Credit_Card_Fraud_Detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Credit_Card_Fraud_Detection.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ioNZ7SCeybBx",
        "colab_type": "text"
      },
      "source": [
        "## **Credit Card Fraud Detection Model using Self Organizing Map & Artifical Neural Network**\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wxyqQ91isb94",
        "colab_type": "text"
      },
      "source": [
        "**Intuition:** We will be taking a sample from a Credit Approval dataset & then use that sample to detect some potential fraudsters with the help of a Self Organizing Map using the 'minisom' package. After that, we will create a dependent variable based on the list of potential fraudsters detected. Finally, we will create an Artificial Neural Network with Keras & use it to predict the probability of potential frauds corresponding to each customer.\n",
        "\n",
        "\n",
        "**Notes:**\n",
        "\n",
        "(1) The 'minisom' package can be installed with 'pip'\n",
        "\n",
        "(2) We only took a sample from the data to lessen the computational time. The main dataset can also be used directly after tweaking the code accordingly.\n",
        "\n",
        "(3) The 'customer_id' column is only created to be used as a refrence and is not really necessary to be added.\n",
        "\n",
        "**Dataset Source:**\n",
        "https://www.kaggle.com/mlg-ulb/creditcardfraud"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "seQfft9rrCST",
        "colab_type": "text"
      },
      "source": [
        "## **Part 1:** Data preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YY9bsibnA7bJ",
        "colab_type": "text"
      },
      "source": [
        "### **Importing the libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0VnmOU7SV-7P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "import pandas as pd\n",
        "import random\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
        "from minisom import MiniSom\n",
        "from pylab import bone, pcolor, colorbar, plot, show\n",
        "import numpy as np\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yUUsQF3kBVGn",
        "colab_type": "text"
      },
      "source": [
        "### **Mounting Google Drive**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EM5D-mVUWTO3",
        "colab_type": "code",
        "outputId": "2188780b-b5fc-46b9-c3a6-94520aadc396",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l6v1U-dhBCLP",
        "colab_type": "text"
      },
      "source": [
        "### **Importing the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZOGki21Wi-5",
        "colab_type": "code",
        "outputId": "f60dcbf5-0def-4bb2-bec9-998b0a2dea2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        }
      },
      "source": [
        "dataset = pd.read_csv('/content/drive/My Drive/Colab Notebooks/SOM/creditcard.csv')\n",
        "dataset.count()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      284807\n",
              "V1        284807\n",
              "V2        284807\n",
              "V3        284807\n",
              "V4        284807\n",
              "V5        284807\n",
              "V6        284807\n",
              "V7        284807\n",
              "V8        284807\n",
              "V9        284807\n",
              "V10       284807\n",
              "V11       284807\n",
              "V12       284807\n",
              "V13       284807\n",
              "V14       284807\n",
              "V15       284807\n",
              "V16       284807\n",
              "V17       284807\n",
              "V18       284807\n",
              "V19       284807\n",
              "V20       284807\n",
              "V21       284807\n",
              "V22       284807\n",
              "V23       284807\n",
              "V24       284807\n",
              "V25       284807\n",
              "V26       284807\n",
              "V27       284807\n",
              "V28       284807\n",
              "Amount    284807\n",
              "Class     284807\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6bnDuTcrly3S",
        "colab_type": "text"
      },
      "source": [
        "### **Creating a new column with randomly generated Customer IDs**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9lcqxFpbfY3-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "a827f61c-68d9-4030-db77-e77b39ffb524"
      },
      "source": [
        "dataset['customer_id'] = np.random.randint(100000, 500000, size = len(dataset))\n",
        "cols = ['customer_id', 'Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7','V8', 'V9',\n",
        "        'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', \n",
        "        'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23',\n",
        "        'V24', 'V25', 'V26', 'V27', 'V28', 'Amount', 'Class']\n",
        "dataset = dataset[cols]\n",
        "dataset.head()"
      ],
      "execution_count": 8,
      "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>customer_id</th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>138377</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.991390</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>1.468177</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>149.62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>220192</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>276043</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>360161</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>335803</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   customer_id  Time        V1        V2  ...       V27       V28  Amount  Class\n",
              "0       138377   0.0 -1.359807 -0.072781  ...  0.133558 -0.021053  149.62      0\n",
              "1       220192   0.0  1.191857  0.266151  ... -0.008983  0.014724    2.69      0\n",
              "2       276043   1.0 -1.358354 -1.340163  ... -0.055353 -0.059752  378.66      0\n",
              "3       360161   1.0 -0.966272 -0.185226  ...  0.062723  0.061458  123.50      0\n",
              "4       335803   2.0 -1.158233  0.877737  ...  0.219422  0.215153   69.99      0\n",
              "\n",
              "[5 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jq9OIKZYp1tV",
        "colab_type": "text"
      },
      "source": [
        "### **Extracting 1000 Random Samples from the Main Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rhGQ3UqtkD0k",
        "colab_type": "code",
        "outputId": "45600ff8-89e8-46b4-cd9c-462e2b68f782",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "sampled_dataset = dataset.sample(n = 1000)\n",
        "sampled_dataset.head()"
      ],
      "execution_count": 9,
      "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>customer_id</th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>253553</th>\n",
              "      <td>352247</td>\n",
              "      <td>156309.0</td>\n",
              "      <td>1.981399</td>\n",
              "      <td>0.185255</td>\n",
              "      <td>-3.650571</td>\n",
              "      <td>0.472477</td>\n",
              "      <td>3.227775</td>\n",
              "      <td>3.064406</td>\n",
              "      <td>0.110535</td>\n",
              "      <td>0.659636</td>\n",
              "      <td>-0.016456</td>\n",
              "      <td>-0.336699</td>\n",
              "      <td>0.193482</td>\n",
              "      <td>0.041694</td>\n",
              "      <td>-0.484864</td>\n",
              "      <td>-0.780075</td>\n",
              "      <td>0.007393</td>\n",
              "      <td>-0.268799</td>\n",
              "      <td>0.889960</td>\n",
              "      <td>-0.033402</td>\n",
              "      <td>-0.505414</td>\n",
              "      <td>-0.138698</td>\n",
              "      <td>-0.030301</td>\n",
              "      <td>-0.005892</td>\n",
              "      <td>-0.007516</td>\n",
              "      <td>0.579525</td>\n",
              "      <td>0.505753</td>\n",
              "      <td>-0.472820</td>\n",
              "      <td>0.016482</td>\n",
              "      <td>-0.032189</td>\n",
              "      <td>24.81</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89871</th>\n",
              "      <td>161669</td>\n",
              "      <td>62761.0</td>\n",
              "      <td>1.184980</td>\n",
              "      <td>-1.562447</td>\n",
              "      <td>1.636660</td>\n",
              "      <td>-0.119947</td>\n",
              "      <td>-2.215818</td>\n",
              "      <td>0.667241</td>\n",
              "      <td>-1.893293</td>\n",
              "      <td>0.446549</td>\n",
              "      <td>1.369551</td>\n",
              "      <td>0.217240</td>\n",
              "      <td>-2.116081</td>\n",
              "      <td>-1.161186</td>\n",
              "      <td>-1.710985</td>\n",
              "      <td>-1.335598</td>\n",
              "      <td>-1.441250</td>\n",
              "      <td>0.748530</td>\n",
              "      <td>0.897897</td>\n",
              "      <td>-0.782924</td>\n",
              "      <td>1.158079</td>\n",
              "      <td>0.067352</td>\n",
              "      <td>0.249439</td>\n",
              "      <td>0.937779</td>\n",
              "      <td>-0.265218</td>\n",
              "      <td>-0.063138</td>\n",
              "      <td>0.516223</td>\n",
              "      <td>0.128006</td>\n",
              "      <td>0.081299</td>\n",
              "      <td>0.032692</td>\n",
              "      <td>73.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>274419</th>\n",
              "      <td>401493</td>\n",
              "      <td>166000.0</td>\n",
              "      <td>-1.527155</td>\n",
              "      <td>0.170900</td>\n",
              "      <td>-0.116909</td>\n",
              "      <td>-0.417367</td>\n",
              "      <td>0.635782</td>\n",
              "      <td>-1.435035</td>\n",
              "      <td>0.827557</td>\n",
              "      <td>-0.357451</td>\n",
              "      <td>0.698918</td>\n",
              "      <td>0.461904</td>\n",
              "      <td>-1.003429</td>\n",
              "      <td>-0.428902</td>\n",
              "      <td>-0.992378</td>\n",
              "      <td>0.038490</td>\n",
              "      <td>-0.134832</td>\n",
              "      <td>-0.015578</td>\n",
              "      <td>-0.435619</td>\n",
              "      <td>-0.686190</td>\n",
              "      <td>0.037416</td>\n",
              "      <td>-0.673027</td>\n",
              "      <td>-0.471623</td>\n",
              "      <td>-0.563613</td>\n",
              "      <td>0.798725</td>\n",
              "      <td>0.013735</td>\n",
              "      <td>-0.305358</td>\n",
              "      <td>0.146980</td>\n",
              "      <td>0.034169</td>\n",
              "      <td>0.147651</td>\n",
              "      <td>2.25</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201603</th>\n",
              "      <td>100320</td>\n",
              "      <td>133959.0</td>\n",
              "      <td>-1.122292</td>\n",
              "      <td>1.804898</td>\n",
              "      <td>-1.246749</td>\n",
              "      <td>-0.696037</td>\n",
              "      <td>1.251809</td>\n",
              "      <td>0.508361</td>\n",
              "      <td>-0.371747</td>\n",
              "      <td>-2.261847</td>\n",
              "      <td>-0.871901</td>\n",
              "      <td>-0.932831</td>\n",
              "      <td>-0.215352</td>\n",
              "      <td>1.344583</td>\n",
              "      <td>0.589481</td>\n",
              "      <td>1.046196</td>\n",
              "      <td>-1.247313</td>\n",
              "      <td>-0.395715</td>\n",
              "      <td>-0.289859</td>\n",
