",
            " 17  imp_cons                        14606 non-null  float64       \n",
            " 18  margin_gross_pow_ele            14606 non-null  float64       \n",
            " 19  margin_net_pow_ele              14606 non-null  float64       \n",
            " 20  nb_prod_act                     14606 non-null  int64         \n",
            " 21  net_margin                      14606 non-null  float64       \n",
            " 22  num_years_antig                 14606 non-null  int64         \n",
            " 23  origin_up                       14606 non-null  object        \n",
            " 24  pow_max                         14606 non-null  float64       \n",
            " 25  churn                           14606 non-null  int64         \n",
            "dtypes: datetime64[ns](4), float64(11), int64(7), object(4)\n",
            "memory usage: 2.9+ MB\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 193002 entries, 0 to 193001\n",
            "Data columns (total 8 columns):\n",
            " #   Column              Non-Null Count   Dtype         \n",
            "---  ------              --------------   -----         \n",
            " 0   id                  193002 non-null  object        \n",
            " 1   price_date          193002 non-null  datetime64[ns]\n",
            " 2   price_off_peak_var  193002 non-null  float64       \n",
            " 3   price_peak_var      193002 non-null  float64       \n",
            " 4   price_mid_peak_var  193002 non-null  float64       \n",
            " 5   price_off_peak_fix  193002 non-null  float64       \n",
            " 6   price_peak_fix      193002 non-null  float64       \n",
            " 7   price_mid_peak_fix  193002 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(6), object(1)\n",
            "memory usage: 11.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "STEP 2: GETTING AVERAGES PRICE FOR EACH MONTH.\n",
        "\"\"\"\n",
        "# Calculating the Average Price for the Verious Time Span. \n",
        "average_year_price = price_df.groupby('id').mean().reset_index()\n",
        "average_6m_price = price_df[price_df['price_date'] >= '2015-07-01'].groupby('id').mean().reset_index()\n",
        "average_3m_price = price_df[price_df['price_date'] >= '2015-10-01'].groupby('id').mean().reset_index()\n",
        "\n",
        "average_year_price"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "rMfJcoNKox1A",
        "outputId": "6a1b4306-c9fe-4b98-8b25-afdbd0e5c39f"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "<p>16096 rows × 7 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe43c345-6c64-42cf-aad3-6592c3a71096')\"\n",
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              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fe43c345-6c64-42cf-aad3-6592c3a71096');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            "text/plain": [
              "                                     id  ...  price_mid_peak_fix\n",
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              "\n",
              "[16096 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "STEP 3: CHANGING COLUMNS NAME AND ADDING COLUMNS WITH SIMILAR DATA.\n",
        "\"\"\"\n",
        "\n",
        "# Changing Columns Names.\n",
        "average_year_price = average_year_price.rename(\n",
        "                                              index=str,\n",
        "                                               columns= {'price_off_peak_var': 'ave_year_price_offpeak_var', \n",
        "                                                'price_peak_var': 'ave_year_price_peak_var',\n",
        "                                                'price_mid_peak_var': 'ave_year_price_midpeak_var',\n",
        "                                                'price_off_peak_fix': 'ave_year_price_offpeak_fix',\n",
        "                                                'price_peak_fix': 'ave_year_price_peak_fix',\n",
        "                                                'price_mid_peak_fix': 'ave_year_price_midpeak_fix'\n",
        "                                                })\n",
        "average_year_price['ave_year_price_offpeak'] = average_year_price['ave_year_price_offpeak_var'] + average_year_price['ave_year_price_offpeak_fix']\n",
        "average_year_price['ave_year_price_peak'] = average_year_price['ave_year_price_peak_var'] + average_year_price['ave_year_price_peak_fix']\n",
        "average_year_price['ave_year_price_midpeak'] = average_year_price['ave_year_price_midpeak_var'] + average_year_price['ave_year_price_midpeak_fix']\n",
        "\n",
        "average_6m_price = average_6m_price.rename(index=str,\n",
        "                                           columns={'price_off_peak_var': 'ave_6m_price_offpeak_var', \n",
        "                                                'price_peak_var': 'ave_6m_price_peak_var',\n",
        "                                                'price_mid_peak_var': 'ave_6m_price_midpeak_var',\n",
        "                                                'price_off_peak_fix': 'ave_6m_price_offpeak_fix',\n",
        "                                                'price_peak_fix': 'ave_6m_price_peak_fix',\n",
        "                                                'price_mid_peak_fix': 'ave_6m_price_midpeak_fix',\n",
        "                                                })\n",
        "\n",
        "average_6m_price['ave_6m_price_offpeak'] = average_6m_price['ave_6m_price_offpeak_var'] + average_6m_price['ave_6m_price_offpeak_fix']\n",
        "average_6m_price['ave_6m_price_peak'] = average_6m_price['ave_6m_price_peak_var'] + average_6m_price['ave_6m_price_peak_fix']\n",
        "average_6m_price['ave_6m_price_midpeak'] = average_6m_price['ave_6m_price_midpeak_var'] + average_6m_price['ave_6m_price_midpeak_fix']\n",
        "\n",
        "average_3m_price = average_3m_price.rename(index= str,\n",
        "                                           columns={'price_off_peak_var': 'ave_3m_price_offpeak_var', \n",
        "                                                'price_peak_var': 'ave_3m_price_peak_var',\n",
        "                                                'price_mid_peak_var': 'ave_3m_price_midpeak_var',\n",
