Credit_card_Fraud_detection_Notebook_Week.ipynb

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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/vicaaa12/Deep-Neural-Networks/blob/main/Credit_card_Fraud_detection_Notebook_Week.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ljrXbm43CuG"
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      "source": [
        "## Introduction to Neural Networks - Fraud Detection\n",
        "\n",
        "**Context**\n",
        "\n",
        "Nielsen reports that U.S. card fraud (credit, debt, etc) was reportedly 9 billion dollars in 2016 and expected to increase to 12 billion dollars by 2020. For perspective, in 2017 both PayPal's and Mastercard's revenue was only $10.8 billion each.\n",
        "Therefore, it is important that credit card companies should be able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.\n",
        "\n",
        "\n",
        "**Objective:**\n",
        "\n",
        "Suppose you are working as a Data scientist in a Credit Card company named **\"CCFraud\"** and  given the credit card transactions, you  need to  build a Model  (i.e. Multilayer perceptrons) for Fraud Detection using Keras."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ilr-y66cDLn7"
      },
      "source": [
        "This notebook covers,\n",
        "\n",
        "1. Creating a Model\n",
        "\n",
        "2. Adding Layers\n",
        "\n",
        "3. Activations\n",
        "\n",
        "4. Optimizers and Loss functions\n",
        "\n",
        "5. Earlystopping\n",
        "\n",
        "6. Weight Initalization\n",
        "\n",
        "7. Dropout\n",
        "\n",
        "8. Model Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g3u3Z-GA1dFK"
      },
      "source": [
        "### Dataset Description\n",
        "\n",
        "The dataset contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.\n",
        "\n",
        "It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, the original features and more background information about the data is not provided. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'.\n",
        "\n",
        "**Time** contains the seconds elapsed between each transaction and the first transaction in the dataset.\n",
        "\n",
        "\n",
        "**Amount** is the transaction Amount, this feature can be used for example-dependant cost-senstive learning.\n",
        "\n",
        "\n",
        "**Class** is the response variable and it takes value 1 in case of fraud and 0 otherwise.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CULdZVQfPHht"
      },
      "source": [
        "##Import all necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "H79BpACkblay",
        "outputId": "fa73f434-f45e-427f-8a59-14d8271398f0"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.14.0\n"
          ]
        }
      ],
      "source": [
        "#importing tensorflow\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2Ro7A1N5y6Hh"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "from sklearn import preprocessing\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, f1_score, precision_recall_curve, auc\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras import optimizers\n",
        "from sklearn.decomposition import PCA\n",
        "import seaborn as sns\n",
        "import keras\n",
        "import tensorflow as tf\n",
        "from keras import backend as K\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BikwTj1_eSKh",
        "outputId": "46556115-761f-4614-ad27-3bec648fee2a"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "#mouting the drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a2pHdSjdPW2f"
      },
      "source": [
        "#### Importing data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q-cL3gdjeSM3"
      },
      "outputs": [],
      "source": [
        "#Defining the path of the dataset\n",
        "project_path = 'My Drive'\n",
        "dataset_file = '/content/drive/MyDrive/IntroductionNN/creditcard.csv'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OM0t4RaJe10y"
      },
      "outputs": [],
      "source": [
        "#reading dataset\n",
        "data = pd.read_csv(dataset_file)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nesE_lsIn0Zb"
      },
      "source": [
        "#### Overview of Dataset"
      ]
    },
    {
      "cell_type": "code",
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              "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
              "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
              "\n",
              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
              "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
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              "\n",
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              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-86237d82-a021-4d6a-9e56-e20de0c97e1d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wRm8mpKlxeXx"
      },
      "source": [
        "###Let's check the missing values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8G0c161tn5ux",
        "outputId": "c271465a-d988-4768-b3bf-28f6a332c7d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 284807 entries, 0 to 284806\n",
            "Data columns (total 31 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   Time    284807 non-null  float64\n",
            " 1   V1      284807 non-null  float64\n",
            " 2   V2      284807 non-null  float64\n",
            " 3   V3      284807 non-null  float64\n",
            " 4   V4      284807 non-null  float64\n",
            " 5   V5      284807 non-null  float64\n",
            " 6   V6      284807 non-null  float64\n",
            " 7   V7      284807 non-null  float64\n",
            " 8   V8      284807 non-null  float64\n",
            " 9   V9      284807 non-null  float64\n",
            " 10  V10     284807 non-null  float64\n",
            " 11  V11     284807 non-null  float64\n",
            " 12  V12     284807 non-null  float64\n",
            " 13  V13     284807 non-null  float64\n",
            " 14  V14     284807 non-null  float64\n",
            " 15  V15     284807 non-null  float64\n",
            " 16  V16     284807 non-null  float64\n",
            " 17  V17     284807 non-null  float64\n",
            " 18  V18     284807 non-null  float64\n",
            " 19  V19     284807 non-null  float64\n",
            " 20  V20     284807 non-null  float64\n",
            " 21  V21     284807 non-null  float64\n",
            " 22  V22     284807 non-null  float64\n",
            " 23  V23     284807 non-null  float64\n",
            " 24  V24     284807 non-null  float64\n",
            " 25  V25     284807 non-null  float64\n",
            " 26  V26     284807 non-null  float64\n",
            " 27  V27     284807 non-null  float64\n",
            " 28  V28     284807 non-null  float64\n",
            " 29  Amount  284807 non-null  float64\n",
            " 30  Class   284807 non-null  int64  \n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 67.4 MB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bUBHYG-x09w"
      },
      "source": [
        "* This shows that there are 284807 instances and 31 attributes including the class attribute.\n",
        "*  As you can see there are no null values in any of the column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ZYBQ41ooZxh",
        "outputId": "b2fe8c67-ea45-405a-c741-d36cc72d63b6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "#Number of distinct categories or classes i.e., Fraudulent and Genuine\n",
        "data['Class'].nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJRyxwYpo7zN"
      },
      "source": [
        "* As expected, there are only 2 classes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Ob0IQROpQMr",
        "outputId": "933f8605-de5f-4539-bf93-74f1666e99d1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.998273\n",
              "1    0.001727\n",
              "Name: Class, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "\n",
        "#checking the percentage of each class in the dataset\n",
        "(data.Class.value_counts())/(data.Class.count())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9bo0U4LpUd7"
      },
      "source": [
        "* This shows a complete imbalance of classes. There are 99.82% 'Genuine' (0) instances and only 0.17% 'Fraudulent' (1) instances. This means that we are aiming to predict anomalous events."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fg7uMScupqzB",
        "outputId": "95ec97e9-dfd1-46d6-82a1-ada2f266c26d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "*********Losses due to fraud:************\n",
            "\n",
            "Total amount lost to fraud\n",
            "60127.97\n",
            "Mean amount per fraudulent transaction\n",
            "122.21132113821139\n",
            "Compare to normal transactions:\n",
            "Total amount from normal transactions\n",
            "25102462.04\n",
            "Mean amount per normal transactions\n",
            "88.29102242231328\n"
          ]
        }
      ],
      "source": [
        "print(\"*********Losses due to fraud:************\\n\")\n",
        "print(\"Total amount lost to fraud\")\n",
        "print(data.Amount[data.Class == 1].sum())\n",
        "print(\"Mean amount per fraudulent transaction\")\n",
        "print(data.Amount[data.Class == 1].mean())\n",
        "print(\"Compare to normal transactions:\")\n",
        "print(\"Total amount from normal transactions\")\n",
        "print(data.Amount[data.Class == 0].sum())\n",
        "print(\"Mean amount per normal transactions\")\n",
        "print(data.Amount[data.Class == 0].mean())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1DaNevzqP8L"
      },
      "source": [
        "##Let's Explore the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "HfxEB2QPqYQw",
        "outputId": "3f6f9850-eed2-4bae-8e9f-532378d2c617"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#visual representation of instances per class\n",
        "data.Class.value_counts().plot.bar()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M39v2Prwq_cO"
      },
      "source": [
        "Above plot does not  give  bettter  visual representation of the class imbalance. The below plot after PCA gives a better visualization of the imbalance in the datasets. PCA helps to visualize the high dimensional data into lower dimensions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "tDHfgvBSrE7Q",
        "outputId": "b42925f0-cdfd-4333-9ba0-3ca2842d5dd4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7d17afb9a590>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 558.875x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#PCA is performed for visualization only\n",
        "\n",
        "pca= PCA(n_components=2)\n",
        "creditcard_2d= pd.DataFrame(pca.fit_transform(data.iloc[:,0:30]))\n",
        "creditcard_2d= pd.concat([creditcard_2d, data['Class']], axis=1)\n",
        "creditcard_2d.columns= ['x', 'y', 'Class']\n",
        "sns.lmplot(x='x', y='y', data=creditcard_2d, fit_reg=False, hue='Class')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Piijx4Wms-iu"
      },
      "source": [
        "* As you can see, PCA gives a better visualization of the imbalance in the datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "DL3ZID8ZtU5t",
        "outputId": "54750a97-9fff-4c24-bb0f-5920fcd1e72b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Histrogram for feature Time\n",
        "f, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(12,4))\n",
        "\n",
        "ax1.hist(data[\"Time\"][data[\"Class\"] == 1], bins = 50)\n",
        "ax1.set_title('Fraudulent')\n",
        "\n",
        "ax2.hist(data[\"Time\"][data[\"Class\"] == 0], bins = 50)\n",
        "ax2.set_title('Genuine')\n",
        "\n",
        "plt.xlabel('Seconds after transaction number zero')\n",
        "plt.ylabel('Number of Transactions')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "roOlATnjuP_t"
      },
      "source": [
        "* The transactions occur in a cyclic way. But the time feature does not provide any useful information as the time when the first transaction was initiated is not given. Thus, we'll drop this feature."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XYR1bqVjr74v"
      },
      "outputs": [],
      "source": [
        "#Dropping time feature\n",
        "data = data.drop(\"Time\", axis = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p2LSjlpKvnf0"
      },
      "source": [
        "###Let's take a look at the V1,...,V28 features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QhD6arijvl6V",
        "outputId": "8d3d34d2-ea01-4210-99b4-bcaa2700fe0d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12',\n",
            "       'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22',\n",
            "       'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "\n",