              "      <td>0.258751</td>\n",
              "      <td>0.966525</td>\n",
              "      <td>0.603396</td>\n",
              "      <td>-1.130747</td>\n",
              "      <td>0.863917</td>\n",
              "      <td>-0.253821</td>\n",
              "      <td>-0.164532</td>\n",
              "      <td>-0.036593</td>\n",
              "      <td>-0.244676</td>\n",
              "      <td>0.125635</td>\n",
              "      <td>0.205119</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103685</th>\n",
              "      <td>145159</td>\n",
              "      <td>68762.0</td>\n",
              "      <td>-0.479516</td>\n",
              "      <td>0.356747</td>\n",
              "      <td>1.974170</td>\n",
              "      <td>-0.376257</td>\n",
              "      <td>-0.092452</td>\n",
              "      <td>0.618588</td>\n",
              "      <td>-0.217359</td>\n",
              "      <td>0.161026</td>\n",
              "      <td>0.555482</td>\n",
              "      <td>0.155061</td>\n",
              "      <td>-0.160528</td>\n",
              "      <td>-0.850359</td>\n",
              "      <td>-1.431053</td>\n",
              "      <td>-0.105780</td>\n",
              "      <td>0.937078</td>\n",
              "      <td>1.229569</td>\n",
              "      <td>-1.390534</td>\n",
              "      <td>1.654196</td>\n",
              "      <td>0.296108</td>\n",
              "      <td>-0.076741</td>\n",
              "      <td>0.280530</td>\n",
              "      <td>0.827117</td>\n",
              "      <td>-0.303968</td>\n",
              "      <td>-0.839495</td>\n",
              "      <td>-0.676149</td>\n",
              "      <td>0.365361</td>\n",
              "      <td>-0.271381</td>\n",
              "      <td>-0.079046</td>\n",
              "      <td>6.95</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        customer_id      Time        V1  ...       V28  Amount  Class\n",
              "253553       352247  156309.0  1.981399  ... -0.032189   24.81      0\n",
              "89871        161669   62761.0  1.184980  ...  0.032692   73.00      0\n",
              "274419       401493  166000.0 -1.527155  ...  0.147651    2.25      0\n",
              "201603       100320  133959.0 -1.122292  ...  0.205119    1.00      0\n",
              "103685       145159   68762.0 -0.479516  ... -0.079046    6.95      0\n",
              "\n",
              "[5 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FkB8nUy1mVKs",
        "colab_type": "text"
      },
      "source": [
        "### **Separating the 'Class' feature**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rMc92WgvW4hz",
        "colab_type": "code",
        "outputId": "5754f790-28b2-407c-c6d4-3d4f27dfd572",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "X = sampled_dataset.iloc[:, :-1].values\n",
        "pd.DataFrame(X).head()"
      ],
      "execution_count": 10,
      "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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>352247.0</td>\n",
              "      <td>156309.0</td>\n",
              "      <td>1.981399</td>\n",
              "      <td>0.185255</td>\n",
              "      <td>-3.650571</td>\n",
              "      <td>0.472477</td>\n",
              "      <td>3.227775</td>\n",
              "      <td>3.064406</td>\n",
              "      <td>0.110535</td>\n",
              "      <td>0.659636</td>\n",
              "      <td>-0.016456</td>\n",
              "      <td>-0.336699</td>\n",
              "      <td>0.193482</td>\n",
              "      <td>0.041694</td>\n",
              "      <td>-0.484864</td>\n",
              "      <td>-0.780075</td>\n",
              "      <td>0.007393</td>\n",
              "      <td>-0.268799</td>\n",
              "      <td>0.889960</td>\n",
              "      <td>-0.033402</td>\n",
              "      <td>-0.505414</td>\n",
              "      <td>-0.138698</td>\n",
              "      <td>-0.030301</td>\n",
              "      <td>-0.005892</td>\n",
              "      <td>-0.007516</td>\n",
              "      <td>0.579525</td>\n",
              "      <td>0.505753</td>\n",
              "      <td>-0.472820</td>\n",
              "      <td>0.016482</td>\n",
              "      <td>-0.032189</td>\n",
              "      <td>24.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>161669.0</td>\n",
              "      <td>62761.0</td>\n",
              "      <td>1.184980</td>\n",
              "      <td>-1.562447</td>\n",
              "      <td>1.636660</td>\n",
              "      <td>-0.119947</td>\n",
              "      <td>-2.215818</td>\n",
              "      <td>0.667241</td>\n",
              "      <td>-1.893293</td>\n",
              "      <td>0.446549</td>\n",
              "      <td>1.369551</td>\n",
              "      <td>0.217240</td>\n",
              "      <td>-2.116081</td>\n",
              "      <td>-1.161186</td>\n",
              "      <td>-1.710985</td>\n",
              "      <td>-1.335598</td>\n",
              "      <td>-1.441250</td>\n",
              "      <td>0.748530</td>\n",
              "      <td>0.897897</td>\n",
              "      <td>-0.782924</td>\n",
              "      <td>1.158079</td>\n",
              "      <td>0.067352</td>\n",
              "      <td>0.249439</td>\n",
              "      <td>0.937779</td>\n",
              "      <td>-0.265218</td>\n",
              "      <td>-0.063138</td>\n",
              "      <td>0.516223</td>\n",
              "      <td>0.128006</td>\n",
              "      <td>0.081299</td>\n",
              "      <td>0.032692</td>\n",
              "      <td>73.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>401493.0</td>\n",
              "      <td>166000.0</td>\n",
              "      <td>-1.527155</td>\n",
              "      <td>0.170900</td>\n",
              "      <td>-0.116909</td>\n",
              "      <td>-0.417367</td>\n",
              "      <td>0.635782</td>\n",
              "      <td>-1.435035</td>\n",
              "      <td>0.827557</td>\n",
              "      <td>-0.357451</td>\n",
              "      <td>0.698918</td>\n",
              "      <td>0.461904</td>\n",
              "      <td>-1.003429</td>\n",
              "      <td>-0.428902</td>\n",
              "      <td>-0.992378</td>\n",
              "      <td>0.038490</td>\n",
              "      <td>-0.134832</td>\n",
              "      <td>-0.015578</td>\n",
              "      <td>-0.435619</td>\n",
              "      <td>-0.686190</td>\n",
              "      <td>0.037416</td>\n",
              "      <td>-0.673027</td>\n",
              "      <td>-0.471623</td>\n",
              "      <td>-0.563613</td>\n",
              "      <td>0.798725</td>\n",
              "      <td>0.013735</td>\n",
              "      <td>-0.305358</td>\n",
              "      <td>0.146980</td>\n",
              "      <td>0.034169</td>\n",
              "      <td>0.147651</td>\n",
              "      <td>2.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100320.0</td>\n",
              "      <td>133959.0</td>\n",
              "      <td>-1.122292</td>\n",
              "      <td>1.804898</td>\n",
              "      <td>-1.246749</td>\n",
              "      <td>-0.696037</td>\n",
              "      <td>1.251809</td>\n",
              "      <td>0.508361</td>\n",
              "      <td>-0.371747</td>\n",
              "      <td>-2.261847</td>\n",
              "      <td>-0.871901</td>\n",
              "      <td>-0.932831</td>\n",
              "      <td>-0.215352</td>\n",
              "      <td>1.344583</td>\n",
              "      <td>0.589481</td>\n",
              "      <td>1.046196</td>\n",
              "      <td>-1.247313</td>\n",
              "      <td>-0.395715</td>\n",
              "      <td>-0.289859</td>\n",
              "      <td>0.258751</td>\n",
              "      <td>0.966525</td>\n",
              "      <td>0.603396</td>\n",
              "      <td>-1.130747</td>\n",
              "      <td>0.863917</td>\n",
              "      <td>-0.253821</td>\n",
              "      <td>-0.164532</td>\n",
              "      <td>-0.036593</td>\n",
              "      <td>-0.244676</td>\n",
              "      <td>0.125635</td>\n",
              "      <td>0.205119</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>145159.0</td>\n",
              "      <td>68762.0</td>\n",
              "      <td>-0.479516</td>\n",
              "      <td>0.356747</td>\n",
              "      <td>1.974170</td>\n",
              "      <td>-0.376257</td>\n",
              "      <td>-0.092452</td>\n",
              "      <td>0.618588</td>\n",
              "      <td>-0.217359</td>\n",
              "      <td>0.161026</td>\n",
              "      <td>0.555482</td>\n",
              "      <td>0.155061</td>\n",
              "      <td>-0.160528</td>\n",
              "      <td>-0.850359</td>\n",
              "      <td>-1.431053</td>\n",
              "      <td>-0.105780</td>\n",
              "      <td>0.937078</td>\n",
              "      <td>1.229569</td>\n",
              "      <td>-1.390534</td>\n",
              "      <td>1.654196</td>\n",
              "      <td>0.296108</td>\n",
              "      <td>-0.076741</td>\n",
              "      <td>0.280530</td>\n",
              "      <td>0.827117</td>\n",
              "      <td>-0.303968</td>\n",
              "      <td>-0.839495</td>\n",
              "      <td>-0.676149</td>\n",
              "      <td>0.365361</td>\n",
              "      <td>-0.271381</td>\n",
              "      <td>-0.079046</td>\n",
              "      <td>6.95</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         0         1         2         3   ...        27        28        29     30\n",
              "0  352247.0  156309.0  1.981399  0.185255  ... -0.472820  0.016482 -0.032189  24.81\n",
              "1  161669.0   62761.0  1.184980 -1.562447  ...  0.128006  0.081299  0.032692  73.00\n",
              "2  401493.0  166000.0 -1.527155  0.170900  ...  0.146980  0.034169  0.147651   2.25\n",
              "3  100320.0  133959.0 -1.122292  1.804898  ... -0.244676  0.125635  0.205119   1.00\n",
              "4  145159.0   68762.0 -0.479516  0.356747  ...  0.365361 -0.271381 -0.079046   6.95\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i5cMev1nW6Ij",
        "colab_type": "code",
        "outputId": "60ba6979-d267-472a-ecba-37edf7165163",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "y = sampled_dataset.iloc[:, -1].values\n",
        "pd.DataFrame(y).head()"
      ],
      "execution_count": 12,
      "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   0\n",
              "0  0\n",
              "1  0\n",
              "2  0\n",
              "3  0\n",
              "4  0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YzEr8DosGbAq",
        "colab_type": "text"
      },
      "source": [
        "### **Feature Scaling**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cbcqCPDqXElV",
        "colab_type": "code",
        "outputId": "f2b9ff65-97c4-4f62-def6-17eef1788b07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "sc = MinMaxScaler(feature_range = (0, 1))\n",
        "X = sc.fit_transform(X)\n",
        "pd.DataFrame(X).head()"
      ],
      "execution_count": 13,
      "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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.630575</td>\n",
              "      <td>0.908183</td>\n",
              "      <td>0.991084</td>\n",
              "      <td>0.899815</td>\n",
              "      <td>0.611435</td>\n",
              "      <td>0.334491</td>\n",
              "      <td>0.330738</td>\n",
              "      <td>0.891396</td>\n",
              "      <td>0.690759</td>\n",