        "                                                'price_off_peak_fix': 'ave_3m_price_offpeak_fix',\n",
        "                                                'price_peak_fix': 'ave_3m_price_peak_fix',\n",
        "                                                'price_mid_peak_fix': 'ave_3m_price_midpeak_fix',\n",
        "                                                })\n",
        "average_3m_price['ave_3m_price_offpeak'] = average_3m_price['ave_3m_price_offpeak_var'] + average_3m_price['ave_3m_price_offpeak_fix']\n",
        "average_3m_price['ave_3m_price_peak'] = average_3m_price['ave_3m_price_peak_var'] + average_3m_price['ave_3m_price_peak_fix']\n",
        "average_3m_price['ave_3m_price_midpeak'] = average_3m_price['ave_3m_price_midpeak_var'] + average_3m_price['ave_3m_price_midpeak_fix']\n"
      ],
      "metadata": {
        "id": "oOFBkwsDSzzR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "STEP 4: MARGING THE CLIENT CHURN WITH THE PRICE DATASET. \n",
        "\"\"\"\n",
        "new_price = pd.merge(average_year_price, average_6m_price, on='id')\n",
        "new_price = new_price.merge(average_3m_price,how='inner', on='id')\n",
        "price_analysis = pd.merge(new_price , client_df[['id','churn']], how='inner', on='id')\n",
        "#.drop(columns=['price_off_peak_var', 'price_peak_var', 'price_mid_peak_var', 'price_off_peak_fix', 'price_peak_fix', 'price_mid_peak_fix'],axis=1)\n",
        "\n",
        "price_analysis"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "4U4jZEPVUkTm",
        "outputId": "6772f465-2a0e-48e4-a22e-d23403d189fd"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "      <td>...</td>\n",
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              "      <th>14601</th>\n",
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              "<p>14606 rows × 29 columns</p>\n",
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            ],
            "text/plain": [
              "                                     id  ...  churn\n",
              "0      0002203ffbb812588b632b9e628cc38d  ...      0\n",
              "1      0004351ebdd665e6ee664792efc4fd13  ...      0\n",
              "2      0010bcc39e42b3c2131ed2ce55246e3c  ...      0\n",
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              "...                                 ...  ...    ...\n",
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              "14605  ffff7fa066f1fb305ae285bb03bf325a  ...      0\n",
              "\n",
              "[14606 rows x 29 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "STEP 5: CALCULATING THE CORRELATION BETWEEN PRICE AND CHURN.\n",
        "\"\"\"\n",
        "corr = price_analysis.corr()\n",
        "\n",
        "corr"
      ],
      "metadata": {
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          "height": 991
        },
        "id": "U1lPLaMIamc1",
        "outputId": "332d0974-074e-4925-865d-67e13bbe6b1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .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>ave_year_price_offpeak_var</th>\n",
              "      <th>ave_year_price_peak_var</th>\n",
              "      <th>ave_year_price_midpeak_var</th>\n",
              "      <th>ave_year_price_offpeak_fix</th>\n",
              "      <th>ave_year_price_peak_fix</th>\n",
              "      <th>ave_year_price_midpeak_fix</th>\n",
              "      <th>ave_year_price_offpeak</th>\n",
              "      <th>ave_year_price_peak</th>\n",
              "      <th>ave_year_price_midpeak</th>\n",
              "      <th>ave_6m_price_offpeak_var</th>\n",
              "      <th>ave_6m_price_peak_var</th>\n",
              "      <th>ave_6m_price_midpeak_var</th>\n",
              "      <th>ave_6m_price_offpeak_fix</th>\n",
              "      <th>ave_6m_price_peak_fix</th>\n",
              "      <th>ave_6m_price_midpeak_fix</th>\n",
              "      <th>ave_6m_price_offpeak</th>\n",
              "      <th>ave_6m_price_peak</th>\n",
              "      <th>ave_6m_price_midpeak</th>\n",
              "      <th>ave_3m_price_offpeak_var</th>\n",
              "      <th>ave_3m_price_peak_var</th>\n",
              "      <th>ave_3m_price_midpeak_var</th>\n",
              "      <th>ave_3m_price_offpeak_fix</th>\n",
              "      <th>ave_3m_price_peak_fix</th>\n",
              "      <th>ave_3m_price_midpeak_fix</th>\n",
              "      <th>ave_3m_price_offpeak</th>\n",
              "      <th>ave_3m_price_peak</th>\n",
              "      <th>ave_3m_price_midpeak</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ave_year_price_offpeak_var</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.317882</td>\n",
              "      <td>-0.609086</td>\n",
              "      <td>0.662064</td>\n",
              "      <td>-0.631643</td>\n",
              "      <td>-0.609196</td>\n",
              "      <td>0.664829</td>\n",
              "      <td>-0.630841</td>\n",
              "      <td>-0.609219</td>\n",
              "      <td>0.989280</td>\n",
              "      <td>-0.306671</td>\n",
              "      <td>-0.606667</td>\n",
              "      <td>0.666802</td>\n",
              "      <td>-0.630444</td>\n",
              "      <td>-0.608025</td>\n",
              "      <td>0.669666</td>\n",
              "      <td>-0.629629</td>\n",
              "      <td>-0.608043</td>\n",
              "      <td>0.977844</td>\n",
              "      <td>-0.301351</td>\n",
              "      <td>-0.604981</td>\n",
              "      <td>0.664172</td>\n",
              "      <td>-0.628797</td>\n",
              "      <td>-0.606468</td>\n",
              "      <td>0.667017</td>\n",
              "      <td>-0.627980</td>\n",
              "      <td>-0.606486</td>\n",
              "      <td>-0.006421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_year_price_peak_var</th>\n",
              "      <td>-0.317882</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.822436</td>\n",
              "      <td>-0.211414</td>\n",
              "      <td>0.810653</td>\n",
              "      <td>0.815675</td>\n",
              "      <td>-0.212290</td>\n",
              "      <td>0.812065</td>\n",
              "      <td>0.815737</td>\n",
              "      <td>-0.326259</td>\n",