        "Vfeatures = data.iloc[:,1:29].columns\n",
        "print(Vfeatures)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "cI3wN8jqftKw",
        "outputId": "89a4ba4f-a99d-4868-adfc-a07dcb6d7ad6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               V1         V2        V3        V4        V5        V6  \\\n",
              "0       -1.359807  -0.072781  2.536347  1.378155 -0.338321  0.462388   \n",
              "1        1.191857   0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
              "2       -1.358354  -1.340163  1.773209  0.379780 -0.503198  1.800499   \n",
              "3       -0.966272  -0.185226  1.792993 -0.863291 -0.010309  1.247203   \n",
              "4       -1.158233   0.877737  1.548718  0.403034 -0.407193  0.095921   \n",
              "...           ...        ...       ...       ...       ...       ...   \n",
              "284802 -11.881118  10.071785 -9.834783 -2.066656 -5.364473 -2.606837   \n",
              "284803  -0.732789  -0.055080  2.035030 -0.738589  0.868229  1.058415   \n",
              "284804   1.919565  -0.301254 -3.249640 -0.557828  2.630515  3.031260   \n",
              "284805  -0.240440   0.530483  0.702510  0.689799 -0.377961  0.623708   \n",
              "284806  -0.533413  -0.189733  0.703337 -0.506271 -0.012546 -0.649617   \n",
              "\n",
              "              V7        V8        V9       V10  ...       V21       V22  \\\n",
              "0       0.239599  0.098698  0.363787  0.090794  ... -0.018307  0.277838   \n",
              "1      -0.078803  0.085102 -0.255425 -0.166974  ... -0.225775 -0.638672   \n",
              "2       0.791461  0.247676 -1.514654  0.207643  ...  0.247998  0.771679   \n",
              "3       0.237609  0.377436 -1.387024 -0.054952  ... -0.108300  0.005274   \n",
              "4       0.592941 -0.270533  0.817739  0.753074  ... -0.009431  0.798278   \n",
              "...          ...       ...       ...       ...  ...       ...       ...   \n",
              "284802 -4.918215  7.305334  1.914428  4.356170  ...  0.213454  0.111864   \n",
              "284803  0.024330  0.294869  0.584800 -0.975926  ...  0.214205  0.924384   \n",
              "284804 -0.296827  0.708417  0.432454 -0.484782  ...  0.232045  0.578229   \n",
              "284805 -0.686180  0.679145  0.392087 -0.399126  ...  0.265245  0.800049   \n",
              "284806  1.577006 -0.414650  0.486180 -0.915427  ...  0.261057  0.643078   \n",
              "\n",
              "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
              "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62   \n",
              "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69   \n",
              "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66   \n",
              "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50   \n",
              "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99   \n",
              "...          ...       ...       ...       ...       ...       ...     ...   \n",
              "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
              "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
              "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
              "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
              "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
              "\n",
              "        Class  \n",
              "0           0  \n",
              "1           0  \n",
              "2           0  \n",
              "3           0  \n",
              "4           0  \n",
              "...       ...  \n",
              "284802      0  \n",
              "284803      0  \n",
              "284804      0  \n",
              "284805      0  \n",
              "284806      0  \n",
              "\n",
              "[284807 rows x 30 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bdb0a3d5-5e9b-4f30-9fcf-2dcd99c202e9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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>...</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>-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>...</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>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>...</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>-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>...</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>-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>...</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>-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>...</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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284802</th>\n",
              "      <td>-11.881118</td>\n",
              "      <td>10.071785</td>\n",
              "      <td>-9.834783</td>\n",
              "      <td>-2.066656</td>\n",
              "      <td>-5.364473</td>\n",
              "      <td>-2.606837</td>\n",
              "      <td>-4.918215</td>\n",
              "      <td>7.305334</td>\n",
              "      <td>1.914428</td>\n",
              "      <td>4.356170</td>\n",
              "      <td>...</td>\n",
              "      <td>0.213454</td>\n",
              "      <td>0.111864</td>\n",
              "      <td>1.014480</td>\n",
              "      <td>-0.509348</td>\n",
              "      <td>1.436807</td>\n",
              "      <td>0.250034</td>\n",
              "      <td>0.943651</td>\n",
              "      <td>0.823731</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284803</th>\n",
              "      <td>-0.732789</td>\n",
              "      <td>-0.055080</td>\n",
              "      <td>2.035030</td>\n",
              "      <td>-0.738589</td>\n",
              "      <td>0.868229</td>\n",
              "      <td>1.058415</td>\n",
              "      <td>0.024330</td>\n",
              "      <td>0.294869</td>\n",
              "      <td>0.584800</td>\n",
              "      <td>-0.975926</td>\n",
              "      <td>...</td>\n",
              "      <td>0.214205</td>\n",
              "      <td>0.924384</td>\n",
              "      <td>0.012463</td>\n",
              "      <td>-1.016226</td>\n",
              "      <td>-0.606624</td>\n",
              "      <td>-0.395255</td>\n",
              "      <td>0.068472</td>\n",
              "      <td>-0.053527</td>\n",
              "      <td>24.79</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284804</th>\n",
              "      <td>1.919565</td>\n",
              "      <td>-0.301254</td>\n",
              "      <td>-3.249640</td>\n",
              "      <td>-0.557828</td>\n",
              "      <td>2.630515</td>\n",
              "      <td>3.031260</td>\n",
              "      <td>-0.296827</td>\n",
              "      <td>0.708417</td>\n",
              "      <td>0.432454</td>\n",
              "      <td>-0.484782</td>\n",
              "      <td>...</td>\n",
              "      <td>0.232045</td>\n",
              "      <td>0.578229</td>\n",
              "      <td>-0.037501</td>\n",
              "      <td>0.640134</td>\n",
              "      <td>0.265745</td>\n",
              "      <td>-0.087371</td>\n",
              "      <td>0.004455</td>\n",
              "      <td>-0.026561</td>\n",
              "      <td>67.88</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284805</th>\n",
              "      <td>-0.240440</td>\n",
              "      <td>0.530483</td>\n",
              "      <td>0.702510</td>\n",
              "      <td>0.689799</td>\n",
              "      <td>-0.377961</td>\n",
              "      <td>0.623708</td>\n",
              "      <td>-0.686180</td>\n",
              "      <td>0.679145</td>\n",
              "      <td>0.392087</td>\n",
              "      <td>-0.399126</td>\n",
              "      <td>...</td>\n",
              "      <td>0.265245</td>\n",
              "      <td>0.800049</td>\n",
              "      <td>-0.163298</td>\n",
              "      <td>0.123205</td>\n",
              "      <td>-0.569159</td>\n",
              "      <td>0.546668</td>\n",
              "      <td>0.108821</td>\n",
              "      <td>0.104533</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284806</th>\n",
              "      <td>-0.533413</td>\n",
              "      <td>-0.189733</td>\n",
              "      <td>0.703337</td>\n",
              "      <td>-0.506271</td>\n",
              "      <td>-0.012546</td>\n",
              "      <td>-0.649617</td>\n",
              "      <td>1.577006</td>\n",
              "      <td>-0.414650</td>\n",
              "      <td>0.486180</td>\n",
              "      <td>-0.915427</td>\n",
              "      <td>...</td>\n",
              "      <td>0.261057</td>\n",
              "      <td>0.643078</td>\n",
              "      <td>0.376777</td>\n",
              "      <td>0.008797</td>\n",
              "      <td>-0.473649</td>\n",
              "      <td>-0.818267</td>\n",
              "      <td>-0.002415</td>\n",
              "      <td>0.013649</td>\n",
              "      <td>217.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>284807 rows × 30 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bdb0a3d5-5e9b-4f30-9fcf-2dcd99c202e9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bdb0a3d5-5e9b-4f30-9fcf-2dcd99c202e9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-243ae39b-020a-4796-a1dc-4170233efee0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-243ae39b-020a-4796-a1dc-4170233efee0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-243ae39b-020a-4796-a1dc-4170233efee0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X-pNuQ-JFRgn"
      },
      "source": [
        "###Separating response variable and predictors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Av0SeUasfxdd"
      },
      "outputs": [],
      "source": [
        "X_data = data.iloc[:,0:29]\n",
        "y_data = data.iloc[:, -1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PxJAWm1pf2Pp",
        "outputId": "958aa1cb-d4ed-4014-917c-08eb4492d7e0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(284807,)\n",
            "(284807, 29)\n"
          ]
        }
      ],
      "source": [
        "#printing the shape of the data\n",
        "print(y_data.shape)\n",
        "print(X_data.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "bYmQbFKOGCnw",
        "outputId": "c83e2669-03fa-4387-c342-0ce6bcdecc72"
      },
      "outputs": [
        {
          "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>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>...</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>-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>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284802</th>\n",
              "      <td>-11.881118</td>\n",
              "      <td>10.071785</td>\n",
              "      <td>-9.834783</td>\n",
              "      <td>-2.066656</td>\n",
              "      <td>-5.364473</td>\n",
              "      <td>-2.606837</td>\n",
              "      <td>-4.918215</td>\n",
              "      <td>7.305334</td>\n",
              "      <td>1.914428</td>\n",
              "      <td>4.356170</td>\n",
              "      <td>...</td>\n",
              "      <td>1.475829</td>\n",
              "      <td>0.213454</td>\n",
              "      <td>0.111864</td>\n",
              "      <td>1.014480</td>\n",
              "      <td>-0.509348</td>\n",
              "      <td>1.436807</td>\n",
              "      <td>0.250034</td>\n",
              "      <td>0.943651</td>\n",
              "      <td>0.823731</td>\n",
              "      <td>0.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284803</th>\n",
              "      <td>-0.732789</td>\n",
              "      <td>-0.055080</td>\n",
              "      <td>2.035030</td>\n",
              "      <td>-0.738589</td>\n",
              "      <td>0.868229</td>\n",
              "      <td>1.058415</td>\n",
              "      <td>0.024330</td>\n",
              "      <td>0.294869</td>\n",
              "      <td>0.584800</td>\n",
              "      <td>-0.975926</td>\n",
              "      <td>...</td>\n",
              "      <td>0.059616</td>\n",
              "      <td>0.214205</td>\n",
              "      <td>0.924384</td>\n",
              "      <td>0.012463</td>\n",
              "      <td>-1.016226</td>\n",
              "      <td>-0.606624</td>\n",
              "      <td>-0.395255</td>\n",
              "      <td>0.068472</td>\n",
              "      <td>-0.053527</td>\n",
              "      <td>24.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284804</th>\n",
              "      <td>1.919565</td>\n",
              "      <td>-0.301254</td>\n",
              "      <td>-3.249640</td>\n",
              "      <td>-0.557828</td>\n",
              "      <td>2.630515</td>\n",
              "      <td>3.031260</td>\n",
              "      <td>-0.296827</td>\n",
