Bank_Loan_Modelling_SR.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Bank_Loan_Modelling_SR.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ep7xzXXagOB2",
        "colab_type": "text"
      },
      "source": [
        "**1. Importing the required libraries for EDA**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MP8g603rggCl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import io\n",
        "import scipy.stats as stats\n",
        "%matplotlib inline\n"
      ],
      "execution_count": 208,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zNI6X18Th_Uk",
        "colab_type": "text"
      },
      "source": [
        "**1.1 Loading the data into the data frame**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1UGyx0sfiGlQ",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "a1fa9197-dc24-4f05-939d-ac53aa5cf438"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded=files.upload()"
      ],
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c4461706-cde1-45fa-8268-2d3aafb1ed3b\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c4461706-cde1-45fa-8268-2d3aafb1ed3b\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving data.csv to data.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLMX7UmGrjq8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "55e5932e-ce15-4310-8676-aa8938c22786"
      },
      "source": [
        "import io\n",
        "df=pd.read_csv(io.BytesIO(uploaded[\"data.csv\"]))\n",
        "df.head()"
      ],
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Experience</th>\n",
              "      <th>Income</th>\n",
              "      <th>ZIP Code</th>\n",
              "      <th>Family</th>\n",
              "      <th>CCAvg</th>\n",
              "      <th>Education</th>\n",
              "      <th>Mortgage</th>\n",
              "      <th>Personal Loan</th>\n",
              "      <th>Securities Account</th>\n",
              "      <th>CD Account</th>\n",
              "      <th>Online</th>\n",
              "      <th>CreditCard</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>49</td>\n",
              "      <td>91107</td>\n",
              "      <td>4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>45</td>\n",
              "      <td>19</td>\n",
              "      <td>34</td>\n",
              "      <td>90089</td>\n",
              "      <td>3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>39</td>\n",
              "      <td>15</td>\n",
              "      <td>11</td>\n",
              "      <td>94720</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>35</td>\n",
              "      <td>9</td>\n",
              "      <td>100</td>\n",
              "      <td>94112</td>\n",
              "      <td>1</td>\n",
              "      <td>2.7</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>35</td>\n",
              "      <td>8</td>\n",
              "      <td>45</td>\n",
              "      <td>91330</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ID  Age  Experience  ...  CD Account  Online  CreditCard\n",
              "0   1   25           1  ...           0       0           0\n",
              "1   2   45          19  ...           0       0           0\n",
              "2   3   39          15  ...           0       0           0\n",
              "3   4   35           9  ...           0       0           0\n",
              "4   5   35           8  ...           0       0           1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3eCYzScKtkh6",
        "colab_type": "text"
      },
      "source": [
        "**1.2 Checking the types of data and its statistics**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pd288jcUtu0e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "3f8b0daf-9586-4695-9198-cb9143f921df"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5000 entries, 0 to 4999\n",
            "Data columns (total 14 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   ID                  5000 non-null   int64  \n",
            " 1   Age                 5000 non-null   int64  \n",
            " 2   Experience          5000 non-null   int64  \n",
            " 3   Income              5000 non-null   int64  \n",
            " 4   ZIP Code            5000 non-null   int64  \n",
            " 5   Family              5000 non-null   int64  \n",
            " 6   CCAvg               5000 non-null   float64\n",
            " 7   Education           5000 non-null   int64  \n",
            " 8   Mortgage            5000 non-null   int64  \n",
            " 9   Personal Loan       5000 non-null   int64  \n",
            " 10  Securities Account  5000 non-null   int64  \n",
            " 11  CD Account          5000 non-null   int64  \n",
            " 12  Online              5000 non-null   int64  \n",
            " 13  CreditCard          5000 non-null   int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 547.0 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cnAebz7quD_w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "27260398-288c-4736-b476-62eb19879d41"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 223,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                      int64\n",
              "Age                     int64\n",
              "Experience              int64\n",
              "Income                  int64\n",
              "ZIP Code                int64\n",
              "Family                  int64\n",
              "CCAvg                 float64\n",
              "Education               int64\n",
              "Mortgage                int64\n",
              "Personal Loan           int64\n",
              "Securities Account      int64\n",
              "CD Account              int64\n",
              "Online                  int64\n",
              "CreditCard              int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 223
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODocaCEeuW3Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6c9e71cd-9488-45ed-9476-9ca97a176e22"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJ33Et96ua2s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "31d09421-d575-4480-c220-85816feab73b"
      },
      "source": [
        "df.isnull().values.any()"
      ],
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "13-BfgpavBae",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "91eec46e-95f2-4cc0-be01-8e25c2a98f48"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Experience</th>\n",
              "      <th>Income</th>\n",
              "      <th>ZIP Code</th>\n",
              "      <th>Family</th>\n",
              "      <th>CCAvg</th>\n",
              "      <th>Education</th>\n",
              "      <th>Mortgage</th>\n",
              "      <th>Personal Loan</th>\n",
              "      <th>Securities Account</th>\n",
              "      <th>CD Account</th>\n",
              "      <th>Online</th>\n",
              "      <th>CreditCard</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.00000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2500.500000</td>\n",
              "      <td>45.338400</td>\n",
              "      <td>20.104600</td>\n",
              "      <td>73.774200</td>\n",
              "      <td>93152.503000</td>\n",
              "      <td>2.396400</td>\n",
              "      <td>1.937938</td>\n",
              "      <td>1.881000</td>\n",
              "      <td>56.498800</td>\n",
              "      <td>0.096000</td>\n",
              "      <td>0.104400</td>\n",
              "      <td>0.06040</td>\n",
              "      <td>0.596800</td>\n",
              "      <td>0.294000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1443.520003</td>\n",
              "      <td>11.463166</td>\n",
              "      <td>11.467954</td>\n",
              "      <td>46.033729</td>\n",
              "      <td>2121.852197</td>\n",
              "      <td>1.147663</td>\n",
              "      <td>1.747659</td>\n",
              "      <td>0.839869</td>\n",
              "      <td>101.713802</td>\n",
              "      <td>0.294621</td>\n",
              "      <td>0.305809</td>\n",
              "      <td>0.23825</td>\n",
              "      <td>0.490589</td>\n",
              "      <td>0.455637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>-3.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9307.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1250.750000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>91911.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2500.500000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>93437.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3750.250000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>94608.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5000.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>224.000000</td>\n",
              "      <td>96651.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>635.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                ID          Age  ...       Online   CreditCard\n",
              "count  5000.000000  5000.000000  ...  5000.000000  5000.000000\n",
              "mean   2500.500000    45.338400  ...     0.596800     0.294000\n",
              "std    1443.520003    11.463166  ...     0.490589     0.455637\n",
              "min       1.000000    23.000000  ...     0.000000     0.000000\n",
              "25%    1250.750000    35.000000  ...     0.000000     0.000000\n",
              "50%    2500.500000    45.000000  ...     1.000000     0.000000\n",
              "75%    3750.250000    55.000000  ...     1.000000     1.000000\n",
              "max    5000.000000    67.000000  ...     1.000000     1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5GFgIbf0o_AI",
        "colab_type": "text"
      },
      "source": [
        "**2. Dropping irrelevant columns**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UBrxs7mJpJs_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "bcc5f283-78ea-4e3b-9568-ab1d63be866b"
      },
      "source": [
        "df.Experience"
      ],
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1\n",
              "1       19\n",
              "2       15\n",
              "3        9\n",
              "4        8\n",
              "        ..\n",
              "4995     3\n",
              "4996     4\n",
              "4997    39\n",
              "4998    40\n",
              "4999     4\n",
              "Name: Experience, Length: 5000, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MrZh9XgzpfsR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a68fb3bc-7276-47de-fcb6-8c8e166a838d"
      },
      "source": [
        "experience = df['Experience']\n",
        "age = df['Age']\n",
        "correlation = age.corr(experience)\n",
        "correlation"
      ],
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9942148569683321"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d-HDxNZXp6nM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "c98f41f6-cca6-4cba-e3f2-14a7b1deda5b"
      },
      "source": [
        "df = df.drop(['ID','Experience'], axis = 1)\n",
        "df.head()"
      ],
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Age</th>\n",
              "      <th>Income</th>\n",
              "      <th>ZIP Code</th>\n",
              "      <th>Family</th>\n",
              "      <th>CCAvg</th>\n",
              "      <th>Education</th>\n",
              "      <th>Mortgage</th>\n",
              "      <th>Personal Loan</th>\n",
              "      <th>Securities Account</th>\n",
              "      <th>CD Account</th>\n",
              "      <th>Online</th>\n",
              "      <th>CreditCard</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>25</td>\n",
              "      <td>49</td>\n",
              "      <td>91107</td>\n",
              "      <td>4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45</td>\n",
              "      <td>34</td>\n",
              "      <td>90089</td>\n",
              "      <td>3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>39</td>\n",
              "      <td>11</td>\n",
              "      <td>94720</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35</td>\n",
              "      <td>100</td>\n",
              "      <td>94112</td>\n",
              "      <td>1</td>\n",
              "      <td>2.7</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>35</td>\n",
              "      <td>45</td>\n",
              "      <td>91330</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Income  ZIP Code  ...  CD Account  Online  CreditCard\n",
              "0   25      49     91107  ...           0       0           0\n",
              "1   45      34     90089  ...           0       0           0\n",
              "2   39      11     94720  ...           0       0           0\n",
              "3   35     100     94112  ...           0       0           0\n",
              "4   35      45     91330  ...           0       0           1\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 229
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qEUVlTVGtOeO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "75bb864f-2497-4cb0-f220-9ed6131ad06e"
      },
      "source": [
        "duplicate = df[df.duplicated()]\n",
        "print(\"No. of duplicate rows: \",duplicate)"
      ],
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No. of duplicate rows:  Empty DataFrame\n",
            "Columns: [Age, Income, ZIP Code, Family, CCAvg, Education, Mortgage, Personal Loan, Securities Account, CD Account, Online, CreditCard]\n",
            "Index: []\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gLCrHNDrt7vG",
        "colab_type": "text"
      },
      "source": [
        "**3. EDA: Studying the data distribution** "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_gpeB7yUv9gx",
        "colab_type": "text"
      },
      "source": [
        "**3.1 No. of unique values in each column**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "snwrOBvIuZpU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "ad035fcc-7bba-4d0a-a692-ac8f1b797898"
      },
      "source": [
        "unique_no = df.nunique(axis = 0, dropna = True)\n",
        "unique_no"
      ],
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                    45\n",
              "Income                162\n",
              "ZIP Code              467\n",
              "Family                  4\n",
              "CCAvg                 108\n",
              "Education               3\n",
              "Mortgage              347\n",
              "Personal Loan           2\n",
              "Securities Account      2\n",
              "CD Account              2\n",
              "Online                  2\n",
              "CreditCard              2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Yln5yinwFMn",
        "colab_type": "text"
      },
      "source": [
        "**3.2 No. of people with zero mortgage**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Uxr_S_VwO7M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "15ac1d1e-2cbf-4b6e-ac40-9fce1e409590"
      },
      "source": [
        "zero_mortgage = df.Mortgage[df.Mortgage == 0].count()\n",
        "print(\"People with Zero Mortgage: \", zero_mortgage)"
      ],
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "People with Zero Mortgage:  3462\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NU_cxbP7zXt2",
        "colab_type": "text"
      },
      "source": [
        "**3.3 No. of people with zero credit card spending per month**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgNetSSjzd9x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4df789e4-5cd7-40b3-8b43-03a42ebb12d1"
      },
      "source": [
        "zero_cc_spend = df.CreditCard[df.CreditCard == 0].count()\n",
        "print(\"People with zero credit card spending per month: \",zero_cc_spend)"
      ],
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "People with zero credit card spending per month:  3530\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JFSqvgmp0-g_",
        "colab_type": "text"
      },
      "source": [
        "**3.4 Value counts of all categorical columns**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WTumO9xf1kUi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "c7085dd5-56f3-4e23-9fc9-adfcb26e1651"
      },
      "source": [
        "for x in df.columns:\n",
        "  if df[x].nunique(dropna = True) == 2:\n",
        "    print(x,\": \")\n",
        "    print(df[x].value_counts())\n"
      ],
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Personal Loan : \n",
            "0    4520\n",
            "1     480\n",
            "Name: Personal Loan, dtype: int64\n",
            "Securities Account : \n",
            "0    4478\n",
            "1     522\n",
            "Name: Securities Account, dtype: int64\n",
            "CD Account : \n",
            "0    4698\n",
            "1     302\n",
            "Name: CD Account, dtype: int64\n",
            "Online : \n",
            "1    2984\n",
            "0    2016\n",
            "Name: Online, dtype: int64\n",
            "CreditCard : \n",
            "0    3530\n",
            "1    1470\n",
            "Name: CreditCard, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rJydqbcP7V4k",
        "colab_type": "text"
      },
      "source": [
        "**3.5 Univariate and Bivariate analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eRW7ln8K7acp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "12eb2742-b1a5-4787-a38d-fd8a2855e1dd"
      },
      "source": [
        "sns.boxplot(x=df['Income'])"
      ],
      "execution_count": 235,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7364201978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 235
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pcBGLkL8wpqX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "7ecf7a31-aadc-4871-af3b-4db2fd1c1da2"
      },
      "source": [
        "sns.boxplot(x=df['CCAvg'])"
      ],
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f73641f5978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 236
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FRQGDoolw6tw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "163d2b90-c785-481e-a7d9-fbba4e018638"
      },
      "source": [
        "sns.boxplot(x=df['Mortgage'])"
      ],
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f735191ddd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 237
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xPUKr-BLzOql",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "e7b93aad-937b-488b-be1e-cead5c0398fb"
      },
      "source": [
        "fig, ax = plt.subplots(figsize = (10,5))\n",
        "ax.scatter(df['Income'],df['Personal Loan'])\n",
        "ax.set_xlabel('Income')\n",
        "ax.set_ylabel('Personal Loan')\n",
        "plt.show()"
      ],
      "execution_count": 238,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aJU6g2pdg7qr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "17226a22-aa68-470d-99e5-14490914478c"
      },
      "source": [
        "sns.countplot(x='Income', data = df , hue = 'Personal Loan')\n",
        "plt.title('Income vs Personal Loan')\n",
        "plt.xlabel('Income')\n",
        "plt.ylabel('Personal Loan')"
      ],
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Personal Loan')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 243