              "      <td>0.998134</td>\n",
              "      <td>0.820929</td>\n",
              "      <td>-0.212790</td>\n",
              "      <td>0.809238</td>\n",
              "      <td>0.814401</td>\n",
              "      <td>-0.213759</td>\n",
              "      <td>0.810659</td>\n",
              "      <td>0.814464</td>\n",
              "      <td>-0.329420</td>\n",
              "      <td>0.995524</td>\n",
              "      <td>0.818918</td>\n",
              "      <td>-0.214372</td>\n",
              "      <td>0.807509</td>\n",
              "      <td>0.812614</td>\n",
              "      <td>-0.215362</td>\n",
              "      <td>0.808933</td>\n",
              "      <td>0.812677</td>\n",
              "      <td>0.029588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_year_price_midpeak_var</th>\n",
              "      <td>-0.609086</td>\n",
              "      <td>0.822436</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.288083</td>\n",
              "      <td>0.987691</td>\n",
              "      <td>0.991544</td>\n",
              "      <td>-0.290144</td>\n",
              "      <td>0.987778</td>\n",
              "      <td>0.991621</td>\n",
              "      <td>-0.613415</td>\n",
              "      <td>0.814275</td>\n",
              "      <td>0.998234</td>\n",
              "      <td>-0.290182</td>\n",
              "      <td>0.986248</td>\n",
              "      <td>0.990285</td>\n",
              "      <td>-0.292369</td>\n",
              "      <td>0.986339</td>\n",
              "      <td>0.990362</td>\n",
              "      <td>-0.615682</td>\n",
              "      <td>0.809222</td>\n",
              "      <td>0.995998</td>\n",
              "      <td>-0.291440</td>\n",
              "      <td>0.984348</td>\n",
              "      <td>0.988324</td>\n",
              "      <td>-0.293656</td>\n",
              "      <td>0.984441</td>\n",
              "      <td>0.988400</td>\n",
              "      <td>0.046544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_year_price_offpeak_fix</th>\n",
              "      <td>0.662064</td>\n",
              "      <td>-0.211414</td>\n",
              "      <td>-0.288083</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.228613</td>\n",
              "      <td>-0.332563</td>\n",
              "      <td>0.999993</td>\n",
              "      <td>-0.228720</td>\n",
              "      <td>-0.332372</td>\n",
              "      <td>0.637495</td>\n",
              "      <td>-0.207397</td>\n",
              "      <td>-0.286759</td>\n",
              "      <td>0.997116</td>\n",
              "      <td>-0.226949</td>\n",
              "      <td>-0.331122</td>\n",
              "      <td>0.997066</td>\n",
              "      <td>-0.227052</td>\n",
              "      <td>-0.330930</td>\n",
              "      <td>0.626322</td>\n",
              "      <td>-0.204704</td>\n",
              "      <td>-0.285598</td>\n",
              "      <td>0.989103</td>\n",
              "      <td>-0.225902</td>\n",
              "      <td>-0.329738</td>\n",
              "      <td>0.989037</td>\n",
              "      <td>-0.226000</td>\n",
              "      <td>-0.329548</td>\n",
              "      <td>0.016800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_year_price_peak_fix</th>\n",
              "      <td>-0.631643</td>\n",
              "      <td>0.810653</td>\n",
              "      <td>0.987691</td>\n",
              "      <td>-0.228613</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.974096</td>\n",
              "      <td>-0.230980</td>\n",
              "      <td>0.999997</td>\n",
              "      <td>0.974196</td>\n",
              "      <td>-0.635726</td>\n",
              "      <td>0.802038</td>\n",
              "      <td>0.985666</td>\n",
              "      <td>-0.231299</td>\n",
              "      <td>0.998610</td>\n",
              "      <td>0.972827</td>\n",
              "      <td>-0.233798</td>\n",
              "      <td>0.998610</td>\n",
              "      <td>0.972926</td>\n",
              "      <td>-0.637362</td>\n",
              "      <td>0.796975</td>\n",
              "      <td>0.983449</td>\n",
              "      <td>-0.233544</td>\n",
              "      <td>0.996619</td>\n",
              "      <td>0.970879</td>\n",
              "      <td>-0.236066</td>\n",
              "      <td>0.996622</td>\n",
              "      <td>0.970976</td>\n",
              "      <td>0.047240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_year_price_midpeak_fix</th>\n",
              "      <td>-0.609196</td>\n",
              "      <td>0.815675</td>\n",
              "      <td>0.991544</td>\n",
              "      <td>-0.332563</td>\n",
              "      <td>0.974096</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.334479</td>\n",
              "      <td>0.974200</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.615074</td>\n",
              "      <td>0.806989</td>\n",
              "      <td>0.989552</td>\n",
              "      <td>-0.334333</td>\n",
              "      <td>0.972446</td>\n",
              "      <td>0.998559</td>\n",
              "      <td>-0.336382</td>\n",
              "      <td>0.972551</td>\n",
              "      <td>0.998558</td>\n",
              "      <td>-0.617603</td>\n",
              "      <td>0.801883</td>\n",
              "      <td>0.987442</td>\n",
              "      <td>-0.334897</td>\n",
              "      <td>0.970597</td>\n",
              "      <td>0.996578</td>\n",
              "      <td>-0.336978</td>\n",
              "      <td>0.970704</td>\n",
              "      <td>0.996576</td>\n",
              "      <td>0.044842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_year_price_offpeak</th>\n",
              "      <td>0.664829</td>\n",
              "      <td>-0.212290</td>\n",
              "      <td>-0.290144</td>\n",
              "      <td>0.999993</td>\n",
              "      <td>-0.230980</td>\n",
              "      <td>-0.334479</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.231083</td>\n",
              "      <td>-0.334288</td>\n",
              "      <td>0.640288</td>\n",
              "      <td>-0.208231</td>\n",