              "      <td>0.708417</td>\n",
              "      <td>0.432454</td>\n",
              "      <td>-0.484782</td>\n",
              "      <td>...</td>\n",
              "      <td>0.001396</td>\n",
              "      <td>0.232045</td>\n",
              "      <td>0.578229</td>\n",
              "      <td>-0.037501</td>\n",
              "      <td>0.640134</td>\n",
              "      <td>0.265745</td>\n",
              "      <td>-0.087371</td>\n",
              "      <td>0.004455</td>\n",
              "      <td>-0.026561</td>\n",
              "      <td>67.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284805</th>\n",
              "      <td>-0.240440</td>\n",
              "      <td>0.530483</td>\n",
              "      <td>0.702510</td>\n",
              "      <td>0.689799</td>\n",
              "      <td>-0.377961</td>\n",
              "      <td>0.623708</td>\n",
              "      <td>-0.686180</td>\n",
              "      <td>0.679145</td>\n",
              "      <td>0.392087</td>\n",
              "      <td>-0.399126</td>\n",
              "      <td>...</td>\n",
              "      <td>0.127434</td>\n",
              "      <td>0.265245</td>\n",
              "      <td>0.800049</td>\n",
              "      <td>-0.163298</td>\n",
              "      <td>0.123205</td>\n",
              "      <td>-0.569159</td>\n",
              "      <td>0.546668</td>\n",
              "      <td>0.108821</td>\n",
              "      <td>0.104533</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284806</th>\n",
              "      <td>-0.533413</td>\n",
              "      <td>-0.189733</td>\n",
              "      <td>0.703337</td>\n",
              "      <td>-0.506271</td>\n",
              "      <td>-0.012546</td>\n",
              "      <td>-0.649617</td>\n",
              "      <td>1.577006</td>\n",
              "      <td>-0.414650</td>\n",
              "      <td>0.486180</td>\n",
              "      <td>-0.915427</td>\n",
              "      <td>...</td>\n",
              "      <td>0.382948</td>\n",
              "      <td>0.261057</td>\n",
              "      <td>0.643078</td>\n",
              "      <td>0.376777</td>\n",
              "      <td>0.008797</td>\n",
              "      <td>-0.473649</td>\n",
              "      <td>-0.818267</td>\n",
              "      <td>-0.002415</td>\n",
              "      <td>0.013649</td>\n",
              "      <td>217.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>284807 rows × 29 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               V1         V2        V3        V4        V5        V6  \\\n",
              "0       -1.359807  -0.072781  2.536347  1.378155 -0.338321  0.462388   \n",
              "1        1.191857   0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
              "2       -1.358354  -1.340163  1.773209  0.379780 -0.503198  1.800499   \n",
              "3       -0.966272  -0.185226  1.792993 -0.863291 -0.010309  1.247203   \n",
              "4       -1.158233   0.877737  1.548718  0.403034 -0.407193  0.095921   \n",
              "...           ...        ...       ...       ...       ...       ...   \n",
              "284802 -11.881118  10.071785 -9.834783 -2.066656 -5.364473 -2.606837   \n",
              "284803  -0.732789  -0.055080  2.035030 -0.738589  0.868229  1.058415   \n",
              "284804   1.919565  -0.301254 -3.249640 -0.557828  2.630515  3.031260   \n",
              "284805  -0.240440   0.530483  0.702510  0.689799 -0.377961  0.623708   \n",
              "284806  -0.533413  -0.189733  0.703337 -0.506271 -0.012546 -0.649617   \n",
              "\n",
              "              V7        V8        V9       V10  ...       V20       V21  \\\n",
              "0       0.239599  0.098698  0.363787  0.090794  ...  0.251412 -0.018307   \n",
              "1      -0.078803  0.085102 -0.255425 -0.166974  ... -0.069083 -0.225775   \n",
              "2       0.791461  0.247676 -1.514654  0.207643  ...  0.524980  0.247998   \n",
              "3       0.237609  0.377436 -1.387024 -0.054952  ... -0.208038 -0.108300   \n",
              "4       0.592941 -0.270533  0.817739  0.753074  ...  0.408542 -0.009431   \n",
              "...          ...       ...       ...       ...  ...       ...       ...   \n",
              "284802 -4.918215  7.305334  1.914428  4.356170  ...  1.475829  0.213454   \n",
              "284803  0.024330  0.294869  0.584800 -0.975926  ...  0.059616  0.214205   \n",
              "284804 -0.296827  0.708417  0.432454 -0.484782  ...  0.001396  0.232045   \n",
              "284805 -0.686180  0.679145  0.392087 -0.399126  ...  0.127434  0.265245   \n",
              "284806  1.577006 -0.414650  0.486180 -0.915427  ...  0.382948  0.261057   \n",
              "\n",
              "             V22       V23       V24       V25       V26       V27       V28  \\\n",
              "0       0.277838 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053   \n",
              "1      -0.638672  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724   \n",
              "2       0.771679  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752   \n",
              "3       0.005274 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458   \n",
              "4       0.798278 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "284802  0.111864  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731   \n",
              "284803  0.924384  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   \n",
              "284804  0.578229 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   \n",
              "284805  0.800049 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   \n",
              "284806  0.643078  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649   \n",
              "\n",
              "        Amount  \n",
              "0       149.62  \n",
              "1         2.69  \n",
              "2       378.66  \n",
              "3       123.50  \n",
              "4        69.99  \n",
              "...        ...  \n",
              "284802    0.77  \n",
              "284803   24.79  \n",
              "284804   67.88  \n",
              "284805   10.00  \n",
              "284806  217.00  \n",
              "\n",
              "[284807 rows x 29 columns]"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bdLlKi8rGapb"
      },
      "source": [
        "###Data Pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MRNMm_VFf1py"
      },
      "outputs": [],
      "source": [
        "#Standardizing the Amount column (All other 'V' columns are already scaled as they've undergone PCA transformation).\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "X_data['normalizedAmount'] = StandardScaler().fit_transform(X_data['Amount'].values.reshape(-1,1))  # Normalize 'Amount' in [-1,+1] range\n",
        "X_data= X_data.drop(['Amount'],axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G0gxPVuSGVaZ"
      },
      "source": [
        "###Splitting the Data into train and test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HeY6L0Pd1ZuF"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size = 0.2, random_state = 7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tTq5DsKV1ayv",
        "outputId": "83e2dfd8-35d0-424d-e3dc-7993f035ec14"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(227845, 29)\n",
            "(56962, 29)\n",
            "(227845,)\n",
            "(56962,)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J7DcOjOuutyW"
      },
      "source": [
        "##Model Building"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2U7H-CIcHd8f"
      },
      "source": [
        "## Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ea3p5mN9Hd8f"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tMfWcf9iHd8f"
      },
      "outputs": [],
      "source": [
        "random_forest = RandomForestClassifier(n_estimators=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "hD-TmahUHd8f",
        "outputId": "c886d67d-0640-4101-ee7c-d52c010573c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# Pandas Series.ravel() function returns the flattened underlying data as an ndarray.\n",
        "random_forest.fit(X_train,y_train.values.ravel())    # np.ravel() Return a contiguous flattened array"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4rODQu64Hd8f"
      },
      "outputs": [],
      "source": [
        "y_pred = random_forest.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GE5-iPOPHd8g",
        "outputId": "92f8b7aa-06ca-45f0-874d-d7edd41eb475"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9995786664794073"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "random_forest.score(X_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u7IYoh-MHd8g"
      },
      "outputs": [],
      "source": [
        "\n",
        "def make_confusion_matrix(cf,\n",
        "                          group_names=None,\n",
        "                          categories='auto',\n",
        "                          count=True,\n",
        "                          percent=True,\n",
        "                          cbar=True,\n",
        "                          xyticks=True,\n",
        "                          xyplotlabels=True,\n",
        "                          sum_stats=True,\n",
        "                          figsize=None,\n",
        "                          cmap='Blues',\n",
        "                          title=None):\n",
        "    '''\n",
        "    This function will make a pretty plot of an sklearn Confusion Matrix cm using a Seaborn heatmap visualization.\n",
        "    Arguments\n",
        "    '''\n",
        "\n",
        "\n",
        "    # CODE TO GENERATE TEXT INSIDE EACH SQUARE\n",
        "    blanks = ['' for i in range(cf.size)]\n",
        "\n",
        "    if group_names and len(group_names)==cf.size:\n",
        "        group_labels = [\"{}\\n\".format(value) for value in group_names]\n",
        "    else:\n",
        "        group_labels = blanks\n",
        "\n",
        "    if count:\n",
        "        group_counts = [\"{0:0.0f}\\n\".format(value) for value in cf.flatten()]\n",
        "    else:\n",
        "        group_counts = blanks\n",
        "\n",
        "    if percent:\n",
        "        group_percentages = [\"{0:.2%}\".format(value) for value in cf.flatten()/np.sum(cf)]\n",
        "    else:\n",
        "        group_percentages = blanks\n",
        "\n",
        "    box_labels = [f\"{v1}{v2}{v3}\".strip() for v1, v2, v3 in zip(group_labels,group_counts,group_percentages)]\n",
        "    box_labels = np.asarray(box_labels).reshape(cf.shape[0],cf.shape[1])\n",
        "\n",
        "\n",
        "    # CODE TO GENERATE SUMMARY STATISTICS & TEXT FOR SUMMARY STATS\n",
        "    if sum_stats:\n",
        "        #Accuracy is sum of diagonal divided by total observations\n",
        "        accuracy  = np.trace(cf) / float(np.sum(cf))\n",
        "\n",
        "        #if it is a binary confusion matrix, show some more stats\n",
        "        if len(cf)==2:\n",
        "            #Metrics for Binary Confusion Matrices\n",
        "            precision = cf[1,1] / sum(cf[:,1])\n",
        "            recall    = cf[1,1] / sum(cf[1,:])\n",
        "            f1_score  = 2*precision*recall / (precision + recall)\n",
        "            stats_text = \"\\n\\nAccuracy={:0.3f}\\nPrecision={:0.3f}\\nRecall={:0.3f}\\nF1 Score={:0.3f}\".format(\n",
        "                accuracy,precision,recall,f1_score)\n",
        "        else:\n",
        "            stats_text = \"\\n\\nAccuracy={:0.3f}\".format(accuracy)\n",
        "    else:\n",
        "        stats_text = \"\"\n",
        "\n",
        "\n",
        "    # SET FIGURE PARAMETERS ACCORDING TO OTHER ARGUMENTS\n",
        "    if figsize==None:\n",
        "        #Get default figure size if not set\n",
        "        figsize = plt.rcParams.get('figure.figsize')\n",
        "\n",
        "    if xyticks==False:\n",
        "        #Do not show categories if xyticks is False\n",
        "        categories=False\n",
        "\n",
        "\n",
        "    # MAKE THE HEATMAP VISUALIZATION\n",
        "    plt.figure(figsize=figsize)\n",
        "    sns.heatmap(cf,annot=box_labels,fmt=\"\",cmap=cmap,cbar=cbar,xticklabels=categories,yticklabels=categories)\n",
        "\n",
        "    if xyplotlabels:\n",
        "        plt.ylabel('True label')\n",
        "        plt.xlabel('Predicted label' + stats_text)\n",
        "    else:\n",
        "        plt.xlabel(stats_text)\n",
        "\n",
        "    if title:\n",
        "        plt.title(title)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "E7JMW_6S_3jI",