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SxgoS/wHcZGb9gGfd/bGM8xcRkQJlGhDcvRb4fN77l4HxWeYpIiLdo0dXiIgIoIAgIiKRAkIXaIC3eFTXIsWngCAiIoACgoiIRAoIIiICbMUBIemjzrKvWv3gItKXbLUBQURENqWAICIigAKCiIhECggiIgIoIBSk9eCwBouLT3Uukj0FBBERARQQREQkUkCQkqauIpHiyfT3EMzsFOACoBn4PjAbmAGUA4uBSe7emGUZRESkMJldIZjZTsAlwKHAkcCXgKnAdHcfD8wDTs8qfwlKqYVdSmURkffLssvoMOAxd1/t7ovd/UxgAvBA/PzBOI+IiJSALLuMxgDbmNkDwI7ApcDgvC6ipYTfXBYRkRKQZUAoA3YCvgJ8CPhznJb/eafq6up6vmRRbW3tJq9dmbeQZTparjt5d9fmLt+TulKW7ta5iHRPlgFhCTDL3ZuBV81sNdBsZoPcfT0wGljUWSLjxo2jsrKy50t360yqq6vT167MW8gyHS3Xnby7a3OX70ldKUt361xkK9fY2NjthnSWYwh/Aj5jZv3iAPMQ4DHgmPj5McDDGeYvm6kYjwgXkdLR6RWCmQ0EjgCGktfN4+63drScu79lZvcBz8RJ3wSeB+4ws7OAN4Dbu1luERHpYYV0GT0MbCScwBM5oMOAAODuNwE3tZp8eMGl6yNGTLmWJdPP7+1iiIhslkICwgB3/2TmJRERkV5VyBjCnDgGICIiW7BCrhA+AMwzs1cIj6AAwN0/lVmppNvUfSUi3VVIQLi6jWm5ni6IiIj0rk67jNz9L0AtMD/+LQKuybhcIiJSZJ0GBDO7AFgIOCEwvBD/RERkC1LIoPKxwHDgGXcfBpwMZPc8CRER6RWFBITV7r4BGADg7g8QHmW91etL3+DtS2UVkd5RyKDyu/GHburM7FfAy8CobIslIiLFVsgVwqnAX4HzgX8SbkM9KctCiYhI8RVyl9G6+O/ngFeAn7j7S5mWSkREiq6Qu4zOJvyWwUnAKcATZnZa1gUTEZHiKqTLaBKwl7sf5+7HAvsCZ2dbrJ7TG4OppTyAWwplK4UyiMj7FRIQmt29IXnj7muBDdkVSUREekMhAWGBmV1vZkfHv+nAm1kXrKdtKa3SQtdjS1lfESmeQgLCmcBbwNeAyYTfRTgzwzKJiEgv6PR7CPEuo00ecGdmE4AnOlouzvNbYE6c9HfgR8AMoBxYDExy98YulllERDLQ3d9U/n6B8/3F3SfEv28CU4Hp7j4emAec3s38S1Jf6qbpS2UVkeLobkAo63yWNk0AHoj/Pwgc1s10RESkhxXy6Iq2FPp7CHub2QPAUOAyYHBeF9FSYGRnCdTVbf5z9Gprazd57Wx6IWn09Gshebf3fnPL2pW0NldP1LmIZKPdgGBmU9v5qAzYrYC0/0kIAvcCHyZ8uS0/v4KuMsaNG0dlZWUhs7bt1plUV1enr51NLySNnn4tJO/23m9uWbtTd93VE3UuIh1qbGzsdkO6oyuElg4+u62zhN39LeCe+PZVM3sbONDMBrn7emA04cd2RESkBLQbENz9ss1JOD4hdaS7X2NmuwAjgF8BxwB3xteHNycPERHpOd0dQyjEA8CvzexLhN9S+Abhl9buMLOzCN9nuD3D/EVEpAsyCwjuvho4qo2PDs8qTxER6b7u3nYqIiJbmI7uMlpA27eXlgE5d/9gZqXK0Igp17Jk+vm9XYwe1xvrtaXWpcjWqqMuo0M7+GzHni6IiIj0ro7uMnoj+d/M9gZ2jm8rgZ8Be2VbNBERKaZCfjFtGvA74A/ATwjfLZiRcblkC6LnJon0DYUMKn/c3fcCXnT3Awl3CW2TbbFERKTYCgkIybOHKs2szN1rgUMyLJMUoDdb3fqRHpEtUyHfQ3AzOweYCTxqZg7skG2xRESk2AoJCGcT7ipaCZxIeATFVVkWSkREiq/TLiN3zwG7Av8CvA28BFRlXC5pRd0vIpK1Tq8QzOx3wH7AgrzJOeD/siqUiIgUXyFdRmPcfY/MSyJbDH2DWaRvKuQuIzezAZmXREREelUhVwgtwMtm9hzQnEx091MzK5X0KboiENkyFBIQHot/IiKyBes0ILj77WY2BjiAMJhc6+5vFpK4mQ0C6oDLgccJj7woBxYDk9y9sYPFRUSkiAp5ltHZwJ8J30E4BXjCzE4rMP2LgRXx/6nAdHcfD8wDTu96caW1UrodtZTKIiJdV8ig8iRgL3c/3t2PBfYlfFmtQ2a2J7A38FCcNIHws5oADwKHdbm0IiKSmULGEJrdvSF54+5rzWxDAcv9BDgXSK4mBud1ES0FRhZSwLq6ukJm61BtbW1Brz2RRndf+0rePV2mruRdyDIi0n2FBIQFZnY98Gh8/zmgwzEEMzsVeNrd55tZW7OUFVrAcePGUVlZWejs73frTKqrqzt/7Yk0uvvaV/Lu6TJ1Z71FpEONjY3dbkgXEhDOBM4DvkYYVH4auKGTZb4IfNjMjgQ+QHhi6hozG+Tu64HRwKJulVhERDJRyF1G68xsurtfbWa7AGNJH4nd3jInJP+b2aXA68AngWOAO+Prw90vtoiI9LRC7jK6HjjezIYCTxHGBW7sRl6XAKeZ2ZPAUOD2bqQhIiIZKaTLaH93/2a8/fR2d7/czB4vNAN3vzTv7eFdLaCIiBRHIbedJgPARxJuFwXYjFFeEREpRYUEhH+a2RxgW3d/Md5BtKKzhUREpG8pJCCcSfiG8mfj+zmAHmwnJUHfjhbpOYWMITzi7p9O3ri7vh0kIrIFKiQgvGhmU4FZwHvfUHZ3/WKaiMgWpJCA8NH4Oj5vmn5CU0RkC1PIF9M+DWBmZe6ey75IIiLSGwr5Ytp+ZlYDvBLff8/MDsq8ZFJySnkAt5TLJtJXFHKX0Q2E3y5YHN/fA/w0sxKJiEivKCQgNLn77OSNu88l77eVRURky1BIQGg2s90IA8mY2efpwuOrRUSkbygkIHwH+ANwiJnVA1cD38y0VD1AfcpbN21/ka4r5C6j2cBHzGwY0Ojuq7IvloiIFFu7AcHMtgMuBvYEZgLXubvGDkREtlAddRn9PL7eDOxN+D2DkqUugq2LtrdIz+uoy2iMu08EMLP/BQr+DQQREel7OgoITck/7t5iZl36lrKZbQPcBowABgKXAy8BM4BywvcaJrl7hz/H2VUjplzLkunn92SSIiJbhY66jFoHgK4+tuIooMbd/wU4nvBltqnAdHcfD8wjfOFNRERKQEdXCJ80szfz3g+P78uAnLt/sKOE3f2evLe7AguBCcDZcdqDhFtau/P7zCIi0sM6CgjWExmY2SzgA4Sf4Hwsr4toKTCys+Xr6uo6/PwLt87kj6d/CoDa2trNeu3I5qatvHsu765+LiIFyuVymf9VVVV9tKqqanZVVdWyvGl7VFVVzWpvmZqamjE1NTW5hoaGXEeGn/PTHnktRh7Ke/Pz7urnIlubhoaGXE1NTa6mpmZMrovn6kK+qdwtZlZtZrsCuPuLhKuR1WY2KM4yGliUVf4ivU23xkpfk1lAAD4FfBvAzEYAQ4DHgGPi58cAD2eYv4iIdEEhv5jWXf8F3GJmTwKDgClADXCHmZ0FvAHcnmH+IiLSBZkFBHdfD5zcxkeHZ5WniL6HItJ9WXYZiYhIH9JnA4IG7EREelafDQgiItKz+kxA0BWBwObvB9qPRNrXZwKCiIhkSwFBREQABQQREYkUEEREBFBAkC2cBpFFCqeAICIigAKCiIhEfS4gqAtASl3rfVT7rPQVfS4giIhINhQQZKugVrpI5xQQREQEyPYHcjCzHwHjYz5XAc8DM4ByYDEwyd0bsyyDiIgUJsvfVP40MM7dDwY+B1wHTAWmu/t4YB5welb5i4hI12TZZTQTOC7+vxIYDEwAHojTHgQOyzB/ERHpgix/QrMFWBvfngH8ETgir4toKTCys3Tq6ure+7+2tjbT144o760r782VdZ5fuHUmfzz9Uz2apgi5XC7Tv6qqqi9VVVU9W1VVtX1VVdXSvOl7VFVVzWpvuZqamjE1NTW5hoaGXC6Xyw0/56eZvnZEeW9deW+uYuRZjPWQvqmhoSFXU1OTq6mpGZPr4vk607uMzOwI4CLg8+5eD6wxs0Hx49HAoizzFxGRwmU5qLw98GPgSHdfESc/BhwT/z8GeDir/EVKjb4LIaUuy9tOTwB2Bu41s2TaacAvzews4A3g9gzzFxGRLshyUPlm4OY2Pjo8qzxF+roRU65lyfTze7sYspXSN5VFRARQQBDpVNL3X8wxgFIebyjlssnmUUAQERFAAUFERCIFBJHNtLV3JZVimaR7FBBERARQQBDptlL4qcws8lSLf+ulgCAiIoACgoiIRAoIIu1Q14lsbRQQREQEUEAQeZ8t8cqgkHXaEtdbukYBQUREAAUEERGJFBBEukhdK7KlyvIHcjCzccAfgGvd/QYz2xWYAZQDi4FJ7t6YZRlERKQwWf6E5mDgeuDxvMlTgenuPh6YB5yeVf4iW5vbHp3aK4/q3hrc9ujU3i5CUWTZZdQIfAFYlDdtAvBA/P9B4LAM8xcRkS7I8ic0m4HmvN9TBhic10W0FBjZWTp1dXXv/V9bW5vpa0eUt/LurbwLLVtnyyfv/77iIfYd+sVu5d16vvbeA3zh1pn88fRPdZhOR2XpLe2Vu5Bt11N59ZpcLpfpX1VV1aVVVVXnxv+X5k3fo6qqalZ7y9XU1IypqanJNTQ05HK5XG74OT/N9LUjylt591behZYtl8vlfvWny9pdvvV83cm7rbQKKWtH2itLb2qr3FmVs5A66qqGhoZcTU1NrqamZkyui+frYt9ltMbMBsX/R7Npd5KIiPSiYgeEx4Bj4v/HAA8XOX+RPmXElGs3GSwuZYWUsdDB2VJf3y11kDmzMQQzqwZ+AowBmszsWOAU4DYzOwt4A7g9q/xFRKRrMrtCcPdad5/g7mPcfWz8/y13P9zdx7v7RHdvyip/kVKURcuy9RVEfh6b29Iudku4J64MWpc5ed/ZurRXh1sTfVNZREQABQQREYkUEER6UHtdDT3ZBVFoWq27Srpahs66bwrt3uluN1B737wuxe6c7nZTlRoFBBERARQQRHpFey3K3tCdvHurvG1dKRSrNZ7cApyvozxL/dbZtiggiIgIoIAgIiKRAoJstYrR7dFZHn2hq2hzHqmd1SD75gx499UB32JQQBAREUABQbZgXW0BdmX+UmhdFrMMnbX0S+VKqDu3p7Z3NdHWIHJ30+vuAHixB6YVEEREBFBAkK3I5rRS22vZdTa9J5XCVUkhutOqLaTPv1hlKTTNnvy50q6mmdWVgwKCiIgACggiIhIpIEif0Vm3TKG3OPaVrpdS1JuDx22lXYxB11L+xnFPl00BQUREgF4ICGZ2rZk9bWazzOzAYucvqZ4YZO3u+/bS64nn6mR5u6kUpi/+sE5PlCHLH0DKf20r7/zX7ipqQDCzfwHGuvvBwBnAz4qZv4iItC+z31Rux78C9wO4+ytmtqOZbefuq9qYtxxgw4YNAAwfUkljY2Nmrx3ZUvOu6NfcYf6dLbs571vnW9FvEI2Nje+9diXP7rzm59WVvHviVXkr747yLrQs7c2fnDOTc2hXlOVyua4u021mdjPwkLv/Ib5/EjjD3ee2nre2tvZQ4MmiFU5EZMsyvrq6+qmuLFDsK4TWyjr47HlgPLAYaClOcURE+rxyYCThHNolxQ4Ii4Bd8t6PIpzw36e6uroR6FJ0ExERAF7tzkLFvsvoT8CxAGZ2ALDI3VcXuQwiItKGoo4hAJjZ1cCngI3AFHd/qagFEBGRNhU9IIiISGnSN5VFRARQQBARkai3bzttl5kNAe4AdgT2BbYj3JFURvhy278By4ARhPXYAKwEhhPuZtopfv5BYDWwDZAjBMGWmE6OMJZRCawDKgi3bK2LfwNjvsn8AI3AgPh/S5y/HKiP5RgY590INMT3/WN6xDw2xGnr4+dLYzkd2C0vv1eB3eM8TXHZxljepphGc3xtia/J9ETyeVksT2WcVp43b1OslwFxnn555VsGDAMGx/nK47qVxf9bWr3m8ta/X9701u/zbyVO0muK65rUPfH/DYTtNwhYGz9rjun1i8snfZ8tcb41cV2Tz0dig7cAAA//SURBVJNtkdR/BWF/2ZF0m75N2H9WxelNefMPyKvLZPn826aT9V4NbBvrbhCwHNghr2765dX/CsJ+mr+dWr8mZUhe86cn25W8NJP9OqmLpK7bSqt1GutiPSfrsIqw/68AhgLvxPIm8yXv29J6P0jeJ3m3/jyRvO+sHpJjJElzQN56w6bbpnUZcrS97Vq/T+ovvwz5dZy/LVfHumpd7rbSyF+P/LIlaXak9Trk8vJ4nXD+2EA4p2wHvAz8HtgfaHT3yR0lXspXCJMJJ8ibCCu9jlB5AKcBswg7wUpCxTwHDAFeIpy85gIfIK3404F3CZW1nrCBlpCeMJrj56sJd0MNJd3xVgNv5pVhRZz/RUIgyAELY9r/JJy0amJZN8a/V2NZ1sZ8WmJ6ycGXrOPamNfKuH4D4nItQF1M9w3gtpj3A3HaP2I+PyYExNtiWX4Tl3031tO/xvTmx/cLYrmT9Xghfv5SXHYkaeC8K5bhv4FnYp5LgDnATMJOuBG4L9bNbMLjSf5JOOluiH+zYnmaYr4bY1pJIGskBP9VwJ9j/ZTHulkZl1tBOMmvin+NsV5aYnrLCCfjJXEd18b0k31oSUz37fh+JbAz8BbhBL6A9GQ6INZpWcwnl5dO8pXrl+O0QfHzZN8ZShoMkjvqXotprY3vl5M2AMpjHZXH9UvKUEE44PvF6ZAG1ZUx/cWkwTFJYxVpQM4RjosKYF583xzTqI9lX084jnKxfjYC28f3O5E2KjbmzUd8bYj1uSq+Xx9fl8Z5lhG2wdOx7PVxnqQOV8fpyXGZBP2m+PnGWPalhP0nF+skCTDJfjw/LtNM2I5lcX035NXDQkKjoSnWW7Iea+P/89j0hL+OTYPGuliO/oTz1HakDZtFcdkFbNoIar09l8S86luln5yP8su7MW86pA28ZD9tJuy3LYRjqZ+7bw8cApwL7EkBSjkgLCfsgGOAGYSd7GekreOngWuB/8l7/33CwTaX0NJbRqjU1+P0d2La+Sem9YSKbQGqSKN9GeHE/A5hQ8wjbZFsE9P8f4SdIQd8l3AS/Le85ZKDNDlJJa2AStLW5LuEHas5Tvs14QS0AhhN2FnK4vv+hCDXHKcPJBy0i4AbY9p/JZys34zpHBTXsV/M51DCATgvlulWwkH8s1jOmwlXZlcDH471Xk9oXXwtrs80QovjGcLVw3eAvYBLYlm/SThJ/QH4MuHEAelV2EOx3E2E4P1OXLdlpCfzZ/LWsX9c/+TksIa0hTgormcLaXBrjtvop6Qt2Z1jusRtl2yDBTHNWYRt/49Yj/8X01kU3z8a81hG+kiAjXmf/y1+vjguV0PYr+YSgkx93EZNhECZnDxyhH39bcJJqj4uvyIuNy9ut9VAbfxbGdchacWuiev9t1i+JtJg+DIhYK8nBOo1Md2NhGMnabVWEk6G5aTB7/W8ukhO7GXAnXH9/0rayt8Q53mOdH9OgvIrpA23WkLg/lvMbxFpoF4e1+d5wr7SEMs7L69+1hMaQUvjfAPjMk0xn4ZYrg2EY6sibpMBwA1xvpdjutfHMiffd2ombZgNiHkRy5k0qpLGh8d1zT+2W2IaK+O0+XFaY3z/TixXErgfje+d9NwC6VVDE5sG3GbSoJDU+Yt5ZUiurtcClWZWSQhUOxGO506V9F1GZvYw8BHCwTyDsCN/l1DBPyHsQGOBcwgt48XAxYSK6Uc4EKoJO9LImGzyoI/XCDviP4EjCQfLE4SKKyPsEPcCJxMOzm0JJ5dkYx1AOOENj/O+RdjgQwknyQFx3kbS7qrksnEFoUviMcK3safF9bqYcGU0JKa7Li43iHCi2Jn0RPIKsDdhxy6P6zg2Th8T50m6VsrY9DIcwk7Uj7CDbxPrchThBFkZy5B0L60ntBKTFlxF/GxZzOO3sQ6vJBxknwD+HTg8Lrcwprd9LGvSTZF0vayL67giruNCQldg0iWUBLSk6yzpzkpa40kr7J2Yx4CY9mzgY3nrXU9oRb1N+gXJ5KRRRwiefwI+G9fjXMIJ3fLeb4jlmE3oyky6CpJupfuAo0m7il4mBPZKwpXVGbHs/YE/Al8ivUraMZZpPeEkuBfhZHwoaTfmLOCEuL4NsQ6Sk0ku1u8a0u1cE+ugnPRElbT0BxNOHoPjvEn95PKWPRD4HfDVWG/DgF8CZ5N2fcKmredk2zYQtntytZJc5SXlHUx6XCRXVP3y0mgk7VYhzpfsdzvGeZM0tyHsG8l69SMEnvpYj0/HaRZfHyIcw1Uxj9nAODbt2ksk+1o9abdQUs9DSPfRZH9syXufHO/JfI1x+SGEfWtXwn5TEdPblvd3Ub5FODZXxc/fIjQM/0DYtsk56Gbg1Fjm+lgn5YTz2O3A5D7bZWRmE4E33X0UoftjEvBp0svrRFne66mEA3ABIeomfceNhK6H1YQNtS0hEHwA+Auh0h4FriCcpHPAPYQDGeD8OM8ThI24kdCCvYUQdACOJ9RnHWFjzCJswEdJW5JJ317St71zLN+HY55jCTvcIsLBu5Cws68lnAzmEA7Odwg7cnJyvIh0R92BEBQ2xLpoJBzYOdJL5OSSPAkayY64MaaTi/O9EdOfR9q9syouc0qcd1vCTrkI+ALhYDibcBC+Qtr6mUl6mft0rP+kZdgc88pvASXr/0as+z/HbVdHGkheYdNWVdJnS0xjLOlJooX0RJe0pJKuiG3YdJ+CTfcrYr32I90/RsV6Wx7fvxvLd3Qsx9utlu9HOPmvIuxbFbHOGgn7xEDCdl5MOJHsGdMcG/MZTNhuXyZs1yRIrozzLYxlmR/XB9Ixkr/HOvs7YXv9lbAfLyftgqqPebSQHmMfjXV0UKzPHeO8k2P+SePqH3E9VsR1eCuu30DCdk66Z9bH8iVdY3NJ98lm0u6aRtJxgaRrLWlpJ10s5YT9KGmgLYv5J91jTwIfIhzjZYRjZvdY5sFxnj2BB2O6H4zpvBrXcQNpg6yCcO5IWvk50v16XZyWBNxk+SFxmSZCI7E/acBOgvjIuK3WxHpMxs2Wx9dkHxpB2u3Vj7AflBHOB6OAC2LZPhnr/vVY5wsIXb8fJ+y/nSrZgEDo+3oEwN3PJGy0ewmV1kDaMklOZEcS1udvhArclbBDDyIcVKNJ+x7XA/sBzwJnETb+jwkbeQ/Chp1ACEBlhMjbSNoCWBHfN5BuPAgDOkkL4UOEneIzhJ3gA4SNP4KwQ5YRul12BI6LZT+N0ALbn9CK24uw0+xA2GG3I7RwJhIOimRHvZ+0T74ylmkpYYfvF9Mpi8svjfMl868l7ECNpP3ly+LnSXDYJ67DTrEc5YSAVx/rbgTh0rWacGCfSLgM3y1ur10JX0asJD1w3orzJvU6i3DgNMc8novp7hjXc0J8/QjhQGmO/78T06sHron/J33XjxJOukm3X3Jy3pa0L/Zh0m6opGWYi3WetOQgnIibSa8sBuetG4QT3UbSk1TSlZK0grcj7TtPljkxrtMesR7Wx7KuJWzXAYSDP2n97kc4iQ6N6VcSroi2i/8nY18LSVve2xP2oXWEq9GZhP1iA2mQGxjnS26q2DmWryL+jSY9wSdXjoNIW7175X329/j/G3H91xBOSpWkg9QfImyvpHEE6YkSwtV7jrSPPRkvSMZcdonrNof0ymgo6bhf0rf/Dum+MiiWabtYz8cSgtXepOMh/Uhb7Ek30xrSln1ys0BFrNPkCnlwnDaAsO++E+erIZxj6kn30dmE43NdTHdRLF+STy6u30bS7Z6MJVTF//eK7/+TMOa3R6yHPQhXC6Njed+O6zMCuA74opldQAdKOSDMAw4ys/3M7B5CRexJ2FBO2JDE1+QknJxsLyP0Va4hHQh+gnCCeiUut4ZQwc8SNuYThEvIbxECyQDSAalfxzx2JlR20nd9AuEEDaGraQBwMGHHeZj0pNMS810Ty/8MYUeYD9xN6BZIxjSWxfJfRhjEXUHad7mKsOP+MOa/Mr4/nXCQlcf1PyCW9Uo27XNsJBwQO8blk508OdiTHXI3woH66Vj+JTGN5bF8OcK4waD4fntCi2xQTKOecMDVxzTeJe33T65Cku2WDLRVxLzKY7kmxDpK+n3XxHnfittgA+lJJ0c4eI4k7VZYBXwu1vdcwkkmuQxfGZdvJDQ8lhBawW8TWpEthG6apOsqqbtmwknnNdIB26RV+4GY7wjSBkAL4eDennCCSH4QqiZ+9n/x/RrScY9RhJPZoPg3P26byljO0fH196R9zIsJ2/QzhO2ZDGSPitsgaVQkAW1Y3jptJASfZXHeFtIxhqSL6QLSQfH1hOOiHjgvfp6MwyWt7kGxnOWEbpjhpHd4JeXfKZZnFWl/+Pq4Th+K+e0YPxtJ2l2VIx1E3yZuI9i0cTaQ0J2XbO8kgL0TP9sm1tn2Md1HSANoMqa0C+kVNHH5BtK7qpaRBpe6vOVX5y03jtBoGRL/tot1MIK0O3Y06X7cENNJBuWTbrbkjqJk8Di50+m1vPKsjWV5hdDrkQT64+M6fpfwpOkf0YGSHUOIt53eSjhA92XTfvCNpJeTrS/1ExtJ+7th01vSEm3dggZpX+Yyws6cjPInLYWKvHn7tVouv0xJS3UwabdF0uecDOAl/enbkZ50kj7x1wmtoGNIB0bXx/QaSQeeBpF2meQPViaX28lAeFu3quZ3jSQ7X5JGC2FnaiRsh2SHhbAzGuGAHBXXawjpuEXSXbEspnUDMJX0FuD8Ok9ui0y218a8z5oIB8iKmN9a0kHkpO5yrZbt3yqNZFtUsuktua23fetb+rZWhdZD8nlyK3RvKGRbtb61tb15Ojqf9EQ5spbcUDGAsE3eIQT3ewlX8J2OIZRsQBARkeIq5S4jEREpIgUEEREBFBBERCRSQBAREUABQUREIgUE2SqZWc7MSvZpvyK9QQFBRESAEv49BJFiMLMJhG9xLiQ8oqMJ+Jy7rzOzM4BvxGl/dvcLzWwE4RlWySMcfuTuvzezSwnfbt2F8IiJHxK+8f4xwrdij3b3nJl9k/Dt0f6EZwCd4+7JUzVFepWuEETC40YudPeDCd94PsLMPkR4aOD4OH2UmRnh29Z/cfcJhIfV3Whm28Z09gK+AnyN8HTUHxMeVzEO2M/MPh4//1RMcyXw9SKto0inFBBE4BV3T37E5Q3Cs4AOBGqT1ru7T3Z3Jzzz6NE4bSnhyiJ5ntXT7p78+MoSd381vn+L8NycCYQHkP3ZzJ4gPC8pedCdSK9Tl5FI+hDCRPIcn7YaTK2f9VKWNy0/nbbSbAQecPdzu1lOkUzpCkGkbc8DHzez7QDM7F4zqyY8qfaIOG0U4UmcXmCafwU+Hx/ciJmdY2YH93jJRbpJAUGkDe7+JnAp8JiZzQJed/daws+EHhq7fP4bONPd17Sb0KZp1gDTgSfM7ClCF9JLPV96ke7R005FRATQFYKIiEQKCCIiAiggiIhIpIAgIiKAAoKIiEQKCCIiAiggiIhI9P8ByfqL83i3IlwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0a6xe6gBhf1E",
        "colab_type": "text"
      },
      "source": [
        "People having higher income have availed Personal Loan."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qyGpCcbN1KC9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "02e6e695-accf-4d3c-b780-560485e4f40d"
      },
      "source": [
        "fig, ax = plt.subplots(figsize = (10,5))\n",
        "ax.scatter(df['CCAvg'],df['Personal Loan'])\n",
        "ax.set_xlabel('CCAvg')\n",
        "ax.set_ylabel('Personal Loan')\n",
        "plt.show()"
      ],
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZFjX8whBf8w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "outputId": "b0749e5a-06f9-4cbe-85d4-9960fb4f2451"
      },
      "source": [
        "df.corr()"
      ],
      "execution_count": 241,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Age</th>\n",
              "      <th>Income</th>\n",
              "      <th>ZIP Code</th>\n",
              "      <th>Family</th>\n",
              "      <th>CCAvg</th>\n",
              "      <th>Education</th>\n",
              "      <th>Mortgage</th>\n",
              "      <th>Personal Loan</th>\n",
              "      <th>Securities Account</th>\n",
              "      <th>CD Account</th>\n",
              "      <th>Online</th>\n",
              "      <th>CreditCard</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.055269</td>\n",
              "      <td>-0.029216</td>\n",
              "      <td>-0.046418</td>\n",
              "      <td>-0.052012</td>\n",
              "      <td>0.041334</td>\n",
              "      <td>-0.012539</td>\n",
              "      <td>-0.007726</td>\n",
              "      <td>-0.000436</td>\n",
              "      <td>0.008043</td>\n",
              "      <td>0.013702</td>\n",
              "      <td>0.007681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income</th>\n",
              "      <td>-0.055269</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.016410</td>\n",
              "      <td>-0.157501</td>\n",
              "      <td>0.645984</td>\n",
              "      <td>-0.187524</td>\n",
              "      <td>0.206806</td>\n",
              "      <td>0.502462</td>\n",
              "      <td>-0.002616</td>\n",
              "      <td>0.169738</td>\n",
              "      <td>0.014206</td>\n",
              "      <td>-0.002385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZIP Code</th>\n",
              "      <td>-0.029216</td>\n",
              "      <td>-0.016410</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.011778</td>\n",
              "      <td>-0.004061</td>\n",
              "      <td>-0.017377</td>\n",
              "      <td>0.007383</td>\n",
              "      <td>0.000107</td>\n",
              "      <td>0.004704</td>\n",
              "      <td>0.019972</td>\n",
              "      <td>0.016990</td>\n",
              "      <td>0.007691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Family</th>\n",
              "      <td>-0.046418</td>\n",
              "      <td>-0.157501</td>\n",
              "      <td>0.011778</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.109275</td>\n",
              "      <td>0.064929</td>\n",
              "      <td>-0.020445</td>\n",
              "      <td>0.061367</td>\n",
              "      <td>0.019994</td>\n",
              "      <td>0.014110</td>\n",
              "      <td>0.010354</td>\n",
              "      <td>0.011588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CCAvg</th>\n",
              "      <td>-0.052012</td>\n",
              "      <td>0.645984</td>\n",
              "      <td>-0.004061</td>\n",
              "      <td>-0.109275</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.136124</td>\n",
              "      <td>0.109905</td>\n",
              "      <td>0.366889</td>\n",
              "      <td>0.015086</td>\n",
              "      <td>0.136534</td>\n",
              "      <td>-0.003611</td>\n",
              "      <td>-0.006689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Education</th>\n",
              "      <td>0.041334</td>\n",
              "      <td>-0.187524</td>\n",
              "      <td>-0.017377</td>\n",
              "      <td>0.064929</td>\n",
              "      <td>-0.136124</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.033327</td>\n",
              "      <td>0.136722</td>\n",
              "      <td>-0.010812</td>\n",
              "      <td>0.013934</td>\n",
              "      <td>-0.015004</td>\n",
              "      <td>-0.011014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mortgage</th>\n",
              "      <td>-0.012539</td>\n",
              "      <td>0.206806</td>\n",
              "      <td>0.007383</td>\n",
              "      <td>-0.020445</td>\n",
              "      <td>0.109905</td>\n",
              "      <td>-0.033327</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.142095</td>\n",
              "      <td>-0.005411</td>\n",
              "      <td>0.089311</td>\n",
              "      <td>-0.005995</td>\n",
              "      <td>-0.007231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Personal Loan</th>\n",
              "      <td>-0.007726</td>\n",
              "      <td>0.502462</td>\n",
              "      <td>0.000107</td>\n",
              "      <td>0.061367</td>\n",
              "      <td>0.366889</td>\n",
              "      <td>0.136722</td>\n",
              "      <td>0.142095</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.021954</td>\n",
              "      <td>0.316355</td>\n",
              "      <td>0.006278</td>\n",
              "      <td>0.002802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Securities Account</th>\n",
              "      <td>-0.000436</td>\n",
              "      <td>-0.002616</td>\n",
              "      <td>0.004704</td>\n",
              "      <td>0.019994</td>\n",
              "      <td>0.015086</td>\n",
              "      <td>-0.010812</td>\n",
              "      <td>-0.005411</td>\n",
              "      <td>0.021954</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.317034</td>\n",
              "      <td>0.012627</td>\n",
              "      <td>-0.015028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CD Account</th>\n",
              "      <td>0.008043</td>\n",
              "      <td>0.169738</td>\n",
              "      <td>0.019972</td>\n",
              "      <td>0.014110</td>\n",
              "      <td>0.136534</td>\n",
              "      <td>0.013934</td>\n",
              "      <td>0.089311</td>\n",
              "      <td>0.316355</td>\n",
              "      <td>0.317034</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.175880</td>\n",
              "      <td>0.278644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Online</th>\n",
              "      <td>0.013702</td>\n",
              "      <td>0.014206</td>\n",
              "      <td>0.016990</td>\n",
              "      <td>0.010354</td>\n",
              "      <td>-0.003611</td>\n",
              "      <td>-0.015004</td>\n",
              "      <td>-0.005995</td>\n",
              "      <td>0.006278</td>\n",
              "      <td>0.012627</td>\n",
              "      <td>0.175880</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.004210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CreditCard</th>\n",
              "      <td>0.007681</td>\n",
              "      <td>-0.002385</td>\n",
              "      <td>0.007691</td>\n",
              "      <td>0.011588</td>\n",
              "      <td>-0.006689</td>\n",
              "      <td>-0.011014</td>\n",
              "      <td>-0.007231</td>\n",
              "      <td>0.002802</td>\n",
              "      <td>-0.015028</td>\n",
              "      <td>0.278644</td>\n",
              "      <td>0.004210</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Age    Income  ...    Online  CreditCard\n",
              "Age                 1.000000 -0.055269  ...  0.013702    0.007681\n",
              "Income             -0.055269  1.000000  ...  0.014206   -0.002385\n",
              "ZIP Code           -0.029216 -0.016410  ...  0.016990    0.007691\n",
              "Family             -0.046418 -0.157501  ...  0.010354    0.011588\n",
              "CCAvg              -0.052012  0.645984  ... -0.003611   -0.006689\n",
              "Education           0.041334 -0.187524  ... -0.015004   -0.011014\n",
              "Mortgage           -0.012539  0.206806  ... -0.005995   -0.007231\n",
              "Personal Loan      -0.007726  0.502462  ...  0.006278    0.002802\n",
              "Securities Account -0.000436 -0.002616  ...  0.012627   -0.015028\n",
              "CD Account          0.008043  0.169738  ...  0.175880    0.278644\n",
              "Online              0.013702  0.014206  ...  1.000000    0.004210\n",
              "CreditCard          0.007681 -0.002385  ...  0.004210    1.000000\n",
              "\n",