              "      <td>0.694798</td>\n",
              "      <td>0.340644</td>\n",
              "      <td>0.380272</td>\n",
              "      <td>0.555262</td>\n",
              "      <td>0.556909</td>\n",
              "      <td>0.399829</td>\n",
              "      <td>0.418499</td>\n",
              "      <td>0.421154</td>\n",
              "      <td>0.365925</td>\n",
              "      <td>0.232340</td>\n",
              "      <td>0.384448</td>\n",
              "      <td>0.412289</td>\n",
              "      <td>0.567529</td>\n",
              "      <td>0.379280</td>\n",
              "      <td>0.537045</td>\n",
              "      <td>0.559064</td>\n",
              "      <td>0.492678</td>\n",
              "      <td>0.645895</td>\n",
              "      <td>0.296416</td>\n",
              "      <td>0.414630</td>\n",
              "      <td>0.458269</td>\n",
              "      <td>0.009299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.154030</td>\n",
              "      <td>0.363485</td>\n",
              "      <td>0.969502</td>\n",
              "      <td>0.864732</td>\n",
              "      <td>0.889115</td>\n",
              "      <td>0.296397</td>\n",
              "      <td>0.201681</td>\n",
              "      <td>0.803509</td>\n",
              "      <td>0.625968</td>\n",
              "      <td>0.681762</td>\n",
              "      <td>0.493031</td>\n",
              "      <td>0.435261</td>\n",
              "      <td>0.166587</td>\n",
              "      <td>0.377051</td>\n",
              "      <td>0.179928</td>\n",
              "      <td>0.353080</td>\n",
              "      <td>0.246322</td>\n",
              "      <td>0.492780</td>\n",
              "      <td>0.233068</td>\n",
              "      <td>0.277872</td>\n",
              "      <td>0.646502</td>\n",
              "      <td>0.584338</td>\n",
              "      <td>0.399629</td>\n",
              "      <td>0.744014</td>\n",
              "      <td>0.533694</td>\n",
              "      <td>0.382864</td>\n",
              "      <td>0.648196</td>\n",
              "      <td>0.454173</td>\n",
              "      <td>0.428479</td>\n",
              "      <td>0.464723</td>\n",
              "      <td>0.027361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.753716</td>\n",
              "      <td>0.964610</td>\n",
              "      <td>0.896006</td>\n",
              "      <td>0.899527</td>\n",
              "      <td>0.797019</td>\n",
              "      <td>0.277273</td>\n",
              "      <td>0.269287</td>\n",
              "      <td>0.726433</td>\n",
              "      <td>0.713943</td>\n",
              "      <td>0.632575</td>\n",
              "      <td>0.419297</td>\n",
              "      <td>0.459548</td>\n",
              "      <td>0.353834</td>\n",
              "      <td>0.486544</td>\n",
              "      <td>0.308808</td>\n",
              "      <td>0.514894</td>\n",
              "      <td>0.403989</td>\n",
              "      <td>0.397500</td>\n",
              "      <td>0.110670</td>\n",
              "      <td>0.291627</td>\n",
              "      <td>0.488717</td>\n",
              "      <td>0.523940</td>\n",
              "      <td>0.347177</td>\n",
              "      <td>0.414723</td>\n",
              "      <td>0.638438</td>\n",
              "      <td>0.396000</td>\n",
              "      <td>0.467611</td>\n",
              "      <td>0.459155</td>\n",
              "      <td>0.418409</td>\n",
              "      <td>0.476158</td>\n",
              "      <td>0.000843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000625</td>\n",
              "      <td>0.778046</td>\n",
              "      <td>0.906977</td>\n",
              "      <td>0.932328</td>\n",
              "      <td>0.737681</td>\n",
              "      <td>0.259354</td>\n",
              "      <td>0.283892</td>\n",
              "      <td>0.797684</td>\n",
              "      <td>0.675165</td>\n",
              "      <td>0.516067</td>\n",
              "      <td>0.246590</td>\n",
              "      <td>0.321095</td>\n",
              "      <td>0.486459</td>\n",
              "      <td>0.751720</td>\n",
              "      <td>0.592509</td>\n",
              "      <td>0.633563</td>\n",
              "      <td>0.269728</td>\n",
              "      <td>0.350100</td>\n",
              "      <td>0.124048</td>\n",
              "      <td>0.425989</td>\n",
              "      <td>0.619532</td>\n",
              "      <td>0.628067</td>\n",
              "      <td>0.299231</td>\n",
              "      <td>0.727815</td>\n",
              "      <td>0.534816</td>\n",
              "      <td>0.365539</td>\n",
              "      <td>0.526686</td>\n",
              "      <td>0.356319</td>\n",
              "      <td>0.437952</td>\n",
              "      <td>0.481874</td>\n",
              "      <td>0.000375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.112746</td>\n",
              "      <td>0.398427</td>\n",
              "      <td>0.924396</td>\n",
              "      <td>0.903258</td>\n",
              "      <td>0.906841</td>\n",
              "      <td>0.279916</td>\n",
              "      <td>0.252022</td>\n",
              "      <td>0.801725</td>\n",
              "      <td>0.680157</td>\n",
              "      <td>0.664294</td>\n",
              "      <td>0.403526</td>\n",
              "      <td>0.429088</td>\n",
              "      <td>0.495685</td>\n",
              "      <td>0.423527</td>\n",
              "      <td>0.230133</td>\n",
              "      <td>0.497905</td>\n",
              "      <td>0.533354</td>\n",
              "      <td>0.552763</td>\n",
              "      <td>0.023022</td>\n",
              "      <td>0.624409</td>\n",
              "      <td>0.525140</td>\n",
              "      <td>0.572583</td>\n",
              "      <td>0.401890</td>\n",
              "      <td>0.719743</td>\n",
              "      <td>0.529879</td>\n",
              "      <td>0.250206</td>\n",
              "      <td>0.386110</td>\n",
              "      <td>0.516494</td>\n",
              "      <td>0.353123</td>\n",
              "      <td>0.453608</td>\n",
              "      <td>0.002605</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         0         1         2   ...        28        29        30\n",
              "0  0.630575  0.908183  0.991084  ...  0.414630  0.458269  0.009299\n",
              "1  0.154030  0.363485  0.969502  ...  0.428479  0.464723  0.027361\n",
              "2  0.753716  0.964610  0.896006  ...  0.418409  0.476158  0.000843\n",
              "3  0.000625  0.778046  0.906977  ...  0.437952  0.481874  0.000375\n",
              "4  0.112746  0.398427  0.924396  ...  0.353123  0.453608  0.002605\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0L9t3Zi3IQ4_",
        "colab_type": "text"
      },
      "source": [
        "### **Training the SOM**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "deVPWZhdH3OI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "som = MiniSom(x = 10, y = 10, input_len = 31, sigma = 1.0, learning_rate = 0.5)\n",
        "som.random_weights_init(X)\n",
        "som.train_random(data = X, num_iteration = 100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v8KVD8o3nKUV",
        "colab_type": "text"
      },
      "source": [
        "### **Visualizing the results**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7HX76DdfYp8z",
        "colab_type": "code",
        "outputId": "5b6efbc5-6c15-49ef-b624-aa1550da5b72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "bone()\n",
        "pcolor(som.distance_map().T)\n",
        "colorbar()\n",
        "markers = ['o', 's']\n",
        "colors = ['r', 'g']\n",
        "for i, x in enumerate(X):\n",
        "    w = som.winner(x)\n",
        "    plot(w[0] + 0.5,\n",
        "         w[1] + 0.5,\n",
        "         markers[y[i]],\n",
        "         markeredgecolor = colors[y[i]],\n",
        "         markerfacecolor = 'None',\n",
        "         markersize = 10,\n",
        "         markeredgewidth = 2)\n",
        "show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAD8CAYAAABJsn7AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOydd3wU1d7Gvyc9QAih915C6IKAqIBy\nRRAFKRYQBBVBFDsqNgQULyp6BVEEuSKCIErv4gUB6TUQEkILvZcAoaSf94/dma3ZnZ3ZhJV3Hj/7\nMZtzOM+eye6zZ35VSCkxYcKECRMFj6Bb/QJMmDBh4v8rTAE2YcKEiVsEU4BNmDBh4hbBFGATJkyY\nuEUwBdiECRMmbhFMATZhwoSJWwSvAiyE+FEIcU4Iscfud8WFEH8KIQ5Y/x+Tvy/ThAkTJm4d3Omg\n07gQQowTQhwUQuwWQtyhZV0tJ+CfgA5OvxsKrJRS1gJWWp+bMGHCxO2Kn3DVQXt0BGpZHwOACVoW\n9SrAUsq1wCWnX3cBplp/ngo8qoXMhAkTJv6JyEMH7dEF+FlasAkoJoQo523dEJ2vp4yU8rT15zNA\nmbwmCiEGYPlGAGgaHKyXUjtCQyPynUOBEKLAuKBgshZzc3MLhAcgLCyywLhyc7MLjEuIgnOvFFQ2\na05OVoHwKEhPv35BSllK77/v0KGDvHDhgqa527dvTwTS7X41SUo5yQe6CsBxu+cnrL877X66BYbV\nUEophRB5vgOsm5gEEBISKqOiihul9IoKFWrnO4eCsLCCE/uc7IL5ANy4mVYgPABVqtQvMK7r1y8X\nGFdISFiBcUlZMF+YFy+eKhAeBcnJm44a+fcXLlxg27ZtmuYKIdKllM2M8OmB3q/ps8rx2vr/c/57\nSSZMmDDhH0gpNT38gJNAJbvnFa2/8wi9ArwQ6Gv9uS+wQOc6JkyYMJEvkEBObq6mhx+wEHjaGg3R\nErhiZ6bNE15NEEKImUBboKQQ4gTwETAa+E0I8RxwFHjcyCs3YcKECf9DIv3kN8lDB0MBpJTfA0uB\nh4CDwA3gGS3rehVgKWXPPIbaaSHQi12Xz1HR7vkxoEmx0vnCtWjvZqrk2uyrh4ND6RLbIl+45u1e\nS6WcTPX50ZAwujdonS9cGxPWYG+hvgm0atDG7zzt0lJ5OfUMRXJzuBYUzNiYsvwVlT+h4UMS1vLA\nuSMILCec5WWq8Z/69+YLV720S/Q8fYio7CzSQkKZUa4mSfm0r+F71tP28ml1X6tiyjGy3t1+5/l5\n6zKqZt5Un6eERdLvzo5+5wGYdWgn9bMy1Oe7QyPoWaNxvnC5hYRcP/knPeigMi6Bl3xdVxRkPWAt\nTri1l8+huGXs4wuUV7kHaO1FiLU64abv30ajrPQ8uXaFRtC7tme7vFYn3JTE9TS0vvHdce0Oi+QZ\nLx84rU641QlrKGb92R3XZaCtByHW6oRrcf0qk08fIhzpwpOBoH+5GmwuXNTjGlqdcP2Tt/DEyWTA\n/Z5mVYhlcmxzj2todcLVuH6Fr5I3Uyor3YXrfGgEb8S24FDhaI9raHXCvbZvK93OW3xN7vY1t1QV\nvq5zp8c1tDjhvtmxksY3r+TJEx8Zzct3eD5TaXXCfX84gdYZ1/PkWhtemBeqNfC6TnLypu1GHGNN\nmzaV6zdt0jQ3MizMEJdeBFQqsiK+yh9N2j2w/r4+8Ndl4z4/RXw9cTXKSmfqge2GuRTx9cTVMPMm\nPyRtMMyliK8nrmLAqoQ1hnhaXL/KtNMHibCunI7gdHAoyhWNQDLt9EHuvHHVEA/YxNfTnp44mUy/\n/VsNc9W4foWfEtZS2vrFfC40gl1FYjhnDW0snZXOTwlrqXb9imEuRXw97avb+aO8ckCbJz8vKOLr\niafxzSuM3bnKEA/YxNcTV+uM63x7xG1CmV8hgVwpNT1uFfI/KNcHKOIrgSPAHXYn3R2Xz1HVOt7I\nD1yK+ErgYHAYXe1OT/OSt1AzJxMB3GF3u6YXivhKICUknMca2G6Zf0/4m+rZGRaujBuGuRTxlcB1\n4G67k+76hDUUto4bDQacfPoQAsgFBpatzuoixdSxttcuM/FMCkHAj6cO0aBmE0NcivhKYHHZaoyt\nZ7t+ryb+zcNnDiOAp47v5afank+L3vBV8mZCkKQHBTGo7t0k25kcYtNSmbB3PRG5uXydvJkuTdsb\n4lLEVwIripfnk7hW6tgHSRtof+kUAuhx9gjjauk/nCniK4ED4YV4tpktoevHbcuplXHD8v67YTxM\nTxFfCewIi6B3dZvJYXpKPHdkWj5396VfM8ylBYHe8SdgTsC7rKdad+KL9flRbN+kOw2cghft3axy\nOYsvQNfY5qQEh6pcC5I36+aat3utyuUsvgCPNbiXIyFhKtechLW6uTZaT7XuxBfr8xvYruEGnafg\ndmmphFvdG87iC7C6SDFeKFMNCYQjuS8tVRcPWGy+4F58AcbWu5elVi6A1/f8rZurXtolSmWlI8FF\nfAGSo2J4qe7dSKBUVjpxBvY1fM96wL34AnwS14r/xZRT9zUscb0unp+3LlN5nMUX4NlmHTgUFqny\n/GSdrwezDu1UuZzFF6B39cbsCo1QuWYeitfNpQVSyoKMgtCFgBHgithum53FV4HihBNAZQNcVXKz\nVC5n8VWgOOEEUM1ABlAl60kacBFfBYoTTgBVsjPdztGCCGzX0Fl8FShOOAHozUF7OfUMAoud11l8\nFfwVFUMGAgG8knpGJxOqww1wEV8FihNOAA+ePaybq6f1VH8+NMJFfBUkRcVwPjQCYZ2vF4rDDXAR\nXwWKE04A96d6jWhyi6p2pi9n8VWgOOEEUN3AHV/9rAyVy1l8FShOOAE0zEp3O8efKMA4YF0IGAE2\n8c9BkdwcAFK9pJVfDg4GIMo6Xw98TfQ28oaOsjo5T4d7/mo6Yx0vauDL0td9FWTC++0EqfG/W4WA\nsgGb+GfgWpBFWGNyPNdWKJZjEd4063w98PWjYeRmMi0kFIByGZ5PgWWt41cNpBv7uq/AtmQGJixO\nuFv9KjwjYE7AJ7C9yXbkYd/daWcnPmaA62iQzb47L3mL2zmK3VdiiQvWi+PBNvvu7wnu7ZNz7Oyc\nRw18qNOxXcP1edh3N9jZifXebH4TU1a177a95t5xc5+dnXhcTFmdTPBn6arqnl5NdH/9FLuvBP4o\nU00318xyNVT7bmwe9t24tFTVTjyzXA3dXKuL2ey7H+QR/aLYfZW4YD04Ymff/XHbcrdzfrKzE6cY\nKI60JzRc5Zqe4t6+q9h9JZa44PyGaYLQiEZ29t2quDrZdl4+RxVst2JGkjIeqWuz79bMyXRxsi1I\n3kz1HJud2EhSRteGNvtu9ewMFyfbnIS1VM222YmNJGXcZWffLYyrk21DwhoKYbuGepMyVtrZdyee\nSXFxst2Xlsr3Zw+rdmIjSRlj7OzjD5857OJke33P3zxk5QIMJWUkRhVX7bsT9q53cbLFpaXy7d71\nqp3YSFLG8Po2+277S6dcnGzDEtfzr1SbnVhvUsbTdvbdWhk3XJxsP21dRg07O7GRpIwnajRRue7I\nTHdxss08FO8Q+pnvSRn/ACdcQJkg9mALRasCXHISYSW8ZZcfuHaFRqhvhuo5WSQkrnPLtcMP5RJ3\nh0WqoWhVszPZvvN/7rnCCxnmuowtFK0QEO8kwgqXp8KmWtC/XA2mnT5IEDDx7GEyzh7hcnAwxXJy\n1MQMCTxbXv8pUcGsCrFqKNpDZw/zkJOjTeH6pVJdw1xvxLbgp4S1ROTmMjnxb86HRnAmPJKyGTfV\nxIxsBK/5IVNybqkqaijav1JP8691sx3GlX3NLlPVEE98ZLQailYj8yZ/r5/rlmdHIfcOVV+wNryw\nGorWKCudpGTHRAiF66+IIoa5vEFihqH5hNbFSrMHx6Bt5QE28b3PDynJvWs3cwiJcce1IyySvrWa\nGuZ6pt7d7La7FXTLFV6I5/PwhvuCtg3acBnP1/AScL/BlOTNhYvSp1xNh8SLsjnZDokZT5WvydZC\nnjPhtGBybHNmVYj1uKdfKtU1HAMMcKhwNP0atHZIvGh4LdUhMePpBq057CUTTgu+rnMnc0tV8biv\n2WWqGooBBnj5jnbER0Z7fv8VKsarTe43xAPwQrUGrA0v7JHrr4givFS1YMqQBnoiRsClIivYefmc\nQ6iZL7UgfK0HvCB5s0OomS+1IHytBzwnYa1DqJkvtSB8rQe8IWGNQ6iZ1loQvtYDvi8tlVdSzxCV\nm0NaUDDjfKgF4Ws94Nf3/M2DZw8ThMXh9ocPtSB8rQccl5ZKz9OHKJqdydWQMGaWq6HZ7OBrPeBh\nieu5P1VfLQhf6gH/tHWZQ6iZL7UgfK0HPPNQvEOoma+1IIymIjdq0kQuW6Utu69C8eK3JBU5YAXY\nCMyC7MZgFmQ3DrMgu3EYF+DGcqlGAa5YvMQtEeCAsgGbMGHChL8gZeCHoZkCbMKEidsWBdnfUA9M\nATZhwsRtCaUaWiDDFGATJkzctgj0MLQCFeCwsAgqVYrNd56+b72S7xwKTh0sOMdEcKj+lF5fUCS6\ncIHwAJwswOtHAX4YRVDBRXjeuGq8jKkWZCYYL81aoPBziJkQogMwFggGJkspRzuNVwF+BEphifbs\nLaU84WnNgIoDNmHChAl/wl+pyEKIYOBboCMQB/QUQsQ5TRsD/CylbAiMBP7tbV1TgE2YMHFbQgI5\nUmp6aEBz4KCUMkVKmQn8CnRxmhMHKHFvf7kZd4EpwCZMmLht4cMJuKQQYpvdY4DTUhWA43bPT1h/\nZ49dQDfrz12BKCFECU+vL2CdcCOPJvHw1QtqxtOC6JKMqOx84vcPXnq6B/Zh85nAtz/Pzmu6IXzw\n+VCi0202u8uRhRn1ltc7FV3oO2UcdVP2qc8Ta9ZlWt/Bfud5+5V+Ltfv83E/+Z0HICbtMo1S9lIo\n4yY3wiPZVb0uqVHGaxi4Q6s9W3hkyyrCsrPIDAllYYt2bKxnPN3ZHT77dphajxgspTHfeWmk33la\n79tB151rCc/JIiM4lDlNWrOuzh1+5wH4Mnkzd1+zVR35O6oEb9Xx3DjV3/DBCXfBD4kYQ4DxQoh+\nwFrgJOCxGHaBZsIVKhQla9b0/Md+7cQBnrFW/7cvQq28yikx5fi6Yi2Pa2h1wg16uoeaquuO6yYw\nwYsQa3XCvTn2I8pdSc2T63R0DF++OsLjGlqdcN1/+5HmCdvz5NrSoClzHn82z3+v1Qk35JV+Xq/f\nGC9CrNUJV/R6Gk+uXkiTlCSC7N6zuUKws3ocv7btzNXCUZ4X0fheb7x/NwP/+A2Ba7dniWDig48T\nX7uhxzW0OuFGTvyEUul5dxA+H1GYYQM/8LiGFidc05QkXl4zN889fdOmG9urez7gJCdoaw76/qGd\ndLZ2QXG3p4UxZRlVw3ufwK3blhnKTqvXsKGctXSpprkNKlXyyCWEuAsYLqV80Pr8XQAppdvTkxCi\nCJAspazoiTegTBCK+HrqqPpM6mlePnnQMJcivp64IoEXnu5hmEsRX09c5a6k8to44yceRXw9cTVP\n2E7XOVMN8Sji6+36vflKP0M8YBHft3+fSNNDieSKILbXqMfSZm3ZXqMeUgiaHkrk7d8nEnXDeAp1\n4/27eeGPWQRZd5KL4GZoGLnWnQYheeGPWTQ8mGiYSxFfT9ewVPp1hk8aZYinaUoSr6yZ47CnGyGO\ne3plzRzuOJJkiAds4utpT51TzzD0kD9qGnqHH+sBbwVqCSGqCSHCgCeBhfYThBAlhRCKpr6LJSLC\nIwLKBPGMXTGS2cVK8bFdecEPj++lx+XzCKD/pVN8U6GmIS5FPCSWQubf2Z10X3y6h9pfzXiBSFTx\nlcClQkX4dMin6th7Y96j+I1rCKDi5QuGuRTxlUBC7XpM7/OiOtZ72nc02J+IAFrGb2Fe9766eZyv\n3xd2J923Xunn1+v35OqFlLp6iaOlyvPdw324HGWrRFYs7QovLp5GlfOn6PnXQiZ1esoQl+Xka9nX\nlHbd2Bxnq4bXImk7z6yciwAGLZvJoJc/McSliK8EroSGMfRF2x3Q6O8+IjrLUie6zE1jHYQtJ18L\nz8S7H2FDbVtBnFb74xm4fhECeOWvufR7xpiZTxFfCawuWoJ3attMDp/t30LbqxcRQNfUU4z2S39z\nz/DXHb6UMlsIMRj4A0sY2o9SykQhxEhgm5RyIdAW+LcQQmIxQbzkbd2AOQGPPGr59nUnvgAfV6rL\n3OiS6jfpR8f0f1u/ZD3VuhNfrM8zsH1rv2TgFPzB50NVLmfxBfh0yKekRtrK973/xbu6ufpOGady\nOYsvwPQ+L7KnZl2Vq8/U8bp43raeat2JL9bn9tfvbQOn4Ji0yzRJSSI7KNhFfAEuR0Uz4eHe5AQF\n0SQliZg0/QV3Wu3ZgrB28XAWX4DNcU2Zev+jSEAguStxq26uz74dBrgXX4ChL47gaoitc4sy31e0\n3rdD3ZOz+AJsqN2YH1p1Uvd0z74dunjAYvMF9+IL8E7t5qyNKqHu6Yt97rvR+AuWKIhcTQ9N60m5\nVEpZW0pZQ0o5yvq7YVbxRUo5W0pZyzqnv5Qyw9uaASPAD1+9oN62OIuvAsUJJ4AuV/SfFsOw3To7\ni68CxQknrPP1Ijr9hsrlLL4KFCecAIrdvK6bq27KPpXLWXwVKE44AdQ7uFcXj/31cxZfBYoTzuj1\na5SylyAp2VUt1kV8FaRGFSO+Wl2CpKRRir49ATyyZZX19CZcxFfBxnp3Iq3dQB7ZrK3SljtEZds6\nrjiLrwLFCSfAwUHnC7ruXKvuyVl8Fayrc4e6p27xa93O0YK7r11S9+QsvgoUJ5wA7k27qJtLK3Kl\ntsetQsAIsK8vJGBeuIl8RSFrA8yzMaU8zjsXU9Jhvh6EWUUuI9RzD0BlPNxAV+SCQri1znV6iOc9\nKeMRBVTytECg0f5r9oTD9262gV3jyIS/cMPaAr5M6nmP80qnXnCYrweZVhEKz/IsQsp4RgHW/NWL\njGBtwqqMexPqfxKUlkSmAGvA4qI2++6Hx93fRip2X4klLlgvMrHZJ1/Mw75rbyc2cs65ElFI5Xpv\nzHtu5yh2X4klLlgv9lavo3L1nvad2zmK3VdiiQvWA/vr91Ye9l17O7GR67erel1yhaDR4WSKpV1x\nOycm7TKND+8lVwh2VdffF25R8/tVW2iLpO1u59yVuFW1qS5qob+FT5qdfXf0dx+5nWNvJ07TKYzz\nmrRW99Rqv/tOxffY2YnnNtbfFHZ9keI2m/V+9/Zdxe4rscQF5zcCvSVRwAjwsCo2+26Py+ddnGwf\nHUui2xWbndhIUoa9fTcCVyfbS0/3IBybndNIUsYnb49WuYrfuObiZHv/i3eJuWkLRTKSlDH1mVdU\nrgb7E12cbH2mjqf+wb0ql96kDHv7bgSuTra3X+nncP2MJGWkRhVjZ/U4QnJzeHHxNBcnW0zaZQYt\nnk5wbi47q8cZSsrYUL+5agt9ZuVcFyfbXYlb6btqvmpTNZKUYW/fjc7KdHGyffbtMIra2Yn1JmWs\ntbPvDly/yMXJds++HTy/YYm6JyNJGW/G2rqNt7160cXJ9sW+LbROu6juqSCSMgL9BBxQYWhTYsqp\noWjdrlygm1MLdyW8ZXLx8oa5bmILpQoH3nASYYXLH3WmTkfHqKFoMTevM2akY6KIwnWimP5TvYIt\nDZqqoWj1D+7lsw8dI2EUrk2Njb35na/fh04i7M/r92vbzlQ+f5oq508xauoY4qvV5VxMSUqnXqDx\n4b0E5+ZyvmhxZt7X2TDXxAcf54U/ZiGAvqvm8/SqBWSEhhKelaUmMUhgQseehrnORxRWQ9GKZmcx\nYazjl7PCdTbSWAfhb9p045U1cxDA8xuW0H/DUtJDQonIdtzTuPu6eVnJOxbGlFVD0VqnXWTztmUO\n4wrXvBjjn2FvkNa29IGMgDkBA3xdsRZTYsp57Kg6uXh5wzHAYMlwu4nn7sE3gO/9kJL85asjOB0d\n45HrRLGSfP2KvlAje8x5/Fm2NGjqkWtT4+aGYoDBkuGm5fp96YeU5KuFo/j8sYFsr1EPISVNDyXS\ncdsamh5KREjJ9hr1+OzxgaQV8pIJpwHxtRvy/YNPOCQpRGZlOiQxfNexF7tr1jPMNWzgB5yP8NxB\n+GxkEYYPeN8Qz/bqcYxr091hT4WyHfc09r7u7KhqPNV/VI0mLIwp63FP82LKM7pG/scAW/i0/Xer\nEHCpyAo+OpZElyv6akH4Wg/YSC0IX+sBv//Fuw6hZr7UgvC1HnCfqeMdQs201oLwtR6wkVoQvtYD\nNlQLwsf3+l2JW3lk8yrCszPJCAljUYv7NZsdfK0HbKQWhC/1gO/Zt4Nu8WuJyM4iPSSUuY2114LQ\nmoqs4It9WxxCzXytBWE0FTm2fn05ac4cTXPbxMbe/l2RfRFgIzALshuDWZDdOG7Hguy+CrBRGBXg\nOvXry0mztR2k2tate0sE2NC7RAjxuhAiUQixRwgxUwhRcD3aTZgwYcILAt0Jp1uAhRAVgFeAZlLK\n+ljyo5/01wszYcKECUOwOuG0PG4VjEZBhACRQogsLHVXCvB+0oQJEybyhpKIEcjQLcBSypNCiDHA\nMSxRSSuklCuc51kryw8AKBJVjNYPPqKXUjOKlc6fAt3u8Gaf7gXG9f2CZd4n+QGH9xwpEB6AnCyP\n9ar9ikun87/2gIJr1/UXBfIVpcrmf0gXwKnThwqEx58I9Lb0RkwQMVh6HlUDygOFhRC9nedJKSdJ\nKZtJKZtFFjIWz2jChAkTviDQw9CMOOH+BRyWUp6XUmYBc4FW/nlZJkyYMGEcUmp73CoYsQEfA1oK\nIQphMUG0Awo2TsWECRMm8oDkNjZBSCk3A7OBHUCCda1JfnpdJkyYMGEMfo6CEEJ0EELsE0IcFEIM\ndTNeWQjxlxBipxBitxDiIW9rGoqCkFJ+BLgv5WQQ7Tat5MEtqwnJySY7OIRlLdvxV/O2+UFF7aWL\naTJjOiHp6WRHRLC9d18OduiYL1y8+CL88APk5kJQELzwAnzzTb5QlU7eS8OF84i4lkZ6kSh2denG\n+TqxfueJS4qn3aqlRGSkkx4ewZ/3P0RynPvi30YxeOEUGp5MUZ/HV6zJd48YS6vOCy9tWUbbU/vU\n+gUrK8Ty/Z0d8oXrrjNH6X04nsI5WVwPDuXnGk3YXLqy33nGzxxDtF0d49SQcF7p+abfeQBeuHqR\nwdcuES4hQ8C4IsWZVDT/K6Ap8GcUhBAiGPgWeABLS/qtQoiFUkr7qmEfAL9JKScIIeKApUBVj+sW\nZJhG6bKV5ON9XvU4p8WuTfT+05I+6K6j6vQHurO5UUuPazT5l/eOqwBV1q7h/n9/gpBuOsUKwap3\nP+Bo6zYe13juwXaauBg2DD7+OO/xDz+EkZ5TT7VGQcQcSaHTyI8ofOmiy76uFy/BkmEjSK1aPc9/\nrzUKotrBZJ6bPoEQu6pdCk92SCj/7T2IwzU9C/7Fk9oiE/r++Rv3HEwA3L8v1tVswNQHHve4htYo\niD67V/NoSnyeXPOrN2Zaw7Ye19AaBdHw4klGxa8iLDfHhSszKJj3G9/P7hIVPK6hJQri89+/oXy6\npWmpuz2dioji7cde9rjGmlXassqeuJbKmCvn8+QaEl2KWUVivK5z8uR+Q9lpNePi5JfTpmma+2iz\nZoa7IgshJgIpUsrPrPO/lFJ69IsFVDEeRXztC3dkBQWpfzgB9P5zDi12bzbMVWXtGtp9+rHa4jxX\nCDIjI8kV1oIlUtLu04+ptG6dYS6v4guWcS8CrAUxR1Lo8earFLlkEZtrxUtwJrYu14pbTh5FLl2k\nx5uvEnP0iCGeageTGfjTOEKt9QuyQkK5XLQYWda6taHZWQz8aRzVDu8zxAM28fXUafeegwn0/p+2\nvH9PUMTXE9ejKfH0SlhjmKvhxZN8seNPwnMtoXgZQcGcCy9ERpAl5Tw8N4cvdvxJ/YunDfEo4utp\nT+XT0/j3bH09Au2hiK89l31/QAGMuXKeHtdSDXNpgQ/1gEsKIbbZPQY4LVUBOG73/IT1d/YYDvQW\nQpzAcvr1/I1GgJWjVMRXArPbPsyaZrbTZ5tta+ixerFFhFfMZnPDFoa47v/3J7burW8MIaW97day\n+orltP1qDAL416gRTFn2pyEuB/F99VX4+mvb89deg7FjLT9/9JFFrA2g08iPCM7JISssnIWffMb5\n2rXVsVL799P5g3cIzcyg04gPmf6jttOBOzw3fQICS6Gkn3oPYl9sA3WsTnIC/aZPIAh4bup3fDB8\nrP4NgSq+EtheqTbfP9xHHXth8TSaHt+PANociGf6v4zFZSviK4EVleKY2LS9OjZw+wraH09CAN0P\n7WRGA893R94wKn6Veg2HNb6fLaVsJofm548xMn4VQcC/4//HI+365LWMVyjiK4ELoRG89uQb6tjX\nv35Fyax0BFDp5lXdHAoU8ZXAR0VL8KNd0fVn0y4ywtoV+T9XzjNbwynYGHwKMbvgh1oQPYGfpJRf\nWk/A04QQ9aXMu+tnwJyA221aCbgXX4A1zdowt3Un9XLet2W1bq7aSxcjpHQrvgAp7Tuw9rU3LZ0E\npKTmcgMJEC/aNcd0Fl+wPB9sV6HsZa9fmnmidPJeCl+6iAQX8QU4X7s2Cz/+NxIofOkipfYl6+KJ\nS4onJDsLiav4AuyLbcDUXgOQQEh2FrFJ7jsxaMHghVMA9+IL8P3DfdhZsab6vnhx0VTdXC9tWaZy\nOYsvwMSm7fmzQqzK9cLW5bq57jpzlLDcHCSu4guwpVRlPmpk6dARlptDi3PHdPGMnzkGcC++AK89\n+QaXQsLVPY2b+aUuHrDYfBUuZ/EF+DGqBB/bdUUecDV/E2O0hqBptMKeBCrZPa9o/Z09ngN+s3DL\njVj6FXgs8h0wAvzgltXqbYuz+CpQnHAC6GAVbD1oMmO65VtaCBfxVXCwQ0eksHQSaDpd/4eaH36w\n/ewsvgrsnXATJ+qmarhwHgKLnddZfBWcrxPL9eIlEECjhfN08bRbtRSBxc7rLL4KkuMakx0SigAe\n+Ev/F1jDkynq+8JZfBUoTjgBND5xUDeX4nADXMRXgeKEE0C7k/q+wAB6H7actDODgl3EV8Hm0pXJ\nDApGAH1S9H2JRWdnqntyFnFz+kkAACAASURBVF8FihNOADHZXjup54nBdl2RncVXgeKEE8Ar1y7p\n5tIKP0ZBbAVqCSGqCSHCsNS9Weg05xiWcFyEEHWxCLDHZoYBI8AhOdkAZHsp46eMh1rn6+JKT7es\nFeG5eJsyrszXBV8LfeToT82NuGZxslwrXdrjvGulLB2GI9L03XJGZFiux/VCnstWXo8sZJmfrr9T\ncUFCeJ9iaL49Clu7FV8JDfc472qopdJykX9CB2brSdLbK1WqHodrO3nqhhIH7I+ecFLKbGAw8Aew\nF0u0Q6IQYqQQQmnF8ibwvBBiFzAT6Ce9RDkEjA04OziEsJxsQrwIljKeFaz/pWdHRMC1a16FVatQ\ne0RQkG8iHKy/5m96EUtHiCLnznmcV+S85Us5PaqoPp5wy/UofOO6x3mFb1rq1KZH6O9UXJDwVQ+M\n6Md1a7fi6CzPJ86iWRY5u/ZP6MAsIFKCt1eqtBfNMPINphH+jPKSUi7F4lyz/90wu5+TgLt9WTNg\nTsB/NG+rvqHbbHPvYVbsvhJY3lJj+Jcb7OzVW7XvVl/h3o5Xc/ky1U68vbeBONPnn7f9/Npr7ufY\n230HDtRNtbtzV5t9d/9+t3NK7UtW7cS7OnfVxbPy/odU+26d5AS3c2Lt7MR/3qc/pnp3heo2m+ti\n905Dxe4rscQF68Xq8rau0gO3u9SVsrwGq91XiQvWi+nVGqv23ebn3dt3W5w7ptqJp1XXF1d9JSRM\n3dPXv37ldo5i95VY4oL1YrxdV+Rn09zbdwfY2YnHFSmum0sTNNYC/kfWA/Y3VloFVQA9Vi92cbLd\nt2U13dYuUW/7jCRl7H/oYdW+2/arMS5OtprLl9H66y9VO7GhpIzv7NrDjx3r6mR7+WUYbxf+YyAp\n41xsXdW+2/mDd1ycbKX2JdP5w3dtdmKdSRlJdvbdftMnuDjZYpPi6TtjkmonNpKUMb7zM4DlfdH0\n+H4XJ9uLi6bS5MRB9X1hJCnj2+YdVa72x5NcnGwvbF3OAyeTbTZpA0kZG8tWUe27I+NXuTjZWpw7\nxohdq1Q7sd6kjME9hwCWPZXMSndxso2b+SXFszPUPRlJyvjezr474upFFyfbgKsX+dCuK3KBJGUE\neDGIgDFBgCXJQglF67Z2Cd3WLiE7KEg1OyjhLdPb9/C0jCasevcD2n36saV769dfcu/Yr8iOiCAk\nPV1NzJDA/973Q6Lfhx/aQtHGj3cUXHuMGGGYasmwEfR481VCMzPo9s4bXC9egmulSlHk/Hk1MSMn\nOJglH3mJS/aC//YexMCfxhEE9JsxieyQUK5HFqLwzRtqYoYE/tv3RS8rece6mg3UULQmJw7yw4QP\nHcYVrjW1jGffza/eWA1Fe+BkMg84OdoUrjk1tCX7eML7je/nix1/EgSM3LWKzKBgroaGUTQrU03M\nkMC7jf9liOdURJQailY8O4Pp0z51GFd4jkfqM0nZY0h0KTUU7cM0i+BmYTM7KFyvR5cyzKUFuTm3\naS2I/MDmRi2Z/kB3h6Dt0Nxc9RtTEV+jMcAAR1u3YeV7HzokXoTdvOmQmPHnB8M5fs89hrkYOdIi\nwp4wYoThGGCA1KrVmf3lWIfEi7L7kh0SM2Z/9Q2pVaoa4jlcM5aJ/V5xSLwolnbFITFj4nOvcrha\nHUM8AFMfeJx1NRt47LS7plZjwzHAANMatmV+9cYeuebUaGI4Bhhgd4kKvHXHAw6JF6UybjokZrx5\nx4PsKVHOEM/bj73MqYgoj3s6HlmUd3t4b9jqDbOKxDAkupQDV5gT1+vRpQogBlg53Aa2CSLgUpEV\n3LdlNR02rSQ0J5us4BCW+1ALQmsqsoKay5fRdPpUXbUgNKciK3j5ZUuoWU6OxeE2cKBms4OvBdlL\n7Uum0cJ5RKRdJT2qKLs6d9VkdvC1IHtsUjwP/LWMiPSbpEdE8ud9HTWbHbSmIit4cdFUh1AzX2pB\n+FqQ/YWty2lnNTkoNl+tZgdfC7K3OHeMPinxFMnO5FpIGNOqN9ZsdvClIPu4mV86hJr5UgtCayqy\nggFXL/KKgVoQRlORq9WJlSMmaqsP1ve+Nrd/V2RfBNgIfBVgI/BZgA3gduyI4asAG4HZEcMYfBVg\nozAuwHXk8O+1CXC/+9veEgEOKBuwCRMmTPgTMjewbcCmAJswYeK2hGIDDmSYAmzChInbFvIWtpzX\nggIV4MgiEcS1ist3nlKl8t/DqmDSEvcB+/mBkLBQ75P8gCIxBdc89Xjyce+T/ISCtMvmGkgp9xWX\nLxaMbTsiwnPqeSAiwA/A5gnYhAkTtymkNG3AJkyYMHGrYNqATZgwYeIWQGIKsG482+MRwuwM6JlB\nwfw427n8pn9Qf9QoqixYgMjNRQYFcbhbN5KGujQ99Qtajh9HvRXLETIXKYJI7PgQmwYZz0Byh8ob\n1nHHjOmE3bhBZqFCbH/qaY7f5bFFlS48/dlwKh2zNco8VqUG0972Qwq3G7Tdv5NuCesIz8kiIziU\n2Q3uZW3t/GkA+mLiBjqdOkAQklwECyvUZmLcXfnCVVAYtWEeDS/bquXFFyvDh60ezReuRjev8dyV\n80TnZnMlKIT/FivNrgK2Iwe6ABdoIkaVmrXkO194bk/zdM/uFLbWm3XX0O96eAQ/z/Tc+6tilbKa\nXk+tCROInTw5T67k/v05MGiQxzXOnL6giavxtKncOWtGnlxbn+hFfB/PGV1BXmolKygbv5MOwz8g\nODPThSsnLIzlwz/hTOO8k1VOH9bWg6z7+C+ouzfvRpl76zZgzuC3PK6xd+NeTVzNjiTx2roFDim0\nCpcEvr6nC9uqenbwXjirbV89D2yn35G89/VT1QbMrNXU4xoF6YQLjyjkdc6Qrctpe/4o4H5Pq0tV\nYYyXLL99+7Zoej21M27w4+kUyuS4Nms9GxzKs+Wqsz/c+2s+dGinoeSIKjVryXe/HKdp7qBHH7ol\niRgBVQtCEV9PzQMLZ6TT+ynjxXgU8fXEFTt5MjUmacuk8QRFfD1x3TlrBo1m6O/RpqBs/E46vfc2\nIZmWOrLZYWFcK1GS7DBLldaQzEw6vfc2ZXfvMsSjiK+nPdXdm0C3Cf8xxAMW8X193QL1zZoL3AgJ\nRbk/CgJeX7eAO47q71ChQBFf+33l4rivfkcSePzATsNcBQVFfD39rdqeP8rr2/8wzFU74wYLTuyj\nrLXg/JngULaHF+aMtf5x2ZwsFpzYR62MginSH+i1IALKBKGIrwTSQ0KY8tsCdeyZx7sQkZ2NAKJu\nGv/jKeIrgYM9e7J3yBB1rO6YMdScORMBxE2cyKEBzg1SfYMivhLY3bkLmwfYKoS1mPQdDRdaTnbN\nZ0xnVy/9zRcBOgz/QC2jufyjEZxsbrtlrrBlIx1GfISQkg7D3uOn+Ut08yjiK4HD1WrxyxBbsaGn\nxnxMtcMHLNdvz07m6maxQDn5SmBCy4dYV7OROnbPwV0M2mRpkfTG3/PoXeVdQ1yK+EpgdsVYfqjb\nUh17fu8mepyw1IZ47sgufqtVcCnvRqCIrwS2xZRj+F2d1bHhGxfSLPW0pcXS2SMY/br88XQKocBN\nIehVvhYJdiaHBunXmXHqAJFSMuX0Ie6pWt8gm3f4U1uFEB2AsUAwMFlKOdpp/D/AfdanhYDSUspi\nntYMmBPwsz0eAdyLL8CU3xaQERRsK/jcozN6UX/UKJXLWXwB9g4ZwqHHHlO54kaPRi9ajh+ncjmL\nL8DmAS+S0OkRlavlBP2twStvWEdwZqalYL2T+AKcbH4XKz4YjgSCMzOptHGDLp6nPxsOuBdfgF+G\nfMjRKjXUPfX5XH+Zzbb7d+YpvgDrajZiYvOOlgL7QOv9+huAvphouR7uxBfgh7otmVvBrmh70kbd\nXAWFURssff/ciS/A8Ls6s7NYGXVPH2+Yr5ur0c1rlMmxFOF3Fl+AhIjC9C5fCwmUycmiUbrnjipG\noTjh/HECFkIEA98CHYE4oKcQwsHmJaV8XUrZWErZGPgGvJ89AkaAw+zKTjqLrwLFCSewdBLQiyoL\nFqhczuKrQHHCCaDaPH3NKwGLw836s7P4KlCccAKot3yp2zlacIe12WhOWJiL+Co4flcrcsLCEMAd\nOk0elY7ZGmU6i68CxQkngMpHD+niAeiWsE4VYGfxVbC2dmNVgHskrNPN1enUAXVfzuKrQHHCCeCR\nU+67jgQSGl4+p+7JWXwVKE44ATS+fFY313PWOsBng0NdxFfBrojCnA22FPN/