        "outputId": "8289322d-748d-438f-e0f0-0775e064e07e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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UAhevx+YpvlzpEiiirYnQRbK/KWmoq+LyzUdfPT4t/QMmLdqLWSN+w7KQE1n2V65QEq4OZTGyl4e4TVVFAm0tDWhrqaOCpQkeJbySmWty4dqDLO20da+GAR3ro0wpI+jqaEJNVQVvkrNWUXLy/FUS/j0ThfZNq+PUpbuwNC+Omg5l4Tsl8wdvRWtzqKmp4srOcTLHaaqr4WXil4cNSPlUr+GC0WMniO+1dbQBAGfCT2PFsiWIibmH5KQkZGRkIDU1Fe/fv4e2tnaWdrp698DE8WMQumcXXGrWgrtHY5S2sAAA3Lp5E7duRWNf6H//2AsQIJVK8fjRI5QtVy5LewDw9u1b1HSuCkGQIjU1FVWrOWH8pClISkrCs6dP4Vi1mkx81arVEB19EwDQ0qsVfu/dEy08G6N2nbqoV/8X1Kr99V8sctK0WXNMGj8Wo8dOgIaGBvbt3YPGTTyhopL5O+yt6JuIvHQRy5YsFo+RSnP+3IgUjQlLIZD8XnZYRioVgM8ycTU1VfHPujqaAIBWAxfhydNEmbi0tNyNUW/cex6DuzbEqN6N8eDJC5l9utqamLJ4H3YejsxyXEpq7tp3qVIGq6Z6Y/Liffg3PAqvk97jNw8nDPpCVScnm/ddwF8j2sL/zy1o38QZV289xvU7TzL7qqOJDx8yUKvTn8j4bEw/+V3ufmMm5aCtrZ3ljqDHjx/Bb8DvaNe+I/wGDYGevj4uXYzAhLGjkZ6enu0/vP19/NDEsxlOHDuGkyePY9GCefjzr9lo6NYI796/Q9t2HdCpc9csx5mZmX2xb0WKFMGmkB1QUVFBCSMjaGlpAQCSkpK+el129hWx758wnDxxHGfDT2PE0MFwqVkLf8+Z99Vjv6T+L79CEMbg+LGjqFSpMi5GXMDwkf/NX3n37h36+/ihoZt7lmM1NTW/+bw/s5+9OpIfmLAUQs9fJaGitewPTwebkkj//109UffikZKajtKmBjnOV8mJIAgYN383Nv3dG8tCTsrsi7z5EOWtjHHv4fNsj731IAGlTAxgbFgUT19mVlmcKlrIxNR0KIPYuJeYseKguM3CTPaOj7T0D1BV+fqo5p6jVxA0tiPca9mjfRNnbAg9+0lfH0FNTRXGhkVx6tLdr7ZFP5ao69chlQoYOmKUWD3458CXh4M+srIqAyurMujq3R0jh/lj145taOjWCHZ29rh3906eb5VWUVHJ9hhdXV0YGRsj8tJFOFevIW6/dOkiKlWuIhPXuElTNG7SFG7uHhjwe2+8TkzMMuTzcc6KVJqRY380NTXxq5s79oXuwcPYB7AqUwZ29hXF/XZ29rh/P6bAbwkvVJivyI0JSyF09PwtDPFuiE7NauDslRh0bFod9uXMcTk6c7gn6V0q5qwJw4yhbaCiooLTl+5CX1cLro7l8CY5Bev3nP3KGTIdOHkd5689QK82tcXEAwCmLT2A7XP74WHcK+z49xKkgoAqFUrBvpwZJi4MRdiZm7j36BmWTeqK0XN3oqiOFib4NAeQWV4HgDuxz1Da1BC/eTjhwvUHaFK3Ilo0cJA5f+yTl7AqWRxVKpTE46eJeJucirT0rBWcdylp2HPkMsYNaAbbMibYcuC/Z8jciX2KjXvPYfnkrhg1awcibz6CkYEufnGxwbVbj3Hg5PW8ffikVEpbWOLDh3RsXL8W9X/5FZcuRSAkh3kYKSkpmPXXDDRy90DJUqWQEB+P69euomGjzEpDj1590LVTe0ybMgmt2/wGbR1t3Lt7B+GnT+OPMeO+2G5OuvfohUUL5qNUaQvY2tpi547tiL55E4Ez/gIArAleBSMjI9ja2UGiooJD/xxAiRJGKKqnl6UtQ8Pi0NLSwqmTJ2BiYgoNTc0v3tLs2aw5/Ab8jrt3b8OzWQuZfX37+2CgTz+YmZnDzd0DKhIVREffxN3bt+A7aMg3XSeRvJiwFEL/hkchcNkBTB3kBS1NNazZdQYb9p5DRWtzMWbiwlA8f5WE4T0aoczYjkh8+x6RUQ8xY+XBHFrOaszcXTi6emiW87cetBh/9G2Mod0bIf1DBm7dT8CqHacBZA5ZtfNfhkXjOuHkuuGIefQCf8zZie3z+olDRnuPXcX89Ycxa+Rv0NRQw4GT1zF92X6M7tdUPM+OsEi0bOiAA8sGwUBPB33GrcW6LyRbm/ZfwK6gATgRcRsP41/J7Os7YR1G9W6M6f6tYG5cDC8Sk3HuSgz2H7+Wp8+ClI+NrS2GjQjAqhXLMG/OLFRzcsbAwf4YEzAy23hVFRW8TkzEmICRePHiOYoZGKChmzsG+GZO4K1gY4sVwWsxf94c9OjWCYIAlC5dGh5NmmbbXm506tINSUlJ+HvmdLx88RLlypXDvKCFsLS0ApA5nLRq5XLEPngAVVUVVKxUGUGLl4oVo0+pqalhZMAYLFm0AAuD5qGakzNWBK/N9rw1XGpCX18f92Ni0NSzucy+2nXqYt6CxVi6aAFWrVgGNTU1WJUpi9Ztfvvm6/zZcUhIfhKhED7+ULsqb7v70bg6lMXhYH/YN5+AmEfZDyWR/F6dz/nJyUQ/I63v8Kt7qQE786WdRwu98qWdHxErLFQgWjSogqR3abgT+xTlLIzw1/C2OP3/24qJiAobVljkx4SFCoRuES1MGeSF0qYGeJGYhMNnozFqVta1YIiIiAAmLFRANoSew4bQcwXdDSKi74MFFrkxYSEiIlIwDgnJj4/mJyIiIqXHCgvJGP17U4zpJ3uLZnRMPBxbTxHfu1Qpgwk+zVC9shUyMqS4cusxmg9YIK7lY21hjGlDvODqUBYa6qq4dvsJJi4MxfELt8U23l/KerdKt1GrEHIw8xkpSyd2Qdds1vK5cTcOTm2n5su1En1vixbMx+KFsn/3rcqUwa7QAwXUI/peWGGRHxMWyuL6nSfiCsgA8CHjv0fWu1Qpg11BA/DXqn/g/2cIPmRIUaVCyczlAP5v+7x+uBP7FE1+n4f3qenw7dQA2+f1Q8XmE2TWD+ozbi0Onb4hvk98+17887CZWzH2kxWW1VRVcXZzALYfupTv10v0PZWzLo+ly1eJ71U/WTaDCi8mLPJjwkJZfFwBOTszhrbGwk1H8deqQ+K22w+ein8uXqwIylsao//E9bh2O3O9nrHzdqFf+3qwtzZHwotoMfb12/dfPM+bpBS8SfpvocPmv1SBgZ421u4Ol+vaiAqamqoqShgZFXQ3iH44nMNCWVhbGOHeP1NxY88ErJrqjdKmBgAAIwNd1KhSBs9eJuFIsD/u/zsN/ywfhFqOZcVjXyQmIzomHp2a1YCOlgZUVVXQu00dJLx4g0s3ZFeVnhPQDg8PT8eJtcPQrWXW4Z9PeXu54vDZaMTGvcoxjkjZPYh9ALdf6qCpR0MEjBiKuCdPCrpL9B1IJJJ8ef3MCrTC8vz5c6xcuRLh4eGIj48HAJiamqJWrVro3r07jPhbyHd3/tp99B23DrceJMC0hD5G/94E/64cAqe2U1GmVAkAmfNcAmbvwJXoR+jcrAb2LfGD02/TcDf2GQDAs18QNs/ui2en/oJUKuDZqyS09FkoM+QzcWEojp27hXcpaXBztcXcgPbQ1dHEwo3HsvTJzEgfHrXt0f2P4O/yGRApSuUqVTB5aiCsrMrg2bNnWLJoAXp064xtu/agSBHdgu4eKdLPnWvkiwJLWM6fPw8PDw/o6OjAzc0NFSpUAAAkJCRg3rx5mD59Og4ePAhnZ+cc20lNTUVqaqrMNkGaAYkKx4W/xT+n/ptTcu32E5y/eh/R+yahjXs1RMdkJpUrtp3E2t1nAACXox/hlxo28G7pinHzdwMAZge0w7OXb+HWcw7ep6ahe6ta2Db3d9TpMhPxz98AAKYv+2+S4eXoR9DR1sSQbm7ZJiydm7sg8e177D5yRWHXTfQ91KlbX/xzBRtbVK7igCaNGuDggf1cp4foKwosYfHz88Nvv/2GxYsXZylzCYKAfv36wc/PD+HhOc9ZCAwMxMSJE2W2qZpUh7pZjS8cQXnxOul95uPzSxvh6LlbAICoe/EyMdEx8eKw0S81KqBp3Uowqz8Cb5Mz56AMDtyChjVt0aW5i8zcl0+dv3off/RtAg11tSwrLnu3rImNe88h/UNGfl8eUYHS09ODpaUVHsbGfj2Yfmg/+3BOfiiwOSyXL1/GkCFDsv2PKJFIMGTIEERGRn61nYCAALx+/VrmpWbipIAe/5yKaGugTKkSiH/+Gg+evMCTp4moYGUsE2NtaYzYuJcAAB0tDQCAVCqViZFKhRy/sFVsSuHl6+QsyUpdp/KwtjBG8E5OtqXC511yMh4+fMhJuD8BzmGRX4FVWExNTXHu3DnY2tpmu//cuXMwMTH5ajuamprQ1NSU2cbhoG8XOKQV9h6/itgnL2FurI8x/TyRIZViy4HM56PMXv0vxvTzxNVbj3E5+hG6NHeBjZUJOg1fAQA4eyUGr968w/LJ3TBt6X68T0lHz9a1YFWyOA6cvA4AaFqvEoyLF8W5K/eRkpaOhjVtMaKXO+asCcvSn+5erjh3JQY37sZ9vw+BSEH+nvkn6v/SAGbm5nj29CkWLZgPVVUVNGnarKC7Rgr2k+ca+aLAEpZhw4ahb9++iIiIQMOGDcXkJCEhAWFhYVi2bBn++uuvgureT6ukSTGsCewBQ30dPH+VhNOR91C/2994/ioJABC04Si0NNUxY2gbGOjr4Oqtx2jWP0hcZflFYjJa+i7EBJ/m2L9kINTVVBB1Lx6/DVmKq7ceAwDSP2Tg93b1MGNoG0gkEtx9+Awj/96OldtPy/RFT1cLXg0dMWzm1u/7IRApSEJCPEYN90diYiIMDA1RtZoT1m7YAkNDw4LuGpHSkwiCIHw9TDE2b96M2bNnIyIiAhkZmfMTVFVV4eTkBH9/f7Rr1+6b2tWu6puf3SQqNF6dz/qEYaKfndZ3+NW9/PD8eZrx7ZmN86WdH1GB3tbcvn17tG/fHunp6Xj+PPM39BIlSkBdXb0gu0VERJSvOCQkP6V40q26ujrMzMwKuhtERESkpJQiYSEiIirMfvY7fPIDExYiIiIFY74iP64lREREREqPFRYiIiIFU1FhiUVerLD85HR1NDFzWBtE75uEl+GzcCTYH072FuJ+Y8OiWDqxC+79MxUvTs/CrqABKGfx9ady+nb6BZd3jMXL8Fm4vX8yZgxtDU2N//LjYT3dcXLdcDw9+RcehAViy6w+KG8p+wTdP4e2xuOjf+L2/sno0ER2TanWblWxdc7vcl49Ud5s2rAeTRr9iupVK6Nzh99w9UrO61v9c3A/WjZrjOpVK6ONV3OcOC67Vta/h/7B7316ol4tFzhUtMHNqKgsbcz8MxB1XWvAvWF97A3dnaV9vwH95L8wUjiJJH9ePzMmLD+5ReM64deatug5ZjWc203Dv+E3sXexH8yN9AEAW2b3RZlSJfDb4CWo2XE6YuNeYt9iP/ER/Nlp39gZkwe2xLQl++HYegr6TVyPth5OmOTXQoypW80aizcfR/1uf6FZ/yCoqakidJGv2G7TepXQrrEzmg9YgNFzd2LhuE4oXqwIgMwHyk3wbY4h07co8JMhknVg/z78NSMQvw/wwaaQHbCxsUX/33vhxYsX2cZHXrqIUcOHolXrtti8dSca/NoQg/18cPv2LTHm/ft3qFq1Ggb7D8u2jaNHDmP/3lAsXrYCQ/yHY+K4MXj1KnMZjLdv32L+3Dn4Y8y4/L9YIiXEhOUnpqWpDq+Gjhg9ZydOXbyLew+fY+qSfbj78Bn6/FYX1hbGcKlSBgOnbkLEjVjcfvAUA6dthpamOto1+fJ6TTUdyiA88h42H7iA2LiXCDtzE1sOXIBzRUsxpqXvQqzbcxZR9+Jx9dZj9B2/DhZmhqhqXxoAYFvGFCcibuPijVhsORCBN8kpsDIvDgCYOsgLy0JO4GH8K8V+QESfWLt6FVq3bQevVm1QztoaY8ZPhJaWFnZu35Zt/Pp1a1CrTl1079kbZcuVg+/AwbCzt8emDevEmOYtvNBvgC9cXF2zbSPm3l0416iBipUqo4lnMxTR1cXjR48AALP/nol27TvCzNw8/y+W8p0yrCU0ffp0SCQSDB48WNyWkpICHx8fFC9eHLq6umjTpg0SEhJkjouNjYWnpyd0dHRgbGyM4cOH48MH2XXfjh49imrVqkFTUxPW1tYIDg7Ocv4FCxbAysoKWlpacHFxwblz5/LUfyYsPzE1VRWoqakiJS1dZntKajpqVS0nDuGkpP33F1MQBKSlfUAtx3JfbPfM5RhUtS8tJihWJYvDo3ZFcS2h7OjpagEAXr1+BwC4cusxqtlZoFhRbVS1Kw1tTXXcffgMtRzLoqpdaSzYePSbrpnoW6SnpSHqxnXUdK0lblNRUUHNmrVw5fKlbI+5EhmJmjVlE5FatevgSi4Wdf2ogo0tbly7hjevX+PG9WtITUmBhYUlLkZcwM0b19GpS9dvuh76/gp6SOj8+fNYsmQJqlSpIrN9yJAh2LNnD0JCQnDs2DE8efIErVu3FvdnZGTA09MTaWlpOH36NFavXo3g4GCMG/dfZS8mJgaenp5o0KABIiMjMXjwYPTu3RsHDx4UYzZv3gx/f3+MHz8eFy9ehIODAzw8PPD06dNcXwMTlp9Y0rtUnLl8DwF9msDMSB8qKhJ0aFodLlXKwLSEHqLvxyM27iUm+7VAsaLaUFdTxdDubihlagDTEvpfbHfzgQuYvGgvwlYNwZtzcxEVOhHHI25j5sp/so2XSCSYOawtTl+6Ky5y+G94FDbuO4+T60Zg6cSu6DNuLZLfp2HuHx3gN3UT+v5WF5d3jMXhVUNgV9ZUIZ8P0UevEl8hIyMDxYsXl9levHhx8Sndn3v+/DmKFy+RNf5F9vHZqV2nLjybt0Cn9m0xdnQAJk/7E9ra2pg6eSLGjJ+ILZs2ooWnB7w7d8CdO7fzfmH03RRkhSUpKQmdO3fGsmXLYGBgIG5//fo1VqxYgVmzZuHXX3+Fk5MTVq1ahdOnT+PMmTMAgH/++Qc3btzAunXr4OjoiCZNmmDy5MlYsGAB0tLSAACLFy9GmTJl8Pfff8POzg6+vr5o27YtZs+eLZ5r1qxZ6NOnD3r06AF7e3ssXrwYOjo6WLlyZa6vgwnLT67nmDWQSIB7/0zF67Nz4NOxPrYcuACpVMCHD1J0GLoM1pbGiDs+Ey/DZ6GecwUcOHkdUkH6xTbrOpXH8J4eGBS4Ga6d/kR7/6VoUqciRvXJfg2MOQHtUNHaDN1GrZLZPnXJPlRqORHV203D7iNXMLynO46cvYn0DxkY2bsxGvacjVU7wrF8crd8/UyIlEl/Hz+EHjiEbTv3oKFbI6xYvhQ1a7pCTU0Ny5YsQvDajWjV9jeMCRhZ0F2l7yA1NRVv3ryReaWmpuZ4jI+PDzw9PeHm5iazPSIiAunp6TLbbW1tYWFhgfDwcABAeHg4KleuLC5QDAAeHh548+YNrl+/LsZ83raHh4fYRlpaGiIiImRiVFRU4ObmJsbkBhOWn1zMo+dw7z0XxV39Ub7JWNTt+hfU1VQR8zjzt8BLUQ9Rs8N0mNQdhjLuo9HSdyGK6xdBzKPsJxoCwPgBnti49xyCd4Tj+p0n2H3kCsYF7cHwHu5ZfkOYPfI3NK1bCR595uHx08QvtlnBygQdPatj4sJQ1HMuj1MX7+D5qyRs++ciqtlbQFdHM18+D6LsGBQzgKqqapYJti9evECJEiWyPaZEiRJ48Vk15cWLFyhRPPv43Ii5dxd79+yGj98gnD9/Dk7OzjA0NIS7RxNE3biO5OSkb26bFCu/KiyBgYHQ19eXeQUGBn7xvJs2bcLFixezjYmPj4eGhgaKFSsms93ExATx8fFizKfJysf9H/flFPPmzRu8f/8ez58/R0ZGRrYxH9vIDSYsBAB4l5KG+OdvUKyoNtxq2SH06FWZ/W+SUvD8VRLKWRihmr0FQo9++XZObS0NSKWyi4BLpZkVmU/zldkjf0OLXx3Q+Pd5ePDkywkQAASN6YCRf29H8vs0qKqoQF1NFQDE/1dV4V9lUhx1DQ3Y2VfE2TP//TYolUpx9mw4qjhUzfaYKo6OOPv/svpHZ8JPo4qj4zf1QRAETJ44HkNHjIJOkSKQZkiR/v+Jjx8nQGZkfLnySQUrv+awBAQE4PXr1zKvgICAbM/58OFDDBo0COvXr4eWltZ3vuL8xwfH/eTcXO0gkQC37j9FudJGmDbEC7diErBmd+YP5tZuVfHsVRIexr9EpfLm+Gt4W+w5egVhZ26KbSyf3BVPnr7GuPmZz4jYd/waBnZpgMvRj3Du6n2UK22Ecf2bYd/xq2IiMyegHdo3ccZvQ5YiKTkFJsWLAgBeJ6UgJVV2EnCPVrXw/FUS9h2/BgAIj7yH0b83RY3KVnCvbY8bd+PwOum9wj8r+rl19e6BsX+MRMWKlVCpchWsW7sa79+/h1erzAmKowNGwNjYBIOGDAUAdO7SDb26d8Xq4JWoV68+Duzfh+vXrmHshElim68TExEXF4dnzzInHt6/HwMgszpTwkj2eUfbt4bAwMAQvzT4FQDgWLUaFi+cjyuXI3HyxHGULWcNPT09hX8OVLA0NTWhqZm7inJERASePn2KatWqidsyMjJw/PhxBAUF4eDBg0hLS0NiYqJMlSUhIQGmpplzA01NTbPczfPxLqJPYz6/syghIQF6enrQ1taGqqoqVFVVs4352EZuMGH5yenramGSXwuUNCmGl6/fYVdYJMYv2IMPHzJ/UzM10sOfQ1vDuHhRxD9/g/WhZxG49IBMG6VNDWUqKtOXH4AgCBg/oBnMjfXx/FUS9h6/hglBe8SY39vVAwAcWj5Ypq0+49Zi3Z6z4ntjw6IY2dsDDbrPErdduP4Ac9eFYfu8/nj28i36jFubb58H0Zc0btIUr16+xMKgeXj+/BlsbO2wcMlyFP//kFB8XBxUJP9V+hyrVkPgjL8QNG8O5s+ZBQtLK8yZvwDly1cQY44eOYxxY/777XjksCEAgH4DfNHfx0/c/uL5cyxfuhir128Ut1WuUgVdvXvAt//vMCxuiMlT/1TYtZP8CmLxw4YNG+LqVdlqeY8ePWBra4uRI0eidOnSUFdXR1hYGNq0aQMAiI6ORmxsLFz/f6u9q6srpk6diqdPn8LYOPPhnocOHYKenh7s7e3FmH379smc59ChQ2IbGhoacHJyQlhYGLy8vABkVijDwsLg6+ub6+uRCIIgfD3sx6JdNfcfANHP5NX5oILuApHS0foOv7pXm3Q4X9q5OO5XuY7/5Zdf4OjoiDlz5gAA+vfvj3379iE4OBh6enrw88tMlE+fPg0gsyLj6OgIc3NzzJgxA/Hx8ejatSt69+6NadOmAci8rblSpUrw8fFBz549cfjwYQwcOBB79+6Fh4cHgMzbmr29vbFkyRLUqFEDc+bMwZYtW3Dz5s0sc1u+hBUWIiKin9Ts2bOhoqKCNm3aIDU1FR4eHli4cKG4X1VVFaGhoejfvz9cXV1RpEgReHt7Y9Kk/4Y2y5Qpg71792LIkCGYO3cuSpUqheXLl4vJCgC0b98ez549w7hx4xAfHw9HR0ccOHAg18kKwAoL0U+FFRairL5HhcVp8pF8aSdibIN8aedHxAoLERGRgv3sCxfmB94LSkREREqPFRYiIiIFK4i7hAobJixEREQKxnxFfkxYiIiIFIwVFvlxDgsREREpPVZYiIiIFIwFFvkxYSEiIlIwDgnJj0NCREREpPRYYSEiIlIwFljkx4SFiIhIwTgkJD8OCREREZHSY4WFiIhIwVhgkR8TFiIiIgXjkJD8OCRERERESo8VFiIiIgVjhUV+TFiIiIgUjPmK/JiwEBERKRgrLPLjHBYiIiJSeqywEBERKRgLLPJjwkJERKRgHBKSH4eEiIiISOmxwkJERKRgLLDIjwkLERGRgqkwY5Ebh4SIiIhI6bHCQkREpGAssMiPCQsREZGC8S4h+TFhISIiUjAV5ity4xwWIiIiUnqssBARESkYh4Tkx4SFiIhIwZivyI9DQkRERKT0WGEhIiJSMAlYYpEXExYiIiIF411C8uOQEBERESk9VliIiIgUjHcJyY8JCxERkYIxX5FfrhKW3bt357rBFi1afHNniIiIiLKTq4TFy8srV41JJBJkZGTI0x8iIqJCR4UlFrnlKmGRSqWK7gcREVGhxXxFfnLNYUlJSYGWllZ+9YWIiKhQ4qRb+eX5tuaMjAxMnjwZJUuWhK6uLu7duwcAGDt2LFasWJHvHSQiIiLKc8IydepUBAcHY8aMGdDQ0BC3V6pUCcuXL8/XzhERERUGEkn+vH5meU5Y1qxZg6VLl6Jz585QVVUVtzs4OODmzZv52jkiIqLCQEUiyZfXzyzPCcvjx49hbW2dZbtUKkV6enq+dIqIiIjoU3lOWOzt7XHixIks27du3YqqVavmS6eIiIgKE0k+vX5meb5LaNy4cfD29sbjx48hlUqxfft2REdHY82aNQgNDVVEH4mIiH5ovEtIfnmusLRs2RJ79uzBv//+iyJFimDcuHGIiorCnj170KhRI0X0kYiIiH5y3/Qclrp16+LQoUP53RciIqJCSYUFFrl984PjLly4gKioKACZ81qcnJzyrVNERESFCYeE5JfnhOXRo0fo2LEjTp06hWLFigEAEhMTUatWLWzatAmlSpXK7z4SERHRTy7Pc1h69+6N9PR0REVF4eXLl3j58iWioqIglUrRu3dvRfSRiIjoh8YHx8kvzxWWY8eO4fTp07CxsRG32djYYP78+ahbt26+do6IiKgw4JCQ/PKcsJQuXTrbB8RlZGTA3Nw8XzpFRERUmHDSrfzyPCQ0c+ZM+Pn54cKFC+K2CxcuYNCgQfjrr7/ytXNERET0bRYtWoQqVapAT08Penp6cHV1xf79+8X9KSkp8PHxQfHixaGrq4s2bdogISFBpo3Y2Fh4enpCR0cHxsbGGD58OD58+CATc/ToUVSrVg2ampqwtrZGcHBwlr4sWLAAVlZW0NLSgouLC86dO5fn68lVhcXAwECmnJWcnAwXFxeoqWUe/uHDB6ipqaFnz57w8vLKcyeIiIgKs4IYEipVqhSmT5+O8uXLQxAErF69Gi1btsSlS5dQsWJFDBkyBHv37kVISAj09fXh6+uL1q1b49SpUwAyR048PT1hamqK06dPIy4uDt26dYO6ujqmTZsGAIiJiYGnpyf69euH9evXIywsDL1794aZmRk8PDwAAJs3b4a/vz8WL14MFxcXzJkzBx4eHoiOjoaxsXGur0ciCILwtaDVq1fnukFvb+9cxyqKdlXfgu4CkVJ6dT6ooLtApHS0vvkBH7nXc9PVfGlnZYfKch1vaGiImTNnom3btjAyMsKGDRvQtm1bAMDNmzdhZ2eH8PBw1KxZE/v370ezZs3w5MkTmJiYAAAWL16MkSNH4tmzZ9DQ0MDIkSOxd+9eXLt2TTxHhw4dkJiYiAMHDgAAXFxcUL16dQQFZf78kUqlKF26NPz8/DBq1Khc9z1X/5mUIQkhIiL62aWmpiI1NVVmm6amJjQ1NXM8LiMjAyEhIUhOToarqysiIiKQnp4ONzc3McbW1hYWFhZiwhIeHo7KlSuLyQoAeHh4oH///rh+/TqqVq2K8PBwmTY+xgwePBgAkJaWhoiICAQEBIj7VVRU4ObmhvDw8Dxde57nsHwqJSUFb968kXkRERGRLBWJJF9egYGB0NfXl3kFBgZ+8bxXr16Frq4uNDU10a9fP+zYsQP29vaIj4+HhoaG+Dy1j0xMTBAfHw8AiI+Pl0lWPu7/uC+nmDdv3uD9+/d4/vw5MjIyso352EZu5bkQlpycjJEjR2LLli148eJFlv0ZGRl5bZKIiKhQy68pLAEBAfD395fZllN1xcbGBpGRkXj9+jW2bt0Kb29vHDt2LH86853lucIyYsQIHD58GIsWLYKmpiaWL1+OiRMnwtzcHGvWrFFEH4mIiAiZycnHu34+vnJKWDQ0NGBtbQ0nJycEBgbCwcEBc+fOhampKdLS0pCYmCgTn5CQAFNTUwCAqalplruGPr7/Woyenh60tbVRokQJqKqqZhvzsY3cynPCsmfPHixcuBBt2rSBmpoa6tatizFjxmDatGlYv359XpsjIiIq9CQSSb685CWVSpGamgonJyeoq6sjLCxM3BcdHY3Y2Fi4uroCAFxdXXH16lU8ffpUjDl06BD09PRgb28vxnzaxseYj21oaGjAyclJJkYqlSIsLEyMya08Dwm9fPkSZcuWBQDo6enh5cuXAIA6deqgf//+eW2OiIio0CuIB90GBASgSZMmsLCwwNu3b7FhwwYcPXoUBw8ehL6+Pnr16gV/f38YGhpCT08Pfn5+cHV1Rc2aNQEA7u7usLe3R9euXTFjxgzEx8djzJgx8PHxEas6/fr1Q1BQEEaMGIGePXvi8OHD2LJlC/bu3Sv2w9/fH97e3nB2dkaNGjUwZ84cJCcno0ePHnm6njwnLGXLlkVMTAwsLCxga2uLLVu2oEaNGtizZ0+WyTtERERUMJ4+fYpu3bohLi4O+vr6qFKlCg4ePIhGjRoBAGbPng0VFRW0adMGqamp8PDwwMKFC8XjVVVVERoaiv79+8PV1RVFihSBt7c3Jk2aJMaUKVMGe/fuxZAhQzB37lyUKlUKy5cvF5/BAgDt27fHs2fPMG7cOMTHx8PR0REHDhzIMhH3a3L1HJZPzZ49G6qqqhg4cCD+/fdfNG/eHIIgID09HbNmzcKgQYPy1AFF4HNYiLLH57AQZfU9nsPSf9uNfGlnURv7fGnnR5Tn/0xDhgwR/+zm5oabN28iIiIC1tbWqFKlSr52joiIqDDg2ofykzuvtLS0hKWlZX70hYiIqFDias3yy1XCMm/evFw3OHDgwG/uDBEREVF2cjWHpUyZMrlrTCLBvXv35O6UvFI+fD2GiIgI+D5zWPx2ROVLO/Nb2eVLOz+iXP1niomJUXQ/iIiICi0OCclPrrWEiIiIiL6H71AIIyIi+rmpsMAiNyYsRERECsaERX4cEiIiIiKlxwoLERGRgnHSrfy+qcJy4sQJdOnSBa6urnj8+DEAYO3atTh58mS+do6IiKgwUJHkz+tnlueEZdu2bfDw8IC2tjYuXbqE1NRUAMDr168xbdq0fO8gERERUZ4TlilTpmDx4sVYtmwZ1NXVxe21a9fGxYsX87VzREREhYFEkj+vn1me57BER0ejXr16Wbbr6+sjMTExP/pERERUqKj87NlGPshzhcXU1BR37tzJsv3kyZMoW7ZsvnSKiIioMFHJp9fPLM/X36dPHwwaNAhnz56FRCLBkydPsH79egwbNgz9+/dXRB+JiIjoJ5fnIaFRo0ZBKpWiYcOGePfuHerVqwdNTU0MGzYMfn5+iugjERHRD40jQvLL1WrN2UlLS8OdO3eQlJQEe3t76Orq5nffvhlXayYiotz6Hqs1jz1wO1/amdy4fL608yP65v9MGhoasLe3z8++EBEREWUrzwlLgwYNcnxi3+HDh+XqEBERUWHDISH55TlhcXR0lHmfnp6OyMhIXLt2Dd7e3vnVLyIiokLjZ39KbX7Ic8Iye/bsbLdPmDABSUlJcneIiIiI6HP5dlt3ly5dsHLlyvxqjoiIqNBQkUjy5fUzy7e50eHh4dDS0sqv5oiIiAqNnzzXyBd5Tlhat24t814QBMTFxeHChQsYO3ZsvnWMiIiI6KM8Jyz6+voy71VUVGBjY4NJkybB3d093zpGRERUWHDSrfzylLBkZGSgR48eqFy5MgwMDBTVJyIiokJFAmYs8srTpFtVVVW4u7tzVWYiIqI8UJHkz+tnlue7hCpVqoR79+4poi9ERERE2cpzwjJlyhQMGzYMoaGhiIuLw5s3b2ReREREJIsVFvnleg7LpEmTMHToUDRt2hQA0KJFC5lH9AuCAIlEgoyMjPzvJRER0Q8spyVtKHdyvVqzqqoq4uLiEBUVlWNc/fr186Vj8uBqzURElFvfY7XmmUfzZyrF8F/K5ks7P6Jc/2f6mNcoQ0JCRET0I/nZh3PyQ57ySpa0iIiI8o7/fMovTwlLhQoVvpq0vHz5Uq4OEREREX0uTwnLxIkTszzploiIiHL2sy9cmB/ylLB06NABxsbGiuoLERFRocQ5LPLL9XNYOH+FiIiICkqe7xIiIiKivOHv/PLLdcIilUoV2Q8iIqJCS4WLH8rtOzwuh4iI6OfGCov88ryWEBEREdH3xgoLERGRgvEuIfkxYSEiIlIwPodFfhwSIiIiIqXHCgsREZGCscAiPyYsRERECsYhIflxSIiIiIiUHissRERECsYCi/yYsBARESkYhzPkx8+QiIiIlB4rLERERAom4ZiQ3JiwEBERKRjTFfkxYSEiIlIw3tYsP85hISIiKoQCAwNRvXp1FC1aFMbGxvDy8kJ0dLRMTEpKCnx8fFC8eHHo6uqiTZs2SEhIkImJjY2Fp6cndHR0YGxsjOHDh+PDhw8yMUePHkW1atWgqakJa2trBAcHZ+nPggULYGVlBS0tLbi4uODcuXN5uh4mLERERAomyadXXhw7dgw+Pj44c+YMDh06hPT0dLi7uyM5OVmMGTJkCPbs2YOQkBAcO3YMT548QevWrcX9GRkZ8PT0RFpaGk6fPo3Vq1cjODgY48aNE2NiYmLg6emJBg0aIDIyEoMHD0bv3r1x8OBBMWbz5s3w9/fH+PHjcfHiRTg4OMDDwwNPnz7N9fVIBEEQ8vgZKL2UD1+PISIiAgCt7zA5YsPFR/nSTqdqpb752GfPnsHY2BjHjh1DvXr18Pr1axgZGWHDhg1o27YtAODmzZuws7NDeHg4atasif3796NZs2Z48uQJTExMAACLFy/GyJEj8ezZM2hoaGDkyJHYu3cvrl27Jp6rQ4cOSExMxIEDBwAALi4uqF69OoKCggAAUqkUpUuXhp+fH0aNGpWr/rPCQkRE9INITU3FmzdvZF6pqam5Ovb169cAAENDQwBAREQE0tPT4ebmJsbY2trCwsIC4eHhAIDw8HBUrlxZTFYAwMPDA2/evMH169fFmE/b+BjzsY20tDRERETIxKioqMDNzU2MyQ0mLERERAomkUjy5RUYGAh9fX2ZV2Bg4FfPL5VKMXjwYNSuXRuVKlUCAMTHx0NDQwPFihWTiTUxMUF8fLwY82my8nH/x305xbx58wbv37/H8+fPkZGRkW3MxzZyg3cJERERKVh+VQcCAgLg7+8vs01TU/Orx/n4+ODatWs4efJkPvXk+2PCQkRE9IPQ1NTMVYLyKV9fX4SGhuL48eMoVeq/OTCmpqZIS0tDYmKiTJUlISEBpqamYsznd/N8vIvo05jP7yxKSEiAnp4etLW1oaqqClVV1WxjPraRGxwSIiIiUrD8GhLKC0EQ4Ovrix07duDw4cMoU6aMzH4nJyeoq6sjLCxM3BYdHY3Y2Fi4uroCAFxdXXH16lWZu3kOHToEPT092NvbizGftvEx5mMbGhoacHJykomRSqUICwsTY3KDFRYiIiIFK4jHxvn4+GDDhg3YtWsXihYtKs4X0dfXh7a2NvT19dGrVy/4+/vD0NAQenp68PPzg6urK2rWrAkAcHd3h729Pbp27YoZM2YgPj4eY8aMgY+Pj1jp6devH4KCgjBixAj07NkThw8fxpYtW7B3716xL/7+/vD29oazszNq1KiBOXPmIDk5GT169Mj19fC2ZiIi+ql9j9uaQyKf5Es7vzma5zr2SxWZVatWoXv37gAyHxw3dOhQbNy4EampqfDw8MDChQtlhmoePHiA/v374+jRoyhSpAi8vb0xffp0qKn998EdPXoUQ4YMwY0bN1CqVCmMHTtWPMdHQUFBmDlzJuLj4+Ho6Ih58+bBxcUl99fDhIWIiH5m3yNh2Xo5Ll/aaetgli/t/Ig4JERERKRgnDAqPyYsRERECpbXCbOUFZM+IiIiUnqssBARESkY6yvyY8JCRESkYBwRkh+HhIiIiEjpscJCRESkYCocFJIbExYiIiIF45CQ/DgkVEjt2rEddWo6F3Q3CtzYP0ZhsN+Agu4GUb7J7XfboaINDof9+x16RPR9sMKixMb+MQq7d+3Isn3Pvn9gYWlZAD36z64d2zFuTABq1a6DRUtXiNvfvHmDuq7VsXzVGlSvkftHLsvr8eNHaOreEJu37oStnZ24fUTAaBTChzlTNhwq2uS4v98AX/T38fsufenVvSsunM9c4VZDQwOlSpVGh06d0b5jZ7nb9mjSFHXq1RffL1owH0fC/sWW7btk4sKOnoSevr7c56P8IeGQkNyYsCi52nXqYtKUQJltBoaGBdQbWWpqajh7Jhznzp5BDZeaBd2dbBUtWrSgu0DfSdjRk+KfDx7Yh4VB87Ar9IC4TUdHR/yzIAjIyMiQWQslv7Vp2w4DfAciJSUFe3bvxLQpk6Cnp48mns3kaldLSwtaWlpfjSthZCTXeSh/cUhIfhwSUnIaGhooYWQk81JVVcWa4FVo49UcLs6OcG9YH1MnTcC75OQvthN98yZ6de8K1+pVUatGNXT4rTWuX7sq7r8YcQHdu3ZCjWpV4N6wPqZPm4J3797l2DdtbW14tWqDubP/zjEuPi4Ow/0HoU5NZ9R1rYFBvv3x+PEjcf+HDx8wfdoU1KnpjHq1XDD775kYEzBSZijn1Inj8O7SUYzxHfA7HsbGivubujcEALRv6wWHijbo1b0rANkhoa1bNsPtlzqQSqUy/Rvk2x/jxgSI748c/hft27ZC9aqV0dSjIRYvDMKHD1ygStl9+h3R1S0KiUQivo+JuQfXGtVw8sQxdPitNZwdK+PSxYhshwxnBE4V//4AgFQqxYplS9DE/VfUqFYFv7VqgUMHD3x++iy0tLRQwsgIpUqXRn8fP1hYWuHokcMAgLgnTzDItz9qOmd+H4f7D8KL58/FY3P6vn46JLRrx3YsXhiE6OibcKhoA4eKNti1YzsA2SGhbp07YPbfM2X69/LlSzg5VETEhfMAgLS0NPw980+4NagLF2dHdO7wG86fO5un/wZEisSE5QeloiLByIDR2L4rFJOnTse5c2ey/ED6VMDIYTAxNcWGzVuxMWQ7evbuAzU1dQDAw9hYDPi9D9wauSNkx27M+Gs2Ll2MQODUyV/tRz8fX9y5feuLP8DT09PRv28v6BQpglVr1mP1uo3Q0dHBgN97Iz0tDQCwasUy7Avdg4lTArF63QYkJyfhyGHZsff379+jq3cPbNi8DUtXBENFIsGQQT5i8rF+UwgAYOmKYIQdPYlZc+Zn6Yu7R2MkJibK/BB+nZiIUydPwLNZCwCZiduYgJHo3KUbduzeh7HjJ2HXzu1YvnTxVz8LUn5zZ/2NQUOGYueefahQIechpI9WLFuCPbt3Ysy4idi+ay+6dOuOP0YNF4d8cktLUxPp6emQSqUY5DcAr1+/xsrVa7F4+So8evQQI4YNEWNz+r5+yqNJU3Tr3hPlrMsj7OhJhB09CY8mTbPENfVsjoP798kMjx7cvw9GRsao5pSZ/AROmYQrkZcw46/Z2Lp9N9w9GmPA773x4MH9PF0nZU8Fknx5/cyYsCi548eOoqZzVfE1bMhAAECXbt1Rw6UmSpYsBZearvD1G4x/Du7/YjvxcU9Qs2YtlClbDpaWVnD3aAIbW1sAwIrlS9C0WXN06dYdlpZWcKxaDSMDRiN0906kpqbm2D9jYxN06tIN8+fNzrYKcfDAPkgFKSZMmoryFWxQtlw5TJoSiPi4OJz//w/8jevXoVefvmjo1ghlypZDwOhxKFpUT6YdN3cPuDVyh4WlJWzt7DBxyjTcvnULd+/eAfDfMJm+fjGUMDKCfrFiWfqip6+POnXrYd/ePeK2Q/8cRDEDA3G+zeKFQejZuy9aeLVCqdKl4VqrNnz8BmHrlk05fg70YxjgNxCutWqjtIVFtn9HPpeWlobly5Zg4uRpqF2nLkqVLo2WrVrDs3kLbN2yOVfnzMjIQOieXbh1Kxo1XGri7Jlw3Ll9C9Nn/A37ipVQpYoDpkybgQvnz+Ha1SsAcv6+fkpLSws6OjpQU1UVq0nZDRe5N26CZ8+e4tLFCHHb/n2haNzUExKJBHFPnmDXzu2YOXsuqjk5o7SFBbx79ELVak5ixYbkI5Hkz+tnxjksSq56DReMHjtBfK+tow0AOBN+GiuWLUFMzD0kJyUhIyMDqampeP/+PbS1tbO009W7ByaOH4PQPbvgUrMW3D0ao7SFBQDg1s2buHUrGvtC//uHXIAAqVSKx48eoWy5cjn2sUevPti6ZTN2bt8G98ZNZPbdir6Jh7GxcK1eTWZ7amoqHj2Mxdu3b/HixXNUqlxF3Keqqgq7ihUhfDJ08+DBfSycPw9Xr15G4qtXkEozf1OMj4tD+fIVcuzfp5o2a45J48di9NgJ0NDQwL69e9C4iSdUVFTE/kZeuohlS/6rqEilOX+29OOwr1g5T/GxsQ+Q8v49fu/dU2Z7enq6zOTu7GzetBHbt21Feno6VFVV0KVbd7Tr0BEbN6yDiakpTM3MxNhy1tYoqqeHmHv3UKlylRy/r9/C0NAQrrVqY2/oblRzcsajRw9xOfISxo6fCAC4ffsWMjIy0KJp48+uMy1XiR193c+ebOQHJixKTltbO8sdQY8fP4LfgN/Rrn1H+A0aAj19fVy6GIEJY0cjPT09239U+/v4oYlnM5w4dgwnTx7HogXz8Odfs9HQrRHevX+Htu06oFPnrlmOM/vkh+qX6OnpoVefvli8KAj1fvlFZt+7d+9gZ18RgX/+leW4vEweHujTD2ZmJTF+4hQYGRlDKkjRpmUzpKen57oNAKj/y68QhDE4fuwoKlWqjIsRFzB85H/zV969e4f+Pn5o6Oae5VhNTc08nYuUz+ffDYmKJMtdZJ9WCj/O4wpatATGxiYycRoaGjmeq2mz5ujTtx80tbRgZGQkJsW5kdP39Vs19WyOPwOnYNQfY7F/byjKV6iA8v8fFnv37h1UVVWxKWQbVFRUZY77dLIyUUFiwvIDirp+HVKpgKEjRok/BP858OXhoI+srMrAyqoMunp3x8hh/ti1YxsaujWCnZ097t29I9et0h07d8WG9Wuxfu0ame12dhVxcP9+GBYvDl1d3WyPLV68BK5duwon5+oAMkvoN2/cEEvgiYmvcD8mBuMnThHH2y9GXJBpQ109c3xfKs3IsZ+ampr41c0d+0L34GHsA1iVKQM7+4qf9Nce9+/HFPht4/R9GBgY4s7t2zLbom9GQe3/f5/KlSsHDQ0NxMU9gXP1Gnlqu6iubrZ/j8qWLYeE+HjEx8WJVZa7d+7g7Zs3MtXML31fP6euro6MzyaSZ+eXXxti0oRxOHXyBPbtDUXzFi3FfbZ2dsjIyMDLly/F7xjlL97WLD/OYfkBlbawxIcP6di4fi0ePXyIPbt3IiSHORYpKSmYNmUSzp87iydPHuPSxQhcv3YVZcpm/nDs0asPLkdewrQpk3AzKgoPHtzHkcP/YtqUSbnuk6amJvr7+GHj+rUy25s2a45iBgYY5NsfFyMu4NGjhzh/7iymT5uChPh4AEDHzl2wctkSHDn8L+7H3MOfgVPx5s1rsYaqp6ePYsWKYWvIZsQ+eICzZ8Lx14zpMucxNCwOLS0tnDp5Ai+eP8fbt2+/2FfPZs1x4vhR7NyxDU09m8vs69vfB6G7d2HxwiDcuXMb9+7exf59exE0d3auPwv6cdRwqYkb169hz66dmcOOQfNw585/CUyRIrrw7t4Tf/0ZiN07d+BhbCyiblzHhvVrsXtn1mck5UZN11qwLl8BASOHIerGdVy9cgVj/hgB5+o1ULFS5a9+Xz9nbl4Sjx89ws2oKLx69RJp/5/M/jkdHR00aNgQC+fPRcy9u2jS9L/bq62syqBps+YYHTAC/x76B48ePcTVK1ewYtkSHD929Juuk2SpSPLn9TNjheUHZGNri2EjArBqxTLMmzML1ZycMXCwP8YEjMw2XlVFBa8TEzEmYCRevHiOYgYGaOjmjgG+mRN4K9jYYkXwWsyfNwc9unWCIAClS5fO9m6DnLRo2Qprglfh3v8nwgKZJfhVq9dhzqy/4D/IF8nJyTA2MYGLiyuK/L/i0qNXHzx//hxjAkZCRUUVbX5rh1q164ilaRUVFfz512z8OW0K2ng1g5VVGYz8Y4zMradqamoYGTAGSxYtwMKgeajm5IwVwbLJ00c1XGpCX18f92NisiQstevUxbwFi7F00QKsWrEMampqsCpTFq3b/Janz4J+DLXr1EXffgMw+++ZSEtLRctWbdCshRfu3L4lxvgMHAwDQ0OsWL4Ejx4+QlG9orCzs0fvvv2+6ZwSiQRz5y/E9GmT0aNbF6ioSFC7Tl2M+mMsgK9/Xz/n5u6BsH8PoXfPbnj75g0mTQlEy1ats431bNYcPv36wsm5OszMzWX2TZoSiGVLFuHvmdPxNOEpDAyKobKDI+rV/+WbrpMov0mEQvgY0BQ+MuOHJpVK4dW8Cdw9msB34OCC7g4RFXJa3+FX98M3X+RLO7/aFs+Xdn5ErLBQgXvy5DHCT52CU/XqSE9Lw8YN6/H40eMs1Q8ioh8V7xKSHxMWKnAqEhXs3rkds/76E4IgwLp8BSxdseqrt1MTEdHPg0NCRET0U/seQ0JHo1/mSzu/2CjHWnIFgRUWIiIiBfvZ7/DJD7ytmYiIiJQeExaSW8SF8/Ab0A9uv9SRWSH2oxfPn2PsH6Pg9ksduDg5oH/fXlxQjQq9jIwMBM2bI67y7NnYDUsWLZB5su6/h/7B7316ol4tFzhUtMHNqKgC7DEpkiSf/vczY8JCcnv//h1sbGwQMGZ8ln2CIGDwQB88evQQc+YvxOatO2BmXhK/9+ohPvacqDBatWIZQjZvRMDocdixZx8GDxmG4JXLseGThyu+f/8OVatWw2D/YQXYU/oeuPih/DiHheRWp2591KlbP9t9Dx7cx5XLkdi2KxTW1uUBAGPGTcCv9WvjwL69aN2WD2Sjwiky8hJ++bWh+OC1kiVLYf++veKKzADQvIUXgMz1wahw+8lzjXzBCgspVPr/HxOuqfHfwoEqKirQ0NCQWeqeqLBxdKyKc2fO4P79GABA9M2buHQpAnXq1ivgnhH9mH74CktqaipSU1NltgmqmlxZV0lYlSkLMzNzzJvzN8aOnwRtbW2sXROMhPh4PHv2rKC7R6QwPXv3RVJSEryaNYGqqioyMjLgN2gIPJu1KOiuUQFQ+dnHc/KBUldYHj58iJ49e+YYExgYCH19fZnXzD8Dv1MP6WvU1dUxa+58PLh/H3Vr1YCLsyPOnzuLOnXrQYX3+VEhdvDAfuzbuweBM/7GppDtmDxtOlavWvnNiybSj02ST6+fmVJXWF6+fInVq1dj5cqVX4wJCAiAv7+/zDZBldUVZWJfsRK2bN+Ft2/fIj09HYaGhujc4TdUrFipoLtGpDCz/56Bnr36oklTTwBA+Qo2iHvyBCuWL0ELr1YF3DuiH0+BJiy7d+/Ocf+9e/e+2oamZtbhHz7pVjkVLVoUQOZE3BvXr8HHb1AB94hIcVLep2SpIqqqqkIqLXQPF6fc+NnLI/mgQBMWLy8vSCQS5LQ6gITjfkrvXXIyYmNjxfePHz3Czago6Ovrw8zcHP8c3A8DA0OYmZnj9u1ozAichga/uqFW7ToF2Gsixar/SwMsW7oYpmbmKGdtjZtRUVi7ehVatmojxrxOTERcXByePXsKAOIE3RIlSqCEkVGB9JsU42d/hkp+KNC1hEqWLImFCxeiZcuW2e6PjIyEk5MTMjIy8tQuKyzf1/lzZ9G7R7cs21u0bIXJ06Zj/bo1WL1qBV48fwEjIyM0a9ESv/cbAHUNjQLoLdH3kZychAXz5uJw2L94+fIFjIyN0aSJJ37v7yP+3d+1YzvGjQnIcmy/Ab7o7+P3vbv80/oeawmdvfs6X9pxKaefL+38iAo0YWnRogUcHR0xadKkbPdfvnwZVatWhVQqzVO7TFiIiCi3vkfCcu5e/iQsNcr+vAlLgQ4JDR8+HMnJyV/cb21tjSNHjnzHHhEREeU/DgjJr0ArLIrCCgsREeXW96iwnM+nCkt1VliIiIhIYVhikRsTFiIiIgXjXULyY8JCRESkYHxCh/yU+tH8VDA2bViPJo1+RfWqldG5w2+4euVKjvH/HNyPls0ao3rVymjj1Rwnjh+T2b9owXy0bNYYLs6OqONaHX17dceVK5fF/Wlpafhj1HDUqlENzZt64Ez4aZnjg1cuR+DUyfl3gUTfKC/fjTt3bsN/kB+aNPoVDhVtsG5NcI5tr1i2FA4VbTAjcKrM9pl/BqKuaw24N6yPvaGyD9v85+B++A3o983XQ/QjYcJCMg7s34e/ZgTi9wE+2BSyAzY2tuj/ey+8ePEi2/jISxcxavhQtGrdFpu37kSDXxtisJ8Pbt++JcZYWlohYPQ4bNuxB8FrN8C8ZEn079MTL1++BABsDdmMqOvXsWbDZrT9rR1GjRgqPkzw0aOH2LY1BH6Dhij+4olykNfvRsr79yhVuhQGDhmKEiVyfgjctatXsDVkEypUsJHZfvTIYezfG4rFy1ZgiP9wTBw3Bq9eZX5v3r59i/lz5+CPMePy5wJJobiWkPyYsJCMtatXoXXbdvBq1QblrK0xZvxEaGlpYef2bdnGr1+3BrXq1EX3nr1Rtlw5+A4cDDt7e2zasE6MadqsOWq61kKp0qVhbV0ew0YEICkpCbdvRQMAYu7eRf0Gv8Laujzad+yMVy9f4tWrVwCAqZMmYLD/MOjq6ir82olyktfvRqXKVeA/bCSaNPWERg4PSXyXnIyAkcMxfuIU6OnL3gESc+8unGvUQMVKldHEsxmK6Ori8aNHAIDZf89Eu/YdYWZunn8XSYrDjEVuTFhIlJ6Whqgb11HTtZa4TUVFBTVr1sKVy5eyPeZKZCRq1nSV2Vardh1ciYz84jm2hWxG0aJFUcEm87fJCra2uHQxAikpKTh96iSMjIxgYGCAvaG7oampiYZujfLnAom+0bd8N3Jr2pRJqFevvkzbH1WwscWNa9fw5vVr3Lh+DakpKbCwsMTFiAu4eeM6OnXpKte5iX4knHRLoleJr5CRkYHixYvLbC9evDhiYrJfiPL58+coXrxElvjnL57LbDt29AhGDvNHSsp7lDAywuJlK2FgYAgA8GrVBrejo9GqRVMYFDPAjL/n4M3r11gYNA8rVq1F0NzZOLB/H0qVtsDEKdNgYmKSj1dN9HXf8t3Ijf379iIq6gY2bN6a7f7aderCs3kLdGrfFppaWpg87U9oa2tj6uSJmDw1EFs2bcTGDWthUMwAYydOhrV1+W/uCykW7xKSHxMW+i6q13DBlm07kZj4Ctu2bsHwoYOxbmMIihcvDnV1dfwxdrxM/NjRAejUuStuRt3A4cNh2LJ9F4JXLsef06Zg1tz5BXQVRPknPi4OM6ZPxZJlK7OsOP+p/j5+MusKLV4YhJo1XaGmpoZlSxZh6849OH7sCMYEjMSmkO3fo+v0DXiXkPw4JEQig2IGUFVVzTKJ8MWLFyhRokS2x5QoUQIvPqumvHjxAiU+q7ro6OjAwtISVRwcMXHyNKipqmHn9ux/qzx39gzu3rmNDp264Pz5c6hbtx50dHTg3rgJLpw/J8cVEn2bb/lufM2NG9fx8sULdPitNapVsUe1Kva4cP4cNqxfi2pV7LNd9DXm3l3s3bMbPn6DcP78OTg5O8PQ0BDuHk0QdeM6kpOTvqkvRD8CJiwkUtfQgJ19RZw9Ey5uk0qlOHs2HFUcqmZ7TBVHR5w9c0Zm25nw06ji6JjjuaSCFGlpaVm2p6amInDKJIydMAmqqqqQSjPw4UPmWgsf0j9AKs3byt1E+eFbvhtf41KzJrbu3IPN23aKr4oVK6Fps+bYvG0nVFVVZeIFQcDkieMxdMQo6BQpAmmGFOkfvxv///+MjLwtFEvfD+fcyo9DQiSjq3cPjP1jJCpWrIRKlatg3drVeP/+PbxatQYAjA4YAWNjEwwaMhQA0LlLN/Tq3hWrg1eiXr36OLB/H65fu4axEzJX4H737h2WL12MXxr8ihJGRkh89QqbNq7H04QENPJonOX8SxcvRJ169WFnZw8AcKxaDbP/momWrVpj08Z1cKxa7Tt9EkSy8vrdSE9Lw927dzP/nJ6Gp08TcDMqSqw2Fimii/LlK8icQ1tHB8X0i2XZDgDbt4bAwMAQvzT4FUDmd2Pxwvm4cjkSJ08cR9ly1tDT01PkR0Dy+NmzjXzAhIVkNG7SFK9evsTCoHl4/vwZbGztsHDJchT/f9k7Pi4OKpL/CnOOVashcMZfCJo3B/PnzIKFpRXmzF8g/sBVVVVFTMw97N61A4mvXqFYsWKoWKkyVq1Zn2WC4O3bt/DPgf3YvG2nuK2Re2NcOHcOPbp1hqVVGUyf8bfiPwSibOT1u/H02VO0b+slvl+9aiVWr1oJ5+o1sCJ4bZ7O/eL5cyxfuhir128Ut1WuUgVdvXvAt//vMCxuiMlT/5TvAomUHFdrJiKin9r3WK35ysP8mV9UpfTP+0wqVliIiIgUjHcJyY8JCxERkYIxX5Ef7xIiIiIqpI4fP47mzZvD3NwcEokEO3fulNkvCALGjRsHMzMzaGtrw83NDbdv35aJefnyJTp37gw9PT0UK1YMvXr1QlKS7BDXlStXULduXWhpaaF06dKYMWNGlr6EhITA1tYWWlpaqFy5Mvbt25ena2HCQkREpGgFdF9zcnIyHBwcsGDBgmz3z5gxA/PmzcPixYtx9uxZFClSBB4eHkhJSRFjOnfujOvXr+PQoUMIDQ3F8ePH0bdvX3H/mzdv4O7uDktLS0RERGDmzJmYMGECli5dKsacPn0aHTt2RK9evXDp0iV4eXnBy8sL165dy/W1cNItERH91L7HpNvrj5PzpZ2KJYt887ESiQQ7duyAl5cXgMzqirm5OYYOHYphw4YBAF6/fg0TExMEBwejQ4cOiIqKgr29Pc6fPw9nZ2cAwIEDB9C0aVM8evQI5ubmWLRoEUaPHo34+Hhxoc9Ro0Zh586duHnzJgCgffv2SE5ORmhoqNifmjVrwtHREYsXL85V/1lhISIi+kGkpqbizZs3Mq/U1NRvaismJgbx8fFwc3MTt+nr68PFxQXh4ZkPSQwPD0exYsXEZAUA3NzcoKKigrNnz4ox9erVk1mV3MPDA9HR0Xj16pUY8+l5PsZ8PE9uMGEhIiJSMIkkf16BgYHQ19eXeQUGBn5Tn+Lj4wEgy4KyJiYm4r74+HgYGxvL7FdTU4OhoaFMTHZtfHqOL8V83J8bvEuIiIhIwfLrLqGAgAD4+/vLbMtp8czChAkLERHRD0JTUzPfEhRTU1MAQEJCAszMzMTtCQkJcPz/enCmpqZ4+vSpzHEfPnzAy5cvxeNNTU2RkJAgE/Px/ddiPu7PDQ4JERERKZoSrn5YpkwZmJqaIiwsTNz25s0bnD17Fq6urgAAV1dXJCYmIiIiQow5fPgwpFIpXFxcxJjjx48jPT1djDl06BBsbGxgYGAgxnx6no8xH8+TG0xYiIiIFEyST//Lq6SkJERGRiIyMhJA5kTbyMhIxMbGQiKRYPDgwZgyZQp2796Nq1evolu3bjA3NxfvJLKzs0Pjxo3Rp08fnDt3DqdOnYKvry86dOgAc3NzAECnTp2goaGBXr164fr169i8eTPmzp0rM3Q1aNAgHDhwAH///Tdu3ryJCRMm4MKFC/D19c39Z8jbmomI6Gf2PW5rvhn3Ll/asTXTyVP80aNH0aBBgyzbvb29ERwcDEEQMH78eCxduhSJiYmoU6cOFi5ciAoV/lsx/OXLl/D19cWePXugoqKCNm3aYN68edDV/W9doytXrsDHxwfnz59HiRIl4Ofnh5EjR8qcMyQkBGPGjMH9+/dRvnx5zJgxA02bNs31tTBhISKin9r3SFii4/MnYbExzVvCUphw0i0REZGCcS0h+TFhISIiUjRmLHLjpFsiIiJSeqywEBERKdi33OFDspiwEBERKZiE+YrcOCRERERESo8VFiIiIgVjgUV+TFiIiIgUjRmL3DgkREREREqPFRYiIiIF411C8mPCQkREpGC8S0h+HBIiIiIipccKCxERkYKxwCI/JixERESKxoxFbkxYiIiIFIyTbuXHOSxERESk9FhhISIiUjDeJSQ/JixEREQKxnxFfhwSIiIiIqXHCgsREZGCcUhIfkxYiIiIFI4Zi7w4JERERERKjxUWIiIiBeOQkPyYsBARESkY8xX5cUiIiIiIlB4rLERERArGISH5MWEhIiJSMK4lJD8mLERERIrGfEVunMNCRERESo8VFiIiIgVjgUV+TFiIiIgUjJNu5cchISIiIlJ6rLAQEREpGO8Skh8TFiIiIkVjviI3DgkRERGR0mOFhYiISMFYYJEfExYiIiIF411C8uOQEBERESk9VliIiIgUjHcJyY8JCxERkYJxSEh+HBIiIiIipceEhYiIiJQeh4SIiIgUjENC8mPCQkREpGCcdCs/DgkRERGR0mOFhYiISME4JCQ/JixEREQKxnxFfhwSIiIiIqXHCgsREZGiscQiNyYsRERECsa7hOTHISEiIiJSeqywEBERKRjvEpIfExYiIiIFY74iPyYsREREisaMRW6cw0JERERKjxUWIiIiBeNdQvJjwkJERKRgnHQrPw4JERERkdKTCIIgFHQnqHBKTU1FYGAgAgICoKmpWdDdIVIa/G4Q5R0TFlKYN2/eQF9fH69fv4aenl5Bd4dIafC7QZR3HBIiIiIipceEhYiIiJQeExYiIiJSekxYSGE0NTUxfvx4Tiok+gy/G0R5x0m3REREpPRYYSEiIiKlx4SFiIiIlB4TFiIiIlJ6TFiIiIhI6TFhIYVZsGABrKysoKWlBRcXF5w7d66gu0RUoI4fP47mzZvD3NwcEokEO3fuLOguEf0wmLCQQmzevBn+/v4YP348Ll68CAcHB3h4eODp06cF3TWiApOcnAwHBwcsWLCgoLtC9MPhbc2kEC4uLqhevTqCgoIAAFKpFKVLl4afnx9GjRpVwL0jKngSiQQ7duyAl5dXQXeF6IfACgvlu7S0NERERMDNzU3cpqKiAjc3N4SHhxdgz4iI6EfFhIXy3fPnz5GRkQETExOZ7SYmJoiPjy+gXhER0Y+MCQsREREpPSYslO9KlCgBVVVVJCQkyGxPSEiAqalpAfWKiIh+ZExYKN9paGjAyckJYWFh4japVIqwsDC4uroWYM+IiOhHpVbQHaDCyd/fH97e3nB2dkaNGjUwZ84cJCcno0ePHgXdNaICk5SUhDt37ojvY2JiEBkZCUNDQ1hYWBRgz4iUH29rJoUJCgrCzJkzER8fD0dHR8ybNw8uLi4F3S2iAnP06FE0aNAgy3Zvb28EBwd//w4R/UCYsBAREZHS4xwWIiIiUnpMWIiIiEjpMWEhIiIipceEhYiIiJQeExYiIiJSekxYiIiISOkxYSEiIiKlx4SFiIiIlB4TFiIl0r17d3h5eYnvf/nlFwwePPi79+Po0aOQSCRITEz8YoxEIsHOnTtz3eaECRPg6OgoV7/u378PiUSCyMhIudohoh8PExair+jevTskEgkkEgk0NDRgbW2NSZMm4cOHDwo/9/bt2zF58uRcxeYmySAi+lFx8UOiXGjcuDFWrVqF1NRU7Nu3Dz4+PlBXV0dAQECW2LS0NGhoaOTLeQ0NDfOlHSKiHx0rLES5oKmpCVNTU1haWqJ///5wc3PD7t27Afw3jDN16lSYm5vDxsYGAPDw4UO0a9cOxYoVg6GhIVq2bIn79++LbWZkZMDf3x/FihVD8eLFMWLECHy+tNfnQ0KpqakYOXIkSpcuDU1NTVhbW2PFihW4f/++uKiegYEBJBIJunfvDgCQSqUIDAxEmTJloK2tDQcHB2zdulXmPPv27UOFChWgra2NBg0ayPQzt0aOHIkKFSpAR0cHZcuWxdixY5Genp4lbsmSJShdujR0dHTQrl07vH79Wmb/8uXLYWdnBy0tLdja2mLhwoV57gsRFT5MWIi+gba2NtLS0sT3YWFhiI6OxqFDhxAaGor09HR4eHigaNGiOHHiBE6dOgVdXV00btxYPO7vv/9GcHAwVq5ciZMnT+Lly5fYsWNHjuft1q0bNm7ciHnz5iEqKgpLliyBrq4uSpcujW3btgEAoqOjERcXh7lz5wIAAgMDsWbNGixevBjXr1/HkCFD0KVLFxw7dgxAZmLVunVrNG/eHJGRkejduzdGjRqV58+kaNGiCA4Oxo0bNzB37lwsW7YMs2fPlom5c+cOtmzZgj179uDAgQO4dOkSBgwYIO5fv349xo0bh6lTpyIqKgrTpk3D2LFjsXr16jz3h4gKGYGIcuTt7S20bNlSEARBkEqlwqFDhwRNTU1h2LBh4n4TExMhNTVVPGbt2rWCjY2NIJVKxW2pqamCtra2cPDgQUEQBMHMzEyYMWOGuD89PV0oVaqUeC5BEIT69esLgwYNEgRBEKKjowUAwqFDh7Lt55EjRwQAwqtXr8RtKSkpgo6OjnD69GmZ2F69egkdO3YUBEEQAgICBHt7e5n9I0eOzNLW5wAIO3bs+OL+mTNnCk5OTuL78ePHC6qqqsKjR4/Ebfv37xdUVFSEuLg4QRAEoVy5csKGDRtk2pk8ebLg6uoqCIIgxMTECACES5cuffG8RFQ4cQ4LUS6EhoZCV1cX6enpkEql6NSpEyZMmCDur1y5ssy8lcuXL+POnTsoWrSoTDspKSm4e/cuXr9+jbi4OLi4uIj71NTU4OzsnGVY6KPIyEioqqqifv36ue73nTt38O7dOzRq1Ehme1paGqpWrQoAiIqKkukHALi6uub6HB9t3rwZ8+bNw927d5GUlIQPHz5AT09PJsbCwgIlS5aUOY9UKkV0dDSKFi2Ku3fvolevXujTp48Y8+HDB+jr6+e5P0RUuDBhIcqFBg0aYNGiRdDQ0IC5uTnU1GS/OkWKFJF5n5SUBCcnJ6xfvz5LW0ZGRt/UB21t7Twfk5SUBADYu3evTKIAZM7LyS/h4eHo3LkzJk6cCA8PD+jr62PTpk34+++/89zXZcuWZUmgVFVV862vRPRjYsJClAtFihSBtbV1ruOrVauGzZs3w9jYOEuV4SMzMzOcPXsW9erVA5BZSYiIiEC1atWyja9cuTKkUimOHTsGNze3LPs/VngyMjLEbfb29tDU1ERsbOwXKzN2dnbiBOKPzpw58/WL/MTp06dhaWmJ0aNHi9sePHiQJS42NhZPnjyBubm5eB4VFRXY2NjAxMQE5ubmuHfvHjp37pyn8xNR4cdJt0QK0LlzZ5QoUQItW7bEiRMnEBMTg6NHj2LgwIF49OgRAGDQoEGYPn06du7ciZs3b2LAgAE5PkPFysoK3t7e6NmzJ3bu3Cm2uWXLFgCApaUlJBIJQkND8ezZMyQlJaFo0aIYNmwYhgwZgtWrV+Pu3bu4ePEi5s+fL05k7devH27fvo3hw4cjOjoaGzZsQHBwcJ6ut3z58oiNjcWmTZtw9+5dzJs3L9sJxFpaWvD29sbly5dx4sQJDBw4EO3atYOpqSkAYOLEiQgMDMS8efNw69YtXL16FatWrcKsWbPy1B8iKnyYsBApgI6ODo4fPw4LCwu0bt0adnZ26NWrF1JSUsSKy9ChQ9G1a1d4e3vD1dUVRYsWRatWrXJsd9GiRWjbti0GDBgAW1tb9OnTB8nJyQCAkiVLYuLEiRg1ahRMTEzg6+sLAJg8eTLGjh2LwMBA2NnZoXHjxti7dy/KlCkDIHNeybZt27Bz5044ODhg8eLFmDZtWp6ut0WLFhgyZAh8fX3h6OiI06dPY+zYsVnirK2t0bp1azRt2hTu7u6oUqWKzG3LvXv3xvLly7Fq1SpUrlwZ9evXR3BwsNhXIvp5SYQvzfAjIiIiUhKssBAREZHSY8JCRERESo8JCxERESk9JixERESk9JiwEBERkdJjwkJERERKjwkLERERKT0mLERERKT0mLAQERGR0mPCQkREREqPCQsREREpPSYsREREpPSYsBAREZHSY8JCRERESo8JCxERESk9JixERESk9JiwEBERkdJjwkJERERKjwkLERERKT0mLERERKT0mLAQERGR0mPCQvSZ8PBwqKqqwtPTs6C7opSuX7+ONm3awMrKChKJBHPmzMnVcVeuXEHdunWhpaWF0qVLY8aMGVliQkJCYGtrCy0tLVSuXBn79u2T2S8IAsaNGwczMzNoa2vDzc0Nt2/fzo/LIiIlx4SF6DMrVqyAn58fjh8/jidPnhRYP9LS0grs3Dl59+4dypYti+nTp8PU1DRXx7x58wbu7u6wtLREREQEZs6ciQkTJmDp0qVizOnTp9GxY0f06tULly5dgpeXF7y8vHDt2jUxZsaMGZg3bx4WL16Ms2fPokiRIvDw8EBKSkq+XycRKRmBiERv374VdHV1hZs3bwrt27cXpk6dKrN/9+7dgrOzs6CpqSkUL15c8PLyEvelpKQII0aMEEqVKiVoaGgI5cqVE5YvXy4IgiCsWrVK0NfXl2lrx44dwqdfwfHjxwsODg7CsmXLBCsrK0EikQiCIAj79+8XateuLejr6wuGhoaCp6encOfOHZm2Hj58KHTo0EEwMDAQdHR0BCcnJ+HMmTNCTEyMIJFIhPPnz8vEz549W7CwsBAyMjLk+rwsLS2F2bNnfzVu4cKFgoGBgZCamipuGzlypGBjYyO+b9euneDp6SlznIuLi/D7778LgiAIUqlUMDU1FWbOnCnuT0xMFDQ1NYWNGzfKdR1EpPxYYSH6xJYtW2BrawsbGxt06dIFK1euhCAIAIC9e/eiVatWaNq0KS5duoSwsDDUqFFDPLZbt27YuHEj5s2bh6ioKCxZsgS6urp5Ov+dO3ewbds2bN++HZGRkQCA5ORk+Pv748KFCwgLC4OKigpatWoFqVQKAEhKSkL9+vXx+PFj7N69G5cvX8aIESMglUphZWUFNzc3rFq1SuY8q1atQvfu3aGikvkjQFdXN8dXv379vvUjBZA5zFavXj1oaGiI2zw8PBAdHY1Xr16JMW5ubjLHeXh4IDw8HAAQExOD+Ph4mRh9fX24uLiIMURUeKkVdAeIlMmKFSvQpUsXAEDjxo3x+vVrHDt2DL/88gumTp2KDh06YOLEiWK8g4MDAODWrVvYsmULDh06JP6DWrZs2TyfPy0tDWvWrIGRkZG4rU2bNjIxK1euhJGREW7cuIFKlSphw4YNePbsGc6fPw9DQ0MAgLW1tRjfu3dv9OvXD7NmzYKmpiYuXryIq1evYteuXWLMx+ToS/T09PJ8LZ+Kj49HmTJlZLaZmJiI+wwMDBAfHy9u+zQmPj5ejPv0uOxiiKjwYoWF6P+io6Nx7tw5dOzYEQCgpqaG9u3bY8WKFQAy/1Fv2LBhtsdGRkZCVVUV9evXl6sPlpaWMskKANy+fRsdO3ZE2bJloaenBysrKwBAbGyseO6qVauKycrnvLy8oKqqih07dgAAgoOD0aBBA7EdIDPByellbGws13UREcmLCQvR/61YsQIfPnyAubk51NTUoKamhkWLFmHbtm14/fo1tLW1v3hsTvsAQEVFRRxa+ig9PT1LXJEiRbJsa968OV6+fIlly5bh7NmzOHv2LID/JuV+7dwaGhro1q0bVq1ahbS0NGzYsAE9e/aUiVH0kJCpqSkSEhJktn18/3Hi7pdiPt3/6XHZxRBR4cUhISIAHz58wJo1a/D333/D3d1dZp+Xlxc2btyIKlWqICwsDD169MhyfOXKlSGVSnHs2LEs8zAAwMjICG/fvkVycrKYlHxtGAYAXrx4gejoaCxbtgx169YFAJw8eVImpkqVKli+fDlevnz5xSpL7969UalSJSxcuBAfPnxA69atZfYrekjI1dUVo0ePRnp6OtTV1QEAhw4dgo2NDQwMDMSYsLAwDB48WDzu0KFDcHV1BQCUKVMGpqamCAsLg6OjI4DMu4/Onj2L/v37y9U/IvoBFPSsXyJlsGPHDkFDQ0NITEzMsm/EiBGCs7OzcOTIEUFFRUUYN26ccOPGDeHKlSvC9OnTxbju3bsLpUuXFnbs2CHcu3dPOHLkiLB582ZBEAThxYsXQpEiRYSBAwcKd+7cEdavXy+Ym5tne5fQpzIyMoTixYsLXbp0EW7fvi2EhYUJ1atXFwAIO3bsEARBEFJTU4UKFSoIdevWFU6ePCncvXtX2Lp1q3D69GmZtmrVqiVoaGgI/fr1k+uzSk1NFS5duiRcunRJMDMzE4YNGyZcunRJuH37thgzf/584ddffxXfJyYmCiYmJkLXrl2Fa9euCZs2bRJ0dHSEJUuWiDGnTp0S1NTUhL/++kuIiooSxo8fL6irqwtXr14VY6ZPny4UK1ZM2LVrl3DlyhWhZcuWQpkyZYT379/LdU1EpPyYsBAJgtCsWTOhadOm2e47e/asAEC4fPmysG3bNsHR0VHQ0NAQSpQoIbRu3VqMe//+vTBkyBDBzMxM0NDQEKytrYWVK1eK+3fs2CFYW1sL2traQrNmzYSlS5d+NWERBEE4dOiQYGdnJ2hqagpVqlQRjh49KpOwCIIg3L9/X2jTpo2gp6cn6OjoCM7OzsLZs2dl2lmxYoUAQDh37tw3fkqZYmJiBABZXvXr15e5FktLS5njLl++LNSpU0fQ1NQUSpYsKZPsfbRlyxahQoUKgoaGhlCxYkVh7969MvulUqkwduxYwcTERNDU1BQaNmwoREdHy3U9RPRjkAjCZwPrRFQoTZ48GSEhIbhy5UpBd4WIKM846ZaokEtKSsK1a9cQFBQEPz+/gu4OEdE3YcJCVMj5+vrCyckJv/zyS5a7g4iIfhQcEiIiIiKlxwoLERERKT0mLERKQCKRYOfOnfkeS0RUWDBhIfpM9+7dIZFIIJFIoKGhAWtra0yaNAkfPnxQ2Dnj4uLQpEmTfI9VlNjYWHh6ekJHRwfGxsYYPnz4Vz+fixcvolGjRihWrBiKFy+Ovn37IikpKdvYFy9eoFSpUpBIJEhMTBS3b9++HY0aNYKRkRH09PTg6uqKgwcP5uelEZGSYsJClI3GjRsjLi4Ot2/fxtChQzFhwgTMnDkzS9zHx+PLy9TUFJqamvkeqwgZGRnw9PREWloaTp8+jdWrVyM4OBjjxo374jFPnjyBm5sbrK2tcfbsWRw4cADXr19H9+7ds43v1asXqlSpkmX78ePH0ahRI+zbtw8RERFo0KABmjdvjkuXLuXX5RGRsirYx8AQKR9vb2+hZcuWMtsaNWok1KxZU9w3ZcoUwczMTLCyshIEQRBiY2OF3377TdDX1xcMDAyEFi1aCDExMTJtrFixQrC3txc0NDQEU1NTwcfHR9yHz55c6+PjI5iamgqampqChYWFMG3atGxjBUEQrly5IjRo0EDQ0tISDA0NhT59+ghv377Ncj0zZ84UTE1NBUNDQ2HAgAFCWlraN30++/btE1RUVIT4+Hhx26JFiwQ9PT0hNTU122OWLFkiGBsbCxkZGTL9BiDzhFxBEISFCxcK9evXF8LCwgQAwqtXr3Lsj729vTBx4sRvuhYi+nGwwkKUC9ra2mI1JSwsDNHR0Th06BBCQ0ORnp4ODw8PFC1aFCdOnMCpU6egq6uLxo0bi8csWrQIPj4+6Nu3L65evYrdu3fD2to623PNmzcPu3fvxpYtWxAdHY3169fLrKz8qeTkZHh4eMDAwADnz59HSEgI/v33X/j6+srEHTlyBHfv3sWRI0fEikhwcLC4v1+/fl9dAPGj8PBwVK5cGSYmJuI2Dw8PvHnzBtevX8+2n6mpqdDQ0ICKyn8/cj4u2vjp2kg3btzApEmTsGbNGpnYL5FKpXj79u0X11AiosKDix8S5UAQBISFheHgwYPw8/PDs2fPUKRIESxfvhwaGhoAgHXr1kEqlWL58uWQSCQAgFWrVqFYsWI4evQo3N3dMWXKFAwdOhSDBg0S265evXq254yNjUX58uVRp04dSCQSWFpafrF/GzZsQEpKCtasWSMuqhgUFITmzZvjzz//FJMKAwMDBAUFQVVVFba2tvD09ERYWBj69OkDAJg0aRKGDRuWq88kPj5eJlkBIL6Pj4/P9phff/0V/v7+mDlzJgYNGoTk5GSMGjUKQOacHCAzqenYsSNmzpwJCwsL3Lt376t9+euvv5CUlIR27drlqu9E9ONiwkKUjdDQUOjq6iI9PR1SqRSdOnXChAkT4OPjg8qVK4vJCgBcvnwZd+7cQdGiRWXaSElJwd27d/H06VM8efIEDRs2zNW5u3fvjkaNGsHGxgaNGzdGs2bNsqwg/VFUVBQcHBzEZAUAateuDalUiujoaDGRqFixIlRVVcUYMzMzXL16VXxvbGwMY2PjXPXvW1SsWBGrV6+Gv78/AgICoKqqioEDB8LExESspAQEBMDOzg5dunTJVZsbNmzAxIkTsWvXLoX2nYiUAxMWomw0aNAAixYtgoaGBszNzaGm9t9X5dPkAMh89L2TkxPWr1+fpR0jI6NcDW18qlq1aoiJicH+/fvx77//ol27dnBzc8PWrVu/7WIAqKury7yXSCSQSqXi+379+mHdunU5tvHxjh5TU1OcO3dOZl9CQoK470s6deqETp06ISEhAUWKFIFEIsGsWbNQtmxZAMDhw4dx9epV8TqF/z/TskSJEhg9ejQmTpwotrVp0yb07t0bISEhcHNzy7HfRFQ4MGEhykaRIkW+OMfkc9WqVcPmzZthbGwMPT29bGOsrKwQFhaGBg0a5KpNPT09tG/fHu3bt0fbtm3RuHFjvHz5MstcDTs7OwQHByM5OVlMpE6dOgUVFRXY2Njk6lxA3oaEXF1dMXXqVDx9+lSsbBw6dAh6enqwt7f/6vEfqz4rV66ElpYWGjVqBADYtm0b3r9/L8adP38ePXv2xIkTJ1CuXDlx+8aNG9GzZ09s2rQJnp6eub5GIvqxMWEhklPnzp0xc+ZMtGzZEpMmTUKpUqXw4MEDbN++HSNGjECpUqUwYcIE9OvXD8bGxmjSpAnevn2LU6dOZbsY4axZs2BmZoaqVatCRUUFISEhMDU1RbFixbI99/jx4+Ht7Y0JEybg2bNn8PPzQ9euXbPMM8lJXoaE3N3dYW9vj65du2LGjBmIj4/HmDFj4OPjI95ufe7cOXTr1g1hYWEoWbIkgMy5NbVq1YKuri4OHTqE4cOHY/r06eJ1fZqUAMDz588BZCZlH2M2bNgAb29vzJ07Fy4uLuKcGW1tbejr6+f6eonox8O7hIjkpKOjg+PHj8PCwgKtW7eGnZ0devXqhZSUFLHi4u3tjTlz5mDhwoWoWLEimjVrhtu3b2fbXtGiRTFjxgw4OzujevXquH//Pvbt25ft0JKOjg4OHjyIly9fonr16mjbti0aNmyIoKAghV2vqqoqQkNDoaqqCldXV3Tp0gXdunXDpEmTxJh3794hOjoa6enp4rZz586hUaNGqFy5MpYuXYolS5Zg4MCBeTr30qVL8eHDB/j4+MDMzEx8fTqZmYgKJy5+SEREREqPFRYiIiJSekxYiIiISOkxYSEiIiKlx4SFiIiIlB4TFiIiIlJ6TFiIfmASiQQ7d+4EANy/fx8SiQSRkZEF2iciIkVgwkL0jbp37w6JRAKJRAJ1dXWUKVMGI0aMQEpKSkF37ZuFhITA1tYWWlpaqFy5Mvbt2/fVY9avXw8HBwfo6OjAzMwMPXv2xIsXL8T9169fR5s2bWBlZQWJRII5c+Zk286CBQtgZWUFLS0tuLi4ZHn8PxH93JiwEMmhcePGiIuLw7179zB79mwsWbIE48ePL+hufZPTp0+jY8eO6NWrFy5dugQvLy94eXnh2rVrXzzm1KlT6NatG3r16oXr168jJCQE586dE1eBBjIfIle2bFlMnz79i2sNbd68Gf7+/hg/fjwuXrwIBwcHeHh44OnTp/l+nUT0Y2LCQiQHTU1NmJqaonTp0vDy8oKbmxsOHToEAJBKpQgMDESZMmWgra0NBweHLAsYXr9+Hc2aNYOenh6KFi2KunXr4u7duwAy19Jp1KgRSpQoAX19fdSvXx8XL15U2LXMnTsXjRs3xvDhw2FnZ4fJkyejWrVqOT41Nzw8HFZWVhg4cCDKlCmDOnXq4Pfff5epjlSvXh0zZ85Ehw4dxEf3f27WrFno06cPevToAXt7eyxevBg6OjpYuXJlvl8nEf2YmLAQ5ZNr167h9OnT0NDQAAAEBgZizZo1WLx4Ma5fv44hQ4agS5cuOHbsGADg8ePHqFevHjQ1NXH48GFERESgZ8+e+PDhAwDg7du38Pb2xsmTJ3HmzBmUL18eTZs2xdu3b3PdJ11d3Rxf/fr1E2PDw8OzrHzs4eGB8PDwL7bv6uqKhw8fYt++fRAEAQkJCdi6dSuaNm2a6z6mpaUhIiJC5twqKipwc3PL8dxE9HPh4odEcggNDYWuri4+fPiA1NRUqKioICgoCKmpqZg2bRr+/fdfuLq6AgDKli2LkydPYsmSJahfvz4WLFgAfX19bNq0Cerq6gCAChUqiG3/+uuvMudaunQpihUrhmPHjqFZs2a56t/XJuB+urp0fHx8lgUTTUxMxAUGs1O7dm2sX78e7du3R0pKCj58+IDmzZtjwYIFueofkLnIYUZGRrbnvnnzZq7bIaLCjQkLkRwaNGiARYsWITk5GbNnz4aamhratGmD69ev4927d2jUqJFMfFpaGqpWrQogM5moW7eumKx8LiEhAWPGjMHRo0fx9OlTZGRk4N27d4iNjc11/6ytrb/94nLhxo0bGDRoEMaNGwcPDw/ExcVh+PDh6NevH1asWKHQcxPRz4UJC5EcihQpIiYFK1euhIODA1asWIFKlSoBAPbu3YuSJUvKHPNxHoe2tnaObXt7e+PFixeYO3cuLC0toampCVdXV6SlpeW6f7q6ujnu79KlCxYvXgwAMDU1RUJCgsz+hISEL06UBTKHvWrXro3hw4cDAKpUqYIiRYqgbt26mDJlCszMzL7axxIlSkBVVTXP5yainwsTFqJ8oqKigj/++AP+/v64desWNDU1ERsbi/r162cbX6VKFaxevRrp6enZVllOnTqFhQsXivNBHj58iOfPn+epT3kZEnJ1dUVYWBgGDx4sbjt06JA4pJWdd+/eQU1N9seIqqoqACC3C8FraGjAyckJYWFh8PLyApA5YTksLAy+vr65aoOICj8mLET56LfffsPw4cOxZMkSDBs2DEOGDIFUKkWdOnXw+vVrnDp1Cnp6evD29oavry/mz5+PDh06ICAgAPr6+jhz5gxq1KgBGxsblC9fHmvXroWzszPevHmD4cOHf7Uq87m8DAkNGjQI9evXx99//w1PT09s2rQJFy5cwNKlS8WYgIAAPH78GGvWrAEANG/eHH369MGiRYvEIaHBgwejRo0aMDc3B5A5DHbjxg3xz48fP0ZkZCR0dXXF/vn7+8Pb2xvOzs6oUaMG5syZg+TkZPTo0SNP10tEhZhARN/E29tbaNmyZZbtgYGBgpGRkZCUlCTMmTNHsLGxEdTV1QUjIyPBw8NDOHbsmBh7+fJlwd3dXdDR0RGKFi0q1K1bV7h7964gCIJw8eJFwdnZWdDS0hLKly8vhISECJaWlsLs2bPF4wEIO3bsEARBEGJiYgQAwqVLl775mrZs2SJUqFBB0NDQECpWrCjs3bs3yzXXr19fZtu8efMEe3t7QVtbWzAzMxM6d+4sPHr0SNz/sV+fvz5vZ/78+YKFhYWgoaEh1KhRQzhz5sw3XwcRFT4SQchl3ZaIiIiogPA5LERERKT0mLAQERGR0mPCQkREREqPCQsREREpPSYsREREpPSYsBB9pnv37pBIJFled+7cAQAcP34czZs3h7m5OSQSCXbu3PnVNjMyMjB9+nTY2tpCW1sbhoaGcHFxwfLlyxV8NfkvJSUFPj4+KF68OHR1ddGmTZssT6n9XFJSEnx9fVGqVCloa2uLKzJ/dP/+/Ww/c4lEgpCQEDFu4MCBcHJygqamJhwdHRV1iUSkhJiwEGWjcePGiIuLk3mVKVMGAJCcnAwHB4c8LfA3ceJEzJ49G5MnT8aNGzdw5MgR9O3bF4mJiQq6AuTpEf55MWTIEOzZswchISE4duwYnjx5gtatW+d4jL+/Pw4cOIB169YhKioKgwcPhq+vL3bv3g0AKF26dJbPe+LEidDV1UWTJk1k2urZsyfat2+vkGsjIiVW0A+CIVI2X3ogXHbwyYPbcuLg4CBMmDAhx5iMjAzhzz//FMqVKydoaGgIpUuXFqZMmSLuv3LlitCgQQNBS0tLMDQ0FPr06SO8ffs2S7+nTJkimJmZCVZWVoIgCEJsbKzw22+/Cfr6+oKBgYHQokULISYmJlfX97nExERBXV1dCAkJEbdFRUUJAITw8PAvHlexYkVh0qRJMtuqVasmjB49+ovHODo6Cj179sx23/jx4wUHB4e8dZ6IfmissBB9B6ampjh8+DCePXv2xZiAgABMnz4dY8eOxY0bN7BhwwaYmJgAyKzqeHh4wMDAAOfPn0dISAj+/fffLGvthIWFITo6GocOHUJoaCjS09Ph4eGBokWL4sSJEzh16hR0dXXRuHFjsQKzfv166Orq5vg6ceIEACAiIgLp6elwc3MTz2lrawsLCwuEh4d/8dpq1aqF3bt34/HjxxAEAUeOHMGtW7fg7u6ebXxERAQiIyPRq1ev3H3ARFTocS0homyEhobKrHTcpEkTmbkUeTVr1iy0bdsWpqamqFixImrVqoWWLVuKwx1v377F3LlzERQUBG9vbwBAuXLlUKdOHQDAhg0bkJKSgjVr1qBIkSIAgKCgIDRv3hx//vmnmNgUKVIEy5cvh4aGBgBg3bp1kEqlWL58OSQSCQBg1apVKFasGI4ePQp3d3e0aNECLi4uOfb/44rT8fHx0NDQQLFixWT2m5iYID4+/ovHz58/H3379kWpUqWgpqYGFRUVLFu2DPXq1cs2fsWKFbCzs0OtWrVy7BcR/TyYsBBlo0GDBli0aJH4/mOS8K3s7e1x7do1RERE4NSpU+LE3e7du2P58uWIiopCamoqGjZsmO3xUVFRcHBwkOlH7dq1IZVKER0dLSYslStXFpMVALh8+TLu3LmDokWLyrSXkpKCu3fvAgCKFi2aZX9+mz9/Ps6cOYPdu3fD0tISx48fh4+PD8zNzWWqNQDw/v17bNiwAWPHjlVon4jox8KEhSgbRYoUydNKx7mhoqKC6tWro3r16hg8eDDWrVuHrl27YvTo0XlehflLPk+skpKS4OTkhPXr12eJNTIyApA5JPT777/n2O7+/ftRt25dmJqaIi0tDYmJiTJVloSEBJiammZ77Pv37/HHH39gx44d8PT0BABUqVIFkZGR+Ouvv7IkLFu3bsW7d+/QrVu3r14vEf08mLAQFRB7e3sAmfNTypcvD21tbYSFhaF3795ZYu3s7BAcHIzk5GQxKTl16hRUVFRgY2PzxXNUq1YNmzdvhrGxMfT09LKNycuQkJOTE9TV1REWFoY2bdoAAKKjoxEbGwtXV9dsj01PT0d6ejpUVGSnzKmqqkIqlWaJX7FiBVq0aCEmVEREABMWojxLSkoSn8kCADExMYiMjIShoSEsLCyyPaZt27aoXbs2atWqBVNTU8TExCAgIAAVKlSAra0t1NTUMHLkSIwYMQIaGhqoXbs2nj17huvXr6NXr17o3Lkzxo8fD29vb0yYMAHPnj2Dn58funbtKg4HZadz586YOXMmWrZsiUmTJqFUqVJ48OABtm/fjhEjRqBUqVJ5GhLS19dHr1694O/vD0NDQ+jp6cHPzw+urq6oWbOmGGdra4vAwEC0atUKenp6qF+/PoYPHw5tbW1YWlri2LFjWLNmDWbNmiXT/p07d3D8+HHs27cv2/PfuXMHSUlJiI+Px/v37xEZGQkgM/n7dCiMiAqhgr5NiUjZfO225iNHjggAsry8vb2/eMzSpUuFBg0aCEZGRoKGhoZgYWEhdO/eXbh//74Yk5GRIUyZMkWwtLQU1NXV/9fOHeMmDANgGE0nUDakTJFgYMkxcgYugFhYuAEXYOMMOV5mJqbk79RKIAbapa703mpbtjx9i53NZpPL5fI9/u6z5mfjOGa/36dpmiwWi2y32xyPx9xutx/dy5f7/Z7T6ZTVapW6rrPb7TKO48OcqqoyDMPDGQ6HQ9q2zXK5TNd1uV6vmef5Yd35fM56vc40TS/37vv+5d3/9pk28H98JMkftRIAwFv8wwIAFE+wAADFEywAQPEECwBQPMECABRPsAAAxRMsAEDxBAsAUDzBAgAUT7AAAMUTLABA8T4BVhE0j2t2HPkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cm3=confusion_matrix(y_test, y_pred)\n",