              "[12 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Il0WIKCy9_VQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "outputId": "61bfaa72-0b12-4560-c6bb-27c62ecff0be"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "c = df.corr()\n",
        "sns.heatmap(c,cmap = \"Blues\",annot = True)"
      ],
      "execution_count": 244,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f735032ca20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 244
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zC8tCafr5xyyalNk1aS89WFnbEqNUatnqYnAQ8XGxTB09hLSnT3lwP5x1qzxp0Kgxzd9sia6eHrXq1MXA0JD42BjMLa0Koel7jms0Zfovuij+y/Lm3dbRiTu3VP4LDjzFzq0bWLLCDyNjk0LZKCuWVjaajBioMkIWlqqsjYWVDXFZ/BoTHYWFlQ0hZ/8i8sF9QgJPooyORF9fHyvr6rRoY1/k8bNycN8uTvyZ+3yytNK2k2WW+aTMMp9y44Z8FTNzS2yq16Bh46akp6cTHxeb75w6uG8XJ/88rNaS6bOYqCgss+2Ry02LpbVtvu2uXAhm5KQZWv0EB/5Fi9bab6wZOkzNtHUoo6NyvOZcdVjZ5tpOT18/h2/19fUJD7uD9+LZqmPKaGZOGIZDx25EPXrItNEDeZycRHxcLD/u3ELfzwfnajtLKxvisr52ZZRmP5pFtjJldCSWVjbo6enn2uburX9p9mZLAFra2XP8j19zHbMgLK3z0WSdi6Y85pMyKpLwe3dYsURlo9iYaGZPHM6SVRtzrZ9DRynaBlR7QBs31V6qBAg58xdTXZcWSlO5IPaQvfxIklQHsAe8JUm6ALwLfJatWsbnhaeAvyzLXdQ/zWRZfumDMYAP+n6Kt+8mXN28SUlO5uGD+6Q/e0bgqRPY2Wvf965NO0dO/KnKnpw89gdtHTqQnJSIy6RRmuzXpfMh1G/YmCOHfmH39i2A6uIbG6ss0v6a3n36477Wn1mLvUhJTuKRWlfQ6RO0aqutq3U7R06qdZ06doQ29qrga8Nabz769EvsHDpo6t76V2aFm+rblsGBp3i9SVMUisKdLh/07Y+njz9zlniRnJKksdXZUydo0y43Taqnafx17A/s7DvQsVsPNuzYy6oN23BdtoLXpWaMmjidNvaOXAgJ4vnz5yTEx/Ek5bHWMkL+mj7Fy2cTc5d4k5KSnK+m7P6zs1f5b2YW/12+oPJfclIiG3yWs9Bzjda3Q4vCW3YOnDlxBIBb/17Dwspas/RoW6MWj1OSiXwYQXr6M0ICT/KWnQNT5i5jme+3uK3dSvf3PuLjL4eXOBgD6PVRf5at9mfmQi8eZ5lP586coHV237V15C91lvDU8SO0yRLMZ+fKxRD2fq/asB4bo+TJ45QC9yL1+qg/S1dvxGWhJynJyTx6EKGa22dO0CqbllZtHTml1nL6+BFat+tA9Zq18mynjI6kmoEh+vra+4/+/edvGrzeJIcOt1VqHSmZ/Z3L5RxraefIqWO56MilXau2Dpw69gcAN69fw9LaBtsatfhm5894+QXg5ReApZU1S1f783+ffMGazbvw8gtg1ORZ2Dl2zDMYy7DH6eNHMvu2stHMqex6gs+cpGVbxzzbmFtacU/9BY1//7lKzTqv5eu3PDXZOXL6RB6aaqh9pZ7nwWdO0tIu9/uJWtnYsn77fjx8A/DwDcDC0rrQwVhp20Zlk7+pn23OADx8EK7Zm1spEBmyV4LPAR9ZlqcCSJKkA/wLGEmS1FT99zvAUeAs4CVJkjtQBfCUZXl8WQts1awuy6b0pV4tS9KepdPn7VZ8NnUDsQkpZTLehOmzcXNVfbLu0r0ndV6rT4wymoANvkxycaVP/y9YtmAmk0cNwtjYBJf5SzEyNqGdoxMThn9BlarVeL1JUzp168HjlBSWzpvB6ZNHeZaWxoTpc3K8SRSWsdNm4z5ftWTWqVtP6rxWjxhlNNv9/RjvPJcP+w3Ac9Espo8ZgpGxCdNdl/DkyWOO/HaAiHthHPr5R9Vr6vEePT/oy4sXz5k04gv0q1TF2dWteLaaNodlri4qTW9n2urbjb5MnOHKR58MwH3BLKaMHoyxsQkz5uU9jrVNdTp2fZuJI74EYMwUl0IHiVkZP202S9X+65xFU8BGXybNcOWjT1T+mzJ6EEbGJrjMy/TfxBGZ/uvYtQcHf/qB+Pg4lszN3BPlPHcJtjVqFlqP9L+3aNi4KbMnDEFHR4fhE1w4emg/hkbG2Dt1Y8TEmaxcMguA9l16UKtOvTz7+mG7P5dCzhIXo8Rt5niaNG/BV19PLLKNAMZMmY3HQtV86ti1J7Xr1iNWGc32TX6Mmz6XDz4egPfiWTiPU82naXOWALDUdRrRkY+4H3YHlwnDePeDj3nv/z5htft8nMcN4WlqKqMmzyyS78ZMmYXnQpdctKxj3PQ5fPDx53gvns2McUMxMjZh6pzFebYD9b40i5zBfIwyGrN8snajp8zCK6O/bpk6dmxex9hpKh3Ll8zGRa1jilpHbu1q163H61IznMcMQkehYNQkl0LboyCavvEWjZo0Y8bYwSh0FHw9yYUjv+7HyNgYh47dGDV5JssXqXzr1PUdlV3q1svRRqV9Nj5ei9DV1cPE1JRxzvNLpmmcuv+JLhz5bT9GRnlruiFfZbPfCiIfRqCnp8fp40dwWeiFSTE//JS2bUA1l7KvaCTEx2FkVPBev3LlJciQ6ZR4D8orjiRJocBAWZavZDk2F3gOfAHcBu4B92VZXiRJ0hLgbUAH8JVleUt+/Ru0GlfpHCAf8a5oCTlIS39e0RJyRVdR+e5to0Pl0xSfUj5fOikKBlWK9uWD8qDA/WEVRGV8m3heGUVVQkmV8HIAQLOaRuWqzKDzwiJ75/Fx13LVKDJkBSDLcutcji2SJOkdYLssy3ckSVqP6pYYyLI8G5hdzjIFAoFAIBDkRSX88JwdEZAVHx1gryRJicAjYE8F6xEIBAKBQJAbL8GSpQjIioksy4eAQxWtQyAQCAQCQQGIRycJBAKBQCAQVDAiQyYQCAQCgUBQwYgMmUAgEAgEAkEFIzJkgoKojLeYkLpPLbhSOXP5kGdFS8gVK+MqFS0hB05uRytaQg7Ozu1e0RJycODvBxUtIQc7giMqWkKuLOvdrKIl5OB2THJFS8hBA4vSeZZqaaL7EmSGBCpEQCYQCAQCgeDV5iUITEVAJhAIBAKB4NVGLFkKBAKBQCAQVDAiQyYQCAQCgUBQwYgMmUAgEAgEAkEFIzJkAoFAIBAIBBWMyJAJSkpoUCCb1q1GoaugnWNHvhw6Uqs8OSkRt3kuJCclYWBgwMwF7piamfFln3exqV4dhUIXgJnzl3I/PIxFs6dRr0EjABo0asy4qTPLTHvzRjXZveJr1mw/yrrvT5TZOBmcDw4k4Js1KBS62Dk48fngr7XKk5MS8Vgwk5TkJKoZGOI8bykmpmY8TU1lrdci7t6+xaqNOzT1jx7+hR92bEWhq8uXw0bTrn2nEuk7G3gan9Ur0VUo6NCxE8NHjslR54/Dv7Fg7mw2b9vJ642bAPDw4QNmz5hGWloaTZs1Z9bc+SXSkcG0dxvToo4ZL168wOPX6/wdkagpq25aFfd+b6Cvq8O1B4ksPiBjV98cz/5vcjNSdbuBG5FJLDt4vVS0ZCXwzGlWr1yOrq4uTh07MXL02Bx1Dh/6FdfZs/h25/c0Vtvp6J9/sGGdH/pVqvDue735/IsvS03TL1vWcu/fq+jo6NB78HjqvN5UU3brynkO7/wGHYUu1jXr0mfUdBQKBY/CbrHNcw7te/fD8d2+paYlN4bY16GJrREvXsCmwHvciE7RlK3r/wbRyU95/kL1/8pjt4lJSSvxmBeCAwnYsBaFQoGdgxOfDcp5vnktmkVychIGBoZMm+uGialZnu02+63k70uhpKen88mXQ2nfqTvPnqWxws2VB/fvYWBoxMyFnhibmBZJ595Nq7l7XeW7PkMn8FrjzFt4pD1NZdc6Lx7eu81Uz40ABP5xgODjmU/Fu3dTxn3H4eKaSQt/Hy+uX70MOjoMHzedxk3/pym7GHKWbRtVdmlt78SnA0cAcPf2DdzmTOHDfgPo3eczrf7OB51mwYxx7DsaWmQt5eG/DEKDTjNv+lh+Pn6+yDpLjZcgIKv8CksJSZLqS5IUXNE6iorPimW4Ll3OyvUBhASd5u7tm1rlP36/jbda2bFy/VacurzN99s2acrclvvh7bsJb99NWNtWB6BFqzaaY2UZjBlWq8LyGZ9wNKj037DzYv1KD2Yt8sbTdwvnz50hLJutftq9nRat7PD03UL7zt3YvX0zAP6+K2jwuqRVNyE+jh2b1+Phu5n57qsJ/OtYifV5LXPDY/kq/AN2EHj6NLdu3tAqDwkO4tRfJ2ncpInW8ZVeHnw5cDABO3ahq1Dw8EHJ71XVpp459SwNGbgxmPk/XWNGL+3XP7VnYwJOh/HFhmDSX0ANs6oqjXfiGL4llOFbQsskGANwd1vM8pVr2LptJ2dOn+LmDW07BZ8L4q+TJ2giZWp+/vw5S5csYu26DWwO2M7xY0d59PBhqei5ffUCyofhjFriS59RzhzYvFqrfN83Xnw+ZQEjF63l6ZMU/r0QxNMnj/l582oavtG6VDTkR/MaxtQ0rcrMn2V8Tt5lmGPdHHUWH7qB68HruB68XirBGMA3qz2YucgLD58tnD8XSNgd7fNt/54dvNnSDo+1m3Hs2I0fdmzJs92l0HPcvX0DL78AFnj6sGGNFwCHft6LmbkFy9dvo2PXd/j7UtECjxt/nyf6QTiTlq3js7Ez+NF/lbbGrb7UbvC61jGHt99n3KI1jFu0hnc/G0rbLu8W0TK5c+VCCA/Cw3D32cq46a5sXOOhVb5hjQfOCzxZumYzF4LPcO/OLZ48fsyG1R60aNU2R39Pn6ayZ8dmLKysi6WnPPwH8DQ1ld3bN2FZTJ2lho5O0X/Kmf9MQPYy8uB+OCamZthWr4FCocqQnQ8+q1XnfPBZOnRWfRJxcOrM+XOBFSE1B6lpz/hovB8PouLLZbwHEeGYmJpio7aVnYMTF0KCtOpcCAnCsVM3AOzbd+aC2paDRo6nvfq4pm7wWVra2WNoaISltQ0TnF1LpC88/B6mZmbUqFEThTpDFnRW21dNm/2PeQuXoKevrzn2/PlzzocG06mLSt+M2a7UqFmrRFoA7Bta8uc/UQDcjk7BtJoeRlVV2VQdHWhdz5xjsqp86S8yD+NTSzxmYQi/p7ZTTZWdOnbqzNmzZ7TqNGvenIWLl6KfxU6xsbGYmJhiaWmJQqHA3sGBwDOnS0XTzcuhNGvrBIBtnXo8Tk7kSUrmTUnHLPsGMytbAAxNzUlJSkBXX59BM90xtbAqFQ350aKWCUF34wC4H/8Eoyp6GOiX7aX9YUQ4xqZm2NhmnG8duJjtfLsYchaHjl0BaNehExdCzubZ7n9vtcZlgermz0bGJqQ+eUx6ejpBp4/TpUcvAN798GPsO3Qpks5/L4XwZruOAFSvUz+H73p/OZIW9nlnvg/v2sI7nwwq0ph5cSk0CHsnlT3q1mtIUmIiKclJgMqeJiaZdmlj78TF0CD0q+gzd9lqLK1tcvS3Z9smen3UHz09/RxlBVFe/gPYtc2f3h99qnVdqxB0FEX/KWf+c0uWkiRtASKANsBrwBeyLIdKkuQM9AOeAzNlWT4qSdJEICNHvE+WZXd1+0h1exvAHRgCWAOdgSTgG6AhoA+4yrL8Z3G0xiijMTe30PxvbmFJxP172eooMbew0JQrldGaslUei3j4III33mrFsNETAbh7+xZzp48nMSGBr4aNok07x+JIK5D09Oekpz8vk75zI1YZjVkWW5lZWPIwm62y1jGzsCRWbStDQyMS4+O06j56GEHqkycscJlIcmICA4aMoqWdfbH1KaOjsbDI1Gdhacn9e9r6jIxy3uU7NjYGIyMjlnsu459rV2nVug3jJk4pto4MrIyrcDUiIXOclDSsjauQnPoYC8MqpKSmM/3dJjSraUJoWByr/1B9em5oY8Sqz1tgZqDPumO3CbwVU2ItWYmOjsLC0lLzv4WlJeE57GSco52lpSUpycncvXuHWrVqcy7oLHZt25WKpsS4GGo1zMxaGpmakxQXQzVDlb8yfifEKrlx8Rw9Ph2Krq4eurrlc3k1N9DnZpYlyoQnaZgb6PM4LTOIHtnhNWyNq3LtURLbgu+XeMzYmGjMzLKfb+HZ6igzzzdzS2KVUXm209XVRdfAAIDff9lHG3sndHV1iXz4gJCzp9i8biUWllaMnjwLE1OzQutMiIuhTqPMTKqRqTkJccpM3xkYkpKY+4fGsH+vYW5tW2pBdWxMNI2aZC6XmpqbExujxMYuPD0AACAASURBVNDImLgYJaZZr1/mljyMuJfnPLp/7y53bl5nwNDRbFm3slhaysN/GTq/HDaGzetWFFlnqfISbOr/r2bIqsqy3BNYBQyUJKkxqmDMAfgS+EKSpAbAYKCj+udTSZIaqds/k2W5O3AZaC/L8tvqv7sCA4AHsix3BT4Cin625MELXhRQIbN80IgxjJwwDW8ff+7cvMHJo79Tu85rfDVsFAs9VuM8dzHebvNISyud5YtKx4vC2yr34hckJsQzZ7E3k2ctZOXSebwoqM/S1JdFR+SjSD7/4iu+2RSAfO0af504Vno61GS9VOnogK1pVbYH3mPo5hCa1jChY2MrwpSPWX/sFhN3XmLO3qvM/79m6OmW8UWukCbX0dFhkdsy5s2ZxeSJ46hdu05hTVwMTTk7ToqP5Vv3mXw4fDKGJoUPGMoCHbR98l1oBFvOhjP3oMxrFtVwrG9e6mMWeG7kUZ69XeBfR/n94D5GTZqhKucFtevWY+mqjdRr8Dq7t2/KrZuiCC101cA/DtCua6+SjZevlmIXssnHmyFjSv7BTDNaGflv41ovho2tJI/iExmySstJ9e9wwB5oBZyVZfk5cAMYLklSXyBQluVnAJIknQLeUrfLyO0+AP5R//0IMEMV1HWUJMlJfdxAkqQqsiw/Lay4n3/8nmN/HMLM3IKYmMyMlzIqEitrW626VtY2xCijMTI2IToqEit1artHrw81ddq1d+L2zX/p1O0duryt2g9Rq05dLC2tiY56RM1adQorrdLxy95dnPzzEKbmFsTGKDXHldGRWOZiq9gYJUbGJurynMsAGVhYWNLsjbfQ1dOjZu26GBgaEh8Xi7mFZZ5tcmPP9zs5fOhXLCwsUUZn+jIyMhJrW9t8WqowN7egZq1a1Kn7GgBt7R24eeMGTp26FElHdqISU7E2qar538akKlGJqikal5JGRNwTwmMfA3D2dgyNbI04+a+SQ39HAhAe+xhlUirVTapyP+5JibQA7PpuB4d++xULC4tsdnqEjU3BdgKwa9uOLd+qvpSxaoU3tWrXLrEuAFMLK5LiMjOBCbFKTLJkTZ6kJLPVbQY9Ph9G47dy7vUpa2JT0rAwyFwOsjDUJ/Zx5getYzcytYeGJ/CahQFn7mhnhAvLwX27OHn0MGZmFsRmuTbFREflOJ8scznfLK1s82wXGnSaXd/6s8DTByNjE0CV9X+jZRsAWrVtz47NfkXSa2ZhTWJs5uuPj43G1KJwe5lu/H2evsMnFWm8/LC0tiEu62tXRmn2VVlkK8vv+qSMiiT83h1WLJkNqLJdsycOZ8mqjQVqKE//KaMiCQ+7g/fi2erXG43LhGEsW+1foM4yQWTIKi3PsvytA6ST0xYv0E4cVEG1nJm9ffa+ngJLZFnuov5pXJRgDOCDvp/i7bsJVzdvUpKTefjgPunPnhF46gR29tpLjG3aOXLiz98BOHnsD9o6dCA5KRGXSaM02a9L50Oo37AxRw79wu7tWwDVyREbq8TapnpRpFU6evfpz7I1/sxa5EVKchKP1LYKOn2C1m21bdWqrSN/HVXZ6tSxI7Sxb59nv63aOXIxNIjnz5+TEB/H48ePMTUremah36ef882mANy9V5KcnETE/fs8e/aMv04cw8Ex7/Ez0NPTo3adOoTdvQPAtWt/U69BgyLryM6ZmzG83Vx1IW1a04SoxFRSnqr2fKQ/f8H92Me8Zqlagmhe05Q70Sn0erM6A9urAkMr4ypYGlfhUWLp7C3r/9kA/Ld8i9eK1SQlJXH/fjjPnj3jxLGjOHboUKg+xowcjlKpJCUlhePHjuLgWDrL8a+/1ZYrgccBuH/rOqYWVlQ1MNSU/xrgS4fe/WjSsvhL2iXhwv0EHBuolpEaWhkQm5LGkzTVpcpQX8Hcnq+jp1Bdyv5Xw5gwdaBdHHp91J+lqzbistCTlJRkHj2I0JxvrbKfb3aOnDqmOt9OHz9C63YdqF6zVq7tkpMS2ey3Etdlq7WWJNvYdyD0rGov4M3rV6n9Wv0i6ZVatuXimWOA6tuSZhbWVMviu7yIj4mmajWDUt331MrOkdMnjgBw8/o1LK1sMFAvnVavUYuU5GQePYwgPf0ZwWdO0tIu9/lrZWPL+u378fANwMM3AAtL60IFY1C+/rOysWXDzp/x8gvAyy8ASyvrigvGUGXRi/pT3vxXM2TZCQHmSpKkB1gB64DJwHz1MVBl0txQLUPmx1ng/4CdkiTZApNkWZ5VXGETps/GzVWV/u3SvSd1XqtPjDKagA2+THJxpU//L1i2YCaTRw3C2NgEl/lLMTI2oZ2jExOGf0GVqtV4vUlTOnXrweOUFJbOm8Hpk0d5lpbGhOlztDZGlyatmtVl2ZS+1KtlSdqzdPq83YrPpm4gNiGl4MbFZOzU2XgsUH1ztGO3ntR+rR4xymi2b/Jj/PS5fNhvAF6LZuE8dghGxiZMm7sEALe504iOfMT9sDu4jB/Gux9+TJcevXDq0oOpo74CYNSkGSgUJfv84jJ7HrNnqNL3PXq+R736DYiOjmK971pmuy5g3497OHhgP9flf1joOpv6DRqy0M2dqc4zmT93Fi+eP6dR4yZ06ty1RDoALt6L51pEIluHteHFC3D7RebDljVJevKMP/+JwuO36yz6qDk6OnAjMpnj16Mx0NdlWb//0bWpDfq6Oiw5IPMsvfTXBee4zsdluspOPd/rRf36DYiOisLXZw2u8xfy4w+7+eXn/cj/XGPe7Jk0aNSIJUs96NuvP6NHDAUdHYaN+BqLImYz86Ke9Aa1GzZh/Zyx6Ojo8MGwSYQe+5WqhsY0fqst508cRvkwnOA/fwGghdPb1G7YhF8DfImNeoiurh5/Bx5nwLRFGBoX7ZYNhUGOTOZmdApu70u8eAEbzoTRtbEVKU/TOXs3jtDwBJZ90JSn6c+5pUwpdnYsO2OmzMJzoQugPt/q1iNWGc32zesYN20OH3z8Od5LZjNj3FCMjE2YOmdxnu1+2/8DCfGxuM931vQ/edYiPvj4c1a6uXL44D4MDAyZNHNhkTQ2aPomdRpJrJo5Gh0dHT4eMYWgPw9SzdCYFg6d2OI5l7joSKLuh7F27ngce3xIm049SIhVYpxlr1Rp0PSNt2jUpBkzxg1GoaPg64kuHPltP0ZGxjh07MaoyTNZvkh1/XLq+g6169bjhnyVzX4riHwYgZ6eHqePH8FloVeR9tHlRXn4z7Z6zRLrLC0qIsAqKjqlui+mEiNJUn1gD3AF2CPL8gFJkt4H+smyPFiSpKnAx6iyXLPUm/rHotoTpgC2y7K8Vr2pP6O9F3BFluUtGX8D21AFdM0BXWC+LMu/5qUrLCa10jlA6l5J1vyzcPmQZ0VLyJXqZlULrlTOOLkdrWgJOTg7t3vBlcqZA38/qGgJOdgRXPJbmpQFy3o3K7hSOXNDmVTREnLQwCLnF3MqGl1F5QxEmtQwLFdhRp9sLvJ7bfLuIeWq8T+TIZNl+Q5gl+3YAeCA+m9vwDtbuQ/gk+3Y4Cx/T8vtb2B4KckWCAQCgUBQQl6GDNl/dQ+ZQCAQCAQCQaXhP5MhEwgEAoFA8N/kZciQiYBMIBAIBALBK40IyAQCgUAgEAgqmLIKyCRJWoHq/qMvgImyLJ/LUjYW1c3m04FgWZbzvbGd2EMmEAgEAoHg1UanGD8FIElSZ6CxLMuOwDBgdZYyU2A60FGWZSeguSRJDvn1JwIygUAgEAgErzRldGPY7sA+AFmWrwEW6kAMVDeJfwoYq+9nagjk+/BfsWRZwaSV4wO4C0tlvOfXmz2nV7SEXAk96F7REnKwe3TZPDC+JITHFP/u8GXF/2xL/yatJWVp74p9DmZePK90d0uE+pXwnl+V0Eyk/0fuNVoQZbRkWQPVjeUziFIfS5Bl+YkkSQuAW8Bj4DtZlq/n15nIkAkEAoFAIHilKadHJ2kaqTNls4AmQAPAXpKkt/JqCCIgEwgEAoFA8IpTRgFZBKqMWAa1gIxHgDQDbsmyHK1+nvVJoE1+nYmATCAQCAQCwatNGWzqBw4D/QAkSWoNRMiynKguuwM0kyTJQP2/HfBvfp2JPWQCgUAgEAheacpiD5ksy6clSQqRJOk08BwYK0nSYCBeluW9kiR5AkclSXoGnJZl+WR+/YmATCAQCAQCwStNWd2HTJZll2yHLmYpWw+sL2xfIiATCAQCgUDwSiPu1C8oMefPBbL1mzUoFLrYOToxYPDXWuXJSYl4LJhJclISBgaGOM9fiompGRdDz7Fl3WoUugrq1K3PRJd5AKz1XMzd2zfQ09Nn3PQ51K3XoOiaggMJyNDk4MTneWhKSU6imoEhzvNUmp6mprLWaxF3b99i1cYdmvpHD//CDzu2otDV5ctho2nXvlMxLFU4mjeqye4VX7Nm+1HWfX+izMYB8F/rxfWrl0FHh+Hjp9O46f80ZReDz7Jt41oUCgWtHZz4dOAIAO7euoHbnCl82G8Avft+BkBU5EPWLJtPevozdHX1mDx7MRZW1kXSciE4kIANqvHsHJz4bFBOn3ktmkVysmoeTZvrhompWb7tUlOfMG7wJ3w6cARvv/chAPv37GCT7wp2HjiOgaFhofWV5pw6dGAvRw8d0LT9V77KD4fPFEpHcXyWWxuPec7Ex8UCkJQYT5PmbzJ22lwAXrx4wczxQ3mrjT2fDxmVp5aNa72Q/76Ejo4OI8Y707hZppYLwYF8q/ZLmyx+ya1NVORDViyZw/P051hYWTNl9mL0q1QhKTEBz4UzMTAwwGWhl6bvKxeCcZ/nzIQZ82mbz7m4ca0X169egoyxmmrry7BVGwcnPh34dYFtQoNOs8B5LD8dO88N+SqbfZdryu7dvcXMxctp9kbLfLyXO5t8vJCvXkZHR4dh47L5NCTTp23sneiv9unWdSu5evk8z9PT6TtgCI6duhd53NLScvf2DZaqrwm9+qiuCR7znUnIOr+avckY9fwqDGU5z+07dGHvdwGa/m79+w8+AT9iaW1TTKuVEpU/Hnu5AzJJktoA3lkONQAOyrI8WpKkaFmWrSVJ6gLsBv5G5RI9YIYsy39l68sIWIFq490TVDdwGy3L8r1C6DAGrsiyXL/kr0qbdas8WOzti5WNLTPGDcOpc3dea9BIU75v13bebGVHvwGD+fWnPezetpmhYyaxxmMhy1ZvxNq2Om5zphFy9hRpT9NITk7Ce10AD+7fY90qDxZ4rCmypvUrPVik1uQyfhgdsmn6afd2WrSy4+MBg/l1/x52b9/M0NGT8PddQYPXJe7evqWpmxAfx47N61nlv5MnKSls2+RXZgGZYbUqLJ/xCUeD8r0VTKlw5UIID8LDcPfdyr27t1jrvgB3362a8g1rPJjn6YOVtS2zJw6nfafu2FSvyYbVHrRo3Varrx3+vrzzQV+cur7Dwb3f89PubQwele8TOHLwzWoPFnj5YmVty8wJw2nfuTuv1c/02f49O3izpR19Px/Eb/t/4IcdWxg8amK+7b4P2IixSea9vP787WfiYmOwtC5asAilO6d6vt+Hnu/3AeDy+WBOHj1cKA3F8Vl8XGyubZwXeGjarXGfT4/efTT///7LXp6lpRWgJZiI8DA8/QK4d+cWq93n4+mX+Sa3YbUH89V+maX2S0JcbK5tdvj70eujT3Hq2oOAb9bw+8F99PqoP77eS2j+Zktu35A1/T64f499u7YVGPhcuRDMg/AwPHwDuHf3Fmvc5+Phm0XfGg/me/pqbOXYSaUvrzZPU1P5YfsmzQeN16XmLFm1EYCkxETc5kxCat4iX0256wwhIjwMdx+1fzwW4O6T6dONazyY5+GDpbUtcyapdMbFKgm7cxN3n60kxMcx9esBpRKQFUeLTfWabFztQYtW2tcE5/na8+vtLPOrMDrKcp43afYGdo4dAXgQHsZmv5UVH4zxcmTIXupvWcqyHCLLchdZlrsAvYEkILe7mh5X1+sMDAQ25lJnBXBHluXWsiy3B7YC35WR9ELx4H44Jiam2FSvocpQODpxISRIq86FkCDad+oGgH2HzlwIPgvAav+dWNtWB8DM3IKE+Hjuh99FavYGADVr1yXy4QPS09OLpikiHBPTLJocctfkmKGpfaamQSPHa7Rq6gafpaWdPYaGRlha2zDB2bVIeopCatozPhrvx4Oo+DIbI4NLoUHYO3UFoG69hiQlJpKSnATAw4hwTEzMsLGtockgXAwNQl9fn7nuq7G00r54jZw0U/OGYGpuQWJC0fQ/jAjH2DRzPDuHDlzM5rOLIWdx6KjS265DJy6EnM233b27t7l35xZt1RdeAIdO3Rg4YlyRL3ylPaeysnPLNzmygXlRHJ/l1wbgftgdkpMSaaI+7xLiYjnxx2/0/ODjfLVcDAnCwamLqt/6DUlK0taS1S9tHDpwKSQozzaXLwRj36EzAO3ad+JiiMp245xdaf5mK61xLa2smbnIG0Nj4/z1hQZhnzFWgbbqwKXQoHzb7N7uT68+n6Kvp59jrH3fB/BBvy9QKIr+dpXdP8nZdBqbmGGdodPeiUuhQTRv0Zrp81SBhpGxCU+ePC7ydbK0tOhX0WfOstV5BjT3w+6QnJw5v4qjoyzmeQbfbVnPZ4MLd/6VNeV0H7IS8VIHZNlYBGyRZflWfpVkWb4JmEqSpJtxTJIkE6An4J6l3m5UQR6SJHWRJOm0JEnHJUnaLklSVUmSTCVJ+l2SpJPA7Cx9dZQk6aQkSX9KkrRVkqQqxX1BsTHRmJlbaP43t7AkRhmlXUeZWcfMwpIYZTQAhkaqC2pMdBSh5wJp6+hE/YaNCQk6TXp6OuFhd3gYEU5CfFzRNCm1NZlZWBJbgKbYDE2GOe+s/ehhBKlPnrDAZSLOY4do3mjLgvT05zxJzT8zUVrExkRjmsVOpubmxMYoAYiLUWqVmZmrbKirp0fVqtVy9FXNwABdXV3S09P5dd8uOnV/t8hazMyy+yw6Wx1lps/UevJrt8l3OcPGTdXqIzf/FkpfKc+pDK5fu4K1bXUsC7m8Wxyf5dcG4OcfdmqWngG2rl/Fl8PHotDVXH7y0JJtPDMLTb+5+SVGGZ1nm9Qnj9GvUkVTNz/bVa2mmmsFEZdlvoDqQ19WfTltFZ1nm/v37nLn5nU6dOmRY5zU1CecP3ca+w5dCtSUu85oTM3y9qlZbuehri7VDFR3KjhycB9t7DsUyiZloyX3a0IGB37YSe8+n+VZnhvlMc9B9d4TG6OkYeOmRdJXVoiArJyQJMkO6Igqy1VQ3XbAPVmWs37kaQTI2Y4hy3JGtLIO+FSdYYsFBqB6gvsVWZY7AheyNFsN/J8sy92AR8AnxXtVOXlR0CMwspXHxcYwf8ZExk6diamZOW0dnWjS7A2cxw1l367t1K3XsOA+CxZVovIXL16QmBDPnMXeTJ61kJVL55VcU2Ukv5dUiNebnp7OSre5vNmqLW+1sS+ZlGL6LKPdn7/9TNP/taBGzdol0lHU8QtdrubQgb283evDEugohoYsh9PS0rh2+Txvqpeb/r4YgkKhS9M38r1Zdx7d5iMmL3/l0qaszq38+s2rLOO4v48XQ8dMzbXO2b+OYufQsVjZsdzHzKcsm73O/nWMPw7+xIgJM0pl7JJoyY20tDSuXcmcX8UXkl9Z0ed5Bn8e+pnOPXqVTNt/jJd6DxmA+qGd64CRsiw/y6NaZ0mSjqHaQxYPDMpW/gLI9SOQJEmWwIsse8mOAp1RBbPH1ceOqetWBxoDP0qSBGAEaKciCsEve3dx4sghrU+dAMqoSKysbbXqWlnbEBujxMjYhOjoSE1qOyU5CdepYxn49That2uvqT/o63Gav4f2fx9zC8tCazr55yFMs2uKjsQyH03KLJpyw8LCkmZvvIWunh41a9fFwNCQ+LjYQuuqrFha2RAXk+n6GGWUJlNjka1MGR2ZY5kyO2vc51OrTl0+Gzyy0BoO7tvFyaOH1ZmSLFqio3L4xDIXn1la2eba7lzgXzyMCOfcmZNERz1CX78K1ja2tLRzKLQ2KLs5lcHl88GMmpT9G+l5Uxyf6enp59nm74shNG6auYRz9tRxbshXcR49kPj4OJ49fUqN2nXo+s77ObVY2xCXxSYx0VGa/VXZ/aJU+0VPXz/XNtUMDElNfULVqtWIKaTtCsLSykbLZzFKbX1x2eeN2lbZ2+jr63M/7A7LF6sWGWJiopk1cRhuq/wBOHfmJO99WPzPtCo7ZmqJzeIf1ZzPqjPTNueDTrNnuz+u7msxMjYp9viloSUvss+vQuso43meQciZv5jqurTI+soKsYesfJgGHJNlOSSfOpo9ZLIsfyjL8j/Zym8BTSVJqpr1oDrz9gLt72dUQXUDOB31b8i041Pgfsa+NlmW28qy7EER6d2nP+5r/Zm12IuU5CQePbhP+rNnBJ0+Qau22g+Obt3OkZN//g7AqWNHaGOvCr42rPXmo0+/xM6hQ+aL/FdmhZvq25bBgad4vUnTQn/y7N2nP8vW+DNrUU5NrbNpatXWkb+O5tSUG63aOXIxNIjnz5+TEB/H48ePMTUzL5Smykyrto6cPn4EgJvXr2FpZYOBeomoes1apKQk8+hBBOnPnhF85iQt2+b9QPDjvx9ET0+fz4eMLpKGXh/1Z+mqjbgs9NQaL7d51MrOkVPHVD47ffwIrdt1yKEzo92M+e6s+GY7Xn4BvNO7D58OHFHkYAzKbk6B6o2kmoEh+vo59yTlRXF8ll+bf//5m/qvN9H0P3TMFFZs3ImHXwD9vxrO270/yjUYU2lx4NSxPzL7tbbRLDFm13JO7Ze82rzVxl6jUeXb/G1XGFq2deD08SxjWeWj78wJWrZ1zLWNbY1arN/xM55+AXj6BWBpaa0JxgBuZLNhkXXaOXLmRKZ/LLL4x7ZGLR4nJxP5MIL0dJVP37JzJDkpka3rVzLbbRUmpqX3sPfiaMmPG//8Tf1GRbdNWc/zDB4+CNfsY64UlM2d+kuVlzpDJknS68BXQIlytrIsJ0qS9BOqfWjO6r4/BiYAXYAXkiS9JstyGKrs2F+AJapvZP4AdFX3EytJEpIkNZdl+aokSeNRBYOXiqtt7LTZuM+fCUCnbj2p81o9YpTRbPf3Y7zzXD7sNwDPRbOYPmYIRsYmTHddwpMnjzny2wEi7oVx6OcfAejS4z16ftCXFy+eM2nEF+hXqYqzq1vxNE2djccClaaO3XpSO0PTJj/GT1dp8lo0C+exKk3T5i4BwG3uNKIjH3E/7A4u44fx7ocf06VHL5y69GDqqK8AGDVpRqktT2SnVbO6LJvSl3q1LEl7lk6ft1vx2dQNxCaklPpYTd94i0ZNmjFj7GAUOgq+nuTCkV/3Y2RsjEPHboyaPJPli1Q2dOr6DrXr1lN/1X8FkQ8j0NPT4/TxI7gs8uLgvl2kPX3K7Imqr5/Xrd+QUZNnFknPmCmz8FyoyhZ17NaT2nXrEauMZvvmdYybNocPPv4c7yWzmTFuKEbGJkydszjPdnnx/bcbuRAcSGyMkvnO42j6vxYMGV24b4OW9pyKUUYXOctaHJ9Rt16ONhnEKqOpWatOkTRk0OyNlrwuNcN5zCB0FApGqbUYGhnj2Kkbo6fMwiubX2rXrZejDcCAoaNYsWQuh/b/gE2NmnR79wPS09OZO2UkyUmJKKMimTVxOJ8O+pqnqans/W4r4WF3uClf4+cfdrLQ2y9XfY2aNMN57CAUOgpGZugzNsaxYzdGT56F9yIXta0y9WVvUxDJSYnF3psIKp82bNIMl3GD0dFR8PVEF/78TWVHh47dGDl5Jt5qn3ZQ+/Twzz+QEB+H14LMpcqJMxdiU71msXUUV8tN+Sqb/bSvCTMWemFiakZsTDQ1ijG/ymOeJ8THYWSU/xdDypuXIUOm8zLv15EkaR3wHnA7y+H7six/ke22F+NkWe5XQF9VAA+gO6p9YveA8bIsx0iS5AQsA54BN4GRgDGwF1WW7C9goCzLDdR1vVFlyyLUx1PzGvdm1ONK54DKOCXe7Dm9oiXkSuhB94IrlTO6lfDCo1BUPk1p6c8LrlTOKCqh76ByXhOeV0ZRlZBKOqVoVtOoXJXVGbOvyBMm3PejctX4UgdkrwIiICscIiArPCIgKxwiICs8lfGaIAKywlFJp1S5B2R1x/5U5Alzz+f/ylXjS71kKRAIBAKBQFAglTQwzYoIyAQCgUAgELzSvAx7yERAJhAIBAKB4JVGBGQCgUAgEAgEFYwIyAQCgUAgEAgqGBGQCQpEtxJ++8zKuNiP3ywzKuO3GQFa9yqbx6qUhLCTKytaQg7uKUv/Xm8lxdSg8DeNLS8SH+f1sJGKpap+5buHeOW7clJJRQmAl8I3IiATCAQCgUDwSiMyZAKBQCAQCAQVjAjIBAKBQCAQCCqYlyAeEwGZQCAQCASCVxuRIRMIBAKBQCCoYF6CeIzK99UZgUAgEAgEgv8YIkNWyQk9F8jmdavRVejStr0TXwwZqVWenJTIsnkzSU5OpJqBIS4LlmFqaqYp3+S3imtXLuHp4w/An4d+Ydf2Lejq6jJw+BjsO3Qqkb6zgafxWb0SXYWCDh07MXzkmBx1/jj8Gwvmzmbztp283rgJAA8fPmD2jGmkpaXRtFlzZs2dXyId/mu9uH71MujoMHz8dBo3/Z+m7GLwWbZtXItCoaC1gxOfDhwBwN1bN3CbM4UP+w2gd9/PAIiKfMiaZfNJT3+Grq4ek2cvxsLKukTaCkPzRjXZveJr1mw/yrrvT5T5eBmcO3uGb3xWolDo4tihI4NHjM5R58/fD7F0wWzWb9lJw9cbA5CamornkvncvnUT/227SqwjwG85N/65AjowaPRUGkmZ/rscepbvN/uiUOjSsm17+n45HIC/jvzKz7u/RVehS79BI2lt74Sf53xu//sPxupz4P1PvqK1vVOJ9Z0PDiTgmzUoFLrYOTjx+eCvtcqTkxLxWDCTlOQkqhkY4jxv3tR4LQAAIABJREFUKSamZlwMPcfW9atRKBTUea0+E2bMQ6Eo/ufgrX7eajvpMDgXO3232Udtpw58nMVO+3cHoKvQ5ZNBozT2+HXvd2z7ZgX+Px6lmoFhsTVlZZOPF/LVy+jo6DBsXLbzMCTzPGxj70R/9Xm4dd1Krl4+z/P0dPoOGIJjp+6losXfJ8s1IR8tre2zXBNuZ7km9PlMq7/zQadZMGMc+46Glo6+UrpmVRYdFXXtLCwvw5LlfzJDJklSfUmSEiVJOpblp1g3b5IkabAkSX0kSeoiSdKe0tbqt8KduW7LWb5+KyFBZ7h7+6ZW+d7vt9OitR3L123FqXN3dn27SVN29/ZNLl/IvHgkxMexbdM6lvttYaHnGs6cPFZifV7L3PBYvgr/gB0Enj7NrZs3tMpDgoM49ddJGjdponV8pZcHXw4cTMCOXegqFDx8EFFsDVcuhPAgPAx3362Mc3Zl42oPrfINazxwXujJ0rWbuXDuDPfu3OLJ48dsWO1Bi9Ztteru8PflnQ/6smTVRhw6duWn3duKrauwGFarwvIZn3A06HqZj5WdVZ5uLPZYid+mbQQFnub2LW3/nQ85R+DpkzRqLGkd913lRWOpaalouHophIcR91i4ahMjp8xlq6+3VvlWX28mz3Vn/oqNXAo9S/jdWyQmxPHDto3MX76B6YtWEHL6uKb+Z0PH4uq1Hlev9aUSjAGsX+nBrEXeePpu4fy5M4RlOw9/2r2dFq3s8PTdQvvO3di9fTMAaz0XMmuRF15+W0lJSSbk7Klia8iw06JVmxk1ZS5bfL20yrf4ejF5rgcLVvhzKTRQY6c92zawYPlGnBetJFhtpxO/HyA+TomFlU2x9WTnyoUQIsLDcPfZytjprmxco30eblzjwYwFnixds5kL/8/eeYdHUfx//JVLKOm5S0IJHYQFFAQSSCEUKWL56k8UaQoCUkRBlCY1dAghlEDoLaCAgogiqKBIk5KQRmeREkiDJHeX3kjI7487LndJSE8IuK/nuSe3O+2dz+zMfXZmdjZQ0w4vh1zgfthtlq7dweylvmxbu/wpuZdcS7RWy7gCtGxe48XUPFp0fUL7jvnyy8zM4Mfd28vNwSjPPquq6HgWfWdJMDIq+aey+U86ZFpEURS7632+KmUmfqIoHihvcQDRkRFYWllRq3YdZDIZnVy7EBrobxAnJNAft249AHB270aIXvimNcsZNmac7jj4wnnaO7lgZm6OrZ09X03zKJO+iIhwrKytqVOnLjLtCFmA/3mDOC1bvcyc+YswqZa7Cefjx48JCQ6ka3eN7m9melCnrkOpdVwKDsDZ/TUAGjRqSnJSEqkpyQA8iIrA0tIa+1oaGzq6uHMxOIBq1aoxe+lqFHl+kMZ8NV13h25lIycpMaHUuopLxqMs3hu/nujYii9Ln8iIcCytrKmtrT/Xzl0JCjC8voSWrZkxZ6FB/QGM+eIrur7Wq1x0XA25gJNbNwDqNWxCSlKirv4eRkdgYWmFrbb+2nV040rIBa4EB/BK+46Ympkjt7Vj1Nczy0VLQURHadqhvbYdOrm4ExoUYBAnNCgA167adujWTddOfbbswa5WbQCsbeQkJZS+jq+EXKCjW3fg6Xay09qpfcfOXAkJ4HJwAG3ad9LZabTWTh07v8bA4V+U669O3naYkqcdWlha6/Q5OrtzKTiA1m07MGWOxgkwt7AkPT2N7OzsctdSaJ/grO0TqldjtudqFHb5ndQfv9vGW+/1x8SkfDYTLs8+q6roeBZ9Z0mQyYxK/Kl0jZVeYhVFEAQTQRB2CYJwUhCEQEEQ/qc9f0IQhDmCIPwjCMIpQRA+FQThtPa8sSAIcwVBGKeXz1hBEBboHf8pCELb0mhSqeKwtpHrjm3kcpTKOIM4alUcNto4NnIFKm340cO/0LadI7X1HJ2H0VFkZKQzZ+qXTBw7zMB5Kw3KuDjk8lx9coUCZWysQRxzc/N86dRqFebm5qxY5smnn3yEr8+KMulQq+Kw0rOTlY0NapUSgHiV0iDM2kaBWhmLsYkJNWrUzJdXTVNTjI2Nyc7O5vef99K15xtl0lYcsrMfk57xqMLLyYtKGYeNXKE7lisUKOMM68+sgPor7HxpiFcpsbLOrSNLGzkJak39JaiUWFob1l+8Ko7Yh9FkZqSzzGMicyeO4kpIroN05OBeFkwZy+pFM0hMiC+zPrXSsB1ayzXX0NPiaMI17dDM3AIAVVwsIRfO4+Ra+hE7jZ1sdMeWNnLi1XrXubV+G5CjVimJfahp88s8vmbOxJFc1trJ1Kz86i9XX1weDYbt0LqgdmhsTE1TUwCO/fYzjs6dMTY2LrMWdRFaCuwTjAvuEyLD7xF2+yadu/cusy4DfeXUZ1UVHc+i7ywJ0gjZ84UCOCqKYjegPzBPLyxaFEV3wBhQiKLYRfu9TQH5/AC8ByAIgjVgK4ripfIQmJNTvAiJiQkcPfwLHwwemi88MSEej8UrmDxzAcsXeZBTZKblKfBJtBxiHsYw6KMhbNq2E/H6df45daIcdRRaeJHJs7OzWbV4Nm3ad+RVR+fy01XFKddroSwUoiNHW7k5OTkkJyYwcY4XYyfPYYP3fHJycujS6y0GfTqO2cvW06hZC/Z/u6lS9