              "      <td>-0.288813</td>\n",
              "      <td>0.997142</td>\n",
              "      <td>-0.229315</td>\n",
              "      <td>-0.333037</td>\n",
              "      <td>0.997106</td>\n",
              "      <td>-0.229413</td>\n",
              "      <td>-0.332846</td>\n",
              "      <td>0.629094</td>\n",
              "      <td>-0.205520</td>\n",
              "      <td>-0.287647</td>\n",
              "      <td>0.989142</td>\n",
              "      <td>-0.228263</td>\n",
              "      <td>-0.331650</td>\n",
              "      <td>0.989091</td>\n",
              "      <td>-0.228357</td>\n",
              "      <td>-0.331460</td>\n",
              "      <td>0.016713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_year_price_peak</th>\n",
              "      <td>-0.630841</td>\n",
              "      <td>0.812065</td>\n",
              "      <td>0.987778</td>\n",
              "      <td>-0.228720</td>\n",
              "      <td>0.999997</td>\n",
              "      <td>0.974200</td>\n",
              "      <td>-0.231083</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.974300</td>\n",
              "      <td>-0.634944</td>\n",
              "      <td>0.803471</td>\n",
              "      <td>0.985754</td>\n",
              "      <td>-0.231403</td>\n",
              "      <td>0.998606</td>\n",
              "      <td>0.972931</td>\n",
              "      <td>-0.233898</td>\n",
              "      <td>0.998612</td>\n",
              "      <td>0.973029</td>\n",
              "      <td>-0.636588</td>\n",
              "      <td>0.798414</td>\n",
              "      <td>0.983536</td>\n",
              "      <td>-0.233647</td>\n",
              "      <td>0.996615</td>\n",
              "      <td>0.970981</td>\n",
              "      <td>-0.236165</td>\n",
              "      <td>0.996624</td>\n",
              "      <td>0.971079</td>\n",
              "      <td>0.047204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_year_price_midpeak</th>\n",
              "      <td>-0.609219</td>\n",
              "      <td>0.815737</td>\n",
              "      <td>0.991621</td>\n",
              "      <td>-0.332372</td>\n",
              "      <td>0.974196</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.334288</td>\n",
              "      <td>0.974300</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.615090</td>\n",
              "      <td>0.807053</td>\n",
              "      <td>0.989630</td>\n",
              "      <td>-0.334143</td>\n",
              "      <td>0.972546</td>\n",
              "      <td>0.998560</td>\n",
              "      <td>-0.336193</td>\n",
              "      <td>0.972652</td>\n",
              "      <td>0.998559</td>\n",
              "      <td>-0.617618</td>\n",
              "      <td>0.801947</td>\n",
              "      <td>0.987520</td>\n",
              "      <td>-0.334711</td>\n",
              "      <td>0.970697</td>\n",
              "      <td>0.996578</td>\n",
              "      <td>-0.336792</td>\n",
              "      <td>0.970805</td>\n",
              "      <td>0.996577</td>\n",
              "      <td>0.044851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_6m_price_offpeak_var</th>\n",
              "      <td>0.989280</td>\n",
              "      <td>-0.326259</td>\n",
              "      <td>-0.613415</td>\n",
              "      <td>0.637495</td>\n",
              "      <td>-0.635726</td>\n",
              "      <td>-0.615074</td>\n",
              "      <td>0.640288</td>\n",
              "      <td>-0.634944</td>\n",
              "      <td>-0.615090</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.313302</td>\n",
              "      <td>-0.610971</td>\n",
              "      <td>0.647610</td>\n",
              "      <td>-0.635642</td>\n",
              "      <td>-0.615011</td>\n",
              "      <td>0.650592</td>\n",
              "      <td>-0.634837</td>\n",
              "      <td>-0.615017</td>\n",
              "      <td>0.994889</td>\n",
              "      <td>-0.307139</td>\n",
              "      <td>-0.609191</td>\n",
              "      <td>0.647613</td>\n",
              "      <td>-0.634213</td>\n",
              "      <td>-0.613667</td>\n",
              "      <td>0.650602</td>\n",
              "      <td>-0.633402</td>\n",
              "      <td>-0.613671</td>\n",
              "      <td>-0.007589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_6m_price_peak_var</th>\n",
              "      <td>-0.306671</td>\n",
              "      <td>0.998134</td>\n",
              "      <td>0.814275</td>\n",
              "      <td>-0.207397</td>\n",
              "      <td>0.802038</td>\n",
              "      <td>0.806989</td>\n",
              "      <td>-0.208231</td>\n",
              "      <td>0.803471</td>\n",
              "      <td>0.807053</td>\n",
              "      <td>-0.313302</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.815162</td>\n",
              "      <td>-0.208287</td>\n",
              "      <td>0.802452</td>\n",
              "      <td>0.807609</td>\n",
              "      <td>-0.209204</td>\n",
              "      <td>0.803903</td>\n",
              "      <td>0.807677</td>\n",
              "      <td>-0.315698</td>\n",
              "      <td>0.998474</td>\n",
              "      <td>0.813943</td>\n",
              "      <td>-0.209336</td>\n",
              "      <td>0.801357</td>\n",
              "      <td>0.806469</td>\n",
              "      <td>-0.210271</td>\n",
              "      <td>0.802813</td>\n",
              "      <td>0.806537</td>\n",
              "      <td>0.028878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_6m_price_midpeak_var</th>\n",
              "      <td>-0.606667</td>\n",
              "      <td>0.820929</td>\n",
              "      <td>0.998234</td>\n",
              "      <td>-0.286759</td>\n",
              "      <td>0.985666</td>\n",
              "      <td>0.989552</td>\n",
              "      <td>-0.288813</td>\n",
              "      <td>0.985754</td>\n",
              "      <td>0.989630</td>\n",
              "      <td>-0.610971</td>\n",
              "      <td>0.815162</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.288891</td>\n",