        "labels = ['True Negative','False Positive','False Negative','True Positive']\n",
        "make_confusion_matrix(cm3,\n",
        "                      group_names=labels,\n",
        "                      #categories=categories,\n",
        "                      cmap='Blues')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2-0bwhNvqdRv"
      },
      "source": [
        "## Model evaluation criterion\n",
        "\n",
        "### Model can make wrong predictions as:\n",
        "* Predicting a transaction is fraud  and the transaction  is not fraud\n",
        "* Predicting a transaction  is not fraud and  transaction is  fraud\n",
        "\n",
        "### Which case is more important?\n",
        "* Predicting that transaction is not fraud but it is Fraud. It might enable lot of criminal activities and heavy loss to the bank\n",
        "\n",
        "### How to reduce this loss i.e need to reduce False Negative?\n",
        "* Company  would want `Recall` to be maximized, greater the Recall higher the chances of minimizing false Negative. Hence, the focus should be on increasing Recall or minimizing the false Negative or in other words identifying the True Positive(i.e. Class 1) so that the Company can identify the fraud transaction."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SoSt0QMfrr71"
      },
      "source": [
        "**Conclusion:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kvi8ppGVHd8h"
      },
      "source": [
        "- while only 5 regular transactions are wrongly predicted as fraudulent, the model only detects 81% of the fraudulent transactions. As a consequence 19 fraudulent transactions are not detected (False Negatives).\n",
        "\n",
        "- Let's see if we can improve this performance with other machine learning / deep learning models in the rest of the notebook."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4dDQSmsyHd8l"
      },
      "source": [
        "## Let's now explore Neural Network models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7m2DeMC1Hd8m"
      },
      "source": [
        "## Deep neural network\n",
        "\n",
        "Model-1\n",
        "\n",
        "- We will use a simple NN made of 5 fully-connected layers with ReLu activation. The NN takes a vector of length 29 as input. This represents the information related to each transactions, ie each line with 29 columns from the dataset. For each transaction, the final layer will output a probability distribution (sigmoid activation function) and classify either as not fraudulent (0) or fraudulent (1).\n",
        "- a dropout step is included to prevent overfitting.\n",
        "\n",
        "\n",
        "\n",
        "**Dropout**\n",
        "\n",
        "Dropout is a regularization technique for neural network models proposed by Srivastava, et al. in their 2014 paper Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Dropout is a technique where randomly selected neurons are ignored during training. They are “dropped-out” randomly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3KKgqAzrhpiH"
      },
      "source": [
        "### Creating a model\n",
        "\n",
        "Keras model object can be created with Sequential class\n",
        "\n",
        "At the outset, the model is empty per se. It is completed by adding additional layers and compilation\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yPZavT1Shw79"
      },
      "source": [
        "### Adding layers [layers and activations]\n",
        "\n",
        "Keras layers can be added to the model\n",
        "\n",
        "Adding layers are like stacking lego blocks one by one\n",
        "\n",
        "It should be noted that as this is a classification problem, sigmoid layer (softmax for multi-class problems) should be added\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DWTqXfxdh7e7"
      },
      "outputs": [],
      "source": [
        "#initialize the model\n",
        "model = Sequential()\n",
        "# This adds the input layer (by specifying input dimension) AND the first hidden layer (units)\n",
        "model.add(Dense(units=16, input_dim = 29,activation='relu'))   # input of 29 columns as shown above\n",
        "# hidden layer\n",
        "model.add(Dense(units=24,activation='relu'))\n",
        "#Adding Dropout to prevent overfitting\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(24,activation='relu'))\n",
        "model.add(Dense(24,activation='relu'))\n",
        "# Adding the output layer\n",
        "# Notice that we do not need to specify input dim.\n",
        "# we have an output of 1 node, which is the the desired dimensions of our output (fraud or not)\n",
        "# We use the sigmoid because we want probability outcomes\n",
        "model.add(Dense(1,activation='sigmoid'))                        # binary classification fraudulent or not"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GBFDOzQrqJRB"
      },
      "source": [
        "### Model compile [optimizers and loss functions]\n",
        "\n",
        "Keras model should be \"compiled\" prior to training\n",
        "\n",
        "Types of loss (function) and optimizer should be designated\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vJ256xbFqK5w"
      },
      "outputs": [],
      "source": [
        "# Create optimizer with default learning rate\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aOMrVli5iTNR"
      },
      "source": [
        "###Let's print the summary of the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UNXlDahCHd8m",
        "outputId": "2d2c153d-6aee-4455-e8f5-0abe08ae8131"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 16)                480       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 24)                408       \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 24)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 24)                600       \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 24)                600       \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1)                 25        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2113 (8.25 KB)\n",
            "Trainable params: 2113 (8.25 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQ4kI81nHd8m"
      },
      "source": [
        "## Training [Forward pass and Backpropagation]\n",
        "\n",
        "Training the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "McRJfmJlHd8n",
        "outputId": "d7f55dad-fcec-4cee-9dfe-540458ce59bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "12152/12152 [==============================] - 66s 5ms/step - loss: 0.0083 - accuracy: 0.9990 - val_loss: 0.0025 - val_accuracy: 0.9995\n",
            "Epoch 2/10\n",
            "12152/12152 [==============================] - 62s 5ms/step - loss: 0.0042 - accuracy: 0.9993 - val_loss: 0.0024 - val_accuracy: 0.9993\n",
            "Epoch 3/10\n",
            "12152/12152 [==============================] - 63s 5ms/step - loss: 0.0043 - accuracy: 0.9993 - val_loss: 0.0021 - val_accuracy: 0.9995\n",
            "Epoch 4/10\n",
            "12152/12152 [==============================] - 62s 5ms/step - loss: 0.0040 - accuracy: 0.9993 - val_loss: 0.0024 - val_accuracy: 0.9993\n",
            "Epoch 5/10\n",
            "12152/12152 [==============================] - 58s 5ms/step - loss: 0.0038 - accuracy: 0.9993 - val_loss: 0.0021 - val_accuracy: 0.9995\n",
            "Epoch 6/10\n",
            "12152/12152 [==============================] - 59s 5ms/step - loss: 0.0036 - accuracy: 0.9993 - val_loss: 0.0026 - val_accuracy: 0.9994\n",
            "Epoch 7/10\n",
            "12152/12152 [==============================] - 58s 5ms/step - loss: 0.0037 - accuracy: 0.9993 - val_loss: 0.0023 - val_accuracy: 0.9994\n",
            "Epoch 8/10\n",
            "12152/12152 [==============================] - 62s 5ms/step - loss: 0.0034 - accuracy: 0.9994 - val_loss: 0.0024 - val_accuracy: 0.9995\n",
            "Epoch 9/10\n",
            "12152/12152 [==============================] - 59s 5ms/step - loss: 0.0035 - accuracy: 0.9993 - val_loss: 0.0027 - val_accuracy: 0.9995\n",
            "Epoch 10/10\n",
            "12152/12152 [==============================] - 62s 5ms/step - loss: 0.0033 - accuracy: 0.9994 - val_loss: 0.0025 - val_accuracy: 0.9995\n"
          ]
        }
      ],
      "source": [
        "#fitting the model\n",
        "history=model.fit(X_train,y_train,batch_size=15,epochs=10,validation_split=0.2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wSpNGpWIiaTw"
      },
      "source": [
        "Plotting the train and test loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "N3EZTH7yPYV1",
        "outputId": "74860620-c613-4215-da59-2e9789ce65b3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7d179a5eb190>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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vb2/IZDKJq3IsgiCgsbERVVVV8Pf3h0KhuKn7MdT0s6BBnogN9kFRjR4FxXW4Iz5E6pKIiKiPqNVqADAHG+odf39/88/yZjDUDICkIQEoqtEj79IVhhoiIicik8kQFhaGkJAQtLW1SV2OQ3J3d7/pHhoThpoBkDQkAP8+VMp5NURETkqhUPTZGzP1HicKDwDTcQmFJXVoM/T8pFMiIiLqOYaaATBs8CD4Kd3Q3GbEyQqd1OUQERE5JYaaASCXy8yroDgERURE1D8YagaI+XBLhhoiIqJ+wVAzQJKGBAIA8hlqiIiI+gVDzQBJiFJBIZehQtuMsromqcshIiJyOgw1A8Tbww2jwv0AcF4NERFRf2CoGUDjTedAXbwicSVERETOh6FmAJlXQBWzp4aIiKivMdQMoOQYMdScrKiHvqVd4mqIiIicC0PNAApTeSFcpYTBKOBwSZ3U5RARETkVhpoBlhQjLu3mfjVERER9i6FmgCVF+wPgCigiIqK+xlAzwJI7emryi2thNAoSV0NEROQ8GGoGWLzaF94eCtQ3t+NsVYPU5RARETkNhpoB5qaQIzHKHwCHoIiIiPoSQ40EOg+35CZ8REREfYWhRgKmUMPDLYmIiPoOQ40ExkUHQCYDLl5uRHV9i9TlEBEROQWGGgmovNxxa4gvAHEVFBEREd08hhqJjDedA8UhKCIioj7BUCORJIYaIiKiPsVQI5HkjlBztFSL5jaDxNUQERE5PoYaiQwJ8kaQjwdaDUYcL9dKXQ4REZHDY6iRiEwm69yv5iKHoIiIiG4WQ42EOK+GiIio7/Qq1GzYsAExMTFQKpVISUnBgQMHum2/bds2xMfHQ6lUYsyYMdi9e7fF1wVBwPLlyxEWFgYvLy+kpaXh7Nmz5q//5z//gUwms/o4ePBgb16CXUiO6Qw1gsDDLYmIiG6GzaFm69atyMrKwooVK5Cfn4+EhASkp6ejqqrKavt9+/Zh5syZmDt3LgoKCpCRkYGMjAwcO3bM3Gb16tVYt24dNm7ciNzcXPj4+CA9PR3Nzc0AgNTUVFRUVFg8/vu//xuxsbFITk7u5UuX3qhwFTwUclzWt+LS5UapyyEiInJoMsHGLoKUlBRMmDAB69evBwAYjUZERUVh3rx5WLx4cZf2mZmZ0Ov12LVrl/napEmTkJiYiI0bN0IQBISHh+P555/HwoULAQBarRahoaF47733MGPGjC73bGtrQ0REBObNm4eXXnqpR3XrdDqoVCpotVr4+fnZ8pL71UN/3YdDl2rx5iMJeDgpUupyiIiI7Iot79829dS0trbi0KFDSEtL67yBXI60tDTk5ORYfU5OTo5FewBIT083ty8qKoJGo7Foo1KpkJKSct177ty5E5cvX8acOXOuW2tLSwt0Op3Fwx5xXg0REVHfsCnU1NTUwGAwIDQ01OJ6aGgoNBqN1edoNJpu25s+2nLPd955B+np6YiMvH7PxqpVq6BSqcyPqKio7l+cRHi4JRERUd9wuNVPpaWl+PLLLzF37txu2y1ZsgRardb8KCkpGaAKbTM+Wgw1Z6rqoW1qk7gaIiIix2VTqAkODoZCoUBlZaXF9crKSqjVaqvPUavV3bY3fezpPd99910EBQXhvvvu67ZWT09P+Pn5WTzs0WBfT8QEeUMQgAIebklERNRrNoUaDw8PJCUlYe/eveZrRqMRe/fuxeTJk60+Z/LkyRbtAWDPnj3m9rGxsVCr1RZtdDodcnNzu9xTEAS8++67mD17Ntzd3W0p3a7xcEsiIqKbZ/PwU1ZWFv7+97/j/fffx8mTJ/HMM89Ar9ebJ+3Onj0bS5YsMbefP38+srOzsWbNGpw6dQovv/wy8vLy8NxzzwEQd9ZdsGABVq5ciZ07d+Lo0aOYPXs2wsPDkZGRYfG9v/nmGxQVFeG///u/b+Il2x9OFiYiIrp5brY+ITMzE9XV1Vi+fDk0Gg0SExORnZ1tnuhbXFwMubwzK6WmpmLz5s1YtmwZli5diri4OOzYsQOjR482t1m0aBH0ej2eeuop1NXVYcqUKcjOzoZSqbT43u+88w5SU1MRHx/f29drl5KHBAIACkvq0G4wwk3hcFOdiIiIJGfzPjWOyl73qQEAo1FAwitfob65HbvmTcHoCJXUJREREdmFftunhvqHXC4zr4LKu3hF4mqIiIgcE0ONnTDPqymuk7YQIiIiB8VQYyeSTaGGPTVERES9wlBjJxKi/KGQy1CubUZ5XZPU5RARETkchho74ePphhFhvgC4tJuIiKg3GGrsSFI096shIiLqLYYaO5IUI+5Xk8/jEoiIiGzGUGNHTCugjpfr0NjaLnE1REREjoWhxo5E+HshTKWEwSjgcIlW6nKIiIgcCkONnek83JJLu4mIiGzBUGNnOFmYiIiodxhq7ExyTGeoMRpd4lguIiKiPsFQY2dGhPnBy10BXXM7zlc3SF0OERGRw2CosTPuCjkSosRTuvM4BEVERNRjDDV2yHy4JUMNERFRjzHU2KHkIeImfAw1REREPcdQY4fGRfsDAIpq9Ljc0CJtMURERA6CocYO+Xt7IC5kEAD21hAREfUUQ42dMs+r4TlQREREPcJQY6fMoeYiQw0REVFPMNTYKVOoOVKmRUu7QeJqiIiI7B9DjZ2KDfZBoI8HWtuNOF6uk7ocIiIiu8dQY6dkMhnGR3MIioiIqKcYauwYN+EjIiLqOYYaO2Y63DLvUi0EgYdbEhERdYehxo6NiVDBXSFDTUMLSq40SV0OERGRXWOosWNKdwVGR5gOt7wicTVERET2jaHGziVFc14NERFRTzDU2DnTvBqGGiIiou4x1Ni58R0roE5X1kPX3CZxNURERPaLocbOhfgqER3oDUEACorrpC6HiIjIbjHUOADuV0NERHRjDDUOoDPUcAUUERHR9TDUOABTqCksrkO7wShxNURERPaJocYB3BrqC19PN+hbDThdWS91OURERHaJocYBKOQyJEb7A+C8GiIiouthqHEQnCxMRETUPYYaB5E8JBAAkHeRoYaIiMgahhoHkRjtD7kMKKtrgkbbLHU5REREdoehxkEM8nRDvNoPAIegiIiIrGGocSCcV0NERHR9DDUOpPNwS27CR0REdC2GGgcyPloMNcfLdWhqNUhcDRERkX3pVajZsGEDYmJioFQqkZKSggMHDnTbftu2bYiPj4dSqcSYMWOwe/dui68LgoDly