9rLn1lmG4d9ylM2B\ng1LKFCllJvAr0MXD/J5Y+sJ5RMAIcEFC+JieKAw4VIT0kctA6mTYDWsPNi+93jKiLL3jwq/n7wnE\nHwi32hLTQzxnASrj4Tn6m1cGWc+B3j6OynhwgHvYCxrRuZZGuSe99K87Zf1bFcvV31hXGyS5Obma\nHkBJIcQ2u4ez3bECYJ+2ecL6OxcIIaoA1YBV3l5hQNmACwoyKMin7sPSQKNMKYJA+sClMdLBHTIL\nWbsQe+l2HJ5m6Z6cUTjwU0szgkOJIoOI7CyP85TxjGD9zStzEQQjvXY7VsZzRAF0lfwH4UqQRU4q\neOngXN76t7oclP/y44OD7YIfoyCeBGZL6f2DHzAn4MygIPVk8czj7k/2it1XYokL1oujXbqoXHXH\njHE7R7H7SuBwV33NKwES23dQuVpM+s7tHMXuK4HEDg/p5tphbTYanJlJhS3u7ZOVNm5Q7cQ7dDr8\njle2Ncp8aoz71kaK3VdiiQvWi7kN7lHNC/ccdB+50Xp/vM1x1kB/B5MlVvskWBxu7qDYfSWwqHxt\n3VwFhd3FSqt7Gr7RfRy9YveVWOKC9eK/1k4YZXKyaJCHfbdR+nXVTvxjsdK6ubTAzx0xTgKV7J5X\ntP7OHZ5Eg/kBAkiAf5y9CLB80CKys12cbM/26Ey4tU+WZb7+pIw977+vctWcOdPFyRY3ejQ1fv9d\n5TKSlLFp8CsqV8OFC1ycbC0njKfBkkUql5GkjGOt7lHtux1GfOTiZKu0cQPtPxmu2on1JmX8/M5w\nwGofP3zAxcnW5/MRVDl6SN2TkaSM1bWbqAI8aNNSFydb6/3xDNyyTBVgI0kZ39WzXA8B9DiR7OJk\nG5i0kW4n96n7+ickZbzfynJ4EECz1NMuTraPN8ynyeWz6p6MJGXsiiyi2ndnnDrg4mRrlH6d6VY7\n+9ng0IJJyvBfU86tQC0hRDUhRBgWkXURISFELBADaPLQBpQJ4np4hBqKFp6bw6BunRzGlQ9ZWmSk\nYa7k/v3VULQav/9Ojd9/d8uVZKBNvIKtT/RSQ9EaLFlEgyWL3HJt6dXbMNfy4Z/Q6b23EVLS/uOP\nyAkLIyMqivC0NDUxQwLLR37qbSmP2Fu3gRqKVuXoIT546WmHcfX61TceqvX1PV143RqKNnDLMgZs\nWUZ6SCgR1uafCtdX9+q/U1HwU9UGaihat5P76HZyn/oFgB3Xf6u6dwgGIlaXqqKGojW5fJbFSyc6\njKstl8pUNcz1bLnqLDixj0gpmX1yP2eDQzkVEkr57Cw1MSMLeKac/rsiX+CjCybvdaTMFkIMBv7A\nEob2o5QyUQgxEtgmpVTE+EngV6nxWB0wJ2CAn2fO4Xp4hMfmgWmRkUz/xXhrlAODBpHcv79HrqSB\nAw3HAAPE9+nL1id6eeTa0qu34RhggDONm7Dk088dEi8KX7zokJixZPQYzjQ0JiBzBr/F3rqeG2Um\n1W/C3EGvG+IB2FY1jv/c08Uh8aJQdpZDYsaX93ZlRxXv/e68YWatpvxU1XFfQTju679VG/1jYoAB\nxtzZgdWlqnj8W60sU5X/NH3QMNf+8EJ0qVjHIfHijowbDokZnSvGciDc+CFKC/yZiCGlXCqlrC2l\nrCGlHGX93TA78UVKOVxKqfmWOeBSkRU826OzQ6iZL7UgtKYiK4gbPZpq8+YhcnKQwcEc7tpVs9lB\nayqygpYTxlNv+VK17kRiB+21ILSmIiuotHEDd8yYRvj162QULsyOXn00mR20piIr6PP5CIdQM19q\nQWhNRVbQen88PRLWEZ6TSUZwGLMb3KPZ7KA1FVnBwKSNPHJqP8FSkiMEi8prrwURaKnICj7eMN8h\n1MyXWhBaU5EVNEq/zrOXz1EsN5vLQSH86GMtCKOpyJWq1ZCvjvhM09y3+j52+zfl9EWAjcBXATYC\nXwXYCHwVYL3wVYCNwFcBNgJfBdgIAlWAjcBXATYKowJcsVoN+epwbUlUb/d7/J9XC0IIUUwIMVsI\nkSyE2CuECHyvhAkTJv5/QFqacmp53CoYdcKNBZZLKXtYPYMF81VswoQJE1oQ4MkyugVYCBENtAb6\nAVjT8/SnIZkwYcKEX3FrK51pgRETRDXgPDBFCLFTCDFZCOFiYRdCDFDS+65dvWKAzoQJEyZ8Q26u\n1PS4VTBigggB7gBellJuFkKMBYYCDtVZpJSTgEkAJUuWl5sXu88w8icyH7gj3zkUXDhRcE64OnfW\nKRCeK+cKrntw9UbVC4zrxrqCq31x9WrBvS9CQ8MLhEf8w1KvpdUGHMgwcgI+AZyQUm62Pp+NRZBN\nmDBhIiAQ6AXZdQuwlPIMcFwIoRzL2gFJfnlVJkyYMOEHBLoAG42CeBn4xRoBkQI8Y/wlmTBhwoQ/\nEPhOOEMCLKWMB/IleLnb1pV0SNxCsMwlRwSxrH5z5jVrlx9UNJ0/hzsXziU4O4uckFA2d+3BzkeM\n1xVwh7tXLqXN6uWEZGeTHRLC6nYPsaGt52aIehH7y3QazviF4KwsckJD2f1Ub5J7PeV3ng9Gv01R\nayNVgKvhkXwyVFsGkq94fMp46ifZqqIlNGjC70+7L3RvFO+tm0+zi8fU51tKVmb03fnTQfiTnf+j\npV2B8g3FyjCsif/f7/13/sWDx/cQJCW5QrCscn1+bHyf93+oA6NPp9Dl+mWCsKSLzy1cjPfLFZzN\nXynIHsgo0Ey4kiXLy05d+nuc037XBnptXwm47946o2k7VjTynE7bRKMTru7KFbSfPCFPrhX9B7G3\nXXuPa2h1wt2xaS1d50zLk2te9z7saNna4xpanXDVFy2k1Zdj8uTa8OYQUh7Ju6XTrr+0tfUZ8tWH\nlEm7kifP2ahoxrzhvlylgogi2moCdPr9Z1pu+TtPrk3N72XJY0+7/Dt77FmXoIlr8NbltLN2u3DH\ntbJ8bcZ76SCs1Qn31p51dDh/LE+u5aUq80V9zyU2ixYt6ZXn8aQN9DywLU+embWa8Vuc589VYqK2\nbiNvnT3K82kX8+T6IaoEX5Sp4nWdgwd3GMpOK1+5mhw4ZLimucNf7ffPy4TzNxTxtS8SkoNj99Ze\n21fyQILxXlyK+NpzZQcHO3C1nzyBuqtWGuZSxNeBK8iRq+ucaTTerL+djgJFfB2uYUiIA1erL8dQ\nban+hpxgE19PnXbLpF3hja+HG+IBm/h64mq55W86zJlumEsRX09c7U7tZ9C2FYa5FPH1xNXh/DFe\nT9TXu0+BIr72PM6dnnse2EaPPGog+wJFfD3t6fm0i7x+7pjbf+9v3O42YL9CEV8JzGt0Dwua2m6N\numz/i667LP3Bntr6P/5sYCzrWRFfCax+qi+7HrbdWjZaPJ+2v0y1iPAP49l7v7FbQUV8JbDk4R5s\namOrOtVyzR90WjwbAXSfPZX4FvoLigOq+Epg26CXSH7iCXUsdtYsmk34FgHc/flnHH6oU17LeIUi\nvhK4HFGIUe/Ycu7f/2woxdJvIIByVy7p5lCgiK8E4hvewZw+g9Sx7tMm0Hj3DsueNq1heXdjJT0V\n8ZXAxlJV+LyVrTnA2xsWcJe1rGP7k8lMaOb57sgbFPGVwJqYcoy0MwUMi/+LNtZuxQ+fO8J/6umr\n3Qyo4iuB+VUbMrVRW3Ws767VPHpkt+VztX8Ls/PohacVivhKYFaRGD4sW00d+/jMYZ64lmqp7Xz1\nAv8pXdkQl1fcYnHVgoA5AXfbajlpuhNfgAVN72NBw1bqN2nXbfpPpk3nz1G5nMUXYNfDj7K219Mq\nV5NF+pty3r1yqcrlLL4Am9o8yLJO3VWuVquX6+aK/WW6yuUsvgDJTzzBtgEDVa7YGb/o4vlg9Nsq\nj7P4Aox6ZzRXwiNVng9Gv6OLByw2X4XLWXwB5vQZxO4GTVSux35233VEC95bZ+sM4Sy+AJ+36sLm\nkpVVrqHr9XcQ/mTn/1QuZ/EFGNn4PtbbdSseuVPf+73/zr9UHmfxBZjaqC2LqtjKbz4b/5cuHrDY\nfBUuZ/EF+LBsNX4vXEzlGmWdn58I9FoQASPAHRK3qLctzuKrQHHCCaDjHv2Vme5cOFflchZfBYoT\nTgDNrYKtB21W27oiO4uvAsUJJ4C2K/V3RW444xeVy1l8FShOOGGdrwdFrUXzARfxVaA44QRQNOOm\nLh6A+km7VC5n8VWgOOEE0CBhp26uZhdt5gBn8VWgOOEE0PyC/tvolnbdip3FV4HihBNAK53dih88\nvkflcRZfBYoTTgAdj+3RxQPQ5fplW2cNJ/FVoDjhBNDtev4n/AS6CSJgBDjYWrreWwF7ZTzYQKn7\nYGtTwBwvzTaV8ZAs/SUuQrItnV9zvPSwU8aV+XoQnGXZV26IZ8uSMh6caZbuuN0RZBUXbxKjjAcZ\nECNfxSS/xUdiCrBm5AjLS/H2gpRxZb4uLmtb7GAvNVuV8exQ/Z12sxWxy/XCZR3P9iKenpATatlX\nkBcRV8ZzwvTvy8Q/A7nW9GFvScTKeK6BdGNfj0R+6haUN6RE5uZqemiBEKKDEGKfEOKgEMJtxwYh\nxONCiCQhRKIQYoa3NQNGgJfXa65+C3fZ7t4Opdh9JbCsfnPdXFs7d1O5Gi12b8dT7L4S2PJod91c\na9rauiK3XPOH2zmK3VcCq9vp74q8u9dTNvvurFlu5yh2X2mdrwdX7dpGvf+Z+84hit1XYokL1os9\ncY1Uru7TJrido9h9JZa4YL3YVsJm3317wwK3cxS7r8QSF6wXm+y6FQ/Lw+6q2H0llrhgPfijUn2V\np++u1W7nKHZfCSyrXF8XD8ACO/vux2cOu50zys5OPLdwMd1cWiFztT28QQgRDHwLdATigJ5CiDin\nObWAd4G7pZT1gNe8rRswAjz3Tpu9q+uudS5Otq7bVtJl9wb1m9pIUsZ2q6AKoO0vU12cbE0WzaP1\njJ9VLiNJGeutgiqATotnuzjZWq1eTsclc1QuI0kZyU/1VrmaTfjWxckWO+MXmk2aaLMT6xTgT4Z+\nrvIUS7/h4mT7YPQ7RGfcVHmMJGX89sxglavx7h0uTrbHfv6Ohgk7VS4jSRmf3mOz7951/qiLk23o\n+vm0uGCzExtJyvigyb9Urjapp12cbCN3ruRuu27FepMyJjex2XcfPbLbxcn2bPxfPHI0QeUxkpQx\n1M6++8S1VBcn26jTKTxmZycuiKQMP5ogmgMHpZQp1tK7vwLOjoLngW+llKlW7nN4QUCFoc1o2k4N\nReuyewNddm8gF9u3hBLe8sud/zLMtaL/IDUUrfWMn2k942dygoNVs4PCteJ5/W3iFczr3kcNReu4\nZA4dl8whJyhYNTsoXHN69DXMteHNIWooWrNJE2k2aSK5ISGq2UHhWv+2/sgEsCRZKKFo0Rk3+WLE\nKw7jCs/p6OKGeMCSZKGEojVM2EnDt553y7W+ZRvDXCvL11ZD0VpcOMa8BePccq2oYLwB6PJSldVQ\ntLsvn2XlX453rArX4tJVDfHMrNVMDUV75GgCjxxNcNvp+Zfa+u8qFfwQVUINRXvs+mUeO7jDYVzh\nmqAhecQwfMuEKymE2Gb3fJK1kqOCCsBxu+cngBZOa9QGEEKsx9I5ebiU0mNYU8CcgAFWNGrFjKbt\nHIK2g3Hs3vrLnf8yHAMMsLdde1b0H+TAFZKT48C14vnBhmOAAXa0bM287n0cuXIdueb06Gs4Bhgg\n5ZHObHhziOM1zM524Fr/9juGYoABxrzxMWejoj122j0dXZyvXhtuiAdgyWNPs6n5vR651rdsYzgG\nGGD8nR1YWb62R64VFWINxwADfFH/HpaXquyRa3HpqoZigAF+i2vFzFrNPHZ6/qV2c8MxwABflKnC\nD1ElPO5pQtGS+R8DjM9OuAtSymZ2j0lelneHEKAW0BboCfwghPBoZwm4VGQFXbetpOMefbUgtKYi\nq/MXzaP5/DmEZGWSHRrGlke7azY7+FoPuNXq5bRduVRXLQhf6wHHzvjFUgsiM5OcsDB293pKk9lB\nayqygg9Gv+MQauZLLQitqcgKHvv5O4dQM19qQWhNRVYwdP18h1AzX2pB+FoPeOTOlQ6hZr7UgtCS\niqzg2fi/6HhMXy0IranICkadTqGbgVoQRlORy1aoLPsM1HanN+ajwR65rP0uh0spH7Q+fxdASvlv\nuznfA5ullFOsz1cCQ6WUW/NcN1AF2Ah8FWAjuB0LsvsqwEbgqwAbga8CbAQFWZDdFwE2Al8F2CgM\nC3D5yrL3wLc1zf1y+MveBDgE2I+l7O5JYCvQS0qZaDenA9BTStlXCFES2Ak0llJezGvdgDJBmDBh\nwoRfIaW2h9dlZDYwGPgD2Av8JqVMFEKMFEIola3+AC4KIZKAv4C3PIkvBJgTzoQJEyb8CX/e4Esp\nlwJLnX43zO5nCbxhfWiCKcAmTJi4LaE44QIZpgCbMGHi9oQk4JtyFqgAZ2RkcCzlYL7z1LxUK985\nFETFFCkwrq3L9Bcg8gUxZY3H7mpF6hnj5Sq1IrJIoQLjChKlC4zrxs20AuEpqO7L/oMkV2Oa8a2C\neQI2YcLEbQvTBGHChAkTtwqmAJswYcJEwUOaNmD9GJy8mc5nDhOEJBfBgnI1+LbOnfnCVSHlAC1W\nLSfyxnVuFirMpnYdOVWtZr5w3blkPnctX6hmwm3o1JVtHR7JF67nf51IvaMH1OcJVevw3yee9/Av\n9OHNoYMokpGhPr8WHsmXo7/1Ow9Ag/0JdFj/JxEZGaSHh7P0nvYk1tJfwcsTHk7YQJe9mwjNySEr\nOJi5cXexrL7xNHh3k+0COAAAIABJREFUmLDke0pl2TpLnwuN5MVOA/3O0+PQLp44sovQ3FyygoKY\nUbUx82o09DsPwPunDvHYlXMEY+nt+Gt0aUaXr5EvXHkhwA/ABZsJFxVVQjZr5r4rhII+h3bxzPEk\nwH1H1SmV4phWo5HHNe5/rKOm11PqxDF6ffclUVdSXbjSomOY8eKbnK/oOWc9LDxUE1eDtSvp+PNk\nwP2+lj3dn4TWnlNPTx44qYnryQW/cFfyzjy5NsY24dcueacka3XCvTzsdUp46Ip8MSqab0b+x+Ma\nWp1wNQ8f4IU5/yU0J9uFKys4hO+7P8fBap6dr+c1Zi22PrCDF7ZYmm6629f3zduztpbnbMuM6+ke\nxxWM+WMK1W7mfQ0PR0Yz5MFnPK6hxQn3wLG9vLF3Q548X9VtxZ+V63pc49ixJK88AIPOHmXwpVN5\nco0vXp4JGroi79270VAmXOmyFeVjvV/VNPe7L982uyIr4mtfuMO5e+szx5PolbLbMFepE8d4/rNh\nFL2SCsDV6BiOV6/J1egYAIpeSeX5z4ZR6uRxT8togiK+9vty7sDc8efJ1Pt7lWEuRXztuZSHwnVX\n8k4eXzzTEI8ivp54SqRd4cURQwzxgEV8X/5tImE5lopumcEhpBaJJjPYcgMXlpPNy79NpMYx4xE2\nivja7ytLBDns64UtK7j3wC7DXIr4erqG1W5e4bMVPxniUcTXnidTCAeeN/ZuoN3xfYZ4wCa+nj7D\ngy+dYkBBdEWWkJubq+lxqxBQJghFfCUwq1wtJtaxfSEN3LeNJ04fQAD9jyUyo7qx26Ze331JcG4u\nWaFhTH3tPU5XtRUJKXckhb5ff0poVia9vh3D2E/HGuJSxFcCKx97iu0PPqyONf1jMe1+t/Ry6zT1\nBxLvvd8QlyK+EthVLZYfH7fV3nj2t8k0OpxsKX+YuJ3fHu6pm6eEXVfkqxGF+Orf49WxN94dTFFr\nV+TSl42Hmb0w578ILB/kSd2eIalWPXUs7kAiA+ZOIQgY9Ntkhgxx36NOM5dVfCXwc5O2LI+zVQjr\nkLSJp3eutnT13bKMv2t5vhPzBkV8JXAmrBAvPTRAHft26STKZlquYc0bxnqnKeIrgYm1mzO/WgN1\n7NHDCQzcb+nHOCRpHSsrGas3ooivBKYUK8MYu+I7Q06n8Iy1xvGrF08yKZ8rokkC3wYcMCfgwcmb\nAffiCzCxTjPmlKupfpO+tC/PAkNeUSHlAFFXUpHgIr4Ap6tW5+dXhyKBqCuplD+s/2R15xJbBwVn\n8QXY/uDDrOrRS91Xs+WLdHM9/+tElctZfAF+fLw/u6vWUbmem/WDLp43hw5SeZzFF+Crf48nza4r\n8ptDX9LFAxabb2hONhJX8QVIqlWPH7r1QwKhOdnUO6C/qeTDCZZbdHfiC7A8riXTGrVR99Vxz0bd\nXBOWfK9yOYsvwEsPDeBsqO0afrdkoi6eHod2qTzO4gswv1oDJtW8U+Xpekj/3eX7pw6pXM7iCzCm\nXHWmRds6gQy1zs9PmD3hNKLzmcPqbYuz+CpQnHAC6Hxa/x+vxSpLp+K06BgX8VVwqlpN0qJjEEDL\nlfpbxd+1fKG6L2fxVaA44QTQaon+Vuf1jh6wdTZ43H3VOcUJJ4AGR/TdchbJyFB5nMVXgeKEE0AR\nA12RO6z/E4HFzussvgoSa9UnKzjEYspZ/6duri57N6n7chZfBYoTTgDdkjbp5iqVZess7Sy+ChQn\nnABKZ+m7hk8csXWVdhZfBYoTTgC9jug3rTx2xdbp2Vl8FShOOAE8ecVrwwiD0FiIxxRgUKxs3i6F\nMh7sdWbeiLxxHYArJUp4nHeleAnr/Gu6udSuyFo7MFs7NpuwIMIaXXE9srDHedcjLVlukenanF/u\nEKo0YfXS8FUZD83R38G6oBBqtW9meWm2mW0dD/XSPNYTlHe49s9wPkMG/gk4YGzAuQiCkXh+m9i8\nqjleZ+aNm4UsH+boix4rxRF96aJ1vv504+yQEEKzsrR3YA7RFlXx/wXp4Zb018I3r3ucV/jmDQBu\nRkTo5soKDiY8N4cQL10alfGs4ID5+OSJrKAgInJzCPUiMiHW8awg/bKYg2MHm7xg+wznP3JzTBuw\nJiwsW039Zhy4b5vbOYrdVwILy+mPJ9x8fwfVvlvuSIrbOeUPH1TtxJva6W+UubFDZ3VfTf9Y7HaO\nYveVwIZO+hs9JlappXI9+9tkt3MUu6/EEhesB9fCw1WeN9513zNPsftKLHHBerH87gdU+27cgUS3\nc+od2KPaiZfd/YBurgV1W6r76pCHeUGx+0pgbh5mCi04H2rrLP3tUvfdbxS7r8QSF6wHs6rauko/\neth9QXrF7iuBGVX1OxZ/t7PvDjnt/nM11M5O/Gt0/tbLUKqhBfIJOGAEeHyspb+dAJ44fcDFyfbS\nvq10P31Q/fY0kpRxsnot1b7b9+tPXZxs5Q8f5Omxo1U7sZGkjK2dbJ122/3+i4uTrdnyRdw/e4a6\nLyNJGT88abMZNjqc7OJke27WDzQ8sk/l0puU8eXoCSpP0fQbLk62N4e+RJRdV2QjSRkJtRuo9t0B\nc6e4ONnqHdjD83N/Uu3ERpIyFjew9F4TwNM7V7s42Tru2UifXWvUfRlJyhjU6QWVq2zmDRcn23dL\nJlImy3YN9SZlzLbGzAtg4P4tLk62rod2M+DgVpXHSFLGKDv77jOXz7o42YaeOkQfOztxvidlmCYI\n3zClUpwaitb99EG6nz7otnvr5MrunTG+YMaLb/L8Z8MIzcrk2TEjSYuO4UrxEkRfuqgmZuQEBTHj\nJeNxrMue7q+Got0/ewb3z57htgPzkr7Gs9Q2xjZRQ9EaHtnH2M8cX7/Ctb5eU0M8F6Oi1VC0qIyb\nDH/9Wbc854oZr6z2fffnePm3iQQBA+b+RFZwCNcjC1H45g01MUMCE/JwPPrE1by9GorWZ9ca+uxa\nQ7YIUs0OKldzbck+nnA4MloNRSuTdZM58792GFe4Dhby2NfRK76q20oNRRtwcCsDDm4lWwjV7KDw\njIkz3hR2fPHyaihanyvn6HPlnNvP8NgSFQxzeYd/xdXacmgsFkvLZCnlaKfxfsAXWFoWAYyXUrq/\nFbUiYE7AANNqNGJKpTiP3VsnV65nOAYY4HzFyvzwzkiHxItKhw86JGb8MPRjzleoZJgroXU7lj3d\n32MH5iV9nzccAwzwa5en2BjbxHMH4XpNDcUAA3wz8j9c9NIV+Vyx4nz30RhDPAAHq9Xim8cHOiRe\nxFy76pCYMa7nCxyqbDx9fG2tO/i+eXuHfYXKXMeuvs07Go4BBhjy4DMcjvR8DQ8WKsY77fsZ4vmz\ncl2+qtvKaU/SgWdM3D2GY4ABJpSpwvji5T1+hseWqJDvMcAK/HUCFkIEA98CHYE4oKcQIs7N1FlS\nysbWh0fxhQBMRVbw0r6tdD59iGAkOQgW+lALQmsqsoLyhw/ScuVyIm9c42ahImxq10Gz2UFrKrKC\nZssX0WrJfEKys8gOCWVDp0c1mx20piIreG7WDw6hZlprQfhaD/jNoS85hJr5UgvC13rA9Q7soeP6\nP4lMT+dmRATL7n5As9lBayqygo57NtItaROhOdlkBYcwN66lZrOD1lRkBd8tmegQauZLLQhf6gF3\nPbSbXkd2EZqbQ1ZQMDOqNtJsdtCaiqxg6KlDPGmgFoTRVOSSpcrLTl203VX+/N+R/uiK3A9oJqV0\n7xhxt26gCrAR+CrARuCrABuBrwKsF7drQXZfBdgIfBVgIyioguy+CrBRGBXgEiXLy06dtZmlpk35\n+Chg/waZJKVUvaNCiB5AByllf+vzPkALe7G1CvC/gfNYOii/LqX0WMvAsA3YejTfBpyUUrrPNDBh\nwoSJWwAfDpgX/FCMZxEwU0qZIYQYCEwFPNoV/WEDfhVLm2YTJkyYCCBos/9qFOmTgL1DqCI2Z5uF\nTcqLUkqlLutkwKun25AACyEqAp2sZCZMmDAROJCWYjxaHhqwFaglhKgmhAgDngQW2k8QQpSze9oZ\nDQdToyaIr4G3gai8JgghBgADwNLU78KFEwYpvSNpQ8HZqnKzC66UXUGVzSvI7KEbaTcKjCumTEyB\nce3f5T5pJF9QQH6cq1c9Z44GIvzl45JSZgshBgN/YAlD+1FKmSiEGAlsk1IuBF4RQnQGsoFLQD9v\n6+oWYCHEw8A5KeV2IURbDy98EjAJIDIyKrDzAk2YMHHbQMmE89t6Ui4Fljr9bpjdz+8C7/qyppET\n8N1AZyHEQ0AEUFQIMV1K2dvAmiZMmDDhJ0hkgLel120DllK+K6WsKKWsisUessoUXxMmTAQMJMhc\nbY9bhYBKRTZhwoQJf+JW1nnQAr8IsJRyNbDaH2spWJ64gXLSVrDupAjmoXqt/Emhou+mZbRL2U2Q\nlOQKwYoajZjeQn8FtEDBa+vnctcpW1Wq9eVrMu5u/dXW8sKnX79HEbs6xmkhYbz/2ii/8wD0Wr2I\nNvt3qH+rv2Kb8WvrTvnC1ePPOdy7Z5vKtbZhc+a065ovXJ9tXUaTq+fV59uiS/NeM/+/Bz/bttyJ\npxTvNc2f9/ri04dpmGt7X8QHhdK5XLV84coLgS7ABZoJFxkZJWvWbOJxzu97N1MnJxNw31F1X3AY\nj9Vt4XGN+o3u1vR6Ho1fw+OJG/Pk+q3eXcxv3MbjGoEYBfHs1j/ocMRSetDdvpZXbcCPd+adkVim\nShlNPB+NH07J9Ot58lyIKMyIwcM9rqE1CuKRLSt5dOfaPLnmN2nNouaeu0pHFNZWK7jD+hU8snll\nnlyLWrRj+d3tPa6hNQri3fhV3H/xRJ5cq0pU5N+NvdQI0fAZfnfXX955Gt3ncY3kfZu98gBMPXuU\n+7Mz8uYKCaevhq7IJ0/uN5QJF1O8rGzXTptVdM7sL82uyIr4euoSWycnk1+TtxjmUsTXU/fWxxM3\n8n/tnXl8FOX9x99P7hMMJIDcKFAMIIIIBhRQDlFErIpHPXpotf5aj6rVqlSr4n3Uo7YVj1otVcGD\n+44iEMKNgASEFAQCBBIIEBJy7vP7Y/aZ3exuZmdnks2SzttXXrKZyXx29vjs7Pf5HldtWmFbK5wo\n8zV6DMf+uIWfr1tkS0eZr5FOekUZf/rbU7Z0wGO+Rs/V1RuXccXab2xrKfM10hq/OpsxK+09fuAx\nX6PH8NIjBfxh01J7Om7zDaqz2Z4OeMzXUKumkvfDMhVZ4qp1mfppKiIqBqzMVwL7omIYl+lpfDI3\nL5dOLq31YGZNZX2HMI0yXwnM7dmfqV5XhDevXci4HVpLxxu/X8Gsfvbb9IULZb4S+LZDT94ecpW+\n7bcrZzF8/w4EMG73Zv410PgqzghlvhI4HhvPpPsm69smvzGJltXam7CNjXFOCmW+EljQezDTL7pC\n3zZxxTzGbl2ttTDdsJR5FxhfxQVDma8ElpyXxVeXekI2P/16BqO+0143E1Zls2iI9ccP0E1RAqvP\naMMkr1DA5PULGHxM6507pngvLzekzgDPa33yhoVeOvts6QC6+UpgXXQsP23nCTl8VbibgbXVmlZ1\nmHplRHgIImKugBds9Uyk9TVfgHGZWRQIzyS4ee79rfDzVfN1LV/zBZh6wWXM636ernXLautDOcPJ\n/TlfAoHNF+DtIVexor1nsvS9OdYGgD73+mO6jq/5Aky6bzInYuJ0nWdff9ySDmgxX6Xla74A0y+6\ngsXnDNK1blw217LWdYu/0LV8zRfgq0uv5ut+nqkZ12Z/ZVnrxbWe16Cv+QJMOn8sa70mTDy3ztpr\n8EX33wUyX4BJAy5jbcsMj85666/1OQd361q+5gvw03bd2BgVq2vNcu/fmEiT/zUVEWPAZ0pPf1xf\n81WoRTgBdJDWJ0qN3LVZ1/I1X4VahBPAGBujusNJ1oFdnokhPuarUItwAhh6ID/gPsFIqanWdXzN\nV6EW4QSQWlNlSQdg+I4Nupav+SrUIpwALtkeeJyVGS7+fp1nMsSlgRcr1SKcAIZtth4K63+iyPMY\n1rMIphbhBDDQ4gThOjoDAr/W1SKcplMUcB8znOvyvC58zVehFuEEcJ6rcQfQSmciRmQS5X7Agz3s\nqpN/VFMmCv6PE/pzZf3NFE4th3AgkRH+3v2fNGCXEERL8xOYXUHGlDs0HqE/V8H2jAwth/AQ6Wlo\nEeMsB73iu3PzcgPuM88rTrxfWB+fnX3WubrWzWsXBtxHxX0lsMjGoMJwktv+LP28frtyVsB9VNxX\nouUFW+FkjCeON/mNSQH3UXFfiZYXbJVvew7QtSaumBdwHxX3lcA3vaxnEi3vM1DX+unXgePjKu4r\ngWXnDrKstbGFJ+46uZ6463Ne8dt1FicI19HZEPi1ruK+mk6GJR2AzV7x3a8KA8d3Z3nFib+Lavxh\nBi6Xy9RPUxExBjzWK77byVXjt8g2b+tKOnrFie0UZfzrwst1rXE7Nvotst2yegFX5H+na50uRRmv\nD70G0M5r+P4dfots9+bM4KIDnsnSVosyHrv/OV2nZXWl3yLbs68/TosaTzqhnaKM/4wYr2uN3bra\nb5HtxmVzGb1tja5lpyjj89HX6lqjvsv1W2S7NvsrLt20SteyU5TxyAWe1+DgY4f9FtmeW7eAC7wm\nCFstynjEK448+Nhhv0W259Yv4ILjnjixnaKMK73iuwNrq/0W2WYd3E1/rzhxYxdlaPFdl6mfpiKi\nQhA/RMfpqWgdZS2bv19eZ7tKb8mLibetNa13lp6KdkX+d1yR/50e2/PW+rTP6ZOCBlqRhUpFu+hA\nPhdNrzsUU0+962bvqr44IVlPRWtRU8Vbr/whoM7hpBRbOqAVWahUtNHb1jB625qAz9UXA0bY1po9\neKSeinbpplVcumlVQK2ZFxoXfZjh69Yd9RSxC44fZnH2R3W2K61F6fYGWNbVKWLx1x/Xo2N/AO3X\nMfF6Klp/VzX79u8IrBVrrijGNk4IwjwTzxnMD9Ge9CXh9QMe872xl/WvfooZ5w1nWu8sw+mtn/a5\n6LTKAQb44ILLWNC1r+FjOLfbubZygAGe+t2fKU5INtQ5nJTCM//3pC0dgNmDRjKj/zDD5+qLASNs\n5wADLBg6htmDRxpqzbxwpO0cYIDnz7uUr1t3NHwMF6V35uV+I+zp9LvEhE4nXj7Xng7Az9t24euY\neGOt2ARuD9dU5AhPQ4u4UmTFvK0r66SahdILwmwpsuKW1QsY89/NREkXLhHForPPNR12iMRSZMW9\nOTPqpJqZ7QVhthRZ8ezrj9dJNQulF0SoDdlvXDaXS7avs9QLwmwpsuLa7K8YtnmNpV4QoTZkf27d\ngjqpZiH1ggjhPfzc+gV1Us1C6QVhthRZMevg7jqpZqH2grBbityyZYYcMsRcmG3BgveapBQ5Yg3Y\nDqEasB0i2YCtEqoB2yGcEzFCNWA7NMeJGKEasF3sG3C6vPDCwPnwvixa9M+gWkKIscAbaBMx3pNS\nvlDPftcCnwMXSCkNE9MjKgTh4ODg0FA0ZCGGe/r728DlQCZwkxAiM8B+qWiDik19WjkG7ODg0Gxp\nwEq4QUC+lHKXlLIK+BSYEGC/Z4AXAVPNLhwDdnBwaLaEYMDpQoh1Xj93+hyqA7DP63aB+3c6QogB\nQCcppemGJGFNQ6utrebYMWs17aFw+OC+4Ds1EMnJZ4RNKyrKevFJKBzacygsOgBtOlsrMLBCYkr4\nYsCpqeGbwHz06MGw6Lhqa8Ki03DIUOLjxXbizUKIKOA1TExC9iai8oAdHBwcGhJJgy1c7we8E6U7\nun+nSAX6AEuFVqLeDpglhLjKaCHOMWAHB4dmiZQNmjm0FughhOiGZrw3Aj/zaMnjQLq6LYRYCjzk\nZEE4ODj8j2Iu/mtmEU5KWQP8DlgIbAOmSSm3CiGeFkKYy3ULQMReAd9QWsIDpUdIlJJTQvBKajrT\nUxsn3tqmooys4gJSqqs4GRvHyvSOFCUkN4rWH1bPZejhH/Xby9t249VBgfvc2uXmLTmM//E7YqSL\nGhHFzLPO45PeDZ8j/dTij+jlFdvPS2vLU6NubXAdgKxNqxi3aglx1dVUxcYyO2sMq200xTFi+PKF\njMzJJqa2hproGJZcPIZlQ0c1itbf5r1L2ypPTnRhXDK/veKOBtd5JG8VlxXv1UuC56V34ZVM4xmL\nVllTuIv2Xrf3A4PbndUoWvXRkH0epJTzgHk+v3uinn1HmDlmWAsx4uISZNu2XQ33GVt2gn+UHKxT\n/gme2Vy/STuTBcktDI/Rs6e5WHpa5Snu3bGWoUUFRHuVI9YiyMnoyJs9L6AkPtHwGGYX4X69MZsr\nC7YDgQcVzunYi3f7G/cXMLsIN37HOn61bWW9Wh+cM4TZBo9RbJy57mUPfvs5g90fJoF0VrfpyqvD\nrzM8htlFuH47NnPH7KkIpJ+WRPDe+JvZ1NO4v4XZRbiBG1Zww+xpQODz+mz89awbYFyi/t9N/zWl\n9dLij+heVlKvVn5yGg+Pvs3wGGYW4e7auYGbDubXq/PJmd15p8cAw2Ps3GGu2f3iwt1kuo8cSCsP\nweh6GrZ7c7Bwl61CjNTUVrJ/f3MfmMuXT3eGco4tO8GUkoMom6kFShGoguRoYErJQcaUn7CtlVZ5\nijfXL2JY0T6tzDSjE1O79GZZRiekgGFF+3hz/SLSqk7Z1lLm610P7zuo8MqC7dxhc/gieMzXW6vW\nR+tX21YybucGWzrKfI3OafDhH7l/+Re2dEAz31/P/jdR7qO7EJyKjcPlVo9C8uvZ/+bcHfYnlyjz\n9T6vmqjoOud1w+xpDNiwyraWMl+jx7B7WQnPL/k44N+bRZmvkc5NB/P5df5GWzrgMV8jrUwk8wt/\ntK1lBmciRgj8o+Sg/tXo/jPa8mWK5+rympPHeP3YIQQw5ehBuiYZXwUH494da2lfcZIdqa34U9/h\nFCck6dvSK8p5Zsu39Cw9yr0/rOWpvsNsaSnzlcDSdmfxF3crQoDfr53PiEJtlND4vVt5z2bjFWW+\nEvikx0CmZXr6Z1yft5Kbdmpjd+7IW8HcIFc8RijzlcCWVmfyzMib9W1/yp5K36PaczmkcDevW1bR\n0K58Na2PLpvImj4X6NsGfb+W2xZORwC/nj2Vex601+VNma8EZoyZQE6W51vJ0Nxsrl40UzOs2f9h\nw4ALbWkp85XA/vgU7rn8dn3bW/Pfp0PlSQTwk5NHbeko85XArIwuvHaOJ+TwwLbVXFW0BwHcfGAn\n79psFaDMVwJ7gCFeIYeVhbvogmbC5zZcdkL9yJDS0JqEiLkCvqG0hCgCmy/Alyln8GDLtki0Oz2x\n9JhlrTYVZQwtKqBaRPmZL0BxQhJP9B1GjRAMLSogo6LMstYfVnuahfuaL8BfLricZW276VcID64J\n3HTcDDdvydG1fM0XYFrmEKZ1P1/XumlrjiWdpxZ/pOv4mi/AMyNvZmtaW13nSRtXcFmbViHc/ap8\nzRdgTZ8L+Hj0de6WkZLBNua0DV+uNSwPZL4AOVkjmT3qKv28huUssaz1t3nv6lq+5gtwz+W3czDO\n023u7XnvWdJ5JG+VruNrvgCvnTOYOelddJ2H8qz3e1hTuEvX8jVf3LcL8ApRufdvLCTgkrWmfpqK\niDHgB0qPINDivL7mq5ieegYutE/QB0uLLWtlFWsx39z0Dn7mqyhKSGZlekeikQwpLrCsNdTra7qv\n+SrUIpwALj4UeJKAGcb/6Gki72u+CrUIJ4AJu76zpNPrmKdRuK/5KtQinAAyS6wXdoxbtcR9RSX8\nzFex+txBSIQWysldbFlrZE62fl6+5qtQi3ACGLV8kWWttlXlupav+SrUIpwA2lVZuwhQC26An/kq\n1CKcAC4v3mNJB6A9npivr/kq1CKcwKeMrFFouCyIxiJiDDjR/SB4XpaBOeXenmTjQUup1lon7gsS\nxihwb1f7Rzox7hXfYF/u1PaYCB9YCBBXrbUzrIw1Hl+jtsfbeK5i3JVetUEWO9X2mNOgMsz43eRP\nxBhCA+EYsElOuQccJgVpjpzo3l5uYyDiyVhtlb9TkMW8ju7tav9Ip8Y9PDTYk6q215wGw0ardGM1\nHmEerxu19eeqJlpbEol2GX8lVdvV/pFMqNYS+R/JoeEYsEleS22tx3evORk4vjux9JgeJ341NT3g\nPmbITe9ILYKs4v2kVwTuR5tRUcaQ4gJqEaxM72hZK6dNV/1N8Pu18wPuo+K+Ei0v2Cqzu56na12f\ntzLgPiruK4GZZ51nSWf7GW10nT9lTw24j4r7SrS8YKvMvXCUHt8d9P3agPsM3rxGjxPPyRptWSt7\nqGcSxtDc7ID7qLivBJZcbH0qxqE4z6XGW/PfD7iPivtKtLxgKyxM76zrPLAtcHxXxX0lMD+9iyUd\ngAN4DH9lPfHd1V5x4v0B92g4tDW4yJ4JFzEG/Flqmh7fff3YIb9Ftomlx3j1+CE9TmynKONwQjI5\nGR2JlS6e2fKt3yJbRkUZT29ZRoyU5GTYK8p4ebA2rUEAIwp3+S2yPbhmHsMO7da/Ktopypja1xPf\nvWnnOr9Ftpu25nB9/npdy2pRxpPuvFQB9D160G+R7cklH9O75JCuY6coI7ffhXp897aF0/0W2QZv\nXsOtiz/X48R2ijK+vfgyQDuvqxfN9FtkG5azhPFLZunnZaco4/+u+LWu1aHypN8i29vz3uPMqjJd\ny2pRxouZF+o6VxXt8VtkeyhvNVcW79F17BRlDPKK73bBf5FtdeEuOuIJizR+UYZEulymfpqKiCrE\nUHnAKo3FhRbzTUTqhRkSuKPVmSwyiN+aKcRQecDtK05SI7Sr3IKkFnQsP8GQ4gJipORAQgr3DhxD\nSVz9xRhmCjG884ADPdrq97M79zZMQzNTiOGdB6y0XHg+adXv38u8qN40NDOFGN55wEbntLJdN16/\n+Np6j2OmEEPlAatjSgSVsbHEV1frhRkSmDL+FjYbFGOYKcTwzgNW51UbFa2HHfQUv/E/M0xDM1OI\n4Z0HbPQY/pATUKYeAAAeIklEQVTSikcNPsSCFWJ45wEb6Uxt38MwDc1MIYZ3HrCR1maiuLxdV8Nj\n2S3ESE5uKTPrWYz2Zd26BU4hxoLkFtyZdmadwosUZJ3CjGDma5aS+ETuPX8MyzI6IaRWePGzPVsZ\nVrQPIWFZRqeg5muWd/uPZE7HXnWS0X0HFQYzX7PM7jmQD84ZUkcr2kfLyHzN8urw61jtFV4JdE7B\nzNcsm3qey7vjb6lTeJFYXVWnMCOY+Zpl3YCL+Gz89XXOK8ZVW+e8gpmvWR4efRv5yWmGj2Ew8zXD\nOz0G8MmZ3Q11gpmvWUa360YewlDLjPk2FJEeA46oK2BvJpYe48HSYpKkpFwIXg2hF4TZUmSFivda\n6QURaj/gB9fMq5NqFkoviFD7Ad+0NYcJu0LvBWG2FFnx5JKP66SahdILItR+wIM3r+HK3MXEV1dR\nGRvHnKzRpsMOofYDHpazhFHLF1nqBWG2FFnx9rz36qSahdILIpR+wA/lreby4j1EoX0zmh9CLwiz\npciK1YW76qSahdoLwv4VcAvZq5e5D8oNGxY3/6GcoRiwHUI1YDs0x4bsoRqwHZprQ/ZQDdgO4WrI\nHqoB28WuAScltZC9epn7cNm4ccnpFYIQQnQSQnwjhMgTQmwVQtzXkHfMwcHBwS6RHoKwk8hYAzwo\npdzgngS6XgixWEqZ10D3zcHBwcEWDdiQvVGwbMBSyoPAQfe/S4UQ29CqCx0DdnBwiAAkRHi1Z4OU\n8gghugL9Ab9Mb/d00TsBok+DyiEHB4fmgwy5FjC82HZEIUQK8AVwv5TSr7ZXSjkFmAKQlJQq02xU\nRZklJSV8E2nDGT8SNsqvQ6FFa/tpfmaJigrPOQFUlFWGTSs6JnwXGzExxn0yGopaV+T3vvBGq4Rr\nuPenEGIs8AZaZud7UsoXfLb/BvgtWsbsSeDOYCFZW3nAQohYNPOdKqX80s6xHBwcHBqahlqEE0JE\nA28DlwOZwE1CiEyf3f4jpewrpTwPeAltTL0hdrIgBPA+sE1KGVTIwcHBIbzIhuwFMQjIl1LuklJW\nAZ8CE+qo1Y0AJGOiF5Kd70lDgVuBLUII1Vj2MffgOgcHB4cmJ4QsiHQhhHei8xR3+FTRAdjndbsA\n8EsyFkL8FngAiAMuDSZqJwtiBaG3GzXNLw7t4Y7iAmKli2oRxTsZHfm4jfVOTUY8njODgUc8TdfX\npHfi+SETDP7COm8t/ohOFaX67b2JLbi3kSYI9zxygKv+u5GU6kpOxsYzo/sA8lud2eA6z334EhkV\nJ/XbhxNTefznf2hwHYC+O7Zw+colJFRVUBGXwNyhY9jao3ejaF25dA6XbswhyuXCFRXFkvOHMW9Y\n4Kb6dhm4fyfX5+WSVFNFeUwcn2Vmsb5DjwbX+dOmbxh25IB++5v0Djx37ogG1wH4+Ggho2o8/ZkX\nxsTxi1btGkUrECHGgIsbohBDSvk28LYQ4mfAJODnRvuHtRIuKSlVdu9u3INgQvEBnjaY3vrEmd2Z\nmd7e7++86dbNXE+A361byKgDO+vVWtK+B38deJnhMcw+fs8u/ZQ+pUfq1fo+tTWPj7jR8Bhms0g6\nHy/iidyZtKoo89M6mpDM01kT2Nsyo96/b9WutSmdSZ+8Qdfj9Z/Tjy1bM/km4/qcpFRzvTa679nJ\n3V98QGxtjZ9WdXQMf7/2V+R3MTYssy/14WuWct0ybZRUoPP6fNg4vh00wvAYhT8WmtLqfWgvj+V8\nSZxXvwmlVRUVzXNDr2Fr286Gxyg5GlzrnrxcJri7kwU6p5ntzuKtzCzDY3z//fKgOgB/KTnMTdUV\n9Wp9EpvA79OCV0AePrzXVnVaQkKy7Nqlj6l9f9ixxlBLCJEF/FlKeZn79qMAUsrn69k/CiiRUrY0\n0o2oZjzKfL0bd1RRd6Lq0wfzGe/1CW4VZb5G01tHHdjJ/623PuJGoczXSKtP6RGeWTbdtlbn40W8\nuvRTWrtbbB5JSGZbqzM54u5t0bqijFeXfkqn40W2dJT5Gp1T1+NH+ONnf7WlA5r53jttCnHuCRRV\n0TGUpLSgyv2BFFdbw73TptB9b75tLWW+3uflO1X6umVzGb72W9tavQ/t5c/LpxPv7rRWGRVNcWIK\nle6S83hXLX9ePp1zDu+1paPM1+i5mlC4i7u3W58Hp1Dma6R1U3UFrx6z9/ozi8Rl6scEa4EeQohu\nQog44EZglvcOQgjvK4BxwM5gB42oxNynvVrmvdSuK1MzPJ/8Nxft5eFCrf3h5AP5zG5tfBUcDGW+\nEsjN6MKLWeP1bY/kzibLPSl2zP4f+Nv51pt8A7r5SmB3UkvuH3mLvu317H/Trfy4Nin2+GFbOgBP\n5M4kRrqojIph0sXXkp/m+crXvaSQycu/IN5Vw5O5M7ljrLUes4BuvhIoTGrBpNse0rdN/ugV2pWf\nQADdS+yf091ffKD3gX7np78kr7tn8TkzP4+7vvonUcDdn7/Pgw8EvCAxjTJfCcy9cCQLLhqrbxu7\nYgHjVmlz4677dg7fXjDcltZjOV/q5/V81tVs7HC2vq3//v/yaO4MooBJK77k5mvut6yjzFcCizM6\n8aLXlO9HtixjdNE+BHDtgXz+brJ3Qn0o85XA3Jh4bm/lSTt9/+ghxtVUIoCbq07xoC0lczTUN3wp\nZY0Q4nfAQrQ0tA+klFuFEE8D66SUs4DfCSFGAdVACUHCDxBBV8C/OKQNAwxkvgBTMzrzSlvP9NZb\nD1sfHvh4zgxdy9d8AV7MGs/q9E661qMrZ1rWestrgrCv+QLcP/IW9iS20LXetDFBuOeRA7SqKEOC\nn/kC5Ke1Y9JF1yCBVhVldLfYxOW5D18CApsvwKTbHuJQYqp+Ts/+62VLOqDFfGNra5D4my9AXvdM\nplz9CyQQW1tD751bLWtduXQOENh8ARZcNJb5gy7Vz+uKZYEnnJhh4P6dxLlqkfibL8DGDmfzYpY2\ngTnOVcv5+4NeTAXkT5u+AQKbL8CLfYfxdXoH/Zwe27zUkg5oMV+l5Wu+ALe3asuCmDhd60MToRN7\nSFwul6kfU0eTcp6UsqeU8mwp5bPu3z3hNl+klPdJKXtLKc+TUl4ipQz6YowYA76juED/2uJrvgq1\nCCeAO4usTyoeeMSj5Wu+CrUIJ4BBxfsC7mOGThWlupav+Sru9Zog3PmU8Zw6I67670YEWpzX13wV\n+a3O5GhCsva1M3+jJZ2MipP6Ofmar0ItwgmgzanSgPuY4fKV2lTk6ugYP/NVbO3Rm+roGARwxUrr\nk4ov3Zijn5ev+SrUIpwARq5fZlnr+rxcBFqc19d8Fes79KAqKhoBXO8eLx8qw44c8LzWfcxXoRbh\nBDCi2PqgoFE1VbqWr/kq1CKcAC6radxht85IohCIdT8IxqMXPdvjIrzGuylIqdYqvQ4HaVhflJQK\nQKp7oSSSSajS7mNZYpLhfmp7YqX1c4pyhTZVOtpGo5ckt/mUxhsvQp6M01poJteEp4ovfHWJ4aE5\nd0NrUKpFFImylmBFlWp71Wkw0TfcnIyNB6BNkGnPGeXaFWlpbPj641qlQhnQqcDDUxVq+6l46+fk\nioqCWpfpqdK1UdZfg+UxWs/l1MpThvulqA+gmHjLWqEQ2Z0TQqcpzdUMEeNi76V31J/8m4sCr/qq\nuK8EpmRYn1S8rrVH65Hc2QH3UXFfiZYXbJV9CZ5Y6OvZ/w64z5teE4T3JlrvwzDr7P6e+G5J4Pha\n96MH9TjxTIsjaIoSUvRzmvzRKwH3UXFfiZYXbJX5Q0bp8d3M/MBl9b13btXjxPOGWJ9U/HX/ofp5\njV2xIOA+Ku4rgezzA3+lN8O0zCw9vtt/f+Dm7ed7xYmnZVobgbSsdXvPa31L4JCJivtKYGl6h4D7\nmGGJV3z3/aOHAu7zoVeceGFMYzf+lyoOEfyniYgYA/6wrSe++3Dhj36LbLce3sNDhzzTW+0UZTw7\n9GpdK6toj98i26MrZzK4eJ+uZaco4x6vCcLdyo/7LbK9ueRjupw6oWvZKcrY0bq9Ht+dvPwLv0W2\n7kcPMnnFl544scWijMd+8TCgnVO78hN+i2zP/utl2p7yxL7tFGVs6dlXj+/e9dU//RbZeu/cyp0z\nPtTjxHaKMuaMuBLQzmvcqmy/RbYrls3n8jVf6+dlpyhjnVd899HcGX6LbOfv38kjubP0OLHVooxn\n+l0CaOc0umif3yLbY5uXcmnxfv2c7BRl3OoV3x1XU+m3yPbh0ULGesWJw1GUIU3+11REVCGGdx6w\nulfVeMIO6veT2nc3TEMzU4jhnQcc6BFQv1/U4SeGaWhmHj/vPGAjrc0t2/CnYRPrPY6ZQgyVBxwj\nXXrhRVFSKhnlpXphRo2I4oERN7KvnmIMM4UY3nnARueUn9aGF274Xb3HMVOIofKA1TGro2MoS0wi\n+VS5XpghgTdvuIv8zt3rPY6Zl7p3HrDaPdBU6c+HX2mYhmamEEPlAatjVkVFczIugZSqCr0wQwJP\nDJvItjb1F2MEK8TwzgM2eq6+aN/dMA3NTCGGdx6wkdbUuEQePKP+YiCwX4gRF5co25kc/rlv3/bT\nayRRYzAzvT1P+ExvjaPuRNVg5muWvw68jCXte9TR8p3eGsx8zfL4iBv5PrW1oVYw8zXL3pYZPDji\nxjqFF72OFtYpzDAyX7NMvuk+fmxpfE7BzNcs+V168Ob1d9YpvEg7eaJOYUYw8zXLt4NG8PmwcXXO\ny3eqdDDzNcvWtp3588UT6xRetK4oq1OYEcx8zfBWZhYz251l+FwFM1+z/D6tDZ/EJhhqmTHfhsHc\nAtz/zFRkM6XIilsP7+HOogLipIsqEcWUEHpBmC1FVjy6cmadVLNQekGE+vi9ueTjOqlmofSCCLWh\nffejB5mQv5HU6gpKYxOY2b2/qbCD2VJkxbP/erlOqlkovSDMliIreu/cyhUrF5FYWcGp+ATmDTHf\nCyLUl/oVy+Yzcv0yol0uaqOiyA6hF4TZUmTF+ft3cn3eKpJrKimLiWda5oWmww5mSpEVj21eygh3\nyEHFfM2GHcyWIis+PFpYJ9Us1F4Q9q+AE2Qbk56xf/+O5j8VORQDtkOoBmyHcD5+4ZooEqoB2yFU\nA7ZDOC90QjVgO4RiwHYI1YDt0hAGnFFPTYEvBw7sbBIDjpg0NAcHB4eGpimLLMzgGLCDg0PzpIlT\nzMzgGLCDg0OzRAIu5wrYwcHBoWlwQhBe1NbWUlZ2vNF1du/e3OgaimPH7LdbNEuSjSq5UEjYnRIW\nHYBz+gwKm9ahA/b66oZCcbH1ZlEha9loTBUKh232JQ4/TZtiZgbnCtjBwaHZ4hiwg4ODQxMQ4ky4\nJiGiKuEcHBwcGg6JdNWa+jGDEGKsEOIHIUS+EOKPAbY/IITIE0JsFkJkCyGCVoFE7BXwhl2b8O6h\nVQoMOKtfo2jNysulc62nE/GP0bFcHWRAoVVGl5/g9yeOkOJycTIqildbpJOdZL1bmBHv79lKVmWZ\nfjsnPplfd2n4CcIv7drESK9eF4tS03n0rMYphnl0wcf0OeJpGr45vSMvXha40b1dzjlexMR920mt\nrqI0No5pnc5he8v0RtH6Zssy0rxuHwFG1tNA3Q53lx7lnvLjxEuoFPBG0hm8k5oW/A8tsBrwrmxY\nAzTOu6p+GqrRjhAiGngbGI02kn6