              "      <td>0.987062</td>\n",
              "      <td>0.991195</td>\n",
              "      <td>-0.291070</td>\n",
              "      <td>0.987153</td>\n",
              "      <td>0.991276</td>\n",
              "      <td>-0.613058</td>\n",
              "      <td>0.810920</td>\n",
              "      <td>0.998781</td>\n",
              "      <td>-0.289599</td>\n",
              "      <td>0.986040</td>\n",
              "      <td>0.990108</td>\n",
              "      <td>-0.291807</td>\n",
              "      <td>0.986134</td>\n",
              "      <td>0.990189</td>\n",
              "      <td>0.046241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_6m_price_offpeak_fix</th>\n",
              "      <td>0.666802</td>\n",
              "      <td>-0.212790</td>\n",
              "      <td>-0.290182</td>\n",
              "      <td>0.997116</td>\n",
              "      <td>-0.231299</td>\n",
              "      <td>-0.334333</td>\n",
              "      <td>0.997142</td>\n",
              "      <td>-0.231403</td>\n",
              "      <td>-0.334143</td>\n",
              "      <td>0.647610</td>\n",
              "      <td>-0.208287</td>\n",
              "      <td>-0.288891</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.229659</td>\n",
              "      <td>-0.333021</td>\n",
              "      <td>0.999992</td>\n",
              "      <td>-0.229756</td>\n",
              "      <td>-0.332831</td>\n",
              "      <td>0.638994</td>\n",
              "      <td>-0.205026</td>\n",
              "      <td>-0.287276</td>\n",
              "      <td>0.995791</td>\n",
              "      <td>-0.228100</td>\n",
              "      <td>-0.331230</td>\n",
              "      <td>0.995769</td>\n",
              "      <td>-0.228193</td>\n",
              "      <td>-0.331040</td>\n",
              "      <td>0.016899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_6m_price_peak_fix</th>\n",
              "      <td>-0.630444</td>\n",
              "      <td>0.809238</td>\n",
              "      <td>0.986248</td>\n",
              "      <td>-0.226949</td>\n",
              "      <td>0.998610</td>\n",
              "      <td>0.972446</td>\n",
              "      <td>-0.229315</td>\n",
              "      <td>0.998606</td>\n",
              "      <td>0.972546</td>\n",
              "      <td>-0.635642</td>\n",
              "      <td>0.802452</td>\n",
              "      <td>0.987062</td>\n",
              "      <td>-0.229659</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.973983</td>\n",
              "      <td>-0.232163</td>\n",
              "      <td>0.999997</td>\n",
              "      <td>0.974082</td>\n",
              "      <td>-0.637388</td>\n",
              "      <td>0.798031</td>\n",
              "      <td>0.985716</td>\n",
              "      <td>-0.231268</td>\n",
              "      <td>0.998870</td>\n",
              "      <td>0.972870</td>\n",
              "      <td>-0.233798</td>\n",
              "      <td>0.998870</td>\n",
              "      <td>0.972970</td>\n",
              "      <td>0.047083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_6m_price_midpeak_fix</th>\n",
              "      <td>-0.608025</td>\n",
              "      <td>0.814401</td>\n",
              "      <td>0.990285</td>\n",
              "      <td>-0.331122</td>\n",
              "      <td>0.972827</td>\n",
              "      <td>0.998559</td>\n",
              "      <td>-0.333037</td>\n",
              "      <td>0.972931</td>\n",
              "      <td>0.998560</td>\n",
              "      <td>-0.615011</td>\n",
              "      <td>0.807609</td>\n",
              "      <td>0.991195</td>\n",
              "      <td>-0.333021</td>\n",
              "      <td>0.973983</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.335074</td>\n",
              "      <td>0.974086</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.617650</td>\n",
              "      <td>0.803159</td>\n",
              "      <td>0.989956</td>\n",
              "      <td>-0.333040</td>\n",
              "      <td>0.972971</td>\n",
              "      <td>0.998869</td>\n",
              "      <td>-0.335126</td>\n",
              "      <td>0.973077</td>\n",
              "      <td>0.998869</td>\n",
              "      <td>0.044779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_6m_price_offpeak</th>\n",
              "      <td>0.669666</td>\n",
              "      <td>-0.213759</td>\n",
              "      <td>-0.292369</td>\n",
              "      <td>0.997066</td>\n",
              "      <td>-0.233798</td>\n",
              "      <td>-0.336382</td>\n",
              "      <td>0.997106</td>\n",
              "      <td>-0.233898</td>\n",
              "      <td>-0.336193</td>\n",
              "      <td>0.650592</td>\n",
              "      <td>-0.209204</td>\n",
              "      <td>-0.291070</td>\n",
              "      <td>0.999992</td>\n",
              "      <td>-0.232163</td>\n",
              "      <td>-0.335074</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.232256</td>\n",
              "      <td>-0.334884</td>\n",
              "      <td>0.641979</td>\n",
              "      <td>-0.205922</td>\n",
              "      <td>-0.289451</td>\n",
              "      <td>0.995798</td>\n",
              "      <td>-0.230602</td>\n",
              "      <td>-0.333281</td>\n",
              "      <td>0.995791</td>\n",
              "      <td>-0.230690</td>\n",
              "      <td>-0.333092</td>\n",
              "      <td>0.016803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_6m_price_peak</th>\n",
              "      <td>-0.629629</td>\n",
              "      <td>0.810659</td>\n",
              "      <td>0.986339</td>\n",
              "      <td>-0.227052</td>\n",
              "      <td>0.998610</td>\n",
              "      <td>0.972551</td>\n",
              "      <td>-0.229413</td>\n",
              "      <td>0.998612</td>\n",
              "      <td>0.972652</td>\n",
              "      <td>-0.634837</td>\n",
              "      <td>0.803903</td>\n",
              "      <td>0.987153</td>\n",
              "      <td>-0.229756</td>\n",