RUUHq9WMW/aBD6fON3AoapwHQA5aO20jLGT57LBe16l1XNhxeTkaaT+/5zgr99+YdSXFfQqskL/5cLtsW3tcoZ/PrFc5ZRIQmW2yxe47zQyMirxp7L5Ly/qFwRBOKF3fBywFwRhNPAYsNULe3L7HQ2EaL8/BKzJgyiKKkEQ/hUEoQMgAPtKKuzXn/Zy6tgRrG3kqJVK3XllbAy2eYbTbe1qoVIqMbewJE4bfjEwgIR4NZPGDudRZibRkRFs8FlGk2bNad2mHcYmJjjUb4CpmRkJahU2Ctu8Egrlxx/2cPTI78jlCpRxuSN2MTEx2NWqVWR6Gxs5dR0cqN+gIQAdnV24fesW7l27l0jHExS29sSrcnWolLEotGs95HnClHExRQ75r1k6F4f6DRg4bEyh8Z5XDuz7nmN//oGNjVw3ogoQGxuDnX35TYcUF00d5V7namUsNorc+nsyWgaakSa5rT01atakeeu2GBubUNuhPjXNzEiMV/NK+066uI6uXdm62rPUug4f2Mvpv4/oRpueoIyLQWFneJ3b2tmjVmnaoSZcY8fUlGQ8Jn/B0NHj6NDJrdRaAOS2dnnsFIdcz07xBnaKQW5rR82aprTQ2qmOQ31MzcxJjFdjrTcyWl4o7AzbmlqvHSrs7FHrt1E9G4UEnOXHXVvxWOqLuYVlhWgx6BPsCugTCpimBE2fGxEexspFmqletSqOmRNGsshnS9n0lXOfVVV0VOW+8zlY0/+fHiEzWEMGhKEZJesC9M0TN+sp359WxTuBD4F3gD0lFfbO+/1ZtnYrsxZ5k5KazIPoSLKzsvA/cwrHTq4GcTt0cuX030cB+OfEXzg5d6ZLj95s3n0An83f4eG5kpeEVnw2YQqOzq6EBgXw+PFjEhPiSU9NMxiSLi79Bgxi07adLF2+ipSUZKIiI8nKyuKfUydwcS36R8fExIR69etz/14YANevX6VRkyYl1vGE9h1dOXvyGAC3b15HYWuvm5KpXdeB1NQUHkZHkZ2VReC507Tr6PrUvE7++RsmJtUYNDz/04YvCn0/HIjvJj8Weq0kJSWZ6ChN/Z09fYKOLp0rXU9bR2f8T2vq7+6/N5Dr1Z99HQdSU5OJfRBFdnYWIf6naevoTFtHF66GBvL48WOSEuPJSEvD0tqGlfOn8jA6AoBrF4No0LhZqXW93bc/nmu2MmOBN6kpyTzUtsOAs6fokOcaat/RlX+O/wnAmRPHcHTWtIMtvst5r//HODmX3a5tHV3y2MlOZ6dadRxIS00hRmunYP9/aOvoorXTBZ2d0tNSsSzPUTo92jm5cu5UbjvUr8dadRxIS8nVF3juNK86uZKSnMSOjauYudgHS6t897elpr2TK2dPPaVPqONAakoKD/W0tHMquE+wta/Fxl0H8Vq3E691O5Er7MrsjEH59llVRUdV7zufhxEyoyozTVGJCILQGPhRFEUnvXOT0ExHztSOks0VRdFBO4o2ThTFK9qnKH1FUTzx5DvQHYgDrmjj9RMEoTqaEbcEURTfKkxLmDK90Aq4HBLE1nWaB0A7v9aLDwd/gkoZx7db1jHhGw/SUlNZOm8GiYnxWFhY8s2cxQZ3mQ+iI1m+0EO37cXhn/fxx6+aZxAGDxuNa5fu+cq0tahemCQDggMvsGaV5smoHr1eZ8iwEcTFxbJxnS8zPebx808/8tuhg9wUb9CwYSMaN2nK/MVLCb9/j7mzZ5Dz+DHNmrdg+qzCtwOIUKUVqmPnxtVcvRSMzEjG6K+mceffG5hbWODSpQdXLwaxc+NqAFy79uS9gUO5JV5j+7qVxDyIwsTEBIVdLaYt8Gbh9Ak8yszUdUoNGjfls6+nP7XcDm+VfYqlfasGeE58n0YOCh5lZRMVk8DASZtRJ6aWKr/7p4v/wHBocCDrV2vW8HXr0ZvBQ4ejjItl68a1TJ05l0M/7+eP337llniD+g0b0qhJM2bPX8KsqV8T8/ABd+/cQmj1Mu/27cfrb/7vqeWEKwv/X/ZsXcP1yyHIjGQMHzeVsNsiZmYWdHR/jeuXgtmz1ReATu6v8b8PhwDw16GfOHHkFwDeGzwCJ9duXA0NZPeW1VSvUZOapmZ8NsnjqaNBVqbFX6B9JTSI7Rt8AHDr1pMPBmna4a5t6xk/ZTZpqal4L5hBUmIC5haWTJ69CGMTEwa81ZVWL+cuIe3W+03efLffU8tJSst6ahjAbp2djBgx7hvuau3USWun3VvXaO3Ug3d0dtrPca2d+g7+FCfXbhzYvZVLwf7cun6FpkJrWrRqw0ejJjy13BrVinfvvnPTaq5dCsbISMboCdO4e+sGZuZ67XCTXjscMJSjv+7n+x0bcajfSJfHhOnzsa9dt8iyivq53LlJr0+YMI07t25gXoiWW+I1tq/P0yfM9zZwFEcNfJvN3x8uvSh9feXUZ5XVkX1WfWeruuaV6vG8OudYiZ2di/N6VqpGySEzPHcQiAW2AROAQ0APSuiQafPbAgSJori+MC1FOWTPgpI4ZJVFUQ7Zs6I8HLLypiQOWWVRlEP2LCiJQ1ZZFOWQPSuK65BVJlVyBqpKiqqaVLZD1m5uyR2y0LmV65D9J9eQiaIYBjgVcE7/achd2r/z9eL0K+D7Cb00JwAEQagJvApMKh/FEhISEhISEqVF2hj2P4ggCC6AP+AjimLV2ohFQkJCQkLiP8jzsO3Ff3KErCIRRfE8mtExCQkJCQkJiSrA8zBCJjlkEhISEhISEi80z4E/JjlkEhISEhISEi820giZhISEhISEhMQzpqL8MUEQVgIuaN5zMEEUxQt6YQ3Q7EVaHQgWRfGzwvKSFvVLSEhISEhIvNBUxMawgiB0A5qLougKfAqszhNlObBcFMVOQLYgCA0Ly08aIXvGGFXBjWvcFx9/1hLysW9sxexUXVaq4p5fDbt89awl5CPqjM+zlpCPG1FJz1pCPn69GfOsJRTIkHb1nrWEfDxMynjWEvLhYF2+L/8uF6reT8wzoYJGyHoCPwOIonhdEAS5IAhWoigmCoIgQ/Pmn0Ha8C+KykwaIZOQkJCQkJCQKDl10Gwm/4RY7TkAeyAJWCkIwj+CICwpKjPJIZOQkJCQkJB4oamkd1ka5fleD/ABugHtBUF4u7DEkkMmISEhISEh8UJTQRvDRpE7IgbgAERrv8cB90RRvC2KYjZwDHi5sMwkh0xCQkJCQkLihaaCRsiOAk/eX90BiBJFMQlAFMUs4I4gCM21cR0BsbDMpEX9EhISEhISEi80FbGoXxTFs4IgBAmCcBZ4DHwhCMIwIEEUxQPAV4CfdoH/ZeDXwvKTHDIJCQkJCQmJF5qK2hhWFMVpeU5d1Au7BbgXNy/JIaviBF84z/YNq5HJZHR068LHw8cYhKckJ7FkzjRSUpIxNTVl2rylWFlZM+T9N7CvXRuZzBiAaXOXYGdfm81rV3AlNJjs7GwGDv0U9+69yqRv8hvNaVvfmpycHLx+v8lVva0EalvVYGm/V6hmbMT16CQWHhJxamzDsv5tuB2TAsCtmGQ8f7tZ6vJDA8+zc7MvMpkMJxd3Bn4y2iA8JTkJ7wUztPYxY/LsxVhaWReaLiMjnXHDPmTA0FH0evNdAA7+uJtt61ay59BJTM3MSqX1gv85Nq1dhUxmjGvnLgwbNTZfnL//PMKSeTPZ6LeHpi811+rJYNmiudy9c5ut3+0tVdmloXWzuuxbOZo1u46z4YdTlVZuwPmzbPD1QSaT4ebelRGj89vp2J9/sHDOTLbs/J5mLzU3CFu3egWXL11k/ZYd5aZp96aV3BavYIQRg8dMpGmL1rqwzMwMdvh6EnnvDnN9csuMCLuNz4Ip9HlvEL3e+bDctDzh4oHNqO6JYGTEq31HoWjYQhd299wRwvz/xMhIhnW9xrT7YCxh/n9yPzB3Sxt1+C3eW7qvzDq2rvXm5rXLYGTEyHFTaN4yd5nMxSB/vtuiaWcdnN0ZMHQUAPfu3mLxrIm8228wb/cdCICP5xxu37yOpZU1AH0HDMXJtUuZ9e3dsoo74lWMjGDAqK9p3Dy37h5lZvDd2qVEhd9l5ortBukyM9KZN/5j3h4wHLeeha7FLpTy7KPS09NYtWQO8WoljzIzGTB0FJ3cupKV9YiViz2IjgzH1Myc6fOXYWFpVbSuTb7IjAvRNV9Pl4eergLSnTj6G/v3+GFsbMJHn46lo2sXPD2mkBCvBiA5MRHh5TaMmzK71LYsC9JO/VUM7VzuKjSPoxoDZ4HJaOywEnAC0gEVMFYUxXC9tDeAP0RRrNRNntat9GTxyg3Y2ddi8hfD6dK9F42aNNOF//TDd7Tt4ET/j4Zz+Ocf2fvtNkZ+8TUAi5avN3AeQoMCCLtzC5/N35GYEM/YYf3L5JA5NrKhkcKMoVsCaWJnxrz3WjN0S6AufFKf5uw8e5+/b8Qy/W2BOtY1AAgKi2fy3sulLlefTau9mOe9Dlu7Wkz/ciRu3XrSsHGufQ7+uJs27Zx4f9An/HFwP/t3+zHsswmFpvth5xaDzuzvP34lXq1CYWdXJq0+yxaz3HcT9rVqM27UJ3Tr2ZsmTV/ShYcEXeD82dM0ay4YpFvn401zoSV379wuU/klwaxmdVZ88yHHA0rvLJeWFV6L8Vm3GftatRk7ciiv9exNk2a5dgoOvMC5M6d5KY+dAO7evkVIcBAmJuXXtd24HMzDqHBmL99K1P27bPVZyOzlW3XhP2xdQ8MmLYi8d0d3LiM9je82eNP6Vady06FP7K3LJMdF8dpX3iQ+DCdojw+vfeUNQFZmOuEhp+g23hOZsQmn1s5EFXaDJi6v08TldV36iNB/yqzjSmgQ0RH3Wbp2B+H37uDrNY+la3Od0s1rvJjjtRZbu1rM/Gokbl17Yl+7LptXe9G2fcd8+Q0ZNY6Orl3LrOsJN68EExMVzrRlm4kOD2PH6kVMW7ZZF/7jdl8aNG1OVPjdfGl/2+uHuUXhTk1xKM8+6t6dWzQXWvPB4GHEPIhi9qSxdHLrypFfD2BtI2eKxxL+OLifq5eCce7cvXBdPtr87Z+ia99u2rTX07XLj2FjJxSYzkZuyx6/jazcspv01FR2b99AR9cuTJu/TJefj+dcXn+7b5ntWVqeA3/sv7OoXxAEY2A/4KXdNfdJT+mBxhkLE0WxgyiKbsAO4Hu9tI5oHmHtp50LrhSiIyOwtLKmVu06mhEy1y6EBPobxAkJ9Kdzt54AuLh3Izjw/FPza9POkVkLNZ22uYUl6WlpZGdnl1qfc1MFf9/QbMFyNy4Vq5ommNfQjMgZGUGHRjacEDXhSw6LPEgo340cH0RFYGFljX2tOtq7yM5cDAowiHMxyB+XLq8B0KlzV0KD/AtNF37vLuFhd+iod2fu0rUHQ0eNK9MdVmREOJZW1tSuUxeZTIZr564EBRjWpdCyNTPmLMSkWjWD82O++Iqur5VtJLOkZDzK4r3x64mOTajUciMjwrGyttHZya1zVy4EGF7TQqvWzJq7KJ+dAHxWePHZuAnlqula6AU6uHYDwKFhE1KSk0hLTdaF9/tkLB3cuhmkMalWjYnzVmJja1+uWp4Q8+9FHNq4AGBVuwGZack8Sk/VlF29Jl0/X4TM2ISszHQepadQw1JukP760e9p9frAMuu4FByAs7umfTVo1JTkpCRSUzS2eRAVgaVlbjtzdHbnYnAA1apXY7bnahR2FWMbfW5cDKSdi6Zu6jZoTGpyImmpKbrwvkM+o51L93zpHkSEER1+lzZObmUqv7z7qC49+vDB4GEAxMY8xM6+NgABZ0/SvfdbALzx7gdFOmO6/GsXU5dbHl150l0M9OdVJ2fMzMxR2NnnGwWLuB9GSlISLVq/Uio7lgeVtO1FmfjPOGRAb+CGKIonAURRzAGmAl5AH2Dpk4iiKO4D9MeoBwNbgHto9hNBEITgJ69BEAShkXZhn7UgCH9qN4GbIQhC/tuuEqBSxWFtk9uR2sgVqJSxBnHUKiU22jia8DhdmI/XAr7+7BO2rltFTk4OxsbGmJpqRsz++PUAnVy7YGxsXGp9thbVUadk5mpJfYSdRXUA5GbVSc3IZsobLfAb4ciXvXLvvJram+MzqC1+IxxxaaoodflqVRzW1rn2sZYrUOv9/5o4Sp0NrW0UqJWxhabbtm4Fn46bZJCHmZl5qTU+QaWMw0ae+7/KFQqUcYZ1aWZecDlPO1+RZGc/Jj3jUaWXq4yLQy7PrRu5wjafncyfYo9DBw/QwbEjdR3Kd1f5BLUSSysb3bGVtQ0JapXu2LSA68PY2ITqNSpu1/b0xHhqmFvrjmuYW5OeqDaII/61jyMLR1O/nTsWdrlP5qvu38TMxo6aVoZOWmlQq+Kw0mtLVjY2qFVKAOJVSqz0+q8n7c/Y2IQaT7HNbwd+YPbE0XjPn0ZigrrAOCUhQa3CQq/uLK3lJKqVuuOaT2nb+7at4cMRZXfsK6KPApjy+Sd4L5jByPGTAYh5EE2Q/xmmTxiJ17xvSEos/EZKrTT8bdGUW4guuVbXU9I9fBBFRno6C6ZN4JtxI7gYZHizefDH3fzvg7LfAJSFCtr2olz5LzlkLYFQ/ROiKKYBTTRfxew8YfEA2hGx/sAPaF4S+uSqOgC8o/3+f2hG34YC10RRdAfiKe+XVuTkFDt86KjPGfPlZLx9txJ25xanj/+pCzt76jh/HPqJcZOml6s8gx3xjKCWVQ12nQ9nxPYgWtaxpEtzW+4r09h44g4T9lxi1oFrzP2/VpgYl4+Zckpgn4LS/f3Hr7R8uS116lb8a2KK1CoBFN9OCQnxHP7lAIOHDKtYQVTVusuvSej1IW/M2szDG8HE3bmmOx92/iiNOlXQiGuhpincbt17v8WQ0V+yYMUmmrwk8L3fxnKVBsWru3N//0ZT4RXs6jhUfvlF9FFPWLZuB7MXr2LFwlnk5OSQQw71GjRiic8WGjV5iX27tpVMVxF181Rd2nQ5OTkkJSYwY+Fyvpo+j1VL5uo0P3r0iOuXQmnbIf8UdWXyPIyQ/ZfWkOWgWTdW3PNP6IZmc7f7giDsBWYJgjAO+AnNi0PXonHIxqJ5xPWENt1BNCNwJebXn37g5LEjWNvIDe5a4mJjsLWrZRDX1s4elTIOcwtLbbhmGqC3djE6QEdXd8Lu/EvXHq8TeP4Me3ZsZtHK9ZhbWJZGno7YpAzsLGvoju0taxCbpBkxi099RFR8OhHqNAD876poVsuc0/8qOXJV876+CHUayuQMalvWIDI+vdjl/vbzXk4fP4q1tRy1Ktc+qrjYfNMgCjt71Col5haWKONiUNjZo7CtVWC6C+f/4UFUBBfOnSYu9iHVqlXHzr4W7ZxcSm4cLQf2fc+xP//AxkZuMHoZGxuDnX3FT9k8L+zf+z1/Hf0duVyOMk7fTg+xs69VSEoNQQH+qNUqxnw6hEeZmUREhLPK25OvJud9AKrk2NjakxCfO6oSr4zDWm5b5nzLgqm1gvSk3BGktASVbsQrMyWJhAf3sG/2CsbVa1C7pSPKu9exa6pZzB576wrt3h9TYL4lRWFnT7x+W1LGorDVrLWU5wl70v6exquOzrrvndy6sWHl4jLrs1HYkahXdwmqouvucuBZ4h5EcfnCGdTKGEyqVUdua0+rdp2KXW5F9VG3xGtYyxXY16pD0+YC2dlZJMSrsZEreKWdIwDtO7qxe/v6p+v6+6jmt0U//9hYFLbF0GVXq8B0NU1NafVKW4xNTKhbrwGmZmY6XVdCg2jeqtD9UCsFaQ1Z1eIGYNCiBEGogWbvkJba7/phT9aYDQYaC4IQimYTODOgtyiKVwEHQRAaADaiKN5EM0j0WJuu1LfR77w/AO+125i9aDmpqSk8iI4kOysL/zOncOxk+JJtx06unPpbM/p1+sRfODl3JiU5ielffcajR5opp8uhQTRu2pyU5CQ2r13B/GVrsLKyzlduSTl3W0Wv1ppG3LKuJbFJGaRmagYasx/nEKlOo6HCFIDWda0Ii0vlrTa1GeqmeeG9rUV1FBbVS/yS4Lfe688Sny1Mm7+M1NQUHkZHkZ2VRcDZU7TvaGif9k6unDmhsc/Zk8fo0Kkztes6FJjum7lLWblpF97rd/L6230ZMHRUmZwxgL4fDsR3kx8LvVaSkpJMdFQkWVlZnD19go4uncuU94vEB/0Hsn7LDhYvW0VKSjJRWjudOXUSZ9ei7dSjdx++/+kQW3d+j+eKNQgtW5eLMwbwSntnAv/RPJ0YdusGNrZ2BU5TVia1hfZEXjwLaJ6WNLVWUK2mZjnC48dZBO5eRVaG5mZIff8mlrU0o75pCUpMatREZpJ//V1paO/kytlTxwC4ffM6ClvUmbCHAAAgAElEQVR7nW1q13EgNSWFhw+iyM7OIvDcado5uT41L0+PyTyIigDgcmggDfUeXiotrdt3IviMpu7u3xaxVtg9dZryCaOnLmTGim1M896Ce+93eXvA8BI5Y1BxfdSVi8H8/MO3gGZKMS0tDStrGxydOxPsr7kebt+8Rr2GjZ+ua7VWV4pe/udO0T7Pb0v7jq6cOV6ArgLSte/oyqXgCzx+/JjEhHjS01KxstZMFf974ypNXmqRT0tlIzMyKvGnsjGqmsPv5Y926jEUmCmK4q/a4xVoXv5pCySLojhVG/cD4EvgdTTrxl4WRVGpDRuKxiEbon1ZaD3gliiK8wVBmAzYiaI4TRCEkcAsURQbF6brnjKj0Aq4FBLI1nWrAHB/rRcfDh6GShnHzi3r+OobD9JSU/GcN52kxHjMLSyZNmcJ5haWHPjhO/78/SDVa9TkpRYt+WLidH77ZT/fbltP/QaNdPlPnb2IWnXqGpT57uriP301oVczOjSyIScHFh8WaVnXkuT0LP6+EUsDhSkL3muNkRHciklh4aEbmFYzxrPfy1jWrEY1YyM2nLjLP/8qiyxn39iCO/IrF4Pw2+ADgFu3Xrw/cChqZRy7tm9g3ORZpKWmsnzRTJISEjC3sGTSrIWYW1gWmE6f3ds3UKuOA73efJcfvt1CaOB5xGuXaS68TMuX2zJ8rOZhW7l2zVxxCA0OZP3qFQB069GbwUOHo4yLZevGtUydOZdDP+/nj99+5ZZ4g/oNG9KoSTNmz1/CrKlfE/PwAXfv3EJo9TLv9u3H62/+76nlNOxS9geB27dqgOfE92nkoOBRVjZRMQkMnLQZdWJqqfKLOuNT7LghQYGs9VkOwGu9evPR0BEo42LZvMGXabPmcfDAfn4/fJB/xRs0aNiIxk2aMmehZ25ZUZEs8JhR5LYXN/S2aCmKvdvXcvNKCEYyI4aMncK92zcxM7fA0a07vouno4p7SNT9uzR6qSXd33iPuvUasmfrauIeRmNiYoyNbS3Gz/TEwrLwG6Ffb8YUW9PlX/2Iu3MVIyMj2n0wlvjI21SraU69tq6EBfzFnX8OYyQzxtqhCe0//BwjIyPU4be4+tu3uI+ZV+xyAIa0e/o0/s5Nq7l6KRiZkYzRE6Zx59YNzM0tcOnSg6sXg9i5aTUArl178t6AodwSr7F9/UpiHkRhYmKCwq4W0+Z7E3b7Jjs2+lCjRk1qmpox/pu5Busu81LcG7mfdqzj36shGBnJGPTZZMLv3MTUzJz2rt3Z6DkDdVwMUeF3adRMoEuf/6NTtz66tL/u3oJt7brF3vbCwTr/2rjy7KMyMtJZvXQecTEPycxMZ9AnY+jUuZtmO4zFHqhUcZiamvHV9PnIFdqRwKf4FVdCg/DbqM2/ay/eH6TVtW0D46ZodS2cSVJiHl0FpAP4/Zcf+fPwzwAMGDoSZ/fuAGxc5UnrNu3p0rOPQfktaptVqsfz+trzJXZ2jn7hUqka/zMOGYAgCHWBTUBdIBP4E5iHZurWC+gJqIFwYDzQBRgiimI/vTzMgJvAS0Ab4BzQVhTFa4Ig2AG/AI+0eY8QRbHQ27yiHLJnQUkcssriaQ7Zs6YkDlllUR4OWXlTEoessiiJQ1ZZlMQhq0wKc8ieFSUdWa8MCnLInjlVdKqush2yPuv8S/xbe+Rz50rV+F9aQ4YoitHkLsTXJxPN+q+8/KL96OeRCtTXHl7A0IbmwHxRFI8IguCK9olMCQkJCQkJiWeHrIo6pvr8pxyySiABmCgIggea+5Ivn7EeCQkJCQmJ/zzSTv3/MbRbZfQpMqKEhISEhISEhB6SQyYhISEhISHxQvMcDJBJDpmEhISEhITEi41RVX26QQ/JIZOQkJCQkJB4oZEW9UtISEhISEhIPGOkRf0SRZKQWvkvcC4K/9k9n7WEfESo0p61hAIJV5Zuo9SKpCru+eXQuewvai5vrh71ftYS8vF6M7tnLaFAjMvpfbPlSW3LGkVHqmSq4o9+ke+p/I9QBasmH5JDJiEhISEhIfFC8yxehVRSJIdMQkJCQkJC4oXmOfDHJIdMQkJCQkJC4sWmKk4n50VyyCQkJCQkJCReaJ4Df0xyyCQkJCQkJCRebKQ1ZBISEhISEhISz5iq745JDlmVx2/dcm5ev4yRkRHDP5/MSy1f1oVdCvJn97a1yGQyOjh3pt/Ho3RhGRnpTBrZnw8+Hslrfd4F4LcDe9i5YSXbfz6BqalZueg7f+4sq1etwNjYGPcuXRkz9ot8cY4e+R2PmTP4ds8PNG/eAoDjf//F5g3rqVa9Om+8+TaDPvq4zFpCAs+zc9MaZDJjnFzcGTRstEF4SnISXvOmk5qSTE1TM6bOWYKllTWZGRn4ei/g3t07+GzZDcCRQwc4fuSQLu2/4jX2Hz1XIj0716/g1o0rYASfjJ1EMyG37i4H+/PD9nXIZMa06+jG+x+PBOCfY7/z675vMZYZ0++TMXRwdmf9srnc/fcGFlbWAPzvwyF0cHYvlY3yEnD+LBt8fZDJZLi5d2XE6LH54hz78w8WzpnJlp3f0+yl5gZh61av4PKli6zfsqNc9BRF62Z12bdyNGt2HWfDD6cqtKyQC+fx27QamcyYjq7uDB42xiA8JTmJpfOmk5KchKmpGd/M9cTSyprfD+7nyKEDGBsb06RZC76YNIOMjHRWLPJArVbyKCODQcNG49y5W5n07d2yijviVYyMYMCor2ncvLUu7FFmBt+tXUpU+F1mrthukC4zI5154z/m7QHDcev5dpk0PCE08Dw7N/kiM5bh5OLOwE/ytz3v+TNISUnG1NSMyR6LdW1vrfdC7ofdZuXm3br49+7cYuGMr/m/Dz/ifx8MLJWmrWu9uXntMhgZMXLcFJrr9Z0Xg/z5bouvtu90Z8BQTd957+4tFs+ayLv9BvN2X8NyQwLOMu+bcfx8PLhUekBjpx2b1iAz1vZRBdhp2fzcPmqKh2EfdT/sDqu0droUcgFPj6k0bNIUgMZNm/PZV9NKpamq1V1F8DysIZM9awGlRRCExoIgJAmCcCLPR6EX53+CIPiVU3n9tH/bCYIwrzzyLIqrF4OIjrzP4jV+jJ3kwba1ywzCt61dxuQ5Xiz02cbFwPOE37ujC9u/aysWlta645NHDxGvViG3tS9XjUsXL2TFqjXs+G4P586e4fatWwbhgRcC+Of0KVoIgu7c48ePWbJoAb4bNrN95y5OnjjOwwcPyqxl4yovZixYzrJ1foRcOMf9u7cNwn/Zt4u27Z1Yts4Pt2492LdL80O1dd1KmrwkGMTt87++eK7ZiuearXw0Yiw933inRFquXQriQVQ48322MWbibHasW24QvmPdcr6evZS5K7dwKdifiHt3SEqMZ/93W5i7YjNTFqwk6OxJXfyBI77Aw3sjHt4by80ZA1jhtZgl3qvY5LcL//NnuHvbsP6CAy9w7sxpXmou5Et79/YtQoKDyk1LUZjVrM6Kbz7keMDNSilvvc9SZi1cwfL1OwgOOMe9PNfTz3s119Py9Ttw69aTvd9tIz09jZPH/sB73XaWr99BxP27XL9yEf8zJ2nesjXLfLcxfcEyNq8p2x5oN68EExMVzrRlmxk6fibfb1ppEP7jdl8aNG1eYNrf9vphbmFVpvLzssnHi+kLvPFa60fIhfPcDzO01cF9u2nT3gmvtdtx7dqD/bv8ANi+fiVN8lxb6WlpbPRZyquOnUqt50poENER91m6dgfjpniwZY2XQfjmNV5MnbeMJWu2Exp4jvCwO6SnpbF5tRdt23fMl19mZgY/7t6O3LZs+8Rt9NH2UWu1fVRY/j5KYyc/3Lr24EdtH7Vt/UqaFtAGX2nniOfqrXiu3loqZwyqXt1VFDKjkn8qXWPlF1muiKIods/zUVVQWdO0BYaKojingsow4HJIAJ06dwegfqMmpCQnkpqSDMDDqAgsLK2wq1VHc5fXqTNXggMAiLx/l4h7dwx+uDu5v8bgEV+U611CRHg4VtbW1KlbF5lMRpeu3fD3NxxFatW6NfMXLqFatWq6c2q1GktLKxQKBTKZDGcXF86fO1smLdFREVhaWWFfW2MPJxd3QoMCDOKEBgXg2rUHAM5u3QgN9AfgkzHjcdOeL4g9fpvy3TUWxdWQCzi5aUZA6jVsQkqSXt1Fa+rOVlt37Tq6cSXkAleCA3ilfUdMzcyR29ox6uuZJSqzpERGhGNlbUPtOpr6c+vclQsB5w3iCK1aM2vuIkz06u8JPiu8+Gxc5W34mvEoi/fGryc6NqHCy4qOjMDSMvd66ujahdAgf4M4oUH+uuvGpbPmeqpZ0xRPn82YmFQjPT2NlORk5Ao7uvV8gw8/Gg5A7MMH2NWqXSZ9Ny4G0s5Fc33VbdCY1ORE0lJTdOF9h3xGO5fu+dI9iAgjOvwubZzcylS+QZ5REVhYWeu1vc5czNP2Lgb549LlNQA6uXXV2XLI6PG4djFse9WqVWOO1xoUZbh5vBQcgLO7prwGjZqSnJSka38PoiKwtLTGXtv+HJ3duRgcQLXq1ZjtuRqFXf5yf/xuG2+91x8Tk/ztoLg8KKCPym+nAJ09Orl109lpaAF2Kg+qYt1VFEZGRiX+VDYv3JSlIAhtgJ2ACritPdcY+FEURSftcSDQD8gBdgDGwD3gE+AVYC3wCHgMfAh8CrwqCMJPwGpgnCiK/QRB6A9MBLKAIFEUJwiCMBewBgSgGfCVKIq/l+Z/iVcpada8le7YylpOvFqJmbkF8WolVjby3DC5godREQDs2LCST8d/w8mjuVNupmbmpZFQKHFxscgVugFJ5AoFEeHhBnHMzS3ypVMoFKSmpHDvXhgODvW4EOCPU8ey3VGplXFY69nDWq7gQWT4U+NYyxWolXEAmJmZk5QQX2C+N69fwa5WbRQlvDOOVylpold3ljZyErR1l6BSYmmtp9VGwcPoCDIz0snMSGeZx0RSkpPoN2QUr7TX2OXIwb0c3r8baxs5w8ZNxcrapkR6CkIZF4dcnqtDrrAlMuK+QRxz84Kvm0MHD9DBsSN1HeqVWUdxyc5+THb240opS60yvJ5s5HKiIyMM4+S5nlTa6wlg77db+fnH3bz34UfUrVdfd37iZ0OJi3nIPK81ZdKXoFbRsFlL3bGltZxEtVLXzmuamZOclJgv3b5taxg0ehLn/v6tTOXrk6/t2Sh4EJXHViplnrYXCzxpe4YOtrGJCcYmZftpUqviaNZCr++0sUGt0vadKsO+U6M3HGNjE4yN85cbGX6PsNs3GTxiLH4bVpVekzLOoFwbGwXRUXn6KFXJ+qjwsDvMnzaB5KQEBg0bQ/uOriXWVNXqrqJ4DmYsn/sRsoKYDcwVRbEnkF1E3EXAClEUuwBRgBNQCxgviuJrwBngI1EUlwEJoii+/yShIAgWwGKglyiK7kBTQRBe0wY3EEXxLWACYLjwpAzk5BTyCgxt2MmjhxBat6V23cr7oczVULxoRkZGLFjsyZxZM/h6wjjq1atPYf9a6bQUkWExCzxy6AC93nq3QvU8ebVJTk4OyYkJTJzjxdjJc9jgPZ+cnBy69HqLQZ+OY/ay9TRq1oL9324qu54CJRbPJgkJ8Rz+5QCDhwyrEB1VkSJNkydC/yGfsn3vYYL8z3D1Uoju/IoNO5mz1AevBTOKbe/i6Ss6r3N//0ZT4RXs6jiUW7kFaimqIyj3xl4MCi2ycD3b1i5n+OcTy1WOptSy2cmhfkMGDRvD7CWr+HrGAlYvncejR2V7FV+VrLtyQhohq3gEQRBO6B2LQGvgyfzXCeDNQtJ3QOM0IYriVG2GbYGlgiCYAQ7ArqekbQH8K4pisl5Z7bXf/9H+jUAzWlYqFLb2xKuVumO1Mg65QjNSI7e1J16VG6aKi0Vua0+Q/z/EREcSdP40yrgYqlWrhq1dbdo6OpdWRj72fr+bI3/8jlwuRxmXOyoQE/MQe/taxcrDqWMn/L7VLAT1Wbkch3qlcyAPH9jL6b+PYGUjR61nD2VcDAo7Qy22dvaoVUrMLSy14UUPq18OCSzV2oy89aNWxmKjV3cJ6vx1V6NmTZq3bouxsQm1HepT08yMxHi1bpQMwNG1K1tXe5ZYjz77937PX0fz119s7EPsilF/QQH+qNUqxnw6hEeZmUREhLPK25OvJpduDUtV4tCBvZw6dgTrvNdTbP7rRWFXS3c9xcXFYGtnT1JiAmF3btGmnSM1atTEycWda5dDqV69BjZyBfa169CseUuys7NJiFdhI7ctlU4bhR2J8bn6ElRxWBeR1+XAs8Q9iOLyhTOolTGYVKuO3NaeVu1KNzr92897Of33Ua2tcq8jVWxsvikrRSnaXllQ2NkTr69JGasb5ZbnCStMjzI2hojwMFYu0iwfUKvimDlhJIt8thRby28/7+XU30+5pmwN21tJ7GRnX5uuPfsAULdeA2wUtihjY6hTjFHrqlx3/2We9xGyvGvIxqB5uvXJvMaT/y+vW/9kIUA2+W3gA/iIotgN2FhI2TkYPklbXa/cLL3zpXazX3Vy4dypYwDc+fc6cls73ZRErToOpKWmEPMgiuzsLILOn+ZVJxcmzvbEc923LPbdQc833+ODj0eWqzMG0H/gYLb6fYv3ytUkJycTGRlBVlYWp04cx7Vz52Ll8fmYkSiVSlJTUzl54jguriUban/C233747lmKzMWeJOakszD6Eiys7IIOHuKDnmG79t3dOWf438CcObEMRydC19Ho4yLoaapmcH6t+LS1tEZ/9Oaurv77w3ktva6urOv40BqajKx2roL8T9NW0dn2jq6cDU0kMePH5OUGE9GWhqW1jasnD+Vh9GaaYRrF4No0LhZifXo80H/gazfsoPFy1aRkpJMVFQkWVlZnDl1EmfXouuvR+8+fP/TIbbu/B7PFWsQWrZ+IZwxgP/17Y+X71ZmLjS8nvwLuJ46dHLl9N9HAThz4i8cnTuTlZXFikWzSUvVvHRevH6F+g0bc+ViEPu/3wlopoDSU1Ox0pu2Limt23ci+MxxAO7fFrFW2FGziGUJo6cuZMaKbUzz3oJ773d5e8DwUjtjAG+9158lq7cwbf4yUlNSeBgdpWl7507RvlP+tndG2/bOnjxGh07F6ydKS3snV85q+87bN6+j0Gt/tes4aPRq21/gudO0cyq4/7G1r8XGXQfxWrcTr3U7kSvsSuSMgcZOnqu3Mn2+N2l619SFc6fokMdOHfT7qJPHcOz09D7q+NHD/LRH83SzWhlHvFqFbTFviKty3VUUz8Oi/ud9hKwgRDRTj0eAJ1OIiUBtQRCMgNpo1nYBXAB6AD8IgjAfOAXYAbcFQagBvAU8WeWc13G7CTQXBMFSFMUkoBuwEOhVXv+I8PKrNG3ekplfDsfIyIiRX07j+JGDmJlb4Ozeg1ETprNq0QwA3Lr3xqF+o6fmtX/XVi4F+ROvUrJ4+nhatG7LkNFlX5A9y2Mu06ZMAqDPm2/RuHET4mJjWbd2DR5z5/PT/n0c/vUg4o3rzJk5nSbNmrFoiRfv9+vP2FEjwMiIT0eNRi5XFFFS0XwxaSZe86YD0KVHH+o1bIRKGceubesZP2U27/YbjPeCGUz9YjjmFpZMnr0IgMWzJxMX85DI+2FMG/8pb7z7Ad17v4VKGYdNKXW1ePlVmrZoicdXI5AZyRg+bionj/6KmZkFHd1f49Px01izZBYArt16U1dbd85deuIxQbP4+5MvJiOTyXj93f6sXjSD6jVqUtPUjM8meZTVVDqmzpiDx7TJAPTq8wYNGzVGGRfL5g2+TJs1j4MH9vP74YP8K95g4ZyZNG7SlDkLyzZCV1rat2qA58T3aeSg4FFWNn17tWfgpM2oE1MrpLxxk2fhOVfjaHbt0Yf6DRujUsbx3dZ1fDnVg//rN5hlC2Yw+fNhmFtYMtVjMeYWlgwePoZvvhyJsbExTV9qgYt7dzIzM1i1ZC6TPx9GZkYGn0+cjkxW+vvhZq3a0vClliydOgojIxmDPpvM2WOHMTUzp71rdzZ6zkAdF8PDyPssn/E5Xfr8H5269Skv0+Tj84kzWDZfY6sur/WhXoNGqJVx7Nq2gXFTZvHOB4NYvnAm34wbgbmFJZNmLQTA02OKtu3dY/qXI+nzzgfUb9iIrWtXEPMgCmNjE86c/IsZC5djaVX8yYaWr7xKsxat+GbcMGRGMkZPmMaxPw5ibm6BS5cefPb1dFYs0PQV7q+9Tr0GjbglXmP7+pXEPIjCxMSEsyePMW2+d4nKLdpOM/GaP70AO61n3JTZvPPBYJYvnMHUcdo+apamj1rioddHffkpb7zzAc7u3Vk2fzrnz5wg69EjPp84o1Q3j1Wt7iqK52HbC6PyXMdQmWgX6l8G8j53PxXYDEQCdwALURSHCYKwHWgDXEQz3TgEzQjZdjQjZveBYWgW8E9A80DAdsAXjWO2ArDU5v9kUf/7wCQ0I2P/iKI4XbuoP04URV9BEF4BfEVR7P60/+NSeHKVq4AWdfMvxH/WRKjSnrWEAklMK9uajYqgiX35P8BRVhw6V97TmMXl6tGybT1REdxXVYxzWVYcbGo+awn5yM6ucl0nJsZVb9KpyHVhz4gWtc0q1UMa8f3lEhti28A2larxuXXIXhQkh6x4SA5Z8ZEcsuIhOWTFR3LIiofkkBWfynbIRv5wpcSG2DLglUrV+CJOWUpISEhISEhI6HgOZiwlh0xCQkJCQkLixeZ5WEMmOWQSEhISEhISLzTPgT8mOWQSEhISEhISLzay58AjkxwyCQkJCQkJiReaivLHBEFYCbig2Zt0giiKFwqIswRwLWzHBZAcsmeOaXXjZy0hH4euRj9rCfl4uZbVs5ZQIFampX/ZcEVxIyrpWUvIR1V8ovHl1yc/awn5OLxn3rOWUDBV8EG95PSsoiNVMtZmVa8/KP3W5C8WFbGGTBCEbkBzURRdBUFoBWwDXPPEaQ10RfN+7EKpes/oSkhISEhISEiUI7JSfIpBT+BnAFEUrwNyQRDyjh4sB2YWV6OEhISEhISExAtLBb1cvA4Qq3ccqz0HgCAIw4CTQFhxMpOmLCUkJCQkJCReaCrp3ZS6UgRBUADD0bxOseg3viONkElISEhISEi84FTQy8Wj0BsRAxyAJ4uwewD2wGngANBB+wDAU5FGyCQkJCQkJCReaCpoY9ijwDxgoyAIHYAoURSTAERR/BH4EXTv3vYTRfHrwjKTHDIJCQkJCQmJF5qKmLIURfGsIAhBgiCcBR4DX2jXjSWIonigpPlJDlkVJzTwPDs2rUFmbIyTizuDPhltEJ6SnMSy+dNJTUmmpqkZUzyWYGllTWZGBr7eC7gfdodVm3cDkJ6exqolHsSrVGRmZjDwk9F0cutaJn2H/XwJ//caRkZGvD1sPPVfaqkLu3MlhKN7NmEkM8aubgP6fjYFmUzGw/t3+G7ZLNze7ofrG++Xuuytvt7cvHYZjIwYOX4KzVu+rAu7GOjPd1t8kclkdHBxZ8DQUU9N4zVnKgnxagCSkxJo0boNX0yeDUBOTg7Tx4/gVUdnBg3/rNRaAUICz7Nz0xpkMm1dDstfl17zcuty6hxNXV4MvsCOjauRyWTUb9iYL7+Zg0xW9tUGuzet5LZ4BSOMGDxmIk1btNaFZWZmsMPXk8h7d5jrs0N3PiLsNj4LptDnvUH0eufDMmt4QsiF8/htWo1MZkxHV3cGDxtjEJ6SnMTSedNJSU7C1NSMb+Z6Ymllze8H93Pk0AGMjY1p0qwFX0yaQUZGOisWeaBWK3mUkcGgYaNx7tyt3LTmpXWzuuxbOZo1u46z4YdTFVZOXvZt8eHuTU39fTjqKxo3z62/R5kZ7FrnRfT9u0xfsc0gXWZGBgu+/Ii3+g/HtefbpS4/NPA8Ozdr2piTizsDC+ibvBfMICUlGVNTMybPXoyllXWB6S6HBOI5ZwoNmzQDoHGTlxjz1TRdXsEBZ5kz5Qt+PRlSbH3fbVzBrRtXMDIy4uMxk2gm5NrnSkgAe/3WIZPJeLVjZ/oO/pT0tFQ2eM8lJTmJrEeZ9P1oJG0dXbl57RK7Nq3CpJoJLV5ux4Bhn1cJmwGc+PM39u/xw9jYhI9GjKWja5eSadnki8y4EC3z9bR46GnJk+7ooQMcP3pYl/aWeI19R85y9/ZNNqz0xMjICAtLKyZ7LKZmTdNS268qIoritDynLhYQJwzoXlRez/0aMkEQGguCkCMIgkue8xcEQfArZh79KkRcObDRx4sZC5azbK0fIRfOcT/stkH4L/t20aa9E15r/XDr2oMfd20HYNv6lTRtLhjEDThzipeE1niu2cq0eV5s8S3b3lB3r4WifBDBZ4vW0fezqRzavtog/OdN3gyaOI8xC3zJTE/l39AAMtPT+HX7apq+0qFMZV8JDSI64j5L1+1g3FQPtqz2MgjfvMaLqfOXscR3O6EXzhEeduepaabO82KRz2YW+WzmJaE1vd/uq8vnz8MHyHpU5PYxxWLjKm1drtPW5d38ddm2vRPL1vnh1q0H+7R16btsPjMWeOO9fgepqSkE+Z8ps5Ybl4N5GBXO7OVbGTFhJrs2LjcI/2HrGho2aWFwLiM9je82eNP6Vacyl5+X9T5LmbVwBcvX7yA44Bz38tjm570a2yxfvwO3bj3Z+9020tPTOHnsD7zXbWf5+h1E3L/L9SsX8T9zkuYtW7PMdxvTFyxj85qK2wPNrGZ1VnzzIccDblZYGQVx80oIMdHhTPXazMfjZ7B3s+HSlJ/81tKgSfMC0/6+dzvmFmXf12/Tai+mL/DGa60fIRfO5+ubDv64mzbtnPDy3Y5rlx7s3+1XaLpX2jmyxGcLS3y2GDhjmRkZ7Nu1DYWtXbG1Xb8UzIOocOau3MbIr2bx7QbDa2Dn+uVMmLUUj+VbuBx8nsh7dzj15yHq1m/EzKXr+XKmJ99uWAGAn68no7+exexlm0hUK7l57VJpzFXo//6EktgsMbuIQF8AACAASURBVCGePX4bWeq7HQ9PH/z/OVEyLT5FaNm3W/vbsh3Xrj3Yv8vvqele/19flqzewpLVWxg84jN69HkHgI2rlvLp5xPxXLMVh/oNOfb7r6UxW7lgZFTyT2Xz3DtkWu4Ag54cCILwEiAvQfq8Hm6V4EFUBJZWVtjXrqO7M7oYFGAQ52JQAK5degDQya0boUH+AAwdPV53/glde/ah3+DhAMTGPMTOvnaZ9N2+HEyrju4A1KrfiLSUJNJTU3Thn3tuwtq2FgBmVjakJidiXK0an0xfipXctkxlXwoOwNn9NQAaNGpKclISqSnJgNZultbY19LYzdHFnYvBAYWmAYi8H0ZKchItWr0CQGK8mlN//UGfdz4ok1aA6ALqMjRPXYYGBeDaVVNnzm7dCA3U1KXPlj3Y1dLUlbWNnKSEhDLruRZ6gQ6umlEjh4ZNSElOIi011xb9PhlLBzfDUSWTatWYOG8lNrb2ZS5fn+jICCwtc23T0bWL7jp+QmiQP25a27h01timZk1TPH02Y2JSjfT0NFKSk5Er7OjW8w0+/Eh7nT98oLNdRZDxKIv3xq8nOrbsdVISxIuBvOqsGd2u26AxqclJpOm1vf/7eAztXPKPCj6ICCM6PIxXnNzKVP6DqAgsrHLbmJNL5wL6Jn9cumjaW6fOXQkN8i9Wurzs/W4rb783AJNqxd909WroBRy113c97fX9pK3HREdiYWmFrX1tZDIZ7Tp25mroBSytbEhO1NRjSnIillY2AMSrlNRr1BSANo4uXA4+X2wd+pS3zS4G+fOqozNmZuYobO0ZN2V2ybXULqYWtzxaCkn3vd9mBn6imZHw8PShRWtNf2ptIycpMb5kRitHZEZGJf5UNi/KlOV5oLcgCMaiKGYDA9EstjMTBKE7sBjNLrkRwAg0ztubaJ6I+At4VRCEn4ABwHdAI+As0F8UxfqCIPQCFgCZgBroj2bv6