tEGKWlNK7Rd9GYKCUslwIcTfwEnCD4XHDeYkeH58kO3bsabhP\nzq5NqF76gSaqHgKGBjHi5GTDQaQ6H21fQz+D6a2bYhO4rZfxIpHZRbisUyf5sHg/CUg/rQoEv0jv\nQG6i8eKX2UW4l/f9wJWntMXOQOc1J7Elf+j0k3r/PiHI/VD8cW8eN7jbXgbS+SytHS90DjzFQmF2\nEe63S79g6P76J1jndOjB2yOuNTyG2UW4bqUlvLB5KelVp/y0iuMS+eO5I9gdxLTMLsLN3pJDZ7Qr\nsEDntZdoxvcdaqxlYhHuhrLjvGYwlfuB1NZ8FuR9c9A9VTkYcwFVsB1Iaz7axEoT2KpOi4mJlamp\nrUzte+zY4Yaeitwf+KuU0vDJi6gQhDJfo4mqbYHluzbZ1lLma6TVr7qCf/6w1rZW1qmTfFpcQKL7\n6KcQHIiKQb29E5F8WlzAoFNlRocxhTJfo/O68tRxXijYYUtHma+Rzg0lhTy0d7stHfCYr5HW0P07\nuXvZDNta3UpLeGf9AjKqtEbpRXGJbGmRTlGcVjKdUXWKd9YvoOvJEttaynyNzqsztcz4fqUtHWW+\n3jqVPjqvlR7hugbIUFLma3ROl+MzTrgRCaEZT7oQYp3Xz50+h+oAeM8mK3D/rj5uR/usMSSiQhDK\nfCVwHDjf60p3/a5NtHRvt9bFti79vFrm/TcmjmvO8TS8/nLbKs529y0dUGU8scAMH7qbn7iAX7bu\nwDdeIYdLykv555H9RAEfFxfwE4MrUzMo85XAt/Ep/KaL5wr0H3vyGF6pzXObUH4MvyBWCCjzlcDc\nFhlM6tZX3zZ59xbGnSjSpt+WHOCVzr1sKKGbrwS+a9OJl0bfrG97ePFUzjus9W6+eN92/m5LCV7Y\nvJQYKamIiub+80axo6WnL0bP40d4/bslJLhqeXHTUm4Y+lNbWsp8JXAYwei+F+vbFm9ZThv3t6Vu\nssaWzmte4aEnU9J4L8Vz9X7HyRKeOlmCAN4oPcLnJr891ocyXwmsBC7y2rYCGOLefqUtFXNo5mo6\nD7i4oXpBCCFuQYu+BG2ZFzFXwBvcV7WBzBf37RN4Pkk32LgKnpWXq2v5mi/ANedcyO7oWF1rhnt/\nK4wuP0GCOxLla74A3ySlcntrbSptApKR5dYHWL6/R2tWHsh8AX7TJZMV8cn6eb27x9oE4Ze8nitf\n8wWY1K0vC1LTdZ3nd1nPy350gWdaiK/5Arw0+mY2eU1TeWRh4KkjZjjneBHpVaeQ4Ge+ADtatuaB\n80YigfSqU/Q6XmxZ6xv3dIpA5gswuu/FFON5vWfXM80iGHeXHtV1fM0X4L2UNJ5OTtN17iq1fmW/\n2v3/QOaL+/ZqPOdk/V1lngZsR7kf8B6N09H9uzq4x9I/DlwlpQw6yC9iDDgVz9cWX/NVqEU44d7f\nKp1rq3UtX/NVqEU4AXStDTYqtH5+f0K7+qhA+JmvIjsplQoEAnjghPU3dVZlmX5evuarUItwAhha\naS3kMdLr66yv+SrUIpwAxpRaP6c+RzzTGnzNV6EW4QRwro0CiIn7tiPQ4ry+5qvY3jKd4rhEhHt/\nq6Theb37mq9CLcIJwGp/unvKPeEoX/NVqEU4AdxXfsyiknbJp7R8zVehFuEEEI4SnAYcS78W6CGE\n6CaEiANuxCeS4o77voNmvqYWhyLGgJsrKe4nt8TddLs+jrm3p9qYtBsuRPBdbO3fVKRWa71rC93N\n7OvjUII2gblFdXgmFdsh3n1xF2wAvNoeb+pi8DSigWbCSSlrgN8BC4FtwDQp5VYhxNNCiKvcu70M\npADThRDfCSGChrojKgbcHDkZFQW1kBYk1/AM9/ZSG5N2w0Wo79HT5T1dGqsNiWxXYfzNoG2FNoH5\nRGx4JhXboVJAotQmyxihtleeLp+WppBIGu6CRko5D5jn87snvP49KtRjRsy7vRTPG3V9PfFd7zix\n9Ugp7PWK7365bVXAfWZ4xYl/jI4NuI8Z/tKitR7fvaSe+O7I8lI9TvxaC+t5prle8d1/7Ak8Qfhd\nrzhxTrzxlV59ZHuNV5q8e0vAfVTcV+UFW+V7d3wctAW3QKi4r0TLC7bK9E699Phuz+NHAu7T63ix\nHiee3sn64mIJntf74i2BG51ne8WJA9+b4LyV1FLXuaOezA0V95VoecFWWYfnnFbUs4+K+0q0vODG\nRFXCRfJIoogxYO/4bkv8F9k27NpECzxfZ+0UZVzlFd89u6bKb5FtRl4u3bzixHaKMhYntdDju/88\nst9vkW1keSnvu+OcFQhbRRm3e8V3h1ee9Ftke3fPVi7yihNbLcp42Ou5GneiyG+R7fldmxlbWqzr\n2CnKeH7srbrWeYf3+S2yPbLw3/QrLtC17BRlbGuZocd3X/9uid8iW6/jxbz2XbYeJ7ZTlHGJV3y3\nDdJvkS17yzLS8bzerRZl/N2dByuAp06W+C2y3VVawhNlJbqOnaIMNVFOoGU7+C6y5br3UVrhKMqI\ndAOOqBDEITypaC2AfB8TVuktB/3+MnQ2xSboqWjdaqvZ5PMGUFob4uyPzPlFegc+LS4gCvjgyH4q\njgiORUVzhqtWL8yQwK02rt4UcxJb6qloF1WWsW1H3esMpTXTxpUOaEUWKhVtbGkxYzd9HVBnapr9\nAao5HXroqWj9igv4z9QXAmott3FFqvjjuSN4Z/0CEly1vL1hEcVxiRxKSKJtRblemFEjBI/0G2Fb\nay/ReipaOtT7Gtwt7L1NH0htraeiPVGmGW4VnrCD0rkv1f4oqvl4UtEGg18AQGnNsa1kDqcXRAgM\nPasfh6ibtO07UfUgcHEDlCTf1msQm4JMb90Ql8gvf3KBba3cxBRuTO9Yp/DiTFdNncKMiemdWJNo\nLSTgzR86/YQ5iS0Nz2tm0hn8MUhFYjBe6JzJZ2ntjKffprW3nQMM8PaIa8npYDzBenmnXvx92NW2\ntXanpnHX+WPrFF70OXGkTmHGnQPH8mM9GQWhML7vUPYSbXheu0UMV/cZYkvns+SWPOAzlTveR+e+\n1Na2c4BBq3Cbj/F7eA5wlf+fNgISl6vW1E9TEXGlyAo7vSDMliIrZuTl1kk1C6UXRKj9gEeWl/LA\niWJSXS5Ko6J4LYReEKH2A353z9Y6qWZme0GYLUVWPL9rM2PcIQcV8zUbdgi1H/AjC/9dJ9UslF4Q\nofYD7nW8mIn7ttOiupITsfFM79TLdNgh1H7A2VuW1Uk1C6UXRCj9gO8qLeG+8mOWekGYLUVW5FI3\n1cxCLwhbpchCRMmYGHPrNzU1Vc1/KnIoBmyHUA3YDs2yIXuIBmwHpyF7A2iFqSF7qAbcANg3YJOT\nxGtqq5vEgG2FIIK1Z3NwcHBoOqTp/5oKy9F9k+3ZHBwcHJqM5jwTbhCQL6XcBSCE+BSYADgG7ODg\nEBGYLDNuMuwYcKD2bIN9d3K3dVOt3Sp37dr0vQ3NSCQdsN7sIHIJy3lt8klfCwPN8flqjucEYK81\noFY2bDZZu0kev0bPA5ZSTgGmAAgh1jVFoLsxaY7nBM55nU40x3MC7bzs/L2UcmxD3ZfGws4inKn2\nbA4ODg4OgbFjwEHbszk4ODg41I/lEISUskYIodqzRQMfSCmDdfieYlUvgmmO5wTOeZ1ONMdzguZ7\nXjphLcRwcHBwcPAQUb0gHBwcHP6XcAzYwcHBoYkIiwE3x5JlIUQnIcQ3Qog8IcRWIcR9TX2fGgoh\nRLQQYqMQIlxdAxsdIcQZQojPhRDbhRDbhBDhaEfb6Aghfu9+/X0vhPhECJHQ1PcpVIQQHwghDgsh\nvvf6XSshxGIhxE73/+23n4tAGt2AvUqWLwcygZuEEIEnRp5e1AAPSikzgQuB3zaT8wK4D23uVXPi\nDWCBlLIX0I9mcH5CiA7AvcBAKWUftMXwG5v2XlniQ8A3Z/ePQLaUsgeQ7b7d7AjHFbBesiylrAJU\nyfJpjZTyoJRyg/vfpWhv6A5Ne6/sI4ToiNbW9b2mvi8NhRCiJTAMeB9ASlklpbQ+/jeyiAEShRAx\nQBJwoInvT8hIKZcBR31+PQH4l/vf/wLsN3uOQMJhwIFKlk97o/JGCNEV6A+sbtp70iC8DjyM/zCD\n05luQBHwT3do5T0hhP3u902MlHI/8AqwF21WwXEp5aKmvVcNRlsppRp+U4g2LKfZ4SzC2UQIkQJ8\nAdwvpTzR1PfHDkKIK4HDUsr1TX1fGpgYYADwdyllf6CMZvCV1h0XnYD2AdMeSBZCWB+KF6FILVe2\nWebLhsOAm23JshAiFs18p0opv2zq+9MADAWuEkL8iBYqulQI8W/jPzktKAAKpJTqG8rnaIZ8ujMK\n2C2lLJJSVgNfos3DbA4cEkKcCeD+f/gmH4SRcBhwsyxZFkIItJjiNinla019fxoCKeWjUsqOUsqu\naM/T11LK0/6KSkpZCOwTQqjuWiNpHm1T9wIXCiGS3K/HkTSDxUU3s4Cfu//9c2BmE96XRiMc3dCs\nlCyfDgwFbgW2CCG+c//uMSnlvCa8Tw71cw8w1X0RsAv4ZRPfH9tIKVcLIT4HNqBl5WzkNCzfFUJ8\nAowA0oUQBcCTwAvANCHE7cAe4Pqmu4eNh1OK7ODg4NBEOItwDg4ODk2EY8AODg4OTYRjwA4ODg5N\nhGPADg4ODk2EY8AODg4OTYRjwA4ODg5NhGPADg4ODk3E/wPUR0tRCYUz/QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LD3vi_c-iIaa",
        "colab_type": "text"
      },
      "source": [
        "The (2, 7) node is surely an outlier and the customers clustered in that neuron are the potential fraudsters."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5L4FZdOwn11Y",
        "colab_type": "text"
      },
      "source": [
        "### **Finding the fraudsters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vsXh_E1lyj6",
        "colab_type": "code",
        "outputId": "70116d78-0829-4824-9354-2826457606ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "mappings = som.win_map(X)\n",
        "frauds = mappings[(2, 7)]\n",
        "frauds = sc.inverse_transform(frauds)\n",
        "fraudsters = pd.DataFrame(frauds)\n",
        "fraudsters.columns = ['customer_id', 'Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7',\n",
        "                      'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', \n",
        "                      'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', \n",
        "                      'V26', 'V27', 'V28', 'Amount']\n",
        "fraudsters"
      ],
      "execution_count": 22,
      "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>customer_id</th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>417209.0</td>\n",
              "      <td>142323.0</td>\n",
              "      <td>-2.115428</td>\n",
              "      <td>-0.356423</td>\n",
              "      <td>-3.719861</td>\n",
              "      <td>0.656568</td>\n",
              "      <td>-7.892220</td>\n",
              "      <td>2.611464</td>\n",
              "      <td>8.115545</td>\n",
              "      <td>-0.789151</td>\n",
              "      <td>-0.904982</td>\n",
              "      <td>-2.544300</td>\n",
              "      <td>0.225305</td>\n",
              "      <td>-0.138968</td>\n",
              "      <td>0.428770</td>\n",
              "      <td>-0.359836</td>\n",
              "      <td>0.796916</td>\n",
              "      <td>0.722632</td>\n",
              "      <td>0.791262</td>\n",
              "      <td>0.704245</td>\n",
              "      <td>-0.753352</td>\n",
              "      <td>0.156679</td>\n",
              "      <td>0.324233</td>\n",
              "      <td>1.216345</td>\n",
              "      <td>1.417695</td>\n",
              "      <td>0.906605</td>\n",
              "      <td>-0.100475</td>\n",
              "      <td>-0.174178</td>\n",
              "      <td>0.729505</td>\n",
              "      <td>-0.366921</td>\n",
              "      <td>1845.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>180681.0</td>\n",
              "      <td>145904.0</td>\n",
              "      <td>-1.326713</td>\n",
              "      <td>0.649403</td>\n",
              "      <td>-3.818230</td>\n",
              "      <td>-2.114035</td>\n",
              "      <td>-10.722687</td>\n",
              "      <td>6.026615</td>\n",
              "      <td>9.674628</td>\n",
              "      <td>-2.731137</td>\n",
              "      <td>1.042109</td>\n",
              "      <td>-1.510719</td>\n",
              "      <td>0.568384</td>\n",
              "      <td>-0.311650</td>\n",
              "      <td>-0.416244</td>\n",
              "      <td>-0.319877</td>\n",
              "      <td>-0.007690</td>\n",
              "      <td>1.875562</td>\n",
              "      <td>-1.641352</td>\n",
              "      <td>0.749190</td>\n",
              "      <td>-0.383020</td>\n",
              "      <td>-3.339235</td>\n",
              "      <td>0.153870</td>\n",
              "      <td>1.328389</td>\n",
              "      <td>-0.437222</td>\n",
              "      <td>-0.049816</td>\n",
              "      <td>-2.249449</td>\n",
              "      <td>0.276496</td>\n",
              "      <td>1.202437</td>\n",
              "      <td>-0.822944</td>\n",
              "      <td>2088.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>252304.0</td>\n",
              "      <td>39138.0</td>\n",
              "      <td>-2.948199</td>\n",
              "      <td>-3.113410</td>\n",
              "      <td>-0.751727</td>\n",
              "      <td>-0.914998</td>\n",
              "      <td>-3.432860</td>\n",
              "      <td>1.161925</td>\n",
              "      <td>4.018882</td>\n",
              "      <td>-0.003248</td>\n",
              "      <td>-2.304612</td>\n",
              "      <td>-1.271708</td>\n",
              "      <td>0.680086</td>\n",
              "      <td>0.037912</td>\n",
              "      <td>0.830897</td>\n",
              "      <td>0.121789</td>\n",
              "      <td>-1.503389</td>\n",
              "      <td>1.347734</td>\n",
              "      <td>0.062355</td>\n",
              "      <td>-0.551259</td>\n",
              "      <td>0.365018</td>\n",
              "      <td>2.708991</td>\n",
              "      <td>0.995586</td>\n",
              "      <td>0.454418</td>\n",
              "      <td>2.361695</td>\n",
              "      <td>-0.069749</td>\n",
              "      <td>1.420331</td>\n",
              "      <td>-0.185002</td>\n",
              "      <td>-0.593091</td>\n",
              "      <td>-0.093805</td>\n",
              "      <td>1200.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>338520.0</td>\n",
              "      <td>56427.0</td>\n",
              "      <td>-1.295631</td>\n",
              "      <td>-0.609158</td>\n",
              "      <td>0.652955</td>\n",
              "      <td>1.340946</td>\n",
              "      <td>-1.607694</td>\n",
              "      <td>0.373082</td>\n",
              "      <td>2.852770</td>\n",
              "      <td>-0.276567</td>\n",
              "      <td>-0.985335</td>\n",
              "      <td>-0.813173</td>\n",
              "      <td>0.215089</td>\n",
              "      <td>0.126594</td>\n",
              "      <td>0.302795</td>\n",
              "      <td>0.411027</td>\n",
              "      <td>1.233781</td>\n",
              "      <td>-0.659800</td>\n",
              "      <td>0.219704</td>\n",
              "      <td>-0.303990</td>\n",
              "      <td>-0.279026</td>\n",
              "      <td>1.298152</td>\n",
              "      <td>0.583004</td>\n",
              "      <td>0.702820</td>\n",
              "      <td>1.355469</td>\n",
              "      <td>0.558617</td>\n",
              "      <td>-0.141912</td>\n",
              "      <td>-0.358738</td>\n",
              "      <td>-0.019109</td>\n",
              "      <td>0.211247</td>\n",
              "      <td>614.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>453564.0</td>\n",
              "      <td>32674.0</td>\n",
              "      <td>-8.108444</td>\n",
              "      <td>-6.803067</td>\n",
              "      <td>-1.221330</td>\n",
              "      <td>0.140158</td>\n",
              "      <td>-5.251149</td>\n",
              "      <td>3.977291</td>\n",
              "      <td>6.195621</td>\n",
              "      <td>-2.379155</td>\n",