              "      <td>0.999997</td>\n",
              "      <td>0.974086</td>\n",
              "      <td>-0.232256</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.974186</td>\n",
              "      <td>-0.636587</td>\n",
              "      <td>0.799491</td>\n",
              "      <td>0.985807</td>\n",
              "      <td>-0.231364</td>\n",
              "      <td>0.998866</td>\n",
              "      <td>0.972973</td>\n",
              "      <td>-0.233890</td>\n",
              "      <td>0.998872</td>\n",
              "      <td>0.973072</td>\n",
              "      <td>0.047047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_6m_price_midpeak</th>\n",
              "      <td>-0.608043</td>\n",
              "      <td>0.814464</td>\n",
              "      <td>0.990362</td>\n",
              "      <td>-0.330930</td>\n",
              "      <td>0.972926</td>\n",
              "      <td>0.998558</td>\n",
              "      <td>-0.332846</td>\n",
              "      <td>0.973029</td>\n",
              "      <td>0.998559</td>\n",
              "      <td>-0.615017</td>\n",
              "      <td>0.807677</td>\n",
              "      <td>0.991276</td>\n",
              "      <td>-0.332831</td>\n",
              "      <td>0.974082</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.334884</td>\n",
              "      <td>0.974186</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.617654</td>\n",
              "      <td>0.803227</td>\n",
              "      <td>0.990037</td>\n",
              "      <td>-0.332852</td>\n",
              "      <td>0.973071</td>\n",
              "      <td>0.998869</td>\n",
              "      <td>-0.334940</td>\n",
              "      <td>0.973176</td>\n",
              "      <td>0.998869</td>\n",
              "      <td>0.044788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_3m_price_offpeak_var</th>\n",
              "      <td>0.977844</td>\n",
              "      <td>-0.329420</td>\n",
              "      <td>-0.615682</td>\n",
              "      <td>0.626322</td>\n",
              "      <td>-0.637362</td>\n",
              "      <td>-0.617603</td>\n",
              "      <td>0.629094</td>\n",
              "      <td>-0.636588</td>\n",
              "      <td>-0.617618</td>\n",
              "      <td>0.994889</td>\n",
              "      <td>-0.315698</td>\n",
              "      <td>-0.613058</td>\n",
              "      <td>0.638994</td>\n",
              "      <td>-0.637388</td>\n",
              "      <td>-0.617650</td>\n",
              "      <td>0.641979</td>\n",
              "      <td>-0.636587</td>\n",
              "      <td>-0.617654</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.308501</td>\n",
              "      <td>-0.611552</td>\n",
              "      <td>0.644497</td>\n",
              "      <td>-0.636643</td>\n",
              "      <td>-0.617005</td>\n",
              "      <td>0.647523</td>\n",
              "      <td>-0.635830</td>\n",
              "      <td>-0.617005</td>\n",
              "      <td>-0.009468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_3m_price_peak_var</th>\n",
              "      <td>-0.301351</td>\n",
              "      <td>0.995524</td>\n",
              "      <td>0.809222</td>\n",
              "      <td>-0.204704</td>\n",
              "      <td>0.796975</td>\n",
              "      <td>0.801883</td>\n",
              "      <td>-0.205520</td>\n",
              "      <td>0.798414</td>\n",
              "      <td>0.801947</td>\n",
              "      <td>-0.307139</td>\n",
              "      <td>0.998474</td>\n",
              "      <td>0.810920</td>\n",
              "      <td>-0.205026</td>\n",
              "      <td>0.798031</td>\n",
              "      <td>0.803159</td>\n",
              "      <td>-0.205922</td>\n",
              "      <td>0.799491</td>\n",
              "      <td>0.803227</td>\n",
              "      <td>-0.308501</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.811096</td>\n",
              "      <td>-0.205022</td>\n",
              "      <td>0.798129</td>\n",
              "      <td>0.803229</td>\n",
              "      <td>-0.205935</td>\n",
              "      <td>0.799601</td>\n",
              "      <td>0.803298</td>\n",
              "      <td>0.028445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_3m_price_midpeak_var</th>\n",
              "      <td>-0.604981</td>\n",
              "      <td>0.818918</td>\n",
              "      <td>0.995998</td>\n",
              "      <td>-0.285598</td>\n",
              "      <td>0.983449</td>\n",
              "      <td>0.987442</td>\n",
              "      <td>-0.287647</td>\n",
              "      <td>0.983536</td>\n",
              "      <td>0.987520</td>\n",
              "      <td>-0.609191</td>\n",
              "      <td>0.813943</td>\n",
              "      <td>0.998781</td>\n",
              "      <td>-0.287276</td>\n",
              "      <td>0.985716</td>\n",
              "      <td>0.989956</td>\n",
              "      <td>-0.289451</td>\n",
              "      <td>0.985807</td>\n",
              "      <td>0.990037</td>\n",
              "      <td>-0.611552</td>\n",
              "      <td>0.811096</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.287342</td>\n",
              "      <td>0.986922</td>\n",
              "      <td>0.991099</td>\n",
              "      <td>-0.289551</td>\n",
              "      <td>0.987014</td>\n",
              "      <td>0.991181</td>\n",
              "      <td>0.045553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_3m_price_offpeak_fix</th>\n",
              "      <td>0.664172</td>\n",
              "      <td>-0.214372</td>\n",
              "      <td>-0.291440</td>\n",
              "      <td>0.989103</td>\n",
              "      <td>-0.233544</td>\n",
              "      <td>-0.334897</td>\n",
              "      <td>0.989142</td>\n",
              "      <td>-0.233647</td>\n",
              "      <td>-0.334711</td>\n",
              "      <td>0.647613</td>\n",
              "      <td>-0.209336</td>\n",
              "      <td>-0.289599</td>\n",
              "      <td>0.995791</td>\n",
              "      <td>-0.231268</td>\n",