oLitgZ8teFJwDBRFEt1FaqVcVjZ5PqVNjYKoqPCDeOo4rDWxrGWK1Ar4wAwMzMnKaHgYiePHYoyNgaPpasLDC8uSfEqHJrmjqKYW9mQHK+ippk5gO5volrJrYsX6D1gBMbGJhgbl/2yU6viaNaile7YysYGtUqJmbkF8Sqlgd2sbRQ8iAonMSH+qWkAft2/h7ffH6gL37HRh49HfkFk+L2y61Xm1hNo6upBZPhT4xjUpVafKi6WkAvnGTLyizLrSVAraaw3vWxlbUOCWoWpmaYsUzNzkpMMnYzyqru86F/DADZyOdGREYZx8thGpbUNwN5vt/Lzj7t578OPqFuvvu78xM+GEhfzkHlea8pd8xOysx+Tnf24wvJ/GonxShq+lDsCbmEtJ1GtxFSv7aUkJeZLt3/bGgaMmcT5v38rU/lqVRzW1nmv5zx1plLm1pmNArUy9qnpGjdtTnjYHRZMn0BSUiKDPhlD+44uRIbfI+z2TT7+9HO2byj0ATUDEtRKmjTPvb4trW1IUGv7B7USS2sbXZiVjZyH0ZG8/n8DOPXXISaOeJ/U5EQmz9OUZ1/HgRuXgxFeac+VkACMZaV7u0p52ywjI52MjHQWTJ9AclISg4eP4VVH5+Jpydsf/T975x0eVdH24Xs3CaT30EOHAaQk1IReFH1RUERpNlAEBaSXEIp0Qgg9FKXzAiqo8FpAUXpvoZdBmgECpG163/D9sZtN7xW+c19XriRnzsz8zswzs8/OzJmxtedpQA5a7PRacol3++Z1HCtUxE4/mpnSd8XFxnLwz9/wmJV+JqMkeRFGn14EjXkhETgDdNH//zaQ0uOsBfpJKTuhc6YG6q9XBzpKKWehW4D3LvAGYCqldAMOonPYQDfaNlCfRgTweg73rgSGSSm7oXMKC//pqed5bueXPM/b+SY+a7YyfcEyFs+ZyvM8xskTWaQVFa7hvwun0GvIWMytbIour0x5509XxjiJiYncvHqRJq6tALh++QJqtRENGjcrOo150ZRNeJgmlFkeoxk+bgrWaT5Mik5O2TkbJ1cpGW7o+9FnbNr5OxfOnOD6ldQ1RkvWbuXrhcvxnuNZpp6vWMjD850+uI9aDRrjWLFKrvfmP/uC9U0p8apUq86AQcOYNn8ZY6fMZoX3LBITE1nv68NnI8YXgcCcpOkCjx/ch6NTJZZs/JkpXqvZsmYRAEPGTOPn7etZOHUUFpZWuffDeZVUyDJ7/vw5keHheM5ZzJgps1jmNbPAdl7Qz5aM8fb/tptX/9Mr3bW42FjmTBlD7/4f41yzdoH0FQUvwpTlyzJCBrALGCCEeAo8BqIAe+C5lDJlKOIQ0AnwA85JKTNaWUMgZYHOXiDlsLQgYL0Qwhiojc4Bq5DNva2BdUIIgPJApoNGc2Pvnp0cPfgnNrZ2aEJDDNdDggKx108BpmDv6IQmNAQLSytCggOxd8x+OumOvIGNrT1OFStRu14DtFot4WEabO3s8ysRAGs7B6LCQg3/R2hCsEozFRkXE82W+ZN5bcBn1GvWqkB5ZIe9gxNhoamjJKEhQYY1JnYZwkKCA7F3cMLY2CTbONcvX6Beg8aGsDMnjnBH3mDSlx8THh5GUkIClapWo0v3t/Kl8/fdOzl28E+sM9ZlcCD2junr0iGbuoyJjmLGhBF8PHQkzVsXbqopBVsHJ8LDUvWEhQRjU8hp5Pzy2+6dHD2QjZ1nsGN7xwqGsgkODsTB0YnIiHAe3LtDE5cWlC9vSku39ty4eoly5cpja6ez8zoGOw/FtoSfrzixsXckQpPa9sJCc6+/a+dPEvzsMVfPnSQsJBBjExNsHSrQ0CXvbXPvnp0cO7QfGxs7NGnbUnBQFnWW2Z7tHSpkGc/BqQIdur4OQOWqztjZO/DPres88n/A4rm6U2dCQ4LxGPUZXis25KrT1sGRME2qTWlCg7C1T+kfHAlPGxYShJ29I/9cv0yTFrqlyDVq10cTEkyyVotzzTp4eq0G4MDen4mOyt9ZscVVZqamZjRs3BQjY2MqV3XGzNw81/58756dHDu4X9/m0qQZFIS9Qx60OFbIMd61S+cZNmay4X9tUhJzPcfQ6dU3MjlqJU1pTEHml5dlhAx0U49d0E1X/qi/9pz0R6uWQ/dqKuimHzOiShP+nNTvVRuBkfoRsv/lcm8M0EVK2VlK6S6lHJXfB+nxTl+8VmxgymwfYqOjePbkMdqkJM6dOkrz1unOLaV5K3eOH/oLgBNHDtAihw/sa5cvsPuHrYBuODouNqZQoy11m7Xi2ukjADy+dxtrOwfKm5kbwvdtXU27N9+jvkvehtHzg2srd04eOQDA3ds3sXdwMkzXVKxchZiYaJ49CUCblMT5U8dwaeWeY5x/bl2nZt3U6ddPh49j6frv8F6zlb4fDeHVN9/JtzMG8Gbvvnit3IDnHB9i0tTl2ZNHad4qfV26pq3Lwwdo0UZXl+t9F/NO3w9p2aZdvvPPjsaubTh//BAAD+7cwtbB0VAWJcVbvfvi7buBqXPTl82ZLMqmeWt3jh3cD8CJw3/Tok07kpKSWDJvOrExMQDIm9eoVr0m1y5f4Kfv09h5TAzWNvlZUlr2aejSBr+TuvrzvyuxtXc0LBHIjiGT5uCxeCOTF62j3Ws96dF3cL6cMdD1TQuWr8dj9qJ0bezsyaO4ZrTnlu6cOKyz55NHDtC8dbtMbTMl3uG/9vJzSp2FBBOmCaFeg1dY992v+KzZis+ardg7OObJGQNo0tyNc8cPAnD/zi3s7FPbulPFKsTGRBP0LACtNomLZ47TpHkbKlZx5q68BkDwsyeYmpmhNjLi2yVz8L//D8laLScO7MO1dfsyUWaurdy54neO5ORkIsLD8tSf93inLwtW6LVEp0nz1FFcW2fuj04cykJLNvFCggMxNTPHJM1avx93bKaJS0u6v9Wb0kYZIStBpJQJQoijwGdAA8AV3RTlcyFEdSmlP7rRseNkfu4Ux/QukPLGZfc099kA/kIIW3RO35Uc7r2MbjpznxCiPxAkpTxQ0OcaPm4q3rOnANChy+tUda6BJiSY7RvXMHLidHr2GcjiuZ5MGjkYC0srJkybB8CCGRMIDnzGY/8HeIz6jDd69uE/b7/PioUzmTRyMAnx8Xwxdkqhtk+oIRpTtXZ9vpk2ApVKRc/PxuB3eB/lzS2p16wVF4/uJ+TpI84f1L0O3bT9q1StXZ99W1ejCXqKkZEx108fYeCEOZjn862vBo2bUad+QyaPGIRapWboGA8O7PsFC0tL3Dp05YuxU1gyR1du7bt0p6pzDXCukSlOCpqQYCpXqZZddkXCiPFT8Z6lr8uur1O1eg1C9XX51cTp9HpvID5zPJk0Ql+X0+cRFxfLgT9/I+CRP/t/+xmATq/9h//0KtyLwfUaNaVG3QbMHT8ElVrFR19O5Nhfv2FuYUmLtp3xnT+F0OBnPH3szwKPL+n8xjtUrlqd7zasIPjZE4yNjTh34iBfTfXCsgimokdOmIbXTF19dOz6OtWq1yQ0JJhtG1YzatIM3n5vIIvmeDJh+CAsLK2YNGM+FpZWDBw8jMmjhmBkZETtuvVxa9+ZhIR4li2YyYThg0iIj2f4uMLZeU64NnTGa9y71KhiT2KSlt6vutJ//Do0ETHFkl8KdRo2oXodwaJJQ1Gp1fQfNp5TB37HzNwSF/dOrFs4ldDgQJ499mfJ1BG07/42rTt1L1INw8d5smi2rs46dE3TN21ay8gJ0+jZZwCL501l8shPsbC0Yvy0udnGs3NwxGe2J2eOHyYpKZHh4zzTfbDnl/qNmlKzbgNmjfsMlUrNJyMmcvSv3zAzt6BVuy4MHjmZVV7TAHDr+BqVq9XAzsGJb5fOYe7EYWiTtQweqdPY+fVefLN4NgBtO7+Oc806ZaLMdHpeZcKXHwMwbPTkfNl5ujTTfbasZeREvZa5uWjpkqpFExKMjV36Lz57d/9AhUpVDC/pNG3eigEZtrQpKUro6KRCoXrR11bod8CdKaUcJIR4ExgupXxTv5h/ELAe8EI3pXgXGAZ8CDSWUk7Qp3EAsAI6Aj8D1sBhYKiUsoIQYjbQC7gN/A7MBNoCG7K4tyHwLbrRs1h0a89S5xYy8M+z2DJXAZeflt6bMNnxSoXCv6ZfHJgYlb1B5qCI+NKWkImKNqalLSETr3SfUNoSMvH7d7NKW0KWVLMte3tHhccUzXY0RYmNecGdyGKjjDoi9Sual6iy2X/dyfdn7YzX6paoxhfeIStK9IeBdpFS/iSEqAockFI2KOy9OaE4ZHlDccjyjuKQ5Q3FIcs7ikOWNxSHLO+UtEM25+/8O2TTXy1Zh+ylmbIsIiKBvkKIieimMXM6dyo/9yooKCgoKCiUEi/ClKXikKVBSpmIbi+yIr1XQUFBQUFBofRQldWhwjQoDpmCgoKCgoLCS40yQqagoKCgoKCgUMooDpmCgoKCgoKCQimjegE2hlUcMgUFBQUFBYWXGmWETCFXiupctKJkx/mA0paQiQVvFuM5mIUgMjYp95tKmF9vB5a2hEx0r+NY2hIyURa3mHhzwNelLSFLzv+2sLQlZOKwf0juN5UwPUWl0paQmbL3EVMqvAADZC/V0UkKCgoKCgoKCi8kygiZgoKCgoKCwkvNi3C4uOKQKSgoKCgoKLzUKGvIFBQUFBQUFBRKmRdggExxyBQUFBQUFBRebtTKTv0KCgoKCgoKCqWLMkKmUGAunT/N1m99URupaenWnv6fDE0XHh0Vic9sT6KjozAzM2fCjPlYWdtkGW//b7s5tP93Q9w78gbf7z3GtLHDDNdCg4Po9p9e9P3oswJrHtymGvUrWPD8OWw8/ZA7wTGGsLV9GxMcnUCy/hXsZYfvExqTWKB81vv6IK9fQaVS8flXk6jX8BVD2KXzp/nvOl/UajUt0pRbVnGCAp+ydN40krXJ2Dk4Mm7qXEzKlSMqMoJFs6dgZmaGx2wfQ9rXLp1n4deTGDV5Jq3adsxV55Y1i7lz6xqoVAz6cjx1RKrOq35n+H7TKtRqI1xataPPh0MAOH5gH7/s2oqR2oj3P/mC5m3aA7Bv9/ds+3YpG34+hKmZeYHKLSOXd68j9F8JKhXNen+OffX6hrD7p/7kwZm/UKnU2FStiUufL3lw5i/8zx8y3KN5eId3Fu4qEi1p2bl+GffkdVQq6Pf5WGrWa2QIS0yIZ9uqhQQ8vM/UJZvSxUuIj2PWVx/yZr/BtO32ZpFq2rV+OfdvX0OFivc/H5NJ0/bV3jzxv8+UJRszaIpnzqgP6NF3MO5FrCknGtWpzK6lQ1m5/RBrfzharHltXOXD7ZtXUaHi05ETqdcg1c4vXzjD9vW6/qh5m/b0/ehzALZ+s4wbVy+i1WrpM2Awbh27ERz4FF/vmSQlJWFsbMxoz7nY2Rd+u5TTO78h8N4tUKlw7zcMp5rCEHbr2D7kiT9RqY1wqFaLtgNGoFKpOPPTBp79c43kZC3N3uhHrebtCpz/el8fbt+4Ail9T4P0/dW29an9Vb+Ph2YbJykpkeULZvDk8UPMzC2YPGsRllbWfL/lG/zOnOD5c2jl3oG+H39eYpqCAp+ywutrtNokjIyMGTd1HnYOjvx3vS/XLp3neXIybh268u6AQQUuv6JCWUOmRwhRE7gKXABUQHlgoZRyd0nkn42mzsBIKeV7Ga4HSylLfdOkb5d7M8tnNQ5OFZgyaghtO3Wjes06hvBfdu2giWtL3h3wCX/88hM/bd/MoC9HZxmv+1u96f5WbwCuXjrP8YN/YWRkxIIV6w3pfT1xBF26F/wDo1ElSypbl2fKr5KqNqaM7FiDKb/KdPfM/fMOcUnJBc4DdE5RwCN/Fq3ZysMH91ixcCaL1mw1hK9b4c1Mn9U4OFbAU//8EWGaLOPs2LCGHu/0o32X19j67Ur+2ruHHu/0ZfXieTRq4sL9O6n6nzx+yJ6d22jY2CVPOm9cucDTgIfMWb6Jx/73Wbt4NnOWpzoQm1f7MGX+SuwdKzBrwlDadOiKjZ09P25bx4JV/yUuNpZdW7+heZv2HP3rN8LDQrBzcCpU2aUl6M5VooID6DLGh4hnD7nw3XK6jNE5n0kJcTy8eJROX3mhNjLm6KqphD64RS237tRy626I/+jS8SLTk8Lta34EBjzEY9E6njx8wJYV8/BYtM4Q/uMmX5xr1yPg4f1Mcffu3IyFpXUxaLpI4JOHTPLWafrvynlM8k7V9PPmVTjXqscT/8ya9u3cVCyacsLctBxLJr/PobO3iz2v65cv8OSxP16+W3j07z18F83Cy3eLIXyDrzczFq7C3rEC08cOwb1DN8I0Ifjfv4uX7xYiw8MYP2wgbh27sWPjal57613ade7Ovj0/8OuubXw8bEyh9D25fYXwwAB6eSxF88SfY1uW0stjKaCz83vnjtBzog9qI2N+X+JB4L2baJMS0QQ8oJfHUuKiItg9d2SBHbJrl87z5JE/3qu38vDfe6xcOBPv1Wn6q5XezFyk66+mjh6Ce0ddf5VVnP2/7cba1o7x0xfw568/cf2KHzVr1+ffe3fwXr0VrVbLiI97063H2zg4VigRTds3rOL1nn1o36U7v+/+gf/t2kaX7m9x9eI5vFdtITk5ma8GvUeX7m9h51C6H6svwluWJbkPmZRSdpZSdgJ6AMuEEGYlmP8Lw9OAR1ha2+BUsRJqtZqWbu24fOFsunsuXziDW4cuALRu25FLF87kKd73m9fR/5P036AunT9N1Wo1cKpY8E0Nm1ax4uy/YQA8Do/DopwxZiZFb16XL5zFrX1nAJxr1iYqKpKY6CggTblVqKT/dteOKxfOZhvn6qXztGnXCdCV4eULZwAYOWkGjZq4psvX3sGRKXMWY25pmSed1y6eo1VbXZ5Vq9ciOjLCoPPZk0dYWlnjqNfp2qod1y6e5arfWZq4tsbM3AI7B0eGjp0KQKt2Xeg/eESRjrkH/nOZKk3cALCu6ExCbBSJcboRTeNypnQcPg+1kTFJCXEkxkVT3souXfyb+7+nYff+RaYnhVuXz+PipquTys41iYmKIDYm2hDe+6MvcHHrnCne00cPePLwPk1ati1yTfLyeZq16ZhGU2Q6TW9/OMygObOmBzQuBk05EZ+YxDtfreFJUHix53XF7yyt2+n6oWo1ahMdmaE9WtkY7Lx5m/Zc8TtLo6bNmfC1NwDmllbExcWi1WoZOnoKbh26AWBta0dkROH1B9y6RA0XdwDsKlcnPiaKhFhd3RmXM6XHOC+DnSfERmNmbUeleo3pNlTX9sqZW5CUEEdysrZA+V/2O0ublL6nRm2iMpSPlVWG/srvbLZxzp08QqdXewDwes8+tGnXmYqVqzB51iIAoiMjUKnVmJvn3EcVpaYvxnji3lFXZza2dkRGhGFuaUliQoL+Jx6VWkV5U9MClV9RolLl/6ekKZUpSyllqBDiCVBJCBEPbADKAVpgiJTSXwjxD+AH7AcSgZFAAnBZSjlCCNEEWAUkA5HAJ0BT/X3JQEPgRynlLCHEq8AcfXwN0Dc/eoUQ1YCNeo3JwGdSyvtCiCVAa8AUWCulXC+E2AwEAC2A6sAHUkq//OSnCQnGxjb1A9DG1p6nAY/S3xMaYrjHxs4eTUhQrvFu37yOY4WKmb6p/PLjd3z+1YT8SMyErZkJd9NMUUbEJWJrZkJsYrzh2rB21algWZ6bz6LYdv5xgfLRhIZQRzQ0/G9jY4cmNARzC0s0ocHY2KR5fjt7nj5+RER4WJZx4uNiMSlXznCvJiQYAHNzi0z5ljfN33eHsNAQatdrYPjfytaOMI1OZ1hoCNZpdFrb2vHsyWPi4+OIj49j0YyxREVF8t5HQw0OWlETFxGGXbW6hv/LW9gQF6HBxDR1OlT+vYs7R3+lbqdeWDqmOuuh/rcxt3XE1Dq9k1YUhGtCqV4nTbnZ2BGhCTGUgam5BVGREZni7dq4kgFDx3Pq4N4i1xQRFkL1uqnTXJZZaIrOQtNPG1fSb9h4TheDppzQapPRags3Ep1XwkKDqVM/tW1Z29oSFpq1nev6o4cYGRlhZKZrTwf27aFF63bprmm1Wvbt2ZmnqbfciAnX4Fi9nuF/UysbYiM0lDNLbVOX/9jJtQN7aNztHaydKgOgLm8EwO3jf+LcuBVqtVGB8g8LDaFumvKxsU3fX1ln0V9HhIdlGSfw6RP8zpxgy9pl2No78MVYT6ysdSeYrFvpzbGDf/Lpl+MwM895SUNRaqrqXAPQ1dnePT/Q7+OhOFWoRLvOrzKkXw+Sk7X0+2Qo5hZ5+yJbnCgjZNmgn8J0AB6ic5QWSym7AcuA6frbagOzpZQbgAlAHylle+C8fmRtOTBRStkZOAKM1sdrDQwC3IGv9NfsgIH60bkI4PV8Sp4NbNDntRqYKYQwBR7oNXXQ35NCeSnl63qNH+czr0zkerzS86zDM8bb/9tuXv1Pr3TXQoICiY+NpXJV50JpzIgqwxst3/sFsPnMI6bvlVS3M8W9pm2R5JNj2eSxXHS3FvP5InlJ/zlERYQz7utFfDlhJmt9ZhW/rrSZZ0C8+j5vTFvHs1t+BN+7Ybj+4PR+arR+tWRU5eH5Tx3cS23RGMdKVUpAEXmqy9MH91GrQWMcK5aQpjJCTkWTsS7PnjjMgb3/Y8ioyYZrWq2WFQum08S1FU2btykRgc3e6Eu/eZt4dP0CT+9cN1z/99Ip5In9tB0wvAizz76AsgtLuf78+XOqONdg3vL11KhVlx+3p65X/PyrSazeupvdP2zh2ZP8fdktjCbQ1dmy+dNo6tqaZi3a8DTgEaePHeLb735l7fZf+OOXHwnThOZLU3GgjJClRwghDqNbQxYHfCylTBJCtNWHTQOMgCD9/dFSypTW8R2wWwixDfhOShkrhGgkpTyjDz8EfK3/7SeljNFnmJJ3ELBeCGGMztE7iG5ULa+0BKakyWuGlDJOCGEvhDiJbuQt7QKfY/rfj4A89yp79+zk2MH9+m8fwYbroUFB2GdYP2Tv6IQmNAQLSytCggOxd3TC3rFCjvGuXTrPsDGT06Vz/vRxmjZvlVeJ2aKJScTOzMTwv525CZrY1EX7h++kNki/RxFUtzPj1IOwfOdj7+hEWGjqGXahwUGGET97h/TPHxIchL2jE8YmJlnGMTUzJz4+jvLlTQnVl2FRYefgmC5PTUiwYYGynYMTYZq0egJ1ekzNqN+oKUZGxlSqUg0zcwsiwjTY2NkXma4UzGzsiYvUGP6PDQ81jHglREcS/vRfnOo0xqhceSo2aEHI/Zs41tYtZA+6cw2Xd4dlmW5hsbV3JCIstWzCQ4OxsXPIMc7V8ycJfhrA1XMn0IQEYmxSDjsHJxq6tC4STTb2jkSk+UAJy4Oma+dPEvzsMVfPnSQsJBBjExNsHSrQ0KXwba0sYefglK7NaULStEdHJ8I0afqj4EBDf3Tx3El+3LaB6Qt9sbC0Mtzj6z2TytWc6fdJ0diXua0DMRGpdh4THoqZja49xUVHonn8gMr1m2BcrjzVGrfk2d0bVKr7Co+uX+DSvu95fdTcdKNp+cXeQddPpxAakr6/CkvbXwfr+mtjY5Ms49ja29PYpQUArq3b8t2mNQQFPiUsNIR6DV7B0sqaho1d+OfWdSpWrloimgBWLPyaytWq03+Qrs7+uXWd+g0bU97UjPJAzdr18L9/B1u7ommPBeVFOCeyVNaQSSlfl1Ke0l9PAN7Xh3WQUr6b5npKxAXAu3q9B4UQGXvDlKlEgKxOe96IbgF/J+B/BdD+HAxDPuWAZCFEJ6Ar0Ek/chaf5v60GvLsZ/d4py8LVqzHY/YiYqKjefYkAG1SEmdPHcW1tXu6e11buXPi0F8AnDxygOat21GxcpVs44UEB2JqZo6JiUm6dP65dZ1adetTWC49jsC9lu4DvbaDGZqYROISdVVibqJm+ut1Mda/5vJKJUv8NbEFyse1lRsnDv8NwN3bN7F3dDJMMVasXIWYmNTnP3fyKK6t3LONyxxB0gAAIABJREFU06xFG04eOQCklGHRrfVp2sKNM8d0ad//5xZ2Do6GKa4KlaoQGxNN4NMAtNok/M4cp2kLN5q2cOP6pXMkJycTGRFGXGwMVjZFM5KYkYrClceXTwK6tyXNbOwN05XJyUmc37GMpHhdHWn8b2NVQdfBx4aHYFzeFLWxSdYJF5JGrq3xO6F7k9P/rsTG3hHTXKZsh06ai+eSjXj4rKf9a714s9/gInPGABq6tMHvZKom2zxoGjJpDh6LNzJ50TravdaTHn0Hv3TOGIBLS3dOHdXZ+d3bN7FzcEpv59Gpdn7+9DFcWroTHRXJ1m+W4Tl/uWHKDeDI33sxNjGh/6Avi0xftUbNue+ne/kk2P8O5jb2lEuxc20SR7csITFOZ+dBDyS2FauREBvNmZ/W033ELEwtrLJNOy+4tHLj5JE0fY9DDv3VqaO4tHLPNk7z1u3wO6trs3fkDao41yQiTMPapfPRJiWh1Wq5c/smVarVKDFNh//ai7GxCQMHp9ZZ5arO3JE3SE5OJikpkX/v3aFi5WqFKseiQKVS5funpCkL216cAd4B1gghugKVpJQ7UgKFEGp005ozpZRLhBCNgBrANSGEu96x6wSczyEPG8BfCGELdAGu5FPjOX2879Lk5Qg8lFImCiF6AUZCiHL5TDdbho/zZNFsDwA6dHmdqs410IQEs33jWkZOnEbPPgNYPHcqk0d+ioWlFeOnzc02HujXpdllXvMTGhJcJCMwMjCau8ExzH9L8Pw5rDvlT5d6DsQkaDnzbxh+jyLw6tmABG0y90JiCjQ6BtCwsQt1RUMmDf8ElVrNF2M8OLDvF8wtLHHv2JUvx3nik/L8XXXPX9W5RqY4AAM//YKl86bz5y8/4VSpMl3f6IlWq2X6uGFER0USEhSI5+gh9PtkKAnx8ez+fguP/B9wV97k15++Y/biNdnqFK80o1b9hkwf8ylqlYpPR07m8P5fMTe3pHX7Lnz2lQcrF+gWDrt3es3Qibbp0I3powcBMGjERNRqNbt3bOCK3xnCQ0NYMHUU9Rs24YPPR2eXdZ5wqNUQ22p1OLR8IiqVSretxdm/MTG1oGpTdxq+3p+jqzxRqY2wqVKLyo11A71xERrKW9rkknrBqdOwKdXrNmDhpM9RqdQM+GICJw/8jpm5Ba7unfnGyxNNcCDPHvuz2HM4HV5/m9ad8rsCIb+amlC9jmDRpKGo1Gr6DxvPqQO/Y2ZuiYt7J9YtnEqoXtOSqSNo3/1tWnfqXqyacsK1oTNe496lRhV7EpO09H7Vlf7j16GJiMk9cj5p0LgZdeo1ZMrIQajUaj4f7cHBP3Tt0a1DV4aOmcKSubrJhXadu1PFuQb7f/uJiPAwFs9OHa0f5TGbP/bsJCEhgeljdWvHqtWozbAxU7LMN69UrNMIx+p1+WXhOFQqFW0HjOD2yb8oZ2ZOTdd2uL45kN+XTEatNsLeuTbVm7khj+0jPiqCA+vmG9LpPHgClvbZv7mYHQ0bu1CnfkMmjfgEtUrNsJT+ytIS9w5d+XKsJ4vn6Pqj9l1S+6uMcQB69hnAsgUz+Pv3PZiamTNmymxs7R1w79iVyV8NhufPaenWgdr1RE6SilTT3j0/kJiQwNTRum17nGvW5ouxnri0csfjq8EAvPZmbypWLv2p+7K/ggxUJbFGRb9m7EcpZcsswqoAmwAzdCNRg/QL5g3bTwghPID3gHDgHjAMaIBuUf9zdAv1BwPNSbOVRUoaQojZQC/gNvA7MBPwRLcuLeO2F4nAiTSXlqBzwDag264jAfgMiAL+AmKBPUBbdOvTjPTP+psQ4i3gPSnloOzK5vazmJJaJJRnPH67WdoSMrHgzYa531QKxMQX7O2r4mTX9SelLSET3euU+k4ymUgusfV5eefNAV+XtoQsOf/bwtKWkIm9/zwrbQmZ6CkK/qb6/zcaVDYvUR9p24VH+W7wH7aoVqIaS8QhU8gexSHLG4pDlncUhyxvKA5Z3lEcsryhOGR5p6Qdsu0FcMg+KGGHrCxMWSooKCgoKCgoFBsvwK4XikOmoKCgoKCg8HJTGov088uL8CaogoKCgoKCgsJLjTJCpqCgoKCgoPBS8yKMPikOmYKCgoKCgsJLTXFNWQohlgJu6HZ8GC2lPJcmrAuwAN2xkBLd0ZDZnmumOGSlTBl80QuvMvhGY3IZLCeA8sVwgHph+cgl+126SwsjozK4fqMM2lRZfJsRoOVbk3O/qYSZvXRcaUvIRFnctcDYqOz1UaVBcfRA+g3i60kp3YUQDdFtQp92F/dvgS5SykdCiF3AG0C2h9sqNaWgoKCgoKDwUlNMO/V3Q7cPKVLKm4CdEMI6TXgLKeUj/d9B6M7wzhbFIVNQUFBQUFB4qVEX4CcPVCL1/G30fxs2o5NSRgAIISoD3clhdAyUKUsFBQUFBQWFl5wS2vYiUyZCiArAr8BwKWVI5iipKA6ZgoKCgoKCwktNMbljAaQZEQOqAIajUvTTl/uAqVLK/bklpkxZKigoKCgoKLzUqFT5/8kD+9Gds40QojkQIKWMTBO+GFgqpfwjL4kpI2QKCgoKCgoKLzXqYhgjk1KeFEJcEEKcBJKBEUKIQUA48CfwMVBPCDFEH2WHlPLb7NJTHDIFBQUFBQWFl5riWkImpfTIcOlymr/L5yctxSErQ1w6f5r/rvNFrVbTwq09/T8Zmi48OiqSxXM8iY6OwtTMnAnT52NlbZNtvPW+PsjrV1CpVHz+1STqNXzFkJbf2ZPMnDiCX45cTJfHolkemJQrx9gps7PUt1WfT8ts9Pno9Zll0JdVvE1rlnH9ih9arZb3P/yUth27kZSUyNL5M3jy+CFm5hZMmb0ISyvrTFpSnu/2jSuQ8nwNUp/v0vnTbFufWib9Ph6aaxy/syeZNWkE/zt8kTvyBptWLzGEPfz3HlPmLqFhY5fsKzAHNq7yQd64ikql4rORE9Ple/nCmVStbdrT9+PPAdiydhk3rl4kWavl3YGDce/YrUB5Z2TDKh9u37gKKhVDctDSvE17+um1/Hv/DvOnjaPXewN5s3d/AJZ7fc3d2zexsrYBoHe/j2np3qFAmi6dP83Wb31RG+VgW7PT2NYMnW0lxMezymcu/g/usnTdDsP9/967w1zPsbz9/ge81ad//rUUkZ1fvXger68nUr1WHQBq1qrLsDGp/bff2ZN8PXEEv2Zoh9mxcZUPt29eRYWKT7Oou+3rdWXYvE17+n6kq7ut3+jsSKvV0mfAYNw6diM48Cm+3jNJSkrC2NiY0Z5zsbN3zFc5FYRGdSqza+lQVm4/xNofjhZ7fgCnd35D4L1boFLh3m8YTjWFIezWsX3IE3+iUhvhUK0WbQeMQKVSceanDTz75xrJyVqavdGPWs3bFYmWomp7t65fZsva5RgZG2NiYsIYz7nY2NoVSNOl86fZ8u1K1EZGtHRrz4As7H3R7CnE6D93Js5YYGh7vj5z8H9wj2X6tnfl4jm8Zkyieq3aANSsXY8vxmT0V0oHVXGtIitCcl1DJoQYIYQ4LYQ4IoQ4K4R4tahFCCHeEEJ8qf87ZT7WRQgxq5DpVhVCaIUQ7xSFzgLk30sIUS6v969b4Y3HHB8WrtrMpXOn8X9wN134Lz/uoLFLSxb6bsK9Q1d+2rE523jXLp0n4JE/i9Zs5atJX/PtitQNJxPi4/lx+0bsHdJ3wBfPneZpwCOy49sV3kyZ44P3qs1czEZfE5eWeGfQl1W8K37n+Pf+HXzWbGXWolWsW+kDwJ+/7sbG1o4l32yjQ5fuXL/il6WWa5fO8+SRP96rdc+3bkX6DTXXrfRm8mwfvHzTl0l2cRLi4/lp+0bs9GVSVzRi3vL1zFu+nilzl1KtRi1Eo6bZlk1OXLt0gYBH/ixctYURE2ewfqV3uvD1K72ZPGsRC1Zu4tL5Uzx8cI+rF8/h/+AuC1dtYfpCXzauWlygvLPS8kSvZWQWWtat9GZSBi1xsbGsW+FNU9dWmdL76PORzFu2jnnL1hXYGQP4dnkutrVrB01cW+K9ahPuHbvy0/bNAGxas5Ra9US6e+NiY/lm+UKatWhdMC1FaOcAjV1asGD5ehYsX5/OGUuIj2dXFu0wO65fvsCTx/54+ersaINv+rrb4Kuru/krNnE5rR3dv4uX7xZmePmycbXOjnZsXM1rb73L3GXradO+C7/u2lagssoP5qblWDL5fQ6dvV3seaXw5PYVwgMD6OWxlA4fj+HU92sNYUkJcdw7d4SeE33oNWkxYU8fEXjvJgHyMpqAB/TyWMobo+Zyeuc3RaKlKNveL7u2M3rKbOYu/RbxSlP2//ZzgXV9s9wbzzmLWbRqMxfPncpk7//btV3f9jbTtmNXfty+CYCNa5ZSO0PbA529e63YgNeKDWXGGYNiW0NWpOTokAkhagKfAx2klJ2AD4DpRS1CSvmHlHKN/l8P/bVLUsqvC5l0f+Af/e/SYByQJ4fsacAjLK1tcKpQST+q044rF86mu+fyhTO4degCQOt2Hbl84Uy28S5fOItb+84AONesTVRUJDHRUQDs2raBHu/0w9jExJB2YkICO/+7jr4fDyErMubT0q0dl3PRdykLfSnxXmnWHI9ZiwCwsLQiPi4WrVbL2ZNH6PxaDwDe6NWHNu06Z6nnst9Z2qQ8X43aREWmPt/TgEdYWWUoE7+zOcbZtX0DPXr3w8TYJFNee37YSs/3PkCtLtg7MFf8ztKmfRdDvtEZtFpa2eCYorVNe674naVR0+ZM/NrbUD5x+vIpLBm15Fhubdpz2e8sJuVMmO61AntHp0LnnxUGG6mYR9tqq7MtgI+GfoV7h67p7jUxMeFr75XYO+Rfb1HbeU7s3LaBNzO0w5y44neW1u10+VbLxY6ap7GjCXo7Mk9jR0NHT8Gtg27E1drWjsiI8LwXUgGJT0zina/W8CSo+PNKIeDWJWq46DZOt6tcnfiYKBJiowEwLmdKj3FeqI2MSUqIIyE2GjNrOyrVa0y3oVMBKGduQVJCHMnJZavtTZrpTaUq1Xj+/DkhQYE4OlUskKanAY+wsrZO0/baZ2HvZw1trHXbToa293EWba8so0aV75+SJrcpSxvAFJ1TkSil/AfoBCCEaAT4ojuAJBIYJKUME0JMQvfWQTIwBbgP/CilbKmPd14fPhNIQLdz7a9AY+AZ0EwI8TOwAhgppXxPCPEuMB5IAs5LKccLIaoD29CdEWUMfCil/DeD/oHASOB7IYSFlDJaCGELbAes0S2866+Pn/GaEbAZsAVMgFFSSj8hRLCU0lH/LD/qy6CzvqwEUAcYAziiO99qnxCim5QyIaeC1oQGY2OTOuRsY2fP08fpR6vCQkMMw9I2tvZoQoKyjRcRHkYdkXoEko2NHZrQEDShIdy/e5sPPhvO5rVLDeG7tm/kP2+/j7m5ZYH1afKhz8jICCMzMwD++n0PLdq0x8jIiMCnT7hw5gSb1i7Dzt6BL8d6GqbEMpZF3fppns9W93zmFpZoQoOxTjN8b2Nrz9MAXZlkFUcTGsKDu7f54NPhbF6zNF0+8fFxXDx3koGffpllueSFsNBg6qTJ19rW1qA1bZ2man2YrnwO7N1DizbtMDIyKrCGFDS5aMlcbg8xMjLGyCjrrmLv7h/4Zdc2bGztGTp6MtY2+Z820YQEZ1EGOdiWnc62AMzNLYgMT/8Bb2RsjJFxwVZjFLWd16xdj4cP7jFnymgiIyMY8MkwXFu58fjhvzy4e5sPPxvOprXpbS47srKjsLR1Z5OLHe3bQ4vW7dJd02q17Nuz0zBNXpxotclotdke41csxIRrcKxez/C/qZUNsREayplZGK5d/mMn1w7soXG3d7B2qgyAuryurd0+/ifOjVuhVpe9tud39gTrVy6iWvVadNJ/ic23ppD0faWtrT1PAh5m0p2+7QUDKW0vLFOaDx/cY7bHaKIiwxkwaBiurdwz3VMalMaIV37J8Su/lPIycBa4L4TYLIToK4RIsY6VwDApZTd0r36OEELUQ+dsuQEfohtRy4lQKWWfNPktAsKllO+mXBNCWALTgK76UTpnIUQ7fT5/SSm7AKOBymkTFkIIwEZK+TdwGOilD5oA/Cml7AAcAF7N5tpo4LQ+/TFAbr2ms5Syhz7eMCnlf4GnwH9yc8ayJLcz0bILz+b6c/3BfRt8ffhsxPh0YQGP/uWOvEHHbm/kQ17B9GWMd/r4If7au4cvxkw26KzqXIMFy9dTo1Zddm3fWGg92YWlXN+wyodPh4/P8p4zxw/R0q1DgUfHss43h7AMByyeOX6Yv/f+j89HFdNZgjlWY8513Pm1Hnw0dBRzlnxLrbqC7zcXzdROxjLIfEPJnRdYWDuvUq06AwYNY9r8ZYydMpsV3rNITExkfRbtMP/acgpLH3j2xGEO7P0fQ9LYkVarZcWC6TRxbUXT5m0KpeWFIYtCa/ZGX/rN28Sj6xd4eue64fq/l04hT+yn7YDhxaSlwIEANG/djlVbd1O1ek1+2rGpiCQVru2l2Pv0BcsY6zmHFQt19l4WeBGmLHP9Giml/Fh/aObrwCTgSyFEV6A1sE7n91AeOAe4Amf0p5nfAYbopz2zI+cxfR2vANWBP/V52QA10DmBu/UjXj9KKU9liDcQ+F7/9w5gEPAd0Bz9tKuUcimAEGJoFtc+Bebpr50XQtTNRedx/e9Heo15Yu+enRw/tB9rGzs0ocGG6yHBQZmGqe0dndCEhmBhaUVIcCD2jk7YO1TIMp6xiQlhoambAocGB2FiYsIj/wcsnqsbjg8NCWbKqM9w69CVoGdPmfDlx8RGRxEepuGnHZvpM3AQe/fs5Nih/foRtuB06RVEX9p4fmdPsvO/G5i1aBUWllYA2NrZ09ilBQCurdqyY9MassLeQZeXId2QIMP6L3uHCoRlzNPBCWNjk0xxTExMeOz/gCUpZRIajOfoz5i/fAMA504d4z+93s9SQ16xd3RKp0cTEmRYN6Qrs7RaAw3lc/HsSX7cvoEZC30N5VNYMmoJTaPFLkNYSBotWdGsReqHeOu2nVi7dH6+tOzds5NjB/frRyrTaAoKyjTdmJVtFSXFZecOThXo0PV1ACpXdcbO3oF/bl3P1A49Rn2G14oNOWq0c0hvK5q0Nu/oRJgmgx3py/DiuZP8uG0D0zPYka/3TCpXc6bfJ8PyVVYvEua2DsREaAz/x4SHYmZjD0BcdCSaxw+oXL8JxuXKU61xS57dvUGluq/w6PoFLu37ntdHzU03mlYYirLtnT52ELcOXVGpVLh37Mb3W/L3ZWjvnp0cPfinYZbAkG9QIPYOFTLpzmvbc3SqSMduqfZua+9ASFAglapUzZe+4uCFX9QvhFAJIUyllDellMuANkA1dA5SDLpTzDtLKd2llKPQTR9mTDOjS512wUReRo4SgAv6fDpLKV2llDuklNeAZsAxYIEQ4uMM8QYA7wkhLgGzgG565y0rjdnpTluDWY1Zp32WpDR/57nme7zTl/nL1+MxexExMdE8exKANimJcyePZhrqdWnpzonDfwFw8sgBmrduR8XKVbKM59rKjROH/wbg7u2b2Ds6UaFSFb797ld81mzFZ81W7B0cWbBiA2+//wErN+3EZ81WvhjrSUv3DvQZOMigb0EW+s5moc81D/pS4kVHRbJpzTJmeK1INyXZok07/M6c1Ou+QdXqNbMsN5dWbpw8kub5HJwwN9d1nJnK5NRRXFq5ZxmnQqUqfLPjVxat2cqiNVuxt3c0OGMAd25dp2bd+nmtzqy1tnTn1NEDhnztHJww02utUKkKsdHRBD4NQKtN4vypYzRrqSufLd8sY+r85VlO2RYU15bunEyjxT6NloqVqhATHc2zNFpcWmY/3eA1Y4JhavHqpfOGNwnzSo93+rJghd62otPYyKmjuLbOYFut3DlxKL1tFSXFZeeH/9rLz99vBXTTQ2GaEOo1eIV1Gdphbs4Y5NOOTuvqLjoqkq3fLMMzgx0d+XsvxiYm9B9U8Kn4F4FqjZpz30/3XTnY/w7mNvaUMzUHIFmbxNEtS0iMiwUg6IHEtmI1EmKjOfPTerqPmIWpRdF8EYKibXvfb/6Ge3ckALdvXqOqc418aenxTl+8VmxgymwfYqOjePbksaGvbJ6h7TVv5c5xfds7ceQALVq3zTbdQ/t/5+fvtgAp9h6Kg1OFbO9XSE9uI2SfAR2FEJ9IKZ+jG/lRA4Ho9tp4A90aqf7oDtW8AEzXT2s6AGuBT4GKQggVUBHdGqucyOgYSaChEKKClDJQ/+blt0AH4J6Uco8QIhjoC2wFEEK0AiKllC1SEhFCbAT6oBvJ6wqcE0IMA+JyuNYFOC2EcAOu6ZN6LoQw1//tmsuzJJOPrUW+HOeJz2zdWykdur5OVecaaEKC2bFpLSMmTKNnnwEsmTcVj5GfYmFpxbhpc7ONV9W5BnVFQyYN/wSVWl0kb7sMH+fJoiz0bd+0lpF6fYvnTWWyXt94vb6s4v3xy09EhGtYOHOSIf2xnnPo2WcAy+bPYP/ePZiZmTMmi+03ABo2dqFO/YZMGvEJapWaYWM8OLDvF8wtLXHv0JUvx3qyeI4uz/ZdUsskY5zciI6KNDh6BaVB42bUrt8Qj5GDUKnUDB3twcE/fsHcwhK3Dl0ZNnYKi+dMAaBdl+5Uda7B/l9/IiI8DJ9ZqVNMo6fMxqli5eyyybOWOvUbMnnkINR6LQf++AULvZYvxk5hiV5Le72WO/IGm9YsJfBpAMbGxpw8cgCP2T682bsfPrM9KF/eFFMzc76aPLPAutLZSJc0trVxLSMn6m1rbmbb8poxkeDAZzz2/5cpo4bwes8+VKtegw2rlhD4NAAjI2NOHPkbz7mL8+zYFqWd2zk44jPbkzPHD5OUlMjwcZ6Y5HERf0YaNG5GnXoNmTJyECq1ms8z2NHQMVNYMldvR527U8W5Bvt/09nR4tmpdjTKYzZ/7NlJQkIC08fq1o5Vq1GbYWOmFEhXXnFt6IzXuHepUcWexCQtvV91pf/4dWgiYootz4p1GuFYvS6/LByHSqWi7YAR3D75F+XMzKnp2g7XNwfy+5LJqNVG2DvXpnozN+SxfcRHRXBgXeqIb+fBE7C0L5xjUZRtb+Skr/lm2QKMjIwoV648YzznFljX8HFT8Z6tyzd921vDyInT6dlnIIvnejJp5GAsLK2YMG0eAAtmTNC3vQd4jPqMN3r2oU37ziyaPYXTJw6TlFg4ey9q1GV/gAxVTmskhBBGwEKgIxCFbkTIS0r5u34a81t0TkcsMFBKGSqEGI/O8VEBnlLKQ0KITUATdE5cfeAjdIv6f5RS/qbf2baxlHKCEOIAYIVuejTton5PIB64CHyFzhlaq9elRbfo/qZe91LgrpTSN82zdABmoFt7thWdcxmJbmpTncU1gE2AvT58hJTyuhBiNtAbuIHuZYfl6Bb1B0spfYUQjQFfKWVnvRPYGugspUwdj06DfBpTcgti8khZtNvkMldKOpJLcD1TXimL9WdkVAZVlb2qI1FbBkUBLd8qpjWMhWD20nGlLSETb9Uv2NuOxYmxUdk8IbFeRbMS7RQO3grJd+Pq2sChRDXm6JApFD+KQ5Y3FIcs75TF+lMcsryhOGR5R3HI8obikOk4JPPvkHURJeuQKTv1KygoKCgoKLzUvAiL+hWHTEFBQUFBQeGl5kVYQ6Y4ZAoKCgoKCgovNcoImYKCgoKCgoJCKfMi7NSvOGQKCgoKCgoKLzUvgD+mOGSlTVl8S+9+aHRpS8hETbui2S27qCmLjfxZZHxpS8hERavypS0hE1FxSbnfVMIc9g/J/aZSoCy+0Thj7JLSlpCJRv+dUdoSMvEwsvj2eSsM9SrWLNH81C/AEJnikCkoKCgoKCi81JR9d0xxyBQUFBQUFBRedl4Aj0xxyBQUFBQUFBReapS3LBUUFBQUFBQUSpkXYAmZ4pApKCgoKCgovNy8AP6Y4pApKCgoKCgovOS8AB6Z4pCVcTb4+nD7xlVQqRjy1UTqNXjFEHb5/Bm2rfdFrVbT3K09/T7+PNs41y9fYNu6VRgZG2NqasqYqXOxtLIutL7dG1fw7+0bqFQqen86iur1GhrCEhPi2bnWh6cP7zN+0XoATv/9G+eP/Gm45+FdycId+wutIy0bV/kgb1xFpVLx2cgMZXYhtcxatGlPX32ZbVm7jBtXL5Ks1fLuwMG4d+xWaB0bVqWphxx0NG+TWnf/3r/D/Gnj6PXeQN7s3T9dehfPnmTW5JHsOeRXaG0AO9cv4568jkoF/T4fS816jQxhiQnxbFu1kICH95m6ZFO6eAnxccz66kPe7DeYtt3eLBItZbGstn2zhDu3rqFSqfhw2HjqiNTyuXbxLDs3r0atVtOsVTt6D/yMuNgY1vrMJDoqkqTEBHp/MISmLdy5feMK279dhrGJMfVfcaHfoOEF1pSW0zu/IfDeLVCpcO83DKeawhB269g+5Ik/UamNcKhWi7YDRqBSqTjz0wae/XON5GQtzd7oR63m7YpES1nWlBuN6lRm19KhrNx+iLU/HC2xfPdsSu073/l0FNXrpu87d32j6zvHeev6zvjYGHasnEdMVCTapES6vz+IBq5tilTT4R1reXr3Fqig88AvqVQ7tf4e3rzE8V2bUKnV2FeuxmuDx5KYEM+f6xYRF63T5Pb2h9Rs0rJINRUVyhqyQiCEqAcsA5wAI+AkMEFKGS+EeAA8BJL1Yd9LKX2zSecboI2U0qUkdGfI2xpwk1IWyOO4dukCTx75s3D1Fh7+ew/fhbNYuHqLIXzdSm++XrQKB8cKTB09hLYduxEepskyzsZVSxg3bR5Vq9dk17YN/PnLT/T5YHChnu/O9YsEP3nEGK+1PHv0gO98vRjjtdYQ/suW1VStVZenD+8brrm9+hZur75liH/pxKFCacjItUsXCHjkz8JV+uf3nsXCValltn6lN197r8LesQLTxgzBvWOMWon7AAAgAElEQVQ3wjQh+D+4y8JVW4gID2P80IGFdsgMdZeNjnV6HQ6OFZg6Rld3ThUrs26FN01dW2VKLyEhnh93bMLOwbFQulK4fc2PwICHeCxax5OHD9iyYh4ei9YZwn/c5Itz7XoEpKm7FPbu3IyFZeGd+RTKYlndvOLH04CHzFy6kcf+91m3dA4zl240hG9ds5jJ81Zg5+DE3EnDaN2uC9cvn6dytRr0GzwCTUgQ8z2Gs2jdLjb7ejFi8lyq1qjNuqVzuH3jCvUbNS2wNoAnt68QHhhAL4+laJ74c2zLUnp5LAUgKSGOe+eO0HOiD2ojY35f4kHgvZtokxLRBDygl8dS4qIi2D13ZJE6P2VRU26Ym5ZjyeT3OXT2donlCal95+gFur7z+1VejF6Q2nf+unU1VWum7zvPHtqHUxVn3vrwC8JDg1nz9Wg8Vm4vMk2Pbl0h7Nlj+k9fRkiAP39tWEL/6csM4X9vXs57k72xsnfiN9+5PLh6nvCgJ9hVqkb79z8lShPCjwsnMchrQ5Fp+v+GurQFZIUQwgj4CfCWUrYGUlzutLvu/UdK2QnoAXQXQnyRRTomQE/ARgjRoJhlZ0VzoHtBI1/xO0ub9l0AcK5Rm6jISGKiowB4GvAIKysbnCpU0o32uLXnst/ZbONY29gSGREOQHRkBNY2toV8NPjnygWatO4AQMVqNYmNjiQuJnVT2Tc/HEbTNh2zjb9/52a6v/9JoXWkJePzR2coM0srGxxTyqxNe674naVR0+ZM/NobAAtLK+LiYtFqtUWqI8e6a6OrO5NyJkz3WoG9o1Om9H7ctpEe7/TF2NikULpSuHX5PC5unQCo7FyTmKgIYtPUXe+PvsDFrXOmeE8fPeDJw/s0adm2SHRA2Syr65fO0cJdVz5Vq9ciOipVU+CTx1haWePgVBG1Wo1Lq3Zcv3QOK2tbolLaWFQEVta6NhYWGkLVGrUBaNLCjat+pwusK4WAW5eo4eIOgF3l6sTHRJEQq6s/43Km9BjnhdrImKSEOBJiozGztqNSvcZ0GzoVgHLmFiQlxJGcXDg7L+uaciM+MYl3vlrDk6DwEssTdH1n47R9Z1T6vrPHB8NokqHvtLS2ISYyAoDYqEgsrG2KVJP/jYvUaa5r1w5VqhMXHUl8bKqmgTN9sbLXtTczaxtioyIws9T9BoiLicTMqmg1FSUqVf5/Spoy6ZABrwG3pJRHAKSUz4FJwOyMN0opI4DhwJgs0nkDuAjsAAakXBRCfCSEOCeEOCOE6JfDtb5CiNNCiONCiOX6azOFECP1fzcWQhzW/31HCOEjhDglhNgnhFADq4B+QoihBSkETWgw1rZ2hv+tbW3RhOp28g4LDUkXZmNrjyYkKNs4n44cz4Jp4xj+UW9uXLlI1zd6FkRSOiLCQrFI49hZWNsSEZa607ipmXm2cf3/uYmtYwWs7RwKrSMtYaHBWNtkX2Y2WZSZkZERpmZmABzYu4cWbdphZGRUKB2aXHRkVXdGRsaUL2+aKa3HD//lwd3btOv8WqE0pSVcE4qldWrdWdnYEaFJU3fmWZ+MsGvjSt7/dHSR6YCyWVbhmpB0mqxsbAnXl0+YJgSrNHZvbWuHRhOCe+fuBAc9Zdyn7zJ34jAGDhkFgFOlKty66sfz58+5dvEsEZrQQmkDiAnXYGaZ+uFnamVDbIQm3T2X/9jJD1M/pXaLDlg7VUatNsJEX2a3j/+Jc+NWqNWFs/Oyrik3tNpk4uITSyy/FCLD0rc/C5vc+07X9q+iCX7GvBH98Z0+kp6fjChSTdHhGszTOFRm1jbEhKfWX3kzXZ8QFRbCv9cuUKtZa4RbZyJDAtk4aRC75k+gY7/Pi1RTUaIqwE9JU1YdsgbApbQXpJSxUsosz4SRUj4CrIUQGadgBwLfA98B/QGEEFboRto6Aq8DA7O5ZgnMB16VUrYHagshuuSguTawVUrpDtgBTYFFwA9Sym/z/OQ5kdMpS9kdwaS/vG65Nx5zFrP6v7tp2MSVff/bVSSS8qQhC07//Rutu/Qoeg0ZyEnS8wwFeub4Yf7e+z8+HzW5GIQUOJCNqxYzeHjxHl3zPA91d+rgXmqLxjhWqlKsWspkWeVkR/qyO35wH45OlViy8WemeK1my5pFAAwZM42ft69n4dRR/8feeYdXVWx9+E1CSwjpgCAIgrBEQekQQAVs97NdO+C9IvYCXLBRBaUovTcRkKJgw6541YuCBZUO0hYCIr2kACH0hO+P2SecJCeFkGSfxHl5eJJdZu/fmZnss/aaNWsoG1ouU7/LH32Zr3n1P+6n3asz2bV+Bfu2rE/b/9fqX9Cfv6FFh/yJZStSmvyVXPz9LV/8NZExFek76V2eHjCOj6aPKWBNmXcdO3KIT8e+zPUduxIcGsbGJQspF12BR4bP4t6ew/ju7UkFq+lCKAIWmb/GkJ3FxIadD6GYmDIARKQsxtP2hKomichJEWmI+cybVPU4cBz4p4g09bGvIfCHqh51LrkIaJDN/Y+o6lrn913ABftuo6LLcyghLm07If4gUU5cTGSGY/FxB4iKLk+JEiV9lvlr2x/UqWfC6Oo3bsbi/311ofIIj4whyett/3BiHGGRuYvb2bJ+FXc/5supeWFExaSvl0SvOouKKU+id93EHUgb8lq1dAnz586g/7CJlA0tl+860rVdjI+28zH0BhB/8AC7dm5nzKtmWCcxIY6+3R7j1XHTL0hfRFRMujfywwlxhOfgrfx9+RLi9u3h92U/kxh/gBIlSxEZXZ469ZtekBZ/rKuI6BgOeXkMExMOEhHl+duLSfOWgeljkVEx/LF+DfUaNQegWo3aJMbHkZqSQtXqNekzdDIACxd8RPLRpPPWk5GQiGiOeXmfjh1OIDg8CoATyUkk7t5Opdr1KFGqNFXqNmb/1g1cdNmV7Fq/gtVfvcvN/xlMqeD8XR/WHzX5K2FRMRw55PXsTMj52bl90++I87d2cfXLOJJg+lfgBXrzPYRGRJPs5RE7eiiesk77AZw8nszHo/rS8p5OVKvbCIA9f6ynuvN7+UtqknwontTUlEL1cuaWohDU768esk1Auqe8iJQWkbq+Tnbiw7aqaqrX7jsxxtePIrIaiMF4yVLI/Ll97TtLehu5FMbg835v8A5SybhS8QW3foMmsSxZvBCArZs3EhVdnmBnKKlipcocO5bM/r17SDlzhuW//Ej9JrFZlomIimbn9m0A/LFpA5WqXHKh8pD6TVjzyyLAzJYMj4zJdpjSw+GEOEqXCaZEyfyJh/KmfuNYfvnh3OeP9KqzChdV5nhyMgf27SElxdTZ1Y1jST6axOypY+n72jjK5VNcRoPGsSz5IYu2u6gyx5KT2e+lo37jWJ/XiS5fgalzP2P45DkMnzyHyKiYCzbGAK5o0JSVzoSKHVuV8KiYLIcpPTzRYzB9Rr9Jr5HTaXXjHdza7uELNsbAP+uqXsPmLPvpOwD+3LKJyKhzmspXrMzxY8kc3G80rfrtJ+o1bEbFylXZqusAiNu/lzLBwQQGBfHG6EHs+PMPUlNS+HnhVzRo2ipPmrypckVD/lz5k7nXji2EhEdRqoz520tNOcMPs0dz+sRxAA5uVyIqVuHU8WR++3A6N3UeQJmyF/7SURQ0+StydRPWOs/OXducv78cnp0xlaqw448NACQc2EfpMsH5ZowBVKvbkD+W/wjA/u1/EBoRTSkvTT+88wYNb76b6ledm0gTUaEye7cpAEfi9lOydLBfGmNQNGLI/NVD9i0wQkRuV9XPnXisYUASsM77RGdocTxmeNGbB4AHVfVz57zqwPfAALMpoRgj6nOM8eZrXy0RKaeqScB1wGCgCVDJuUdOT9ZULqCOL697NTVr16Fn504EBgTyRPdeLPzqM8qGhtL8mrY89WxvRg/qbYS0uYmLq1aDqtUylQF4+rm+TBo5iKCgEpQLC6NLj1fyKiuNSy+vR5WawrjeTxMQEMA9jz/H0u8WUCYklKuaX8usEf04FHeAg7t3MLFfV2JvvING197IkcR4Qr3ic/KTy+teTY3adejVpRMBAYE80a0X3/33M0LKmjp78tnejHLqrKVTZ998/iFHDh9i5IBzQ5Xdeg+kfMVKWd0mVzpq1q5Dzy5OO3TrxcL/fkbZslm33RbdwMwpYziwbw8lSpRgyeKF9Bo4Mt+MRG9q1rmKSy67nGE9HicgIJAOT73AkoVfEhxSlgaxrZk6tA+JcQfYv3sHo/o8wzU3/5Om192c7zrAP+uq9hVXUf2yyxnw3KMEBATyUOcX+eHbLwgOKUuTlm14uEtPJg19CYDm195IpSrViIwuzxtjBjH4xSdJSU3h4S7mb6/1zXcwdZQJf23R+maqVq95wfoq1ryCmEsu47NhzxEQEECLDp3ZvORbSgWHUL1BSxrc+gBfju5JYGAQUVVrcMnVzdEfv+Lk0SMsnHbuUdn64RcIjapwwXr8VVNONKhTlaHP3U21ylGcPpPCXTc0oP3z00g8cqxA73vp5fWoUkMY38c8O+/2PDvLhnJVs2uZPbIfic6zc1L/rjS/8Q5ib7yDdycNZWK/LqSmpHDvky/kq6bKta6kQrVavDu4OwEBgbR9sDPrf/yG0iFlqVa3ERuXLOTQ/j2sW/xfACS2DfXa3Mo3M0bx/pAXSE1J4fqH/pOvmvIT//ePQUBuYkfcQEQqAW9gjJ9TGCNtgKqmeqW9AIgA3lTVMV5lozExaJeq6hmv/QsxsWLVAE/PGaOq74nIAz723Q08jzGsflLV3iJSDfgS2Av8AFyvqq1FJE5VY5z7zAcmAnGO7lGqOtLX59y4N9nvGuDPhOScTypkqkf651CGP/6R70/yGWrpKhXLlXZbQiaOnsjo1HafRTvicz7JAkD/Z0e7LSET89/qn/NJhczOpII1LvPKU7HVC/XxuWZn0nl/115dtVyhavRbg+zvgjXIcoc1yHKPNchyhzXIijbWIMsd1iAzrN159Ly/a6+qGlqoGv11yNJisVgsFoslX7CLi1ssFovFYrG4TBGwx6xBZrFYLBaLpZhTBCwya5BZLBaLxWIp1hSFPGTWILNYLBaLxVKssTFkFovFYrFYLC5TUPaYiIwBmmOSxndT1WVex27A5EhNARao6qDsrmUNMrfxu6QXcKkfppjww2oy+OFbV+XwzItuu02AH76ehofk/0oRF8rtcpHbEnzij+mRrvDDFBP3PjjQbQmZWLVguNsS/IMCeASJyHVALVWNFZE6wJuA91Ii4zHrY+8GFovIh6q6Iavr+evSSRaLxWKxWCz5QkAe/uWC64FPAFR1IxApImEAIlIDSFDVnc6yjguc87PEGmQWi8VisVgs589FwEGv7YPOPl/HDnBu2UWf2CFLi8VisVgsxZpCiprI7i45KrAGmcVisVgslmJNAdljezjnEQOojFnn2texi519WWKHLC0Wi8VisRRvAvLwP2e+Ae4FEJGGwB5VTQJQ1e1AmIhUF5ESwG3O+VliPWQWi8VisViKNQWRGFZVl4jIChFZAqQCnUWkE3BYVT8GngbecU5/T1U3Z3c9a5D5OTMmjWTzht8hIIDHurxIrcuvTDu2ZsVvvD19IoGBgTRs1op2HR8H4K8/t/DaS89xx70PcOtd7dNdb9XSJQzo2YVPvl9ZrDS9OWkkuuF3AgICeDQbTY2ateJ+L01DHE23OJqGv9KDI4cSATiadJjaderxzAv98qzLw4yJXnXWNYO+5V511tyrzrZ51dnd7bO6dK5YvfxX5kwz92jcvBXtH3oi3fHko0mMHNSH5OSjBAeH8EK/1ygXFu6z3IkTxxk75GUOJcZz+tQp2nV8nKYtruXMmdOMea0/e3fvJDikLL0HjiC0XFiu9c1+YwKBQUE0bt6KDj70jRjYm2PJRykTHMKL/YdQLiycUydPMnHkIHZs38bYafMAWLtqGUP79+CSS2sAUL1GLZ7q3svVOgNY9O0CPnxnFkFBJfjXI0/TJPaaXOmYPnEkmzeshYAAHu/aI13fWb3813N9u3kr2nV8IssyZ86cZtyQc+3Tc4Bpn3dnT2Xlbz9z9iw0ib0m7e8jt+TX82DT+jXMfn0cQSVKULJkSbr3GUx4ROR5afHFJzPH89fmDQQEBHDnI//hksvqpB07feokH0wdyb6df/Lc8OkAnDx+jHkTXuXY0SRSzpzmpvs6cXmDZhes43y4omYlPhjzBBPmfs/r7/2Q79fPrz7lYeXSJQzo0ZlPF60C4MfvvubT998iICCQqxo15cHHuuT7ZzhfCiqGTFUzPlzWeB37gfRpMLLlbztkKSI1ReRzEVkmIitFZIKIBGdxbnURWe78/m5W5+U361avYO+uHQybNJsuL/Zn+oT0+WSmTRhOjwEjGDJhJquX/8LO7ds4cfw408YP56oGTTJd79Spk8yfN5PI6Jhip2mPo6mzD03TJwynpw9N031o6vHKcAaPncbgsdOoWfsKbrj1rjzr8ta3d9cOhk2eTZce/Zk+3kedDRzBkIkzWb0sQ501zFxneeGN8cPpPWgkwyfNYtWyX9mxfWu645/Nn0e9+o0ZPnEmsde05cN5s7Ist2zJD9SSKxg6fgY9XxnGjEmjAPj6848Jj4hk9NS3uabNTaxfm3sDe+q44fQZNIoRk2axatkvmfR9+sFc6jVozPBJs2hxbVvmz50JwJtTxlCjlmS6Xt36jRg6fgZDx8/IkzGW1Wf35nzq7MjhQ7wzayrDJs6k/9Bx/PbTolxpWLd6OXt37WD45Dl07fEy08YPS3d82oTh9Bw4kqETZ7HauVdWZb754mPCIiIZ+frbtHLaZ//ePfy1bQvDJ89h6MSZfPf158THHch1HeXn8+CzD+bSrfdABo95A7nyKr754qNc68iKLetXEbd3F92GvE67Z3ry8Yxx6Y5/PmcyF1e/LN2+pd9/RfnKVek8cDwPvTCIT94cf8E6zoeQMqUY3fM+vl+arTMlz+RnnwI4dfIkH859M+0ZfvLEceZMHcfA0VMZPnk2a1b8lulvxw0KZsQyf/lbGmQiEgh8CIxV1Saq2hDYDryRU1lVba+qxwtYIgBrVy6lWas2AFStVoOjSUkcSz4KwL49uyhXLpzyFS5K8/ysWbmUkqVK0m/oeKJiyme63vy33+SWO++nRIm8J8QsCpqSM2gKLRdOjJemtY6ml7LQBLB7x3aSk5OoXadunnVlpS/bOmvu1FnJkvQbNp6oaN/6zod9e3YRGnbuHo2bt2TNiqXpzlmz4jeaX2M0Nm15LatX/JZluWva3sw9D3QC4OCB/cSUrwjA0iWLaX3jLQD84457aNayda71lQsLo3zFi9K8Spn1LSX2mrZGX4vrWL3iNwA6PtE1bX9+kt91tmbFb1zdqBkhIWWJii5Plxdz53Vds3IpzVq1BnLTd1qyduXSLMssW7KY624w7XPz7aZ9KlaqTM8BIwBITjpCQGAgISGhua6n/Hwe9HhlOBdVrsLZs2eJP3ggrV9dCH+sXUHdpsYTWbFKdY4fTeLEseS047f860nqNbs2XZnQsHCOJR0B4PjRJMqGhV+wjvPh5Okz3Nl1CnsPHi6Q6+dnnwL4YO4MbrmrHSWdZ3jpMsGMm/kBISFlCQgIICwsnKQjBfNZzosiYJH9LQ0y4CZgs6ou9No3GmgmIgtE5DUR+VpENjqBemmIyHYRCRWRWb7OE5HOIvKziPwoIs9fiMjEhDjCws+57MMiIkhMiAfgUEI8YV7u/PCIKBLjDxIUVILSpTNnat+98y+2b91My9Y3Xogkv9R0KAdN4eehycMXH76TaWg1ryQmxKWrl1zVWYns9Z3v/cO96ic8MorE+LgM55yrJ4+GnMq9+MxDjBzUh8e6vgDAgX17WfHbz/Tu9hjDB/TM9UM4MT59/URERJEQfzD9OQlx5/R56QgJ8b2qxM7t2xjYqxs9Ondi1bJfcqUj0/3ysc7279vDyZMnGNS7Gz27PMIax6DMicz9NzKt72TsV0ZDXJZlDuzby8rffqZvt8cYkaF9pk0YTpeH76Xdg48THBKSK21pGvLpeQCwcunPdO54F4cTE7jOMe4vhKRDCYSGRaRtlw2P4Mih+LTtMsGZP2uDVjeQGLefVzu3Z2K/Ltz+UOcL1nE+pKSkcuLk6QK7fn72qaye4Z6/y+3b/mD/vj3IFfUK7PPklgJKDJuv/F0NssuBVd47VPUssA4oCZRW1ZuBcUDHbK6T7jwRuRQz46IVcC1wj4hckm+qs129JPulTd6cNIqHn3ku36Tk7rbuaMpulZezuViE6fTp02xct4p6PoZY84XsJBTCEjU5LoOTxfGM5UZMnk2/18YyevBLnD17lrOc5eKq1RgybjrVLr2MD+a+mTd9ObVRDvorV7mEDp2epN+QsTzbZxDjhw3g9OkL+4K70Do7e/YsSYcP02fQKLr3HsDYoa/kaTmi7MpkdcxbQ+Wq1XjVaZ/5Xu3zeNceTJ7zMR+/N5v9e3eft65zN8vzQQAaNm3JpDkfc/El1flw3sy868hSQs4ali/+msiYivSd9C5PDxjHR9PH5L8OP+JC+tSMSSN55Bnffoc9u/5i9KA+PP/SkAsaAckvAgLO/39h83c1yM4CQT72B2AWAf3R2d4FZOevznheU6AW8L3zvxxQPa8io2LKcyjh3Ft5QvxBopxx+sgMx+LjDmQ5/BZ/8AC7dm5nzKt96fFMRxIT4ujb7bFiqynRS1NUTHkSvfVmo8nD+jUrqHX5hQ9VpumLzqbOon3UWT4MUwIs+OR9end7jE/fn5uhDg5mqgNTT/HnNMSUJyq6gs9yW3QDBw/sA6BGLSEl5QyHDyUSERlF3fqNAGjQpAU7/sw+bmTBJ+/T6z+P8skHb6fdG0zfiIqukKO+rIgpX5Frr7+ZgIAAKl1clYioaOIP5i4uqqDqLDIymjp1ryKoRAkqXVyV4JAQDjuTR7IjKrp8urpJiD+YFqsTFV0hfb+KO0hUdPksy0REebVP0xbs3L6Vgwf28cem9QCElgujTt36adu5Ib+eBwC//vgdYNY9jb32ejauW51rHVkRFhXDkUMJaduHE+IIi8w+XnX7pt+R+k0BuLj6ZRxJiCM1JeWCtfgL+dWnSpYsye4d2xk9uC8vPt2RhIQ4+nR7FIC4A/t57aXn6NZ7oM8YTzcoAiOWf1uDbBPQ2HuHiAQAVwKngDNeh7Jrl4znnQK+VNXWzv96ziyLPNGgcSxLfjCjqls3byQqujzBjiu44kWVOZaczP59e0hJOcPyX36kfmPfkzmiy1dg6tzPGD55DsMnzyEyKoZXx00vNprqN47lFy9NkV6aKlxUmePJyRzw0nR1Fpo8bNm0nuo1a+dJiy8aNIllyeIs6qxSZY4dS2b/3j2knHHqrEmuJ+Vkyy133s+QcdPpNXBEunssXfIDDTLco0HjWH5e9C0ASxYvpGHTlpm0ecqtW7OST957CzDDdsePHycsPIJGzVqy8rclzufcwMWXVM9R39DxM+g9cCTHk4+yf+9uUs6cYdkvP9CwaXp9DZvE8tP3Rt/PixfSqGmLLK/7/Tdf8tE7s42++DgOJSYQXb5ClucXRp01aBLL2pXLSE1N5cjhQ5w4foyw8AhfEtJRv0lzliz+H3Cu74Rk0XeW/fID9ZvEZlmmYdOWrFxq2meLbqBy1eocOZTI62NeI+XMGVJSUtiyeSOVq1TLVV156iA/ngcA786ayrYtCsDmjeu4uGrudWSFXN2Etb8sAmDXNiU8KsbnMKU3MZWqsOMPs/5zwoF9lC4TTGCQr/f3okl+9akKF1Vm6rzPGTFlDiOmzCEqKobXxs0AYMKIATz9bB9q1q7jW4QbFAGLLCAvbvOijhPUvwroraoLnH3PYQyyIGC+qn4hIrdhhiBfcfY1FpHtQF1goo/z+gP/A+oDx4GxQK/sJgFs3JOcbQPMeWM869euJDAgkCe69WLblk2ULRtK82vasn7NCua8YWYAxV57PXe268gW3cDMKWM4sG8PJUqUICqmAr0GjqScV2Dq4+1vZdq7X553vbmpKadeOueN8WxYu5IAR9OfWzYRko2mrT409XQ0TRs/jDp169Oq7c051kVu3dpzpnrVWfdebPtjE2VDvfRN9dLX3qmzyRnqbFD6OsuKIB+i1q1ZwazXzQyzFtfdwN3tO5IYH8fcma/T5YWXOH7sGKNe7UvS4cOUDS3H8y8NpmxoOZ/lTp48wfhhA4g7sJ9Tp07Q4aEnadryOpMO47X+JCTEERwcQvfeA4mMinbqKfuKWrd6BTOnmvu0vPZ67u7wkNH35hS6vNjP6BvchyNHjL4XXnqVsqHlGNL/BeIO7GfHn1upKXX4x+330LTldYwY2Jvko0mcOX2aDp2e9JliIqdnX37WGcBXn83n2y8/AaBdx8d8TnpI9SFp9tRxaX3nye692PaHEhIaSqzTd2Y79RZ77Q3c5dwrY5lLLxNOnjjO2CH9SYyPo4zTPhFR0cyfO4Nff1oEZ8/SuPk1tO/05HnVVX49D/bv3c20CcMJCgqiVKnSdO8zmIjIqCzvuy0hOctj3nzx1uts27iGgIAA7n78OXZv20yZsqFc1exaZo/sR2LcAfbv/JMqNYXmN95B3cYteXfSUJIOJ5CaksL/dXiMWvUa5epe9z44MFfnZUeDOlUZ+tzdVKscxekzKew5cJj2z08j8cixPF1v1YLhmfblV5/y5vF2tzDtvQXs3vkX3R9rT22vtBh33P/vTP398kohhWry/BV/8ryNnWrRpQtV49/SIAMQkYuA14EqGE/hcuBZYBJ5NMhUtZOIPAM8ghn6/ERVh2SnIyeDzGLw10pyI84gJ3wZZG6Tk0HmBv747PNlkPkD/lhXuTXICpP8MMjyG18GmT9Q2AbZjoTzN8guibIG2d8Ka5DlDn+tJD+0M6xBlkv88dlnDbLcYw2y3GENMsPOPBhkVQvZILOZ+i0Wi8VisRRr/AnjgcEAACAASURBVPCdMBPWILNYLBaLxVLM8X+L7O86y9JisVgsFovFb7AeMovFYrFYLMUaO2RpsVgsFovF4jJFwB6zBpnr+GEv8cdZeil+OMvLb/G/5svVklWFjh/Wkz9WE0CJIP+LbtmZlLe8XAWJP85obHBLD7cl+OT4qomFej8//FrLhDXILBaLxWKxFGvcWCz8fLEGmcVisVgsluKN/9tj1iCzWCwWi8VSvCkC9pg1yCwWi8VisRRvbAyZxWKxWCwWi8vYGDKLxWKxWCwWt/F/e8waZBaLxWKxWIo3RcAeswaZPzJj4kg2b/gdAgJ4rOuL1Lr8yrRja5b/xtvTJxIYGEjD5q1o1/HxLMsMf7kHhw8lAnA06TC1r6hHs5at+fjdOWnX2/bHJibN+YiomPI56lq9/FfmTDP3bty8Fe0feiLd8eSjSYwc1Ifk5KMEB4fwQr/XKBcWnmW5mVPGsn7tSlJSUrjv34/Q4trr0661cukSXn6xM58vXlUk6yqv2v7atoXXXnqOO+59gFvvbg/AwQP7mDD0FVJSzhAUVIJn+w4mMjomVxo8rF7+K3PemEhgUDZtN9Cr7fp7tZ2Pcou+WcCH78wiKKgE/3r0aZrEXsPQ/i+eq8MjR5Ar69HlxX4Fqu/UyZNMGjmYHdu3MmbavLTz/9q2hcF9nuWf9/2L2+5p71pdffPFx3z/zZdpZbfoBj74egl/bt3M62OGEhAQQGi5MF7o/xplygT71DN94kg2b1gLAQE83rVHun60evmvaf2oUfNWtOv4RJZlDh7Yx/ihL6f1o+f6vkpkdAxvTZ/IutXLOZuaSvNr2nJ3h07nVV8eHbPfmEBgUBCNm7eig486GzGwN8eSj1ImOIQX+w9Ja7+JIwexY/s2xjrtt3bVMob278Ell9YAoHqNWjzVvdd5a/KwaN7r7Nu6CQKg9QNPc1ENSTu2c+NqfvpgJgGBgURVqsKNDz/L6VMn+XraCE4kJ5Fy5jTN//lvqtdrnOf751f7eVi5dAkDenTm00Xm2fjjd1/z6ftvERAQyFWNmvLgY13yrDU7rqhZiQ/GPMGEud/z+ns/FMg9ChIbQ5bPiEgtYCxQHggClgAvqOrJ87xOnKrGiMhYYBwQDzRX1W+c4/8AXsakaSwNzFDVyXnQextwr6p2ym2ZdatXsHfXDoZNns3Ov7YxcdgAhk2enXZ82oThvDxiEtExFejb7TFaXHs9hw8l+izTY8C5JIUThr3CjbfeRe06dWkcew0Ae3ftYOaUsbk2MN4YP5wBIycTHVOB3v95jBbXXc8l1WumHf9s/jzq1W/M3R0e4r+ffciH82bR6aluPssdSkjgrz+3MHLKHI4cPkS3xzqkGWSnTp7kg7lvEpWD0eHPdZUXbeUrVmLa+OFc1bBJumvNmzGZm26/m1ZtbmLBx+/x6Qdv0+mp7rnS4eGNcU4blM+i7T6YR70GXm03dxadnu7ms1xEZDTvzJrKmOnzOHHsGPNmvk6T2GvoNXBE2vXGDX2Fm269q8D1zZwyhktrCTu2b00798Tx40wdN4yrGzU9rzoqiLq66ba7uOk2Uw+/r17OT999C8DUscN49JnnqH1FXd6cPIaFX33OrXfdn0nLutXL2btrB8Mnz2HnX9uYMOwVhk8+95IwbcJwXhkxOa0fxV57PUecPp6xzNwZk7j59nto1eYmvnT6UZubbuP3VcsYPmk2qampdO10L21uuu28Df6p44Yz0Pnsvf7zKC0z1NmnH8ylXoPG3NOhE//9bD7z587k4ae78+aUMdSoJezYvi3d9erWb0SfQSPPS4Mvdm1ay6H9u2nfbyzxe3bw7YzRtO83Nu34/2aN496ewykXVZ4vJg5m++/LOXxwL5EXVaHVfY9wNDGe+cN60GnojDzdPz/bD8yz8cO5b6a1z8kTx5kzdRzjZn5AcHAILz7Tketu+L90dZ8fhJQpxeie9/H90s35et3CpCjEkPlf+uUsEJEg4ENguKo2BTyvLP3zek1V7a6qfwINgZuc+1QHxgD3qWoL4DrgIRG58QLk55q1K5fSrFUbAKpWq8HRpCSOJR8FYN+eXZQrF075ChelvVGtWbk02zIAu3dsJ/loErXr1E13r3dnTaV9p/Rvslmxb88uQsPO3btx85asWbE03TlrVvxG82uMjqYtr2X1it+yLHfl1Q3pNcB8gZcNLcfJE8dJSUkB4P23Z3Drne0oUbJkkayrvGorWbIk/YaNJyo6vdH3ZPfexDrGalhEJElHDudah+d+oWHhlK+Yy7ZrkaHtMpRbs/w3rm7cjJCQskTFlM/kBdu1YzvJSUnUviJ9Hea3PoAHn+hK7DVt051bsmRJXh4+IVM9FqSW3JR7d9Y02j9kPKH9h45Lq5/wiEiSjhzyqWfNyqU0a9UayE0/asnalUuzLPNU9z5p/chzz5DQUE6fOuX8P0lAYACly5Q57zorFxbm9dlb+aizpWnt1LTFdWnt19FH++UnOzasombDFgBEV76EE8lJnDyenHb8gVcmUi7K9JPgsHCOHz1CcKj5CXDiWBLB5cLzfP/8bD+AD+bO4Ja72lGyhHk2li4TzLiZHxASUpaAgADCwsLP+/mQG06ePsOdXaew92D+X7uwCAg4//+FTVHykN0IbFLVxQCqelZEegCXiMhPwFFgInAYeA04DewEHgdSgXlAVWCZ54IisgjoAkwCwkRkM1ADmKCqu5z7HBWRm1T1sIiEOdcpC4QAXVV1qYj8ASwADgCfAXOABODca3suSUyIo2btOmnbYRERJCbEE1I2lEMJ8YRFRKYdC4+IYt+enRw5fCjLMgCff/hO2vCXh4S4gyQmxFOj1uW51hUe7nXvyCj27d6V4Zx4wh194RFRJMYfzLJcUFAQQcFmiObbLz+hUbNWBAUFsXvnX2zfupl/P/oMM18fUyTrKq/agkqUIKhE5j/JMk49paSk8NUn73O/M7yZay3xcWntcu5+2bRdpNN2WZQ7eeIEJ0+cYFCvbhw9msQDDz/J1Y2apZ332fx55zVMmFd9ACEhZUk6nP5LIqt6LEgtOZXbvHE9MRUqpnk2PP3txPHjfPf1F/Qa4HvJnUMJ8Vzm1Y/CIyLT+lFiQpyPfrSLI4cP+SxzcdVqgOlHCz55j3Ydn6B8hYto2foGHmt3C6mpKbR76Ik0bedTZ946IiKi2LtnZ4Y6i8tQZ3GmHkLKknQ4szG6c/s2BvbqxtGkw3To9CQNmsSelyYPyYcTqVi9Vtp2cFg4xw4nUjq4LEDaz6OH4vlr3Qpa3P0QwaFhrP/pG97s0YmTyUe589lBebo35G/7JSbEs33rZv71yDPMmnLu2RgSYj7D9m1/sH/fHuSKennWmxUpKamkpKTm+3Ut6SkyHjLgcmC19w5VPQ6cBBoA/1LVL4DxwD9VtS2wH7gP4/0qqaqxwFwgOsO1RwDvqeobWdzH88S/CJiuqm2A3kBPZ39J4CtVfRXoB7yiqtcDKRf8qbNb2y6r9R29dp8+fZqNv6+iXoP0w2Dfff051914S95l5bS2ZBbHM5b79afv+XbBJzzV3VTl9IkjebTz83kUdf56CqOu8qzNi5SUFMa+1o96DZqkM37yJiWPbeeUO3v2LElHDtNn8Ci69x7A2CGvpLXr6dOn2bh2daZh18LQVxBcaF15+OaLj7nh/+5It+/E8eMM6t2du9p3pGr1GrnTk81nz+qY937Tj17iqgZNubpRM/bt2cWvP37PG+98zutzP+O/n83nUGJCrrRkqeMC269ylUvo0OlJ+g0Zy7N9BjF+2ABOnz59QZq8xGXi2JFDfDr2Za7v2JXg0DA2LllIuegKPDJ8Fvf2HMZ3b0/Kn3tzYe03Y9JIHnnG97Nxz66/GD2oD8+/NIQSJbIfWfi7Yj1k+ctZTNyYL7aqaryIVARqAR+JCBhPVhxQCRNvhqr+JiLH83if/UA/EXkBE1uW7HXM46O/wnMvYBHwf9ncKxNR0eU5lBCXtp0QfzAtlioyw7H4uANERZenRImSWZZZv2YFtS7PPHS04pefeL7/kBz1LPjkfX78/hvCwyNJ9L5H3MFM8VRRMeVJTIinbGg5oy2mPFHRFbIst3LpEt5/awYDRkwyZQ4eYNeO7Ywa3Nf5HHH0+s+jDB3vO37D3+rqQrVlx4Rhr1C5SlXad3oy1xoWfPI+P373jfOG7aXl4MFM9/PZdjEVfJYrExxMnbpXEVSiBJUurkpwSAiHDyUSERnFutUrqFXnSnLDherLTwqqrjysW72cJ7v3TNtOOXOGwX26c90N/8hkqKW7V7S5V9p14w+medmioiuk72NxB9P6eFZlxg97mUpVLknrR39sWk/tOnUpXSaY0pgA+h1/biEiMuf4uwWfvM8P332d5sHxEH/wAFHRFXKss6yIKV+Ra6+/GYBKF1clIiqa+IMHuKjyxTlqykhoRDTJhxPTto8eiqdseFTa9snjyXw8qi8t7+lEtbqNANjzx3qqO7+Xv6QmyYfiSU1NITAwq6+FrMmv9itZsiS7d2xntOfZmBBHn26P8tq4GcQd2M9rLz3Hs30GU6PWuQkLlvTYGLL8ZROQ7ikhIqWBUOCUs+sUsFtVWzv/m6jqcMyMV29/a3af29d9qolIDNDduX4r4OkM5TwavO913vXboEksSxYvBGDr5o1ERZcn2HFJV6xUmWPHktm/dw8pZ86w/Jcfqd8kNtsyf2xaT/XLame6z769u4ipUDFHPbfceT9Dxk2n18AR6e69dMkPmYYRGjSO5edFJmh5yeKFNGzaMpNmT7nko0nMnDKW/kPHUy7MxGhEl6/AtHc+Z+SUOYycMoeo6JgsjTF/rKsL1ZYVi79dQIkSJenwcMYulz233Hk/Q8Y7bZfs1Qa//ECDphnarkksP3/vo+18lGvQJJa1K5eRmprKkcOHOHH8GGHhEYCpw0t91GFB6MtPCqquwBjcZYJDKOkVEzl/3izq1W+cFvCfFfWbNGfJ4v8B5/pRSBb9aNkvP1C/SWyWZRY5/egBr35U6eKqbNENpKamcubMaf7atoWKlarkus6Gjp9B74EjOZ58lP17d6fpaJihzho2ieUnp85+XryQRk1bZHnd77/5ko/eMRNgEuPjOJSYQHT5Clmenx3V6jbkj+U/ArB/+x+ERkRTKjgk7fgP77xBw5vvpvpV5zy6ERUqs3ebAnAkbj8lSwfnyRiD/Gu/ChdVZuq8zxkxZQ4jpswhKiqG18aZZ+OEEQN4+tk+6UIkLJmxHrL85VtghIjcrqqfi0ggMAyv4UdVTRQRROQKVd0gIl2BxYACHQBEpAXGu+VNKufqYgqwSEQ+U9U/RKQc8DYwAIgB1jrn3QWU8qFTMRMOvgbanO+HvLzu1dSsXYeenTsRGBDIE917sfCrzygbGkrza9ry1LO9GT2oNwCt2txk4kKqVstUxkNifByVKqd/wB45fIiy5xknAvDMc30YMdBc+5q2N3Nx1Wokxscxd+brdHnhJW6/pwOjXu1Lzy6PUDa0HM+/NDjLcv/97EOOHE5k2Cs90q7/bJ9BVKhYqVjUVV60bdENzJw8hgP79lCiRAmWLF5Ir0EjWfDJ+5w+dYq+3UzsWNXqNXjq2d7npSddG7Txars3X6fLi07bDc6h7ZxyAC2uu4EXnuoIwJPdehIYGOjU4UEqVW5w3vWVV31D+79I3IH97N7xF73/8xg3334PVS6pxoxJozmwbw9BQSX4efH/6DN4VJrhX9h1lRgfR3hkZLp7LPj4PSpcVDktuP2qhk3o4MP7WadufWrWrkOPzg8RGBDIk04/CgkNJfaatjz9bB9GDTL3bOXc82Knj3uXAVjwyXtOP3oM8PSjPtRvEkuvrg8DcOOtd1GxUuVc1VP6OuvL8IG9fdTZFLq82I/b73mAUYP70KPLw5QNLccLL70KwJD+Lzjtt51e/3mUf9x+D81atWbEwN78+vMizpw+zTPP9UlnzJ4PlWtdSYVqtXh3cHcCAgJp+2Bn1v/4DaVDylKtbiM2LlnIof17WLf4vwBIbBvqtbmVb2aM4v0hL5CaksL1D/0nT/eG/G0/X+ze+Rcb1q5i3ptT0vbdcf+/adaydZ41+6JBnaoMfe5uqlWO4vSZFO66oQHtn59G4pFj+XqfvzsBOcYC+REiUgl4AzMEeQpjpM0G3lfVxs45rYBRzvE9QEeMwfUBEAmsAe5W1SpeQf041xqlqiNFJBaTXiPV+T9WVT8QkSaYgP2dmAkEY4FBmBQZdZ0JAA2BmcBuYBsQml3ai417k/2uAYL8MGFLShHqp24TFOh/7WfJHal+Gjftj31q4bb9bkvIROvqefPkFSQNbumR80kucHzVxELtVEknUs/7S6RcmcLt+EXKICuOWIMsd1iDLPf445enJXdYgyz3WIMsd1iDzJB0Mg8GWenC7fhFacjSYrFYLBaL5bwpCkH91iCzWCwWi8VSrPHDgZ9MWIPMYrFYLBZLsaYI2GPWILNYLBaLxVLMKQIWmTXILBaLxWKxFGtsDJnFYrFYLBaLyxSFGDKb9sJisVgsFovFZYrS0kkWi8VisVgsxRJrkFksFovFYrG4jDXILBaLxWKxWFzGGmQWi8VisVgsLmMNMovFYrFYLBaXsQaZxWKxWCwWi8tYg8xisVgsBYaIZMp3KSJRbmixWPwZm4fMUuxxvhDuAy5W1ZEiUhdQVT3tsjREpK6qrnNbh6XoIyL1gY5AOF4LxajqIy7pKQGUBhYA//DSVBL4UVWvckOXo+0xVZ2eYd9zqjraLU0Wi83UX8QRkdJAJVXd7raWjIhICVU947YOYBpwAGgNjHR+9gU6uCcpjdGOt+AD4B1V3eG2IH9ERG4GngLCSG9stHVRUxjQBaigqt1FpA2wSlUPuSRpLjAe2OXS/TPyf8BzQFNgg9f+VGCRG4JE5EbgJuB+EantdagkcD/gukEmIlWA6qr6k4iUVtWTLumYCWTpsXHL0C/OWIOsCCMi7YGXnM26IjIeWK6qc1yUhfPFNBbzdny5iLwK/KCqX7skqaqqPiwi3wOo6kQRuc8lLelQ1ZucL/ZbgNdEJAL4HJinqkmFrUdEtvnYnQJsBfqo6spCluRhLNAd/zE2AGYB3wK3OtsVgHmYtnSDnao61aV7Z0JVPwc+F5F/q+rbbutx+BU4jTEW13vtTwWm+yxRiIjIs8C9QChwNTBMRPaq6jAX5Mx3ft6BeQYswoQ5tQFcMRKLO9YgK9p0BhoCHkOnB+aPxlWDDBgAtOXcH/Q44FPO6SxsSjmGzlkAEamDMRb9AlU9IiKbgKuA6kBj4BERGauq7xSynGnAIeAzTH3dApQHvsd4X1oVsh4P21w06LOinKpOEZH7AVT1PRF5ykU9K0RkBPAjkOaZVtUF7kkCIE5EPiLzUGqhezedl5xFIlIPqJdBU3Rh6/HBnara0vPyCDwLLAEK3SBT1S8BRKS7qt7odehdEfmisPX8HbAGWdEmRVVPiYjHrewvby2nVTXeo0tVD4hIqot6+gLfAbUcw+cs8JiLetIQkYHAncBmjCH9sqqeFpEymLf5wjbI/k9Vr/Xani4i36nqEBEpZCnpUBF5H/iJ9MbGZPckESgiNTln6P8DCHJRT2Xn511e+85iYrjcZAz+5938AogEdnvtOwv84I6cNDz9x/NML4P739PRInIb8AvGk9gYqOKupOKJ2w1tuTB+EpG3gCoi0hPjWv6fy5oA/nQMjRgRaYcxODbkUKbAUNUfgYYiUgE4qaqH3dLig93AtRnjjlT1hIg86YKeEyIyBviZcw/fUk7szVEX9Hg47PyPdFFDRroCU4HGIrIPWA084ZYYVX3Ye1tESgJuGqwe/NG7GamqLdwW4YN5IuJ5eZyCGR4c57KmjkA/YAjGm7gJeDjbEpY8YWdZFnFEpBXQAuMdW6qqv7gsCREJBB7gnK7fgPdVtVC9ZCLyJ1kHpZ5V1ZqFqccbEfkAoy0AHxpV9f5CF0VaoHpHoA5G21ZMrFRZ4LCbxqyItAYaYOJZlqvqEre0+CMi8ggwCIjB/N0FAV+oajuXdY3GeFT8xrspIsOB2aq6PseTCxkRqY6ZCHESWKmqO13W01tVh7ip4e+C9ZAVYUSkv9dmGeBGEWmL+RKd7+IMx7LAEYyLOwAoBfybwo9tq+vcvw/Ge7EIE5TaFqhVyFoyMtHl+2fFd5jYvzGqusVrf7xLegBwvHY1gMVACNBPRFao6kvZlyxQTTuBShgj4yzmeRoPJADdVfWbQpb0FFAT+EpV24jIHcClhazBF768m257Au4CnheRI5wzEs+qagUXNflKXfJPEXF7RmMFx0O+DDjl2amqx9yTVDyxBlnRpgLGY7AA84C7CTM0WBXzwHHrzXgx8Dsm1YRrqGoygIi0VNU+Xofmici3LsnyEKGqn4rIM1kcX1yoas5xN/BP4HURCcdMxpivqptc0uOhUYbYtqEi4lYdeXgfY8B6YrRuAlpihjE/BArbIDvhDHWXEpFAVf3MCQ53e8jr+5xPKVxU1e0Xsqzwt9QlYGYR35lh31nMC5IlH7EGWdGmNtBKVT1BxcOAT1T1dpe/rOJV9SEX75+RkyIyCjNbKRVogrvB1wARzs/yPo655j1w