              "      <td>1.104709</td>\n",
              "      <td>1.325456</td>\n",
              "      <td>2.089125</td>\n",
              "      <td>-0.539110</td>\n",
              "      <td>0.094206</td>\n",
              "      <td>-2.417577</td>\n",
              "      <td>-0.092852</td>\n",
              "      <td>1.757298</td>\n",
              "      <td>-0.178962</td>\n",
              "      <td>-2.414106</td>\n",
              "      <td>0.753855</td>\n",
              "      <td>-6.512620</td>\n",
              "      <td>-2.320990</td>\n",
              "      <td>0.990359</td>\n",
              "      <td>-0.218009</td>\n",
              "      <td>0.120944</td>\n",
              "      <td>0.735611</td>\n",
              "      <td>-0.243044</td>\n",
              "      <td>-0.795626</td>\n",
              "      <td>3.287298</td>\n",
              "      <td>1481.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>326573.0</td>\n",
              "      <td>151579.0</td>\n",
              "      <td>-3.204030</td>\n",
              "      <td>-9.381823</td>\n",
              "      <td>-3.991731</td>\n",
              "      <td>0.920507</td>\n",
              "      <td>-2.432941</td>\n",
              "      <td>2.475750</td>\n",
              "      <td>2.442080</td>\n",
              "      <td>-0.088620</td>\n",
              "      <td>1.160074</td>\n",
              "      <td>-1.553717</td>\n",
              "      <td>0.893585</td>\n",
              "      <td>0.684618</td>\n",
              "      <td>-0.289517</td>\n",
              "      <td>0.947351</td>\n",
              "      <td>1.770902</td>\n",
              "      <td>0.182886</td>\n",
              "      <td>-0.078592</td>\n",
              "      <td>0.148469</td>\n",
              "      <td>-1.279562</td>\n",
              "      <td>5.162657</td>\n",
              "      <td>1.860382</td>\n",
              "      <td>-0.518024</td>\n",
              "      <td>-2.131111</td>\n",
              "      <td>-0.762765</td>\n",
              "      <td>-1.481573</td>\n",
              "      <td>0.505958</td>\n",
              "      <td>-0.506516</td>\n",
              "      <td>0.388478</td>\n",
              "      <td>2668.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>449415.0</td>\n",
              "      <td>73457.0</td>\n",
              "      <td>-3.305218</td>\n",
              "      <td>-7.172688</td>\n",
              "      <td>-2.813947</td>\n",
              "      <td>3.029481</td>\n",
              "      <td>-1.814998</td>\n",
              "      <td>0.957197</td>\n",
              "      <td>3.421911</td>\n",
              "      <td>-0.715084</td>\n",
              "      <td>-0.322254</td>\n",
              "      <td>-1.176477</td>\n",
              "      <td>-0.964187</td>\n",
              "      <td>0.752231</td>\n",
              "      <td>1.421585</td>\n",
              "      <td>0.562575</td>\n",
              "      <td>0.489041</td>\n",
              "      <td>-0.160505</td>\n",
              "      <td>-0.125537</td>\n",
              "      <td>-0.253973</td>\n",
              "      <td>-0.933089</td>\n",
              "      <td>4.700579</td>\n",
              "      <td>1.379742</td>\n",
              "      <td>-1.142250</td>\n",
              "      <td>-2.314699</td>\n",
              "      <td>-0.601968</td>\n",
              "      <td>0.045135</td>\n",
              "      <td>-0.434085</td>\n",
              "      <td>-0.414941</td>\n",
              "      <td>0.415110</td>\n",
              "      <td>2364.68</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   customer_id      Time        V1  ...       V27       V28   Amount\n",
              "0     417209.0  142323.0 -2.115428  ...  0.729505 -0.366921  1845.81\n",
              "1     180681.0  145904.0 -1.326713  ...  1.202437 -0.822944  2088.00\n",
              "2     252304.0   39138.0 -2.948199  ... -0.593091 -0.093805  1200.00\n",
              "3     338520.0   56427.0 -1.295631  ... -0.019109  0.211247   614.10\n",
              "4     453564.0   32674.0 -8.108444  ... -0.795626  3.287298  1481.74\n",
              "5     326573.0  151579.0 -3.204030  ... -0.506516  0.388478  2668.00\n",
              "6     449415.0   73457.0 -3.305218  ... -0.414941  0.415110  2364.68\n",
              "\n",
              "[7 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FyQKNYRIi2AU",
        "colab_type": "text"
      },
      "source": [
        "The above table gives us the list of all the potential fraudsters."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ye_iObc5qXi-",
        "colab_type": "text"
      },
      "source": [
        "## **Part 3:** Using Artificial Neural Network to predict the probabilities of fraud."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_4g3xkJoLHO",
        "colab_type": "text"
      },
      "source": [
        "### **Creating a dependent variable**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ORyOEZXwpa4t",
        "colab_type": "code",
        "outputId": "be19d8b4-2d0f-4f15-a1b6-d080e9ce6a13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "is_fraud = np.zeros(len(sampled_dataset))\n",
        "for i in range(len(sampled_dataset)):\n",
        "    if sampled_dataset.iloc[i, 0] in frauds:\n",
        "        is_fraud[i] = 1\n",
        "pd.DataFrame(is_fraud)"
      ],
      "execution_count": 23,
      "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       0\n",
              "0    0.0\n",
              "1    0.0\n",
              "2    0.0\n",
              "3    0.0\n",
              "4    0.0\n",
              "..   ...\n",
              "995  0.0\n",
              "996  0.0\n",
              "997  0.0\n",
              "998  0.0\n",
              "999  0.0\n",
              "\n",
              "[1000 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Tw7EgUEoY4O",
        "colab_type": "text"
      },
      "source": [
        "### **Creating & Feature Scaling the Matrix of Features**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rnOdYujmrukv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "customers = X.copy()\n",
        "sc = StandardScaler()\n",
        "customers = sc.fit_transform(customers)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u95gS8_Do7wo",
        "colab_type": "text"
      },
      "source": [
        "### **Building an ANN & training it over the Matrix of Features & the new Dependent Variable**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c7gk-wB-sKdH",
        "colab_type": "code",
        "outputId": "efe1f42e-215d-4246-8369-4c94cbaceba1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "classifier = Sequential()\n",
        "classifier.add(Dense(units = 15, kernel_initializer = 'uniform', activation = 'relu', input_dim = 31))\n",
        "classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n",
        "classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
        "classifier.fit(customers, is_fraud, batch_size = 1, epochs = 20)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1922 - acc: 0.9930\n",
            "Epoch 2/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0157 - acc: 0.9940\n",
            "Epoch 3/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0106 - acc: 0.9940\n",
            "Epoch 4/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0086 - acc: 0.9940\n",
            "Epoch 5/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0076 - acc: 0.9940\n",
            "Epoch 6/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0062 - acc: 0.9940\n",
            "Epoch 7/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0073 - acc: 0.9940\n",
            "Epoch 8/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0060 - acc: 0.9940\n",
            "Epoch 9/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0054 - acc: 0.9940\n",
            "Epoch 10/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0049 - acc: 0.9940\n",
            "Epoch 11/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0046 - acc: 0.9960\n",
            "Epoch 12/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0049 - acc: 0.9980\n",
            "Epoch 13/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0041 - acc: 0.9990\n",
            "Epoch 14/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0041 - acc: 0.9970\n",
            "Epoch 15/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0037 - acc: 0.9990\n",
            "Epoch 16/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0034 - acc: 1.0000\n",
            "Epoch 17/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0031 - acc: 1.0000\n",
            "Epoch 18/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0031 - acc: 1.0000\n",
            "Epoch 19/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0033 - acc: 0.9990\n",
            "Epoch 20/20\n",
            "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0029 - acc: 1.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7375c64320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L17E75ukpOfx",
        "colab_type": "text"
      },
      "source": [
        "### **Predicting the Probabilities of Frauds**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADItCYTWvGgx",
        "colab_type": "code",
        "outputId": "c6ca6807-2703-4177-c287-16aea9fc5c7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "y_pred = classifier.predict(customers)\n",
        "y_pred = np.concatenate((sampled_dataset.iloc[:, 0:1].values, y_pred), axis = 1)\n",
        "y_pred = y_pred[y_pred[:, 1].argsort()]\n",
        "pd.DataFrame(y_pred)"
      ],
      "execution_count": 29,
      "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>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>164360.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>401483.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>334406.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>167496.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>144490.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>252304.0</td>\n",
              "      <td>0.661621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>449415.0</td>\n",
              "      <td>0.661621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>180681.0</td>\n",
              "      <td>0.661621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>417209.0</td>\n",
              "      <td>0.661621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>338520.0</td>\n",
              "      <td>0.661621</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            0         1\n",
              "0    164360.0  0.000000\n",
              "1    401483.0  0.000000\n",
              "2    334406.0  0.000000\n",
              "3    167496.0  0.000000\n",
              "4    144490.0  0.000000\n",
              "..        ...       ...\n",
              "995  252304.0  0.661621\n",
              "996  449415.0  0.661621\n",
              "997  180681.0  0.661621\n",
              "998  417209.0  0.661621\n",
              "999  338520.0  0.661621\n",
              "\n",
              "[1000 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zTlieq5KpbLf",
        "colab_type": "text"
      },
      "source": [
        "### **Exporting the Predictions with corresponding Customer IDs as a CSV File**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TXc65tYhj48Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pd.DataFrame(y_pred).to_csv('/content/drive/My Drive/Colab Notebooks/SOM/fraud.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D-DXoJtar9ZE",
        "colab_type": "text"
      },
      "source": [
        "The exported .CSV file contains the list of all the customers(from the sampled dataset only) with the probabilities of committing fraud."
      ]
    }
  ]
}