              "      <td>-0.333040</td>\n",
              "      <td>0.995798</td>\n",
              "      <td>-0.231364</td>\n",
              "      <td>-0.332852</td>\n",
              "      <td>0.644497</td>\n",
              "      <td>-0.205022</td>\n",
              "      <td>-0.287342</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.228921</td>\n",
              "      <td>-0.330679</td>\n",
              "      <td>0.999992</td>\n",
              "      <td>-0.229011</td>\n",
              "      <td>-0.330492</td>\n",
              "      <td>0.014859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_3m_price_peak_fix</th>\n",
              "      <td>-0.628797</td>\n",
              "      <td>0.807509</td>\n",
              "      <td>0.984348</td>\n",
              "      <td>-0.225902</td>\n",
              "      <td>0.996619</td>\n",
              "      <td>0.970597</td>\n",
              "      <td>-0.228263</td>\n",
              "      <td>0.996615</td>\n",
              "      <td>0.970697</td>\n",
              "      <td>-0.634213</td>\n",
              "      <td>0.801357</td>\n",
              "      <td>0.986040</td>\n",
              "      <td>-0.228100</td>\n",
              "      <td>0.998870</td>\n",
              "      <td>0.972971</td>\n",
              "      <td>-0.230602</td>\n",
              "      <td>0.998866</td>\n",
              "      <td>0.973071</td>\n",
              "      <td>-0.636643</td>\n",
              "      <td>0.798129</td>\n",
              "      <td>0.986922</td>\n",
              "      <td>-0.228921</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.974078</td>\n",
              "      <td>-0.231455</td>\n",
              "      <td>0.999997</td>\n",
              "      <td>0.974178</td>\n",
              "      <td>0.046186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_3m_price_midpeak_fix</th>\n",
              "      <td>-0.606468</td>\n",
              "      <td>0.812614</td>\n",
              "      <td>0.988324</td>\n",
              "      <td>-0.329738</td>\n",
              "      <td>0.970879</td>\n",
              "      <td>0.996578</td>\n",
              "      <td>-0.331650</td>\n",
              "      <td>0.970981</td>\n",
              "      <td>0.996578</td>\n",
              "      <td>-0.613667</td>\n",
              "      <td>0.806469</td>\n",
              "      <td>0.990108</td>\n",
              "      <td>-0.331230</td>\n",
              "      <td>0.972870</td>\n",
              "      <td>0.998869</td>\n",
              "      <td>-0.333281</td>\n",
              "      <td>0.972973</td>\n",
              "      <td>0.998869</td>\n",
              "      <td>-0.617005</td>\n",
              "      <td>0.803229</td>\n",
              "      <td>0.991099</td>\n",
              "      <td>-0.330679</td>\n",
              "      <td>0.974078</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.332771</td>\n",
              "      <td>0.974180</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.043989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_3m_price_offpeak</th>\n",
              "      <td>0.667017</td>\n",
              "      <td>-0.215362</td>\n",
              "      <td>-0.293656</td>\n",
              "      <td>0.989037</td>\n",
              "      <td>-0.236066</td>\n",
              "      <td>-0.336978</td>\n",
              "      <td>0.989091</td>\n",
              "      <td>-0.236165</td>\n",
              "      <td>-0.336792</td>\n",
              "      <td>0.650602</td>\n",
              "      <td>-0.210271</td>\n",
              "      <td>-0.291807</td>\n",
              "      <td>0.995769</td>\n",
              "      <td>-0.233798</td>\n",
              "      <td>-0.335126</td>\n",
              "      <td>0.995791</td>\n",
              "      <td>-0.233890</td>\n",
              "      <td>-0.334940</td>\n",
              "      <td>0.647523</td>\n",
              "      <td>-0.205935</td>\n",
              "      <td>-0.289551</td>\n",
              "      <td>0.999992</td>\n",
              "      <td>-0.231455</td>\n",
              "      <td>-0.332771</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.231541</td>\n",
              "      <td>-0.332584</td>\n",
              "      <td>0.014760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_3m_price_peak</th>\n",
              "      <td>-0.627980</td>\n",
              "      <td>0.808933</td>\n",
              "      <td>0.984441</td>\n",
              "      <td>-0.226000</td>\n",
              "      <td>0.996622</td>\n",
              "      <td>0.970704</td>\n",
              "      <td>-0.228357</td>\n",
              "      <td>0.996624</td>\n",
              "      <td>0.970805</td>\n",
              "      <td>-0.633402</td>\n",
              "      <td>0.802813</td>\n",
              "      <td>0.986134</td>\n",
              "      <td>-0.228193</td>\n",
              "      <td>0.998870</td>\n",
              "      <td>0.973077</td>\n",
              "      <td>-0.230690</td>\n",
              "      <td>0.998872</td>\n",
              "      <td>0.973176</td>\n",
              "      <td>-0.635830</td>\n",
              "      <td>0.799601</td>\n",
              "      <td>0.987014</td>\n",
              "      <td>-0.229011</td>\n",
              "      <td>0.999997</td>\n",
              "      <td>0.974180</td>\n",
              "      <td>-0.231541</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.974280</td>\n",
              "      <td>0.046152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ave_3m_price_midpeak</th>\n",
              "      <td>-0.606486</td>\n",
              "      <td>0.812677</td>\n",
              "      <td>0.988400</td>\n",
              "      <td>-0.329548</td>\n",
              "      <td>0.970976</td>\n",
              "      <td>0.996576</td>\n",
              "      <td>-0.331460</td>\n",
              "      <td>0.971079</td>\n",
              "      <td>0.996577</td>\n",
              "      <td>-0.613671</td>\n",
              "      <td>0.806537</td>\n",
              "      <td>0.990189</td>\n",
              "      <td>-0.331040</td>\n",
              "      <td>0.972970</td>\n",
              "      <td>0.998869</td>\n",