8qBNACY4+ZBeBdZivJxuUlJEglX1OICIlMX9NoxV1ee9tr8Wkb6q2t9rok1hskxEumAMwe8cD16ICzoy0tXr95KYl8jluBhA7xiqvkIFXMtr5+BXqUsAVLV2xn0i0skFKcUea5AVbSphpm6vdbZrApeKyCVAOddUwUwRmQCsIn3MiFvpOO7BDJlehxkGUNLPRCt0VHW28+tAMk+/dw3HCLvd+V8Z4/3xh+DnMcBaEdmMGXa+DJPmxU12iMjHpJ8AkSQidwN/FbYYVX1eREo5M6+/x6RxWFjYOnzoSpfzT0RCgBkuyfHQxev3kph0LuEuafHG71KXiEhjoCfn0oKUAi7CxJZa8hFrkBVtngXedAwwMLFbgwEBermmyvzx/o4JDPfgZszICeC4oyEFE+NT6ElXs2AhxtPjPbzr5vT7T4GPgBdUdQOAiLjtHUNV3xeRLzFe4bPAZj+IYfk3ZkmgOphn6YeYwPVTmDxuhYqIVAMGOHFIqRgv1K+YeE5/IhW4wk0BPoL5V4vI1xiPsJv4Y+qSCZg43GHA0xhtv7qop9hiDbIijKr+zwncbQe0B6KAQFV1Oz7qoKr+22UN3rwJJGKC+kthPGVtgMdd1OShRIbYKFdR1UYAIhLk5NV6ALPU1CXZlSsoRORlVR3gNSvV+5hrs1G9OAvsd36PBJa4OHt3BjAFs1yRp5/PwL2VAwAQkYOcm1EMxiCb4p4i8BG7WZlzxlCh47VEUme3NGTDMVX9XkROquoKjBfvv5hcbpZ8xBpkRRBn7cN7MV+Wl2HezMP9KFB1hYgMBpbiH273Kqr6oNf2u06uH39glog8T+bhXVc8ZCJyHca4vxMIxgztuJEPzcMnzk9fs1Ldnqn3PsbT2hrjEWsDvOKiniBV/dBr+z0RcS0vmgdV9RUn6Tbems4CcZxbAssNZmKe5+tJ3689aXHcDKA/5rz4/ykir2Fm8bvyglbcsQZZ0WQfsAV4HvhaVVNFZJXLmrzxTB33F7d7KRGprKp7IC1OqqRLWjLyEGbIsrnXvkIfsnRyRd2LiX16B3gZ07dcXYNQVdc4v3ZV1Xu9j4nIr6Svt8ImUlXvFpFFqtrVWZ7rdeAtl/ScctZoXYT5Im+LH6ze4QyhjsXEuAYB64BuqrrRLU2O17U16fPa7XBRzwPOT39IU5KRBzAxY10wKy5cjUnNYclnrEFWNHkI6IAZivtcRN51WU86nIW8a2D+cFOAVS4nN+wLLHSWbwrEDJn4w3AlmCFmt9aH9OYW4BjGI/WZs9yV2x4oROQeTDzk1SJygHPDXoGAWwudeyjtxG2dEZHawE5M/KZbPIKZJPISpo8vc/a5zXjgWWe4CxFpDkzCGIyukEVeu5Wq2tclPcvIxuOrqk0LUU4aInK9qi7EibkVkUnA1aq63A09xR2bqb8IIyKRwH2YN5hmmGGdmZ5gbBd1vYiJa/sZs4h3U2CaqrodNxKJSf54KMeTCwknue8eMg/vFnobikhDTF+6H9iOWej8Kn+oLxF5QVVHZthXT1V/d1HT9Zi4sYOYl6MwYLKqvuyWpoyIyG2q6mqsj5i1UNtm2LdQVa93UdMPGWM3RWSxql7nkp5q2R1X1UKftSsiT2M8YTc5i7LjTCB7Gxirqh8VtqbijjXIigkicjHGa9ZeVRu7rOVnzPqMKc52CWCxqrYsZB1VMMNvt6rqEWdfI2AkcJefGBq+kmaedTMfkogEYALCH8Ckv/jR7eB5x5h+gPRT7x9S1aruqfJ/fBlDLmj4CLNqxyLODaU2ypgOo5A1/QK0zZDX7mu3vdU+MvUDuJKp3/HatfYk2PbaXw5YoKrXFLam4o4dsiwmqOpujKExMqdzC4EAzJCJh1TcCcCeDIzzGGMAqrpCRCZipnI/mGXJQkJV22TcJyL93NDiwUk0vAhY5KS8+IebehzexyT2bQ+8gTEYu2RbooDxw6WTfOF6bjugE9ANM5R6FuMNdntxal957V50VxLgX5n6T2Y0xgBUNUnMesWWfMYaZJaC4D3MTEvPWpaxmC/RwiZKVedn3KmqH4pIV18FChsRuQUT9xPl7CqFeRgPKmQd2XoTC1NLFgSq6ssicp2qjnKM6vcwedPcwt+WTvKFPwyBnAHWqOogABHpiMu6/DSvHfhXpv5AEamkqnu9d4rIZbi/SkaxxBpklnxHVceJyKeYGUxngaEuzWAKzuZYVDbHCpNXMHGAszGGzz24k7TW372JpUTkaswU/BuBbRivhpv4xdJJXnm+MhKAiWtzm3dJv2JAGWAeZs1UVxCR+4EOqnqXs/2NiLzh6wWukPGnTP0DMZOhxgOrMUZYM+AZ4F8u6Cn2WIPMx54pMAAAD75JREFUku84uaz+papPONsfichYF3JrLReRXsAwr/U+S2K8T64vKeOQrKp/ilkMOh54Q8zC5+8Usg5/9yZ2xuSO6olZLDsa9xfN3ukPSyf5aZ4vbyJUNa2tVPUNEengpiDMKifeQ/F3YLydbhtk9THfy1diVnxIcPYXukGmqv8VkU2YPIQ3Y4z+TUAbl2fNF1usQWYpCIaQ3qPyNGY5nkIN6sc8dMcA20REMf1dMMNc3QtZS1bsFpEHgVUi8jbwJ+fyuBUm/u5N/B1o7MyqbOvMcHQ7ue+/yLx00ueYJcwKfekkP+aImEXPf8bEa10PHHZXEkGY5dQ8BOJivJ2IlMGEdVyGydMWgDHKfgD+45YuVd0uIltVtbf3fhF5TlVHu6WruGINMktBEKSqW722D7ohwokJeVJEQjFJKc8C21T1qBt6vBGR0ar6HCanXBRwKSZvVAzmbb2wycqbOBj/8CbOxqQHWeZsX4uZjfaQa4rgXSdZbVpaCRH5VVXdTFbrj/wLeAHTl1IwQf1uJxadAKwTkY0Y46w20N9FPcOA1aqarl5ExPNS+VRhC3JCA24C7nfy7HkoiUmNYw2yfMYaZJaC4EMni/pvmIddC0zuGldwDLA1OZ5YuNQHcFKDHBSR1i6nJ8jOm/isi7o8VPP+snIC/H2lDClwfCSr9RCIibWxpOc0sExV+0FaUH+Km4JU9S1nuLmOo2WTy0H9LVW1W8adqjpGRNxKwvorpu3+D7Okk4dUYLorioo51iCz5DuqOtzJPdQAE5g6wo3Ehn5OxuERt9MTnAEOYFZXuBTjTUwF7lPV024Kc0gVkVsxqS8CMbmszmRfpGBw1ov8UET6q+pANzR4Iz4WXvfG7Rxy2KD+3JCdgZqazbGCJEpVF4lIO/xjtm6xxxpklnzHR3LD20XEleSGjp4AzzCcH5FRj9v6Rjg/T3rWj3RykIWJyMuqOsA9aYAZmnwVGI4xxJbhfi6r6zEz0dzG18LrHi4qNBVZY4P6c2a/4yVf5L1TRP4B7HZHEt0x9eSrf53FxaWviivWILMUBH6R3FBEWmJc6+WcJJ4PqepmNzV50VhEljq/BwDibAdgMvUX9tp1LVS1ifcOVT0lIs9jAovdNsjArEnq8SSexXjK3GSvsyrFMsyMOABUtUdhilDVxZC2IsbNpF/NoDcmX5ub2KD+nOmO8boqsAqjrwlm+bKb3RCkqs86PzMlr7YUDNYgsxQE/pLccCgm0ek2EWmK8a7c6bImD/XcFpABn0MmqprqeMrc5kPOeRFLYRaGXoXJ2O8WX7l4b1+8j8lh1xozy7MNJs+d2/gK6nc7r51fBfU7z6iGmCD6yzF9fQrwrVve/Qz57aIxBmwgZn3iXaqa7fqblvPHGmSWgsBfkhumqOo2595LRSSikO+fJX4YUxcnIq1U9SfvnU7c1j6XNKWR0XsnIhdRyKsZ+OAdzPqaDTCGxnJMvJRbRKrq3SKySFW7Ov39deAtFzWhqocBT0B/OCb58Xxc8vw4mjIF9WPiJ13DMby+dv67jie/nYiMA+aq6lJnuwXQzk1txRVrkFkKgsqYN6uM3qjCNsgyBsO6FRxbFPAMmWwkfVbuS3DxizMrVHWfk7nfTWYAiZh1P0thvHVtgMdd0lNaRKoBZ5w0BTsxM2VdRUSCMTFaHTDDle/hvjGNqh4VkdOY9VHvw6z+cKO7qvySxt4zQFV1iYi86qag4oo1yCz5hogswxhivmIx3HC71xWR953fAzJs+8PsM79BVbeISAPMF5JnyGQiLg6ZeOPVt8C0ZUXgf+4pAqCKqnoPvb0rIm4mq+2HiTsahBlODcMsieUKInIHxti5AWO0zgZqqOpjbmlydNXGeDbbAycxw3Et/dBr7S/sEpEPMTOcUzF97JC7koon1iCz5Cf3ui0gA/dl2M5uNtrfHlVNxY+GTDLg3bfOAkdU1e0vhVIiUllV90DaAu0l3RKjqt6pJWq6pcOLj4DNQHtV/Q5ARPq5KUhEVmEM1XnAPaq6XkRWWWMsWx7AxLZdgfGcv4P/xU8WC6xBZsk3/O2h5pl9Zim6iMhMsvCuuplKxaEP8D9nIfEAjE63hisRkf5AVzLUl6q6sRQXmOHu9sBwEQnDxNeVcUmLh6XAbZjEzBtFZDvup5zxdwKASCBAVUeKSF3cz5tYLLEGmaXYkmGWkDee1BJufVFZco8nL9QdmODrRZiZXm0ww02Fjoi86bX5G2bpq7OYeLLHMEM7bnAPUF1Vk126fzocz+FoYLTXMGGQM/w8U1ULfThVVZ900oP8w9EzztF0C/Bfx0tsSc80TNLo1sBI52dfTEygJR+xBpml2OKZJWQpuqjqlwAi0l1VvQOu3xWRL7IoVtDUAyIwQ7sLgKP4h8dgEy6tXpATTv6/V4BXRKQJLn6Zq+oZzPqjX4hICGby0TOYNBM2lUNmqqrqw56lylR1oohkDAex5APWILMUW0RkEjDc11CqiLyVISDb4t9Ei8htwC+YwOLGQBU3hKhqExGpiRmOewWTAHk+8LmqJrmhySEQUBFZiTHMPJ5gv5q8oqrLOLdIvKs461fOA+aJSJTbevyUUk4KlbMAIlIHk4vMks9Yg8xSnLkVuFZEZgFjMgxHuPJlbjk/RCTCCd7viJlF+BrG8NiEGR50BVXdilnK6VURuRJjnI0QkZWqertLsuyklQtAVRPc1uCn9MEsK1XLSYsD8KiLeootbi89YrEUJH9icmldDPzirLHpwQbyFg0+AlDVdaraDliiqnVV9V7MyguuISIBItIWs97fA8A3mGEvt1iDia17FugGtARWuKgHABGp6qyUgYj8W0TGiog/5Eez33+5I0xVG2Jm7saqah1VdStOslhjPWSW4sxZZ0jiORFpDExz8kS97LIuS+7JGJtVO5tjhYJjXHTA5Gz7DfgAeFpVT7uhx4vZmHVHB3IuUe1MMqd/KWzeBrqJSHPgEYynczwuJRwWkScxiZDDRKQssBV4VVU/ckNPEaCLiCxR1QNuCynuWIPMUpxJ+8JW1eXOF8ILmCVuyrqmynI+ZPRkBmRzrLD4FfMl/htmlKEdcL/H6eNiKo5yqjrKa/tXEXE7eS7AGVVd7SynNlZVfxaRIDeEiEhnTE6tG1R1t7PvcmCciFRR1fFu6PJzwoCdIrIVOOXZqapN3ZNUPLEGmaU487z3hqqmAMOcrNPP+y5i8XP8Yaj5UrcFZEGQiDRW1eUAItIM/whLKSEifYF/Av2cWZblXNLSDrhFVY96dqjqJhG5B/gZ47mzACJSCuPNfIRzKWbKAT1wcSH24ow1yCzFFlVdmcX+LcDThSzHkjcai8hS5/cAQJztANIPXxYa/pYA2YvOGE/PFRjDdZ2zz23+jVlp4S5VPSEiNYCnXNKS6m2MeXDWtTzshiA/ZoTz8y9VPQlpRlo88BAwwC1hxRVrkFksFn+mntsCigqquk5EbnWMniigmqpuzLFgwevaKSK/YdpyPfCDqu51SU6As9i5v6y368+0UNUm3jtU9ZSIPI+JVbQGWT5jDTKLxeK3+LE3yu8QkQnAchFZgElT8IuInFXVJ13WNQKzjNJlmOWTnhSRKFX9jwtyLsEYhdYgy5kUXztVNdXxlFnyGWuQWSwWS/HgalXtKiLdgDdVdYyIfOu2KKCxqrbxyvT+ioj86IYQVfXX+D9/JE5EWqn+f3v3G6pnXcdx/L1zmEeh5gRxi2SONfgkMmIwRDDCPxGJgS3aEKQHY6AgPujBGIHgg0RFkUAIZJJsMYj+7FiP2mRUEEFropYM9KsIWkKp2IYiq1WbD67rsHuHNYK89rvu2/cLbu77ug43fB4czvny+/2u77d+N3kzye3A3xplmmkWZJI0GxaSfJbuzNbWfmbj6saZAFYmWcnZTu9X0mjIeJ/jAeC7S21K+sa+26vKdjjn+jaw2DeD/SMwT9fXcR2NWpbMujE8gSNJ+v99n2625oGqeoturNOBC37j4vgeXauQTUkO0rWdebhRlseByzn3f99rdD3JLMgm9A8/babrZXcS+IDud2xzVb3dMtusWnHmjNvmkjTtkuyoqr0T1yuqahR/4PsGrNfRtU94tapONsrx3PKD6v39ObqHDb7YIJYEuEImSbPiK32TUwBaF2NLK05Jfka3yrILuB/4YZKfNor1Xw+q0003kJrxDJkkzYYtwLEkH9KtRK2gGx92VaM8v+jfzzf0vFWx6EF1jZZblpKkwSQ50A+Dn7x3pKpuaJBlI7AInPegumej1JIFmSTNgCRX0z1BeEVVbUtyJ/D7Vr3c+nFE3wG+AJzgbO+vOeDFqvpyo1xzdIPhP0+3UvcKcLj1Fq/klqUkzYYfAE/QFUEA7wD7gJtbhKmqRbq2Cbuq6vEWGc6nPy/2bP+SRsOCTJJmw3xVHUyyG6Cqft2ylUOSe6pqD7AmyWPLf15VuxvEkkbLgkySZsO/ktwCzCdZA2yl6x/Vyhv9+7GGGaSpYUEmSbNhJ/AgcCVwCPgDsKNVmKpa2hL8WlVta5VDmhYe6pekKZdkAVgL/KU/IzUaSfYA7wFHgVNL96vql81CSSNkY1hJmmJJvg4U8GPg5STXN4603CXAZ4A7gG3965sX/Ib0CeSWpSRNt9108wWPJ1kPPAnc1jbSOZwRKf0PLMgkabqdqqrjAFX1RpLLWgdaZpGznfkvATYALwA3tQokjZEFmSRNt+VnxkZ1hmz5MO8ka+kePpA0wUP9kjTFkrxP120eum746a+XZlmO7UwZSY6OMZfUkitkkjTdNrUOcCFJnuPcYeJrgcON4kij5QqZJGkwSa6ZuDwDvF9VJ1rlkcbKtheSpCGtB+6vqjer6s/A00m+1DiTNDpuWUqShvQI8K2J63uBZ4Ab28SRxskVMknSkOar6vWJ63ebJZFGzBUySdKQFpMcoZutOUe3Mra/bSRpfDzUL0kaVJKNwGbgP8DzVfVm40jS6LhlKUkaTJJVwHbgxqp6BtiQZHXjWNLoWJBJkoa0DzgOLHXsvwr4UbM00khZkEmShvTpqnoSOAVQVT8BxjZvU2rOgkySNKS5JJ+j79af5KvAfNtI0vj4lKUkaUj3AXuALUn+CvwJuLttJGl8fMpSknRRJFkHvF1V/2ydRRobtywlSR+7JLcm+U3/eT7Jr+iGih/rty0lTXDLUpI0hIeAu/rP3wBWAdcCq4GfA4ca5ZJGyRUySdIQ/jExMuk2YH9Vna6qvwP/bphLGiVXyCRJQ1hIMgdcCtwOPDrxs0+1iSSNlwWZJGkI+4HngQXgUFVVkgXgKeC3TZNJI+RTlpKkQSS5Bri8ql6auLcT2FtVp9slk8bHgkySJKkxD/VLkiQ1ZkEmSZLUmAWZJElSYxZkkiRJjVmQSZIkNfYRwGneAtRNSVwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1wTyh3jjBH3n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "4fb7f38a-ba3d-42c2-ae05-7261610f30c4"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5000 entries, 0 to 4999\n",
            "Data columns (total 12 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   Age                 5000 non-null   int64  \n",
            " 1   Income              5000 non-null   int64  \n",
            " 2   ZIP Code            5000 non-null   int64  \n",
            " 3   Family              5000 non-null   int64  \n",
            " 4   CCAvg               5000 non-null   float64\n",
            " 5   Education           5000 non-null   int64  \n",
            " 6   Mortgage            5000 non-null   int64  \n",
            " 7   Personal Loan       5000 non-null   int64  \n",
            " 8   Securities Account  5000 non-null   int64  \n",
            " 9   CD Account          5000 non-null   int64  \n",
            " 10  Online              5000 non-null   int64  \n",
            " 11  CreditCard          5000 non-null   int64  \n",
            "dtypes: float64(1), int64(11)\n",
            "memory usage: 468.9 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fdbfn7WlBzev",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "7f8c5911-9f36-4367-f4be-a0fb2787eecf"
      },
      "source": [
        "sns.countplot(y='CreditCard', data = df , hue = 'Personal Loan')\n",
        "plt.title('CreditCard vs Personal Loan')\n",
        "plt.ylabel('CreditCard')\n",
        "plt.xlabel('Personal Loan')"
      ],
      "execution_count": 246,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Personal Loan')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 246
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJ6dslIIijbE",
        "colab_type": "text"
      },
      "source": [
        "Among people having credit card,a greater fraction has availed Personal Loan than people having no credit card."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ry9Oh4cdefIB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "8e14d8bc-664e-45a5-e901-9be4af56f2aa"
      },
      "source": [
        "sns.countplot(y='Securities Account', data = df , hue = 'Personal Loan')\n",
        "plt.title('Securities Account vs Personal Loan')\n",
        "plt.ylabel('Securities Account')\n",
        "plt.xlabel('Personal Loan')"
      ],
      "execution_count": 247,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Personal Loan')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 247
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQfBLp42DEfn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "ff1aac2f-8dc8-4276-9d35-eb0cf3213b7f"
      },
      "source": [
        "sns.countplot(y='Education', data = df , hue = 'Personal Loan')\n",
        "plt.title('Education vs Personal Loan')\n",
        "plt.ylabel('Education')\n",
        "plt.xlabel('Personal Loan')"
      ],
      "execution_count": 248,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Personal Loan')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 248
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZObpEaFjtIE",
        "colab_type": "text"
      },
      "source": [
        "From the above graph, people with increasing education have availed Personal Loan."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pqm1o2-5gOa1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "outputId": "0ff2cfc2-f146-4e95-d563-e5ddabaf5c2f"
      },
      "source": [
        "dframe=pd.DataFrame(df['Personal Loan'].value_counts()).reset_index()\n",
        "dframe.columns=['Labels','Personal Loan']\n",
        "fig1,ax1=plt.subplots(figsize=(10,8))\n",
        "explode=(0,0.15)\n",
        "ax1.pie(dframe['Personal Loan'],explode=explode,autopct='%1.1f%%',shadow=True,startangle=70)\n",
        "ax1.axis('equal')\n",
        "plt.title('Personal Loan Percentage')\n",
        "plt.show()"
      ],
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3mq2r8C5j7kb",
        "colab_type": "text"
      },
      "source": [
        "A total of **9.6%** people have availed Personal Loan"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZJK7weMUFQUf",
        "colab_type": "text"
      },
      "source": [
        "**4. Applying necessary transformations for feature variables**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wMiTEc5aFcCc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=df[['Age','Income','CCAvg','Education','Mortgage','Securities Account','Online',\"CreditCard\"]].values\n",
        "y=df['Personal Loan'].values\n"
      ],
      "execution_count": 250,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kk0pP84VGevk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "1c97e1ae-0563-4447-b82c-567d62309dcf"
      },
      "source": [
        "X"
      ],
      "execution_count": 251,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[25. , 49. ,  1.6, ...,  1. ,  0. ,  0. ],\n",
              "       [45. , 34. ,  1.5, ...,  1. ,  0. ,  0. ],\n",
              "       [39. , 11. ,  1. , ...,  0. ,  0. ,  0. ],\n",
              "       ...,\n",
              "       [63. , 24. ,  0.3, ...,  0. ,  0. ,  0. ],\n",
              "       [65. , 49. ,  0.5, ...,  0. ,  1. ,  0. ],\n",
              "       [28. , 83. ,  0.8, ...,  0. ,  1. ,  1. ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 251
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sJ35JvkhGjgL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7909505a-b8a7-42ea-bde2-abaf5d7eec8e"
      },
      "source": [
        "y"
      ],
      "execution_count": 252,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "loPR4DwsGm5L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ca3f6254-1d45-40a9-b486-27a1fff8a2c5"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 253,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 253
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NeBjURZGzmb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "dd035417-8587-48a1-90a4-5a2c352e523c"
      },
      "source": [
        "y.shape"
      ],
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9ZRqSW9G3X5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X = sc.fit_transform(X)\n"
      ],
      "execution_count": 255,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i8hcedbMH-vU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "335f3faa-56b2-4235-d740-eef16966544d"
      },
      "source": [
        "X"
      ],
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.77441684, -0.53822878, -0.19338544, ...,  2.92891517,\n",
              "        -1.21661774, -0.64531434],\n",
              "       [-0.02952359, -0.8641094 , -0.25061056, ...,  2.92891517,\n",
              "        -1.21661774, -0.64531434],\n",
              "       [-0.55299157, -1.36379301, -0.53673618, ..., -0.34142334,\n",
              "        -1.21661774, -0.64531434],\n",
              "       ...,\n",
              "       [ 1.54088033, -1.08136314, -0.93731205, ..., -0.34142334,\n",
              "        -1.21661774, -0.64531434],\n",
              "       [ 1.71536965, -0.53822878, -0.8228618 , ..., -0.34142334,\n",
              "         0.82195086, -0.64531434],\n",
              "       [-1.51268285,  0.20043396, -0.65118643, ..., -0.34142334,\n",
              "         0.82195086,  1.54963239]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 256
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqYKnjJQIBV7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b9358950-708b-4df4-cb06-1989c26024b1"
      },
      "source": [
        "y"
      ],
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7u2HhVSqLw2e",
        "colab_type": "text"
      },
      "source": [
        "**5. Normalizing the data and splitting the data into train and test set**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JdVuL9CLMjsN",
        "colab_type": "text"
      },
      "source": [
        "**5.1 Normalizing the data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HcvbH6IwMdup",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "a74f5d62-9ace-4d38-f0a7-4d16ebe1e008"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "X = preprocessing.normalize(X)\n",
        "X"
      ],
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.45275242, -0.13733209, -0.04934338, ...,  0.74732915,\n",
              "        -0.31042685, -0.16465558],\n",
              "       [-0.00828626, -0.24252587, -0.07033779, ...,  0.82204603,\n",
              "        -0.34146287, -0.1811176 ],\n",
              "       [-0.22812463, -0.5626031 , -0.22141882, ..., -0.14084676,\n",
              "        -0.50188915, -0.26621037],\n",
              "       ...,\n",
              "       [ 0.52791266, -0.37047997, -0.3211274 , ..., -0.1169732 ,\n",
              "        -0.41681881, -0.22108765],\n",
              "       [ 0.73501462, -0.23062435, -0.35258607, ..., -0.14629567,\n",
              "         0.35219575, -0.27650919],\n",
              "       [-0.55776679,  0.07390538, -0.24010992, ..., -0.12589195,\n",
              "         0.30307535,  0.57139107]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8FNeBcOeNI_z",
        "colab_type": "text"
      },
      "source": [
        "**5.2 Splitting the data into train and test set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6RXkE6mNRUT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)"
      ],
      "execution_count": 259,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_hJPiFDSNznf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "dd94438b-a8fb-42a7-e789-f1dc4d1d99cc"
      },
      "source": [
        "X_train"
      ],
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.18273183,  0.41040748,  0.75238478, ..., -0.0765766 ,\n",
              "         0.18435236,  0.3475614 ],\n",
              "       [ 0.61750792,  0.31769885,  0.31487539, ..., -0.17688675,\n",
              "         0.42584148, -0.33432851],\n",
              "       [ 0.49414667, -0.29907662, -0.08650719, ..., -0.15272904,\n",
              "         0.36768361, -0.28866872],\n",
              "       ...,\n",
              "       [-0.51894968, -0.33261161, -0.18269388, ..., -0.10501687,\n",
              "        -0.37421396,  0.47664444],\n",
              "       [ 0.25015183, -0.13233465, -0.06161778, ...,  0.72013425,\n",
              "         0.20209358,  0.38100911],\n",
              "       [-0.4349237 , -0.31401657,  0.03746997, ..., -0.13794583,\n",
              "        -0.49155205, -0.2607274 ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 260
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o15f22XPN4Au",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1e51862f-c7d3-4a81-93ae-bbe38498c180"
      },
      "source": [
        "y_train"
      ],
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2c9ESG6OAcB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "16e6c1e1-7d7e-4e76-add5-6da321b35f3e"
      },
      "source": [
        "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
      ],
      "execution_count": 262,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3500, 8) (1500, 8) (3500,) (1500,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-DUbc94WOX5P",
        "colab_type": "text"
      },
      "source": [
        "**6. Using Logistic Regression model to predict the likelihood of a customer buying personal loans**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hxOmuXpjPB_z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "91b78c41-869f-45e4-ef6e-a6ed244af6d9"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)"
      ],
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 263
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y4NYqFFxW9Zx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c4a26582-759a-474f-a8eb-98b0e5c6bba2"
      },
      "source": [
        "y_predict = model.predict(X_test)\n",
        "print(y_predict)"
      ],
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 ... 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_D8pvmilmCD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9cd1cc2c-77d0-49ad-a179-223254a594dc"
      },
      "source": [
        "y_predict_train = model.predict(X_train)\n",
        "print(y_predict_train)"
      ],
      "execution_count": 265,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 ... 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7xFpNSzXi1l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "73b3a44a-b14b-4d4b-85ff-3da78713ecfc"