              "      <td>-0.333092</td>\n",
              "      <td>0.973072</td>\n",
              "      <td>0.998869</td>\n",
              "      <td>-0.617005</td>\n",
              "      <td>0.803298</td>\n",
              "      <td>0.991181</td>\n",
              "      <td>-0.330492</td>\n",
              "      <td>0.974178</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.332584</td>\n",
              "      <td>0.974280</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.043998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>churn</th>\n",
              "      <td>-0.006421</td>\n",
              "      <td>0.029588</td>\n",
              "      <td>0.046544</td>\n",
              "      <td>0.016800</td>\n",
              "      <td>0.047240</td>\n",
              "      <td>0.044842</td>\n",
              "      <td>0.016713</td>\n",
              "      <td>0.047204</td>\n",
              "      <td>0.044851</td>\n",
              "      <td>-0.007589</td>\n",
              "      <td>0.028878</td>\n",
              "      <td>0.046241</td>\n",
              "      <td>0.016899</td>\n",
              "      <td>0.047083</td>\n",
              "      <td>0.044779</td>\n",
              "      <td>0.016803</td>\n",
              "      <td>0.047047</td>\n",
              "      <td>0.044788</td>\n",
              "      <td>-0.009468</td>\n",
              "      <td>0.028445</td>\n",
              "      <td>0.045553</td>\n",
              "      <td>0.014859</td>\n",
              "      <td>0.046186</td>\n",
              "      <td>0.043989</td>\n",
              "      <td>0.014760</td>\n",
              "      <td>0.046152</td>\n",
              "      <td>0.043998</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-292f1468-9c69-4089-bdfe-0069007173f0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-292f1468-9c69-4089-bdfe-0069007173f0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-292f1468-9c69-4089-bdfe-0069007173f0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                            ave_year_price_offpeak_var  ...     churn\n",
              "ave_year_price_offpeak_var                    1.000000  ... -0.006421\n",
              "ave_year_price_peak_var                      -0.317882  ...  0.029588\n",
              "ave_year_price_midpeak_var                   -0.609086  ...  0.046544\n",
              "ave_year_price_offpeak_fix                    0.662064  ...  0.016800\n",
              "ave_year_price_peak_fix                      -0.631643  ...  0.047240\n",
              "ave_year_price_midpeak_fix                   -0.609196  ...  0.044842\n",
              "ave_year_price_offpeak                        0.664829  ...  0.016713\n",
              "ave_year_price_peak                          -0.630841  ...  0.047204\n",
              "ave_year_price_midpeak                       -0.609219  ...  0.044851\n",
              "ave_6m_price_offpeak_var                      0.989280  ... -0.007589\n",
              "ave_6m_price_peak_var                        -0.306671  ...  0.028878\n",
              "ave_6m_price_midpeak_var                     -0.606667  ...  0.046241\n",
              "ave_6m_price_offpeak_fix                      0.666802  ...  0.016899\n",
              "ave_6m_price_peak_fix                        -0.630444  ...  0.047083\n",
              "ave_6m_price_midpeak_fix                     -0.608025  ...  0.044779\n",
              "ave_6m_price_offpeak                          0.669666  ...  0.016803\n",
              "ave_6m_price_peak                            -0.629629  ...  0.047047\n",
              "ave_6m_price_midpeak                         -0.608043  ...  0.044788\n",
              "ave_3m_price_offpeak_var                      0.977844  ... -0.009468\n",
              "ave_3m_price_peak_var                        -0.301351  ...  0.028445\n",
              "ave_3m_price_midpeak_var                     -0.604981  ...  0.045553\n",
              "ave_3m_price_offpeak_fix                      0.664172  ...  0.014859\n",
              "ave_3m_price_peak_fix                        -0.628797  ...  0.046186\n",
              "ave_3m_price_midpeak_fix                     -0.606468  ...  0.043989\n",
              "ave_3m_price_offpeak                          0.667017  ...  0.014760\n",
              "ave_3m_price_peak                            -0.627980  ...  0.046152\n",
              "ave_3m_price_midpeak                         -0.606486  ...  0.043998\n",
              "churn                                        -0.006421  ...  1.000000\n",
              "\n",
              "[28 rows x 28 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy.ma.core import size\n",
        "\"\"\"\n",
        "STEP 6: HEAT MAP DIAGRAM FOR THE PRICE ANALYSIS CORRELATION. \n",
        "\"\"\"\n",
        "plt.figure(figsize=(20,18))\n",
        "sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values, annot = True, annot_kws={'size':10})\n",
        "# Axis ticks size\n",
        "plt.xticks(fontsize=10)\n",
        "plt.yticks(fontsize=10)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xkstUrI6dx2T",
        "outputId": "998af4c2-86d6-4281-b758-f757eb008145"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1296 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observation: From the graph above we can observe that there seem to be a positive relationship between churn and price, but this relationship can also be seen to be a weak relationship between churn and price, and to indicate that price is a major factor for churn prediction further feature enginnering is needed."
      ],
      "metadata": {
        "id": "7m1euf309Jv7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Merging the Price_analysis dataset with the client_df data set. \n",
        "merged_data = pd.merge(client_df.drop(columns=['churn']), price_analysis, on='id')\n",
        "\n",
        "merged_data.to_csv('powerco_data_post_eda.csv')"
      ],
      "metadata": {
        "id": "ayWAp8tnwkxH"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "PowerCo_EDA.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "Ocdt_2go5cYb"
      ],
      "mount_file_id": "1MoOQMXasGBm60UpPAxNZCiKeUhrQZBEx",
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