      },
      "source": [
        "sns.distplot((y_test-y_predict), bins = 50)"
      ],
      "execution_count": 266,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f734fd1a198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 266
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4va37EHGX8Q9",
        "colab_type": "text"
      },
      "source": [
        "**7. Metrics related for evaluating the model performance**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e8FdbeXYmAjC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "b9d7433f-5d1a-4e61-ddaa-4e2184ada17a"
      },
      "source": [
        "from sklearn import metrics\n",
        "print('TRAIN:\\n')\n",
        "print('Mean Absolute Error: ', metrics.mean_absolute_error(y_train, y_predict_train))\n",
        "print('Root Mean Squared Error: ',np.sqrt(metrics.mean_absolute_error(y_train,y_predict_train)))"
      ],
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TRAIN:\n",
            "\n",
            "Mean Absolute Error:  0.052571428571428575\n",
            "Root Mean Squared Error:  0.22928460168844433\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aNoUL5kKYAEK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "bda5fde6-da6f-43f0-f4db-5037614c9ddc"
      },
      "source": [
        "print('TEST:\\n')\n",
        "print('Mean Absolute Error: ', metrics.mean_absolute_error(y_test, y_predict))\n",
        "print('Root Mean Squared Error: ',np.sqrt(metrics.mean_absolute_error(y_test,y_predict)))"
      ],
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TEST:\n",
            "\n",
            "Mean Absolute Error:  0.048\n",
            "Root Mean Squared Error:  0.21908902300206645\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TbL4RbQmZybt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "78aff7e5-fed1-4faa-bcb2-3f8a32ed70cb"
      },
      "source": [
        "accuracy_train = metrics.accuracy_score(y_train,y_predict_train)\n",
        "print('Train Accuracy: ',accuracy_train)\n",
        "accuracy_test = metrics.accuracy_score(y_test,y_predict)\n",
        "print('Test Accuracy: ',accuracy_test)"
      ],
      "execution_count": 269,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy:  0.9474285714285714\n",
            "Test Accuracy:  0.952\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qTSIfgrC_Pol",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "def plot_classification_report(y_tru, y_prd, figsize=(10, 5), ax=None):\n",
        "    plt.figure(figsize=figsize)\n",
        "    xticks = ['precision', 'recall', 'f1-score', 'support']\n",
        "    yticks = ['Will not avail loan','Will avail loan']\n",
        "    rep = np.array(precision_recall_fscore_support(y_tru, y_prd)).T\n",
        "    sns.heatmap(rep,\n",
        "                annot=True, \n",
        "                cbar=False, \n",
        "                xticklabels=xticks, \n",
        "                yticklabels=yticks,\n",
        "                ax=ax)"
      ],
      "execution_count": 270,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FWCbNgML_n0A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "779fa125-8602-41aa-ed86-db52e0c1428c"
      },
      "source": [
        "print('Classification Report for Logistic Regression:\\n')\n",
        "plot_classification_report(y_test,y_predict)"
      ],
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classification Report for Logistic Regression:\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mrVxIXQz7Ywx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "1afd06d1-6e77-475d-aa57-0f487f3a41ef"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "print('Confusion Matrix for Logistic Regression\\n')\n",
        "class_name = ['Will not avail loan', 'Will avail loan']\n",
        "cf_matrix = confusion_matrix(y_test, y_predict)\n",
        "sns.heatmap(cf_matrix, annot = True, xticklabels = class_name, yticklabels = class_name)"
      ],
      "execution_count": 277,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix for Logistic Regression\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f734fc10748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 277
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y1EBLYvN8gME",
        "colab_type": "text"
      },
      "source": [
        "**8. Comparison with other classification algorithms**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oEeQw_Tc9BHF",
        "colab_type": "text"
      },
      "source": [
        "**8.1 Support Vector Machine**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GjcLqwlK8nst",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "a81aef27-4e0e-4e01-d2eb-1700df840485"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "SVM = SVC(kernel = 'linear')\n",
        "SVM.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FT1f9xx954x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b136ffcb-9554-46ee-a0f3-302f453951bd"
      },
      "source": [
        "y_predict_SVM = SVM.predict(X_test)\n",
        "print(y_predict_SVM)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 ... 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zzoBCjltnHqc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "71f7d638-49f7-491a-87c5-0aacf4442b3c"
      },
      "source": [
        "y_predict_SVM_train = SVM.predict(X_train)\n",
        "print(y_predict_SVM_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 ... 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6lXAZDLYCxDP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "7a1eead0-cbe8-484c-99ff-193db1853e80"
      },
      "source": [
        "sns.distplot((y_test-y_predict_SVM), bins = 50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7351aaabe0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGH48ChmnjUj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "da4513e8-62f5-4336-f591-d0616c3b2928"
      },
      "source": [
        "print('TRAIN:\\n')\n",
        "print('Mean Absolute Error: ', metrics.mean_absolute_error(y_train, y_predict_SVM_train))\n",
        "print('Root Mean Squared Error: ',np.sqrt(metrics.mean_absolute_error(y_train,y_predict_SVM_train)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TRAIN:\n",
            "\n",
            "Mean Absolute Error:  0.04971428571428571\n",
            "Root Mean Squared Error:  0.22296700588716195\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26jB_w9w-dW8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "15330a7d-4d2f-4cc2-bc76-2a1f768a6fe7"
      },
      "source": [
        "print('TEST:\\n')\n",
        "print('Mean Absolute Error: ', metrics.mean_absolute_error(y_test, y_predict_SVM))\n",
        "print('Root Mean Squared Error: ',np.sqrt(metrics.mean_absolute_error(y_test,y_predict_SVM)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TEST:\n",
            "\n",
            "Mean Absolute Error:  0.042\n",
            "Root Mean Squared Error:  0.20493901531919198\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Agcj5Dem-a0M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "83e87c4d-13a4-4a31-b4c4-fc3c35dd7515"
      },
      "source": [
        "accuracy_train = metrics.accuracy_score(y_train,y_predict_SVM_train)\n",
        "print('Train Accuracy: ',accuracy_train)\n",
        "accuracy_test = metrics.accuracy_score(y_test,y_predict_SVM)\n",
        "print('Test Accuracy: ',accuracy_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy:  0.9502857142857143\n",
            "Test Accuracy:  0.958\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hE2xrr9Z-y7i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "f47359dc-a174-4718-9938-aa3a04b058fd"
      },
      "source": [
        "print('Classification Report for Support Vector Machine:\\n')\n",
        "plot_classification_report(y_test,y_predict_SVM)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classification Report for Support Vector Machine:\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khYcKrKs-2f6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "e70fc0f5-7cd5-49f5-9bbd-254595075889"
      },
      "source": [
        "print('Confusion Matrix for Support Vector Machine\\n')\n",
        "cf_matrix = confusion_matrix(y_test, y_predict_SVM)\n",
        "sns.heatmap(cf_matrix, annot = True, xticklabels = class_name, yticklabels = class_name)"
      ],
      "execution_count": 279,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix for Support Vector Machine\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f734f7aeeb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 279
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "30-UeYFp_CAl",
        "colab_type": "text"
      },
      "source": [
        "**8.2 K-Neighbor Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EDd-Qos9_RsV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "7db90caa-d6f6-4659-dace-da95167face4"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors = 7)\n",
        "knn.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=7, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9S9xj6pTAI-m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c57f09dc-3a0f-403c-a861-5a6b60e0f995"
      },
      "source": [
        "y_predict_knn = knn.predict(X_test)\n",
        "print(y_predict_knn)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 ... 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BERu9d4josTJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "30b055fb-7e14-4ef0-ee05-c71473a2d26e"
      },
      "source": [
        "y_predict_knn_train = knn.predict(X_train)\n",
        "print(y_predict_knn_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 0 ... 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tfP-GZNNC6T3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "667021ce-ca2a-43b9-f957-fe200c4408c6"
      },
      "source": [
        "sns.distplot((y_test-y_predict_knn), bins = 50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f73517814e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 187
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "771fpdlxo-48",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "0071d8f3-203b-40ab-dd86-fa16e14dec40"
      },
      "source": [
        "print(\"TRAIN:\\n\")\n",
        "print('Mean Absolute Error: ', metrics.mean_absolute_error(y_train,y_predict_knn_train))\n",
        "print('Root Mean Squared Error', np.sqrt(metrics.mean_squared_error(y_train,y_predict_knn_train)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TRAIN:\n",
            "\n",
            "Mean Absolute Error:  0.03942857142857143\n",
            "Root Mean Squared Error 0.1985662897587892\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eon_Q9jsAnwR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "a6c7b169-c766-4a9a-c1c8-9aa190e9f53f"
      },
      "source": [
        "print('TEST:\\n')\n",
        "print('Mean Absolute Error: ', metrics.mean_absolute_error(y_test, y_predict_knn))\n",
        "print('Root Mean Squared Error: ',np.sqrt(metrics.mean_absolute_error(y_test,y_predict_knn)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TEST:\n",
            "\n",
            "Mean Absolute Error:  0.042666666666666665\n",
            "Root Mean Squared Error:  0.2065591117977289\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-N0lIXMNAy7U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "e6ac4817-1005-4d5e-fb38-6b1e98b55c88"
      },
      "source": [
        "accuracy_train_knn = metrics.accuracy_score(y_train,y_predict_knn_train)\n",
        "print('Train Accuracy: ',accuracy_train_knn)\n",
        "accuracy = metrics.accuracy_score(y_test,y_predict_knn)\n",
        "print('Test Accuracy: ',accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy:  0.9605714285714285\n",
            "Test Accuracy:  0.9573333333333334\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZKG_Q8uBAzSu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "311aa89b-16d6-4f76-924a-f471a0c954b7"
      },
      "source": [
        "print('Classification Report for KNeighbors Classifier:\\n')\n",
        "plot_classification_report(y_test,y_predict_knn)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classification Report for KNeighbors Classifier:\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzQn6SGlA-29",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "97ad12ee-bdfd-43f2-865c-fd2248af49b6"
      },
      "source": [
        "print('Confusion Matrix for KNeighbors Classifier\\n')\n",
        "cf_matrix = confusion_matrix(y_test, y_predict_knn)\n",
        "sns.heatmap(cf_matrix, annot = True, xticklabels = class_name, yticklabels = class_name)"
      ],
      "execution_count": 280,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix for Logistic Regression\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f734f736780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 280
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4bLL47g1C-nO",
        "colab_type": "text"
      },
      "source": [
        "**8.3 Naive-Bayes Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYyF8bdLDYSd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2c5c21dc-fad4-4947-8143-3a8507d8b024"
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "nb = GaussianNB()\n",
        "nb.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB(priors=None, var_smoothing=1e-09)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3gQToBVSDrK4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2c5f0a09-74ed-4e60-de5e-4e0c7ded98b2"
      },
      "source": [
        "y_predict_nb = nb.predict(X_test)\n",
        "print(y_predict_nb)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 ... 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uTz1h9iIpq1q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3e6b0496-d739-4075-ab22-df0807e7fae9"
      },
      "source": [
        "y_predict_nb_train = nb.predict(X_train)\n",
        "print(y_predict_nb_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 0 0 ... 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dl2swbJdD2hd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "7cac19a8-5add-429e-df9a-63e751554d86"
      },
      "source": [
        "sns.distplot((y_test-y_predict_nb), bins = 50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7351604668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 196
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jRpeBUmHqBh8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "a8e7e564-5ba9-4b76-e2a2-c66f447d4578"
      },
      "source": [
        "print('TRAIN:\\n')\n",
        "print('Mean Absolute Error: ', metrics.mean_absolute_error(y_train, y_predict_nb_train))\n",
        "print('Root Mean Squared Error: ',np.sqrt(metrics.mean_absolute_error(y_train,y_predict_nb_train)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TRAIN:\n",
            "\n",
            "Mean Absolute Error:  0.09857142857142857\n",
            "Root Mean Squared Error:  0.3139608710833702\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AS6bhUayD2sb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "ee5b2a24-eb39-4fd7-b7a3-d3d55ee42dee"
      },
      "source": [
        "print('TEST:\\n')\n",
        "print('Mean Absolute Error: ', metrics.mean_absolute_error(y_test, y_predict_nb))\n",
        "print('Root Mean Squared Error: ',np.sqrt(metrics.mean_absolute_error(y_test,y_predict_nb)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TEST:\n",
            "\n",
            "Mean Absolute Error:  0.094\n",
            "Root Mean Squared Error:  0.3065941943351178\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ERn9-JdIEFrJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "8cb968da-62fc-4681-bf2b-4aa674ef6ba7"
      },
      "source": [
        "accuracy_train_nb = metrics.accuracy_score(y_train,y_predict_nb_train)\n",
        "print('Train Accuracy: ',accuracy_train_nb)\n",
        "accuracy = metrics.accuracy_score(y_test,y_predict_nb)\n",
        "print('Accuracy: ',accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy:  0.9014285714285715\n",
            "Accuracy:  0.906\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oB5VTvFtEUth",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "ab74116d-49d6-4f42-f50e-79f44d0e59f4"
      },
      "source": [
        "print('Classification Report for Naive-Bayes Classifier:\\n')\n",
        "plot_classification_report(y_test,y_predict_nb)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classification Report for Naive-Bayes Classifier:\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tt6CjiN7EVDn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "6c7eeb43-94b6-461e-95ba-8993e7f0ea6f"
      },
      "source": [
        "print('Confusion Matrix for Naive-Bayes Classifier\\n')\n",
        "cf_matrix = confusion_matrix(y_test, y_predict_nb)\n",
        "sns.heatmap(cf_matrix, annot = True, xticklabels = class_name, yticklabels = class_name)"
      ],
      "execution_count": 281,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix for Naive-Bayes Classifier\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f734f6c1da0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 281
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sfG91zHHEgxn",
        "colab_type": "text"
      },
      "source": [
        "On comparing, I have found that for this particular dataset **Support Vector Machine** predicted  the likelihood of a liability customer buying personal loans with highest accuracy, followed by **KNeighbors Classifier**. **Logistic Regression** followed being a little less accurate and **Naive-Bayes Classifier** had the minimum accuracy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YBJd6veBN3v3",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "**9. Business understanding of the model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P26TaCJeN_0D",
        "colab_type": "text"
      },
      "source": [
        "Bank Service is an essential worldwide service. Considering this fact, I have decided to build a Machine Learning Classification Model to support this service by helping it to predict the likelihood of a liability customer buying personal loans. \n",
        "\n",
        "The model has been built keeping in mind that identification of potential customers who have a higher probability of purchasing Personal Loans will not only lead to an increase the bank's profit and success rate but also will reduce the cost of organizing manual campaigns to identify the same.\n",
        "\n",
        "The dataset contains certain basic information about the bank's customers along with the particulars whether they have purchased Personal Loans or not. The dataset after preprocessing is used to train and later test our model in order to make it befitting for achieving our goals.\n",
        "\n",
        "The model aims at providing the maximum accuracy and help in boosting the bank's overall income from its customers by leading the bank to approach the customers having a higher probability of purchasing Personal Loans.\n",
        "\n",
        "We have tested 4 classification algorithms to predict if a customer avails Personal Loan or not, namely Logistic Regression, Support Vector Machine, KNeighbors Classifier and Naive-Bayes Classifier. As a result of testing we have found the following accuracies:\n",
        "\n",
        "\n",
        "*   **Logistic Regression: 0.952**\n",
        "*   **Support Vector Machine: 0.958**\n",
        "*   **KNeighbors Classifier: 0.957**\n",
        "*   **Naive-Bayes Classifier: 0.906**\n",
        "\n",
        "From a business point of view, the bank is likely to have higher success rate and profit and a potential reduction in its campaign costs if it implements SVM or KNeighbors Classifier or Logistic Regression model. Since the Naive-Bayes Classifier has a significantly lower accuracy, it would not as profitable to the bank as the other classifiers.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        " \n"
      ]
    }
  ]
}