5cjLCwMXl5eSEtLw9mzZ7vc5/PPP0dKSgq8vLwQEBCAjIyM3pTvsCIDvBDq54l2o4DDpXVSl0NERGRXbA41W7duRVZWFlasWIH8/HwkJCQgPT0dVVVVVtvv27cPM2fOxNy5c1FQUICMjAxkZGTg2LFj5jarV6/GunXrsHHjRuTm5sLHxwfp6elobu5c5bN9+3b8+te/xpw5c3D48GH89NNPeOyxx3rxkh2XTCbjvBoiIqLrkAmCINjyhJSUFEyYMAHr168HABiNRkRFRWHevHlYvHhxl/aZmZnQ6/XYtWuX+dqkSZOQmJiIjRs3QhAEhIeH4/nnn8fChQsBAFqtFqGhoXjvvfcwY8YMtLe3IyYmBr///e8xd+7cXr1QnU4HlUoFrVYLPz+/Xt3DHrzzYxFe3XUCd8aH4B9PTJC6HCIion5ly/u3TT01ra2tOHToENLS0jpvIJcjLS0NOTk5Vp+Tk5Nj0R4A0tPTze2Lioqg0Wgs2qhUKqSkpJjb5Ofno6ysDHK5HOPGjUNYWBjuuecei96ea7W0tECn01k8nIGppya/uBZGo015lIiIyKnZFGpqampgMBgQGhpqcT00NBQajcbqczQaTbftTR+7a3PhwgUAwMsvv4xly5Zh165dCAgIwO23344rV6yvBFq1ahVUKpX5ERUVZctLtVujwv2gdJejrrENF2oapC6HiIjIbjjE6iej0QgAePHFF/HQQw8hKSkJ7777LmQyGbZt22b1OUuWLIFWqzU/SkpKBrLkfuOukGNspD8AzqshIiK6mk2hJjg4GAqFApWVlRbXKysroVarrT5HrVZ32970sbs2YWFhAICRI0eav+7p6YmhQ4eiuLjY6vf19PSEn5+fxcNZcLIwERFRVzaFGg8PDyQlJWHv3r3ma0ajEXv37sXkyZOtPmfy5MkW7QFgz5495vaxsbFQq9UWbXQ6HXJzc81tkpKS4OnpidOnT5vbtLW14eLFixgyZIgtL8EpJHeEmjyGGiIiIjM3W5+QlZWFxx9/HMnJyZg4cSLWrl0LvV6POXPmAABmz56NiIgIrFq1CgAwf/58TJ06FWvWrMG0adOwZcsW5OXlYdOmTQDEZcoLFizAypUrERcXh9jYWLz00ksIDw8370Pj5+eH//mf/8GKFSsQFRWFIUOG4I033gAAPPLII33xc3Aopk34LlTrcUXfikAfD4krIiIikp7NoSYzMxPV1dVYvnw5NBoNEhMTkZ2dbZ7oW1xcDLm8swMoNTUVmzdvxrJly7B06VLExcVhx44dGD16tLnNokWLoNfr8dRTT6Gurg5TpkxBdnY2lEqluc0bb7wBNzc3/PrXv0ZTUxNSUlLwzTffICAg4GZev0MK8PHAsME+OF+tR/6lWqSNDL3xk4iIiJyczfvUOCpn2afGZNG/D+OjvFI8c/sw/O/d8VKXQ0RE1C/6bZ8ash/JQwIBAIcucl4NERERwFDjsMZ3TBY+XFqH1najxNUQERFJj6HGQQ0b7AN/b3e0tBtxvFwrdTlERESSY6hxUDKZDEnR3K+GiIjIhKHGgSXFMNQQERGZMNQ4MFNPTd6lWrjIIjYiIqLrYqhxYAlR/nCTy1Bd34LS2iapyyEiIpIUQ40DU7orMCpCBYBDUERERAw1Do6ThYmIiEQMNQ4uOYaHWxIREQEMNQ4vqWMTvtMaHeqb2ySuhoiISDoMNQ4u1E+JyAAvGAWgsKRO6nKIiIgkw1DjBEy9NZxXQ0REroyhxgkkM9QQEREx1DgD0+GWBcV1MBi5CR8REbkmhhonEK/2g4+HAg0t7TitqZe6HCIiIkkw1DgBhVyGcab9aoo5BEVERK6JocZJmCcLX7wicSVERETSYKhxEuZQw54aIiJyUQw1TmJctD9kMqDkShOqdM1Sl0NERDTgGGqchK/SHcNDfQFwaTcREbkmhhonYhqC4jlQRETkihhqnIjpcEv21BARkStiqHEiSdGBAIDj5Vo0txkkroaIiGhgMdQ4kahALwz29USbQcCRUq3U5RAREQ0ohhonIpPJkBTNISgiInJNDDVOpnNeDTfhIyIi18JQ42TGX3VityDwcEsiInIdDDVOZnS4Ch5uctQ2tuFCjV7qcoiIiAYMQ42T8XCTIyFSBYDzaoiIyLUw1DihpCHi0u5DFxlqiIjIdTDUOCEebklERK6IocYJjY/2BwCcq2pAXWOrtMUQERENEIYaJxQ0yBNDg30AAPnsrSEiIhfBUOOkTEu78zivhoiIXARDjZNKHsKdhYmIyLUw1Dgp02Thw6V1aDMYJa6GiIio/zHUOKlhgwdB5eWO5jYjTpTrpC6HiIio3zHUOCm5XGZeBcUhKCIicgUMNU4sOaZjEz6GGiIicgEMNU5sfHTHCqhLV3i4JREROT2GGieWGOUPhVyGSl0LyuqapC6HiIioX/Uq1GzYsAExMTFQKpVISUnBgQMHum2/bds2xMfHQ6lUYsyYMdi9e7fF1wVBwPLlyxEWFgYvLy+kpaXh7NmzFm1iYmIgk8ksHn/4wx96U77L8PJQYFS4HwAOQRERkfOzOdRs3boVWVlZWLFiBfLz85GQkID09HRUVVVZbb9v3z7MnDkTc+fORUFBATIyMpCRkYFjx46Z26xevRrr1q3Dxo0bkZubCx8fH6Snp6O5udniXq+88goqKirMj3nz5tlavstJ4n41RETkImwONW+99RaefPJJzJkzByNHjsTGjRvh7e2Nf/zjH1bb//nPf8bdd9+NF154ASNGjMCrr76K8ePHY/369QDEXpq1a9di2bJluP/++zF27Fh88MEHKC8vx44dOyzu5evrC7VabX74+PjY/opdDEMNERG5CptCTWtrKw4dOoS0tLTOG8jlSEtLQ05OjtXn5OTkWLQHgPT0dHP7oqIiaDQaizYqlQopKSld7vmHP/wBQUFBGDduHN544w20t7fbUr5LMoWakxU6NLTw50VERM7LzZbGNTU1MBgMCA0NtbgeGhqKU6dOWX2ORqOx2l6j0Zi/brp2vTYA8Nvf/hbjx49HYGAg9u3bhyVLlqCiogJvvfWW1e/b0tKClpYW8+c6nWtuQBem8kKEvxfK6ppwuKQOt90SLHVJRERE/cKmUCOlrKws85/Hjh0LDw8PPP3001i1ahU8PT27tF+1ahV+//vfD2SJdmv8kACU1TUh72ItQw0RETktm4afgoODoVAoUFlZaXG9srISarXa6nPUanW37U0fbbknAKSkpKC9vR0XL160+vUlS5ZAq9WaHyUlJd2+NmdmPtyymPNqiIjIedkUajw8PJCUlIS9e/earxmNRuzduxeTJ0+2+pzJkydbtAeAPXv2mNvHxsZCrVZbtNHpdMjNzb3uPQGgsLAQcrkcISEhVr/u6ekJPz8/i4erMs2rKbhUC4ORm/AREZFzsnn4KSsrC48//jiSk5MxceJErF27Fnq9HnPmzAEAzJ49GxEREVi1ahUAYP78+Zg6dSrWrFmDadOmYcuWLcjLy8OmTZsAADKZDAsWLMDKlSsRFxeH2NhYvPTSSwgPD0dGRgYAcbJxbm4u7rjjDvj6+iInJwe/+93v8Ktf/QoBAQF99KNwXvFqX3h7KFDf0o6zVfWIV7tuwCMiIudlc6jJzMxEdXU1li9fDo1Gg8TERGRnZ5sn+hYXF0Mu7+wASk1NxebNm7Fs2TIsXboUcXFx2LFjB0aPHm1us2jRIuj1ejz11FOoq6vDlClTkJ2dDaVSCUDsddmyZQtefvlltLS0IDY2Fr/73e8s5tnQ9bkp5EiM8se+85dx6FItQw0RETklmeAihwLpdDqoVCpotVqXHIp666vTWPfNOTw4LgJvZSZKXQ4REVGP2PL+zbOfXMR4ThYmIiInx1DjIsZFB0AmAy5dbkR1fcuNn0BERORgGGpchMrLHbeG+ALgkQlEROScGGpcSFKM6RyoKxJXQkRE1PcYalxIUjQPtyQiIufFUONCTJvwHSvTobnNIHE1REREfYuhxoUMCfJG8CAPtBqMOFamlbocIiKiPsVQ40JkMhnGdwxB5XEIioiInAxDjYtJjuG8GiIick4MNS7GNK8m/1ItXGQzaSIichEMNS5mdIQKHgo5LutbcfFyo9TlEBER9RmGGhfj6abAmEgVAA5BERGRc2GocUHJQ7gJHxEROR+GGhdkPtySPTVEROREGGpckGmy8JnKBmgb2ySuhoiIqG8w1Lig4EGeiAnyBgDkl7C3hoiInANDjYtKGhIIADh0kaGGiIicA0ONi0rivBoiInIyDDUuyhRqCkvq0GYwSlwNERHRzWOocVFxIYPgq3RDU5sBpyrqpS6HiIjopjHUuCi5/OrDLblfDREROT6GGheWzHk1RETkRBhqXBgnCxMRkTNhqHFhCVH+UMhlqNA2o7yuSepyiIiIbgpDjQvz8XTDiDBfAEAee2uIiMjBMdS4uOSOTfjyGWqIiMjBMdS4OB5uSUREzoKhxsWZVkCdqNBB39IucTVERES9x1Dj4sL9vRCmUsJgFHC4tE7qcnpNEARU17egoLgWu46UI/fCZRiNgtRlERHRAHKTugCSXtKQAOw6UoFDF2uROixY6nKsMhoFVDe0oLS2CaW1jSitbUJZXZP587LaJrS0Wx73EBXohUeSovBQUiQi/L0kqpyIiAYKQw11hppi6ebVGIwCquqbUVbbZDW4lNU2ofUGZ1TJZIDaT4kwlRJnKxtQcqUJb+05gz99fQY/ixuMR5Mj8fORofB0UwzQqyIiooHEUEPmTfjyL9XCaBQgl8v6/HsYjAI0umaUXmm06GExBZfyuia0GbofLpLLgDCVFyICvBAZ4IVIfy9EBniLfw7whlqlhIebOKLa1GpA9vEKbD1Ygv0XruD7M9X4/kw1/L3dkZEYgUeTozAy3K/PXycREUlHJgiCS0w80Ol0UKlU0Gq18PPjm9nV2gxGjH35KzS1GfDV7/4Lt4b62nyPdoMRFdrmzuGgq4eG6ppQUdeM9hvMcVHIZQhTKc0hJTLACxFXBRe1Sgl3he3TwC5d1mNbXin+fagUGl2z+fqYCBUeTY7EfQkRUHm723xfIiLqf7a8fzPUEABgxqYc7L9wBa8/MAaPpUR3+XpruxEabbO5d6W07qqeltomaHTNMNwgtLgrZAj3NwWVa4JLoDdCfT3h1ovQ0lMGo4Afzlbjo7wS7DlRae4Z8nST4+7RajyaHIXJQ4P6paeKiIh6x5b3bw4/EQBxE779F64g+7gGchmums8iBpdKXTNutJjIQyE3Dw11CS4BXgjxVUIhYWBQyGW4fXgIbh8egiv6VnxSUIaPDpbgdGU9Pi0sx6eF5YgMECcXP5zMycVERI6GPTUEAPj2VBXmvHew2zaebqbQ4m01uAwe5OlwvRyCIOBIqRYf5ZVgZ2E56jv26pHJgCm3BCNzQhQnFxMRSYjDT1Yw1HSvuc2Aue8fRKWupSOoiGHl6uASPMgDMpljhRZbmCYXf3SwFDkXLpuvc3IxEZF0GGqsYKghW1y6rMe/D4mTiyu0nZOLR0f4ITM5ipOLiYgGCEONFQw11BumycXb8krx1QmNeXKxh5scd49SI3MCJxcTEfUnhhorGGroZl3Rt2JHQRk+yivBKU29+TonFxMR9R+GGisYaqivCIKAo2Xi5OJPC8tR32w5ufjR5Cj8YhQnFxMR9QWGGisYaqg/NLUa8OVxDbYeLLE6ufiR5EiMCldJWCERkWNjqLGCoYb6W/HlRmw7VGJ1cvGjyVG4n5OLiYhsZsv7d6+2b92wYQNiYmKgVCqRkpKCAwcOdNt+27ZtiI+Ph1KpxJgxY7B7926LrwuCgOXLlyMsLAxeXl5IS0vD2bNnrd6rpaUFiYmJkMlkKCws7E35RP0iOsgbz/9iOH783zvx3pwJmDYmDO4KGY6V6bD80+OY8PrX+O2HBfjxbA2MN9rJkIiIbGZzqNm6dSuysrKwYsUK5OfnIyEhAenp6aiqqrLaft++fZg5cybmzp2LgoICZGRkICMjA8eOHTO3Wb16NdatW4eNGzciNzcXPj4+SE9PR3Nzc5f7LVq0COHh4baWTTRgTDsXb5g1HrlL07D83pGIV/uitd2InYfL8at3cvGz1d9i7ddnUFrbKHW5REROw+bhp5SUFEyYMAHr168HABiNRkRFRWHevHlYvHhxl/aZmZnQ6/XYtWuX+dqkSZOQmJiIjRs3QhAEhIeH4/nnn8fChQsBAFqtFqGhoXjvvfcwY8YM8/O++OILZGVlYfv27Rg1ahQKCgqQmJjYo7o5/ERSutHk4keSo/CLkaFQunNyMRHR1fpt+Km1tRWHDh1CWlpa5w3kcqSlpSEnJ8fqc3JycizaA0B6erq5fVFRETQajUUblUqFlJQUi3tWVlbiySefxD//+U94e3vfsNaWlhbodDqLB5FUZDIZxkb6Y2XGGBx8MQ1rMxOROiwIggD8cLYGv/2wACmv78WKT4/heLlW6nKJiBySTaGmpqYGBoMBoaGhFtdDQ0Oh0WisPkej0XTb3vSxuzaCIOCJJ57A//zP/yA5OblHta5atQoqlcr8iIqK6tHziPqb0l2BjHER2PzkJHz/wh347Z23IEylhLapDe/nXMK0dT9i2rof8EHORVxuaJG6XCIih+EQp3S//fbbqK+vx5IlS3r8nCVLliArK8v8uU6nY7AhuxMd5I2sXwzH/LRb8eO5Gnx0sARfndDgeLk4uXj5p8eh9lMiPswXw9W+iFf7Il7th2GDB8HDrVfz/ImInJZNoSY4OBgKhQKVlZUW1ysrK6FWq60+R61Wd9ve9LGyshJhYWEWbUzzZb755hvk5OTA09PT4j7JycmYNWsW3n///S7f19PTs0t7InulkMsw9dbBmHrrYFzRt+LTwjJ8lFeKkxU6aHTN0Oia8Z/T1eb2bnIZhg72QbzarzPshPkhXKV06kNHiYi606uJwhMnTsTbb78NQJwoHB0djeeee+66E4UbGxvx2Wefma+lpqZi7NixFhOFFy5ciOeffx6A2KsSEhJinihcXFxsMSemvLwc6enp+Pe//42UlBRERkbesG5OFCZHpGtuwxlNPU5p6nFKo8Ppjj+bJhpfy1fphuGhvh09O36IV4s9PH5K7o9DRI7Jlvdvm4efsrKy8PjjjyM5ORkTJ07E2rVrodfrMWfOHADA7NmzERERgVWrVgEA5s+fj6lTp2LNmjWYNm0atmzZgry8PGzatAmAOIFywYIFWLlyJeLi4hAbG4uXXnoJ4eHhyMjIAABER0db1DBo0CAAwLBhw3oUaIgclZ/SHckxgUiOCTRfEwQB5dpmnNboxLBTUY/Tmnqcr25AfXM78i7VIu9SrcV9Ivy9MFxtOYQ1dLAP3BUcwiIi52FzqMnMzER1dTWWL18OjUaDxMREZGdnmyf6FhcXQy7v/IsyNTUVmzdvxrJly7B06VLExcVhx44dGD16tLnNokWLoNfr8dRTT6Gurg5TpkxBdnY2lEplH7xEIucik8kQ4e+FCH8v3BnfOcG+td2I89UN5t4cU+ip0DajrK4JZXVN+OZU535S7goZhg0e1NGb44f4MDHwqP04hEVEjonHJBA5OW1jG05XisNXYtgRHw0t1oewVF7u5h6d4R29OsPVvhjk6RDrCojIyfDsJysYaog6CYKA0tqmjl6dzrBzoUYPw3WOcIgM8EL8VfN0RoT5IibIB24cwiKifsRQYwVDDdGNNbcZLIawTMNYlTrr++V4uMlxy+BB5qGr4Wo/jFD7YrCvJ4ewiKhP9OtEYSJyXkp3BUaFqzAqXGVxvVbfajFP55SmHmcq69HYasCJCh1OVFju2B3g7W4euopX+2JEmDhnx9ONx0AQUf9hTw0R9YrRKKCkttE8dGUaxrpYo4e1ESwPhRwjwv2QGKlCQpQ/EqL8ERvkA7mcPTpEdH0cfrKCoYZoYDS3GXCuqgEnKzr31TlerkVtY1uXtr5KNyRE+iMhSoWESH8kRvkjxI+rHomoE0ONFQw1RNIRBAElV5pQWFqHwyXi42iZFi3txi5tw1TKjqAjhp0xESr4cvNAIpfFUGMFQw2RfWkzGHGmsh6FJaago8WZqnpc+zeSTAbcMngQEqLEnpzEKH8MV/ty40AiF8FQYwVDDZH9a2hpx7EyrRhySsWgU1bX1KWdh5sco8P9zEEnIdIfQ4K8ueLqKoIgQNfUjtK6RpTVipsvltY2oay2CY1tBtwVH4LpCeEI9PGQulSibjHUWMFQQ+SYquqbcaREi8OldeZeHZ2Vs69UXu5iyLlqInLwIOc91FYQBFQ3tJgDy7XBpayu6bobLJq4yWW4fXgIHhofgTtHhHB1GtklhhorGGqInIMgCLh4uRGHSzpCTmkdjpfr0Gplfk6Ev5fYk9MxEXl0hAo+DrIzcrvBCI2uuUtoMQeXuiarr/laQT4eiAgQj9WI7PjYbhSw83A5jpRqze1UXu64d2wYHhwfifHR/uz1IrvBUGMFQw2R82ptN+K0pt48EbmwpA7nqxu6zM+Ry4BbQ307go44bHVr6CBJdkVubjOgvO6awFLbhNKOjxpd83V3dzaRyQC1n1I8C8wcXLzNf47w94KXx/V7X85W1uPjgjLsKChDhbbZfD0myBsPjIvEA+MiEB3k3Wevmag3GGqsYKghci265jYcK9VeteJKC42uuUs7pbscYyJU5hVXiVH+iAzwuumeioaWdjGk1DZ2CSxldU2orre+S/PV3BUyhPt3BhRzWAnwQlSAN9QqZZ9MmDYYBeReuIzt+WX44lgFGlsN5q9NjAnEA+Mj8MsxYVB5cRUaDTyGGisYaohIo23umIAsDlsdKdGi3sq8k0AfDyRcNTcnIdLfYkKtIAiobWzrCCiNKK3tHBIyhRZtU9d9ea7l5a4Qh4QCvK7pbfFChL83Qnw9B3xzwsbWdnx5XIOP88vw47kac2+Xh5scPx8ZiofGR+BncYO5+owGDEONFQw1RHQto1HAhRr9Vaut6nCiQoc2Q9e/FqMDvREd6G2e59LUZrByR0sqL3dzWIm8JrBEBHghwNvdrueuaLTN+LSwDNvzS3GmssF8PcjHA/clhuOh8ZEYFe5n16+BHB9DjRUMNUTUEy3tBpysqDdvElhYWocL1XqrbQf7enaGFovwIoaWQQ4yKflGBEHA8XIdPs4vw87DZahpaDV/7dbQQXhwfCQyEiOgVnE3aOp7DDVWMNQQUW9pm9pwtFSLcm0TwlRKRAZ4I0ylhNLd9ZZAtxmM+PFsDbbnl+KrE5XmFVgyGXDbsGA8OD4C6aPUDrPKjOwfQ40VDDVERH1L29SGL45W4OOCMhwoumK+7u2hwN2j1XhwXCQmDwuCgoeW0k1gqLGCoYaIqP+UXGnEJwVl+Di/FBcvN5qvq/2UyBgXgQfHR+DWUF8JKyRHxVBjBUMNEVH/EwQB+cV1+Di/FLuOVFisAhsd4YcHx0XivsRwp97tuScEQUBNQysuXdajUteCW0MH4ZaQQZx0bQVDjRUMNUREA6ul3YBvT1Vhe34Zvj1VhfaOzQQVchluv3UwHhwfibtGhDjt3CSDUYBG14xLNXpcutKIi5f1uFTTiEtXGlF8WQ99q+UKuiAfD0waGoRJQwMxaWgQQ04HhhorGGqIiKRzRd+KXUfKsT2/DIdL6szXfZVu5uMZkocEONybeGu7EaW1YlAxhZdLlxtx6bIeJVea0Gq4/lEWMhkQrvJCsK8nTmt0aG6zbBs8yAMpsWLImTwsCMMGu2bIYaixgqGGiMg+nKtqwCcFpfgkvwzlVx3PEB3ojQc65t8MCfKRsEJLja3tKL7SiIs1jSi+osfFy40oviz2vJTXNaG70yzcFTJEBnhjSJA3hgR6Y0iQD2KCvREd6IOoQC/zIaIt7QYcKdVi//nL2F90GXkXa9HSbiXkDA3CpKFBmDw00GVCDkONFQw1RET2xWgUkFt0BR/nl2L30QqL4ZikIQF4cHwE7h0TDpV3/x/PoG1swyVzYLEMLlU3ONJC6S5HTJAPogO9ERPc8THIB0OCxKX/vTlbjCGnE0ONFQw1RET2q6nVgK9OaLA9vww/nq029354KOS4a0QIHhwfiam3DoaHW++OZxAEAdUNLR1DQ+LwkPnjlUbUNXZ/rIWf0g0xwT4YEuTT0ePS0esS5I3Bvp79HiZMISfn/GXsv3AZhy5ZCzme5vk4k4YGYdhgH6cIOQw1VjDUEBE5hipdMz4tLMf2/FKc0tSbrwf6eOC+hHA8OD4CYyJUXd6wDUYBFdqmLsHl4mU9iq80WhzUac1gX0/zEJEYWjqDi7+3R7fPHWgt7QYcLtFi/wXnDzkMNVYw1BAROZ4T5Tp8nF+KHYXlqGnoHAYaNtgH94wOQ0NLu7m3pbSHE3NNc1pirgou0YHeDr0LsinkmHtyimvNuz2bDPb1tFhdNTTYMUIOQ40VDDVERI6r3WDED+dq8El+Gb48runSK2HirpAhKqAzrFzd4xIZ0Dkx19k1txlwuKQO+y9ccfiQw1BjBUMNEZFzqG9uwxdHNdh/4TIG+3oiOsjbPFE33N+LxzJYYWvImTw0CLF2EnIYaqxgqCEiIhI1txlQWFJnnpOTX1zXJeSEmEOOGHSkCjkMNVYw1BAREVlna8iZPCwIMUHeAxJyGGqsYKghIiLqGVPIMU08Liiu6zIJO9Tv6p6c/gs5DDVWMNQQERH1TnObAQXFnT051ws56aPUeOX+0X36vW15/3bc9WtEREQ0IJTuCkweJg47AZ0hJ6cj5BQW16FS14JKXfMN7tS/GGqIiIjIJtZCTn5xreQnrjPUEBER0U1RuiuQOixY6jLQu0M0iIiIiOwMQw0RERE5BYYaIiIicgoMNUREROQUGGqIiIjIKTDUEBERkVNgqCEiIiKnwFBDRERETqFXoWbDhg2IiYmBUqlESkoKDhw40G37bdu2IT4+HkqlEmPGjMHu3bstvi4IApYvX46wsDB4eXkhLS0NZ8+etWhz3333ITo6GkqlEmFhYfj1r3+N8vLy3pRPRERETsjmULN161ZkZWVhxYoVyM/PR0JCAtLT01FVVWW1/b59+zBz5kzMnTsXBQUFyMjIQEZGBo4dO2Zus3r1aqxbtw4bN25Ebm4ufHx8kJ6ejubmzjMk7rjjDnz00Uc4ffo0tm/fjvPnz+Phhx/uxUsmIiIiZ2TzKd0pKSmYMGEC1q9fDwAwGo2IiorCvHnzsHjx4i7tMzMzodfrsWvXLvO1SZMmITExERs3boQgCAgPD8fzzz+PhQsXAgC0Wi1CQ0Px3nvvYcaMGVbr2LlzJzIyMtDS0gJ3d/cb1s1TuomIiByPLe/fNvXUtLa24tChQ0hLS+u8gVyOtLQ05OTkWH1OTk6ORXsASE9PN7cvKiqCRqOxaKNSqZCSknLde165cgX/+te/kJqaet1A09LSAp1OZ/EgIiIi52VTqKmpqYHBYEBoaKjF9dDQUGg0GqvP0Wg03bY3fezJPf/3f/8XPj4+CAoKQnFxMT799NPr1rpq1SqoVCrzIyoqqmcvkoiIiBySQ61+euGFF1BQUICvvvoKCoUCs2fPxvVGz5YsWQKtVmt+lJSUDHC1RERENJDcbGkcHBwMhUKByspKi+uVlZVQq9VWn6NWq7ttb/pYWVmJsLAwizaJiYldvn9wcDBuvfVWjBgxAlFRUdi/fz8mT57c5ft6enrC09PTlpdHREREDsymnhoPDw8kJSVh79695mtGoxF79+61GiwAYPLkyRbtAWDPnj3m9rGxsVCr1RZtdDodcnNzr3tP0/cFxLkzRERERDb11ABAVlYWHn/8cSQnJ2PixIlYu3Yt9Ho95syZAwCYPXs2IiIisGrVKgDA/PnzMXXqVKxZswbTpk3Dli1bkJeXh02bNgEAZDIZFixYgJUrVyIuLg6xsbF46aWXEB4ejoyMDABAbm4uDh48iClTpiAgIADnz5/HSy+9hGHDhnUbfIiIiMh12BxqMjMzUV1djeXLl0Oj0SAxMRHZ2dnmib7FxcWQyzs7gFJTU7F582YsW7YMS5cuRVxcHHbs2IHRo0eb2yxatAh6vR5PPfUU6urqMGXKFGRnZ0OpVAIAvL298fHHH2PFihXQ6/UICwvD3XffjWXLlnGIiYiIiAD0Yp8aR8V9aoiIiBxPv+1TQ0RERGSvGGqIiIjIKTDUEBERkVNgqCEiIiKnwFBDREREToGhhoiIiJwCQw0RERE5BYYaIiIicgoMNUREROQUbD4mgYiIiG6SoR0o+g9wZBtQ9B2gigQikoCIZCBiPBA4FJDJpK7S4TDUEBERDQRBAErzgKPbgOMfA/rqzq/VVwClBzs/9wq4KuQkiQ+foIGv2cEw1BAREfWn6jPA0Y/EMFN7sfO6dxAw6kEgfpoYcErzgLJDgOYI0FQLnPtafJgExFgGnbCxgLvXQL8au8YDLYmIiPqarhw4th048pEYUkzcfcQQM/ZRYOjtgMK963PbW4DKY0DpITHklOUBl891bSd3A0JHdYacyGQgKA6QO9d0WVvevxlqiIhu5PJ54Mul4r+kh6QC8fcCt/5CHCIgMmmqBU7sFHtkLv4IoOPtVe4G3JIGjHkEGH4P4OHTu3uXF1gGnauHr0w8/YDwcZ0hJyIJ8FXf1MuSGkONFQw1RGSztibgxz+JD0Or5ddkCiBmihhw4n8pTvQk19PWBJz5UgwyZ7+y/O8kerIYZEZm9P18GEEAtCWdQ1Zlh4DyQqC9qWtbv4jOeTmRyUBYIuA5qG/r6UcMNVYw1BCRTc58BXzxQucciGF3ApOfBYr3A6c+B6pOWLYPS+wIONOAkBFcueLMjAZxxdLRfwMnPwNadJ1fCxkpBpkxDwP+0QNbl6Fd/O/SFHLKDgFVJ2HuMTKRyYHBI8RVVqbenMEjAIV9TrNlqLGCoYaIeqSuBMheDJzaJX7uGw7c/br4r+2rg8rl88Dp3WLAKd4PizeOgFgx3MTfC0RNBOSKgXwF1B8EASjPF5dgH/8YaKjs/JpfpBhixj4qznGxJy31Yg+OaciqLB/QlXVt5+4tBvPIpM5eHVWUXYRzhhor+i3UVJ0Cdi8EfMMAv/DOh2844BcGDArlX2hEjqC9Fdi/AfhuNdDWKA4vTXoGuH0x4Onb/XMbqoEzX4gB5/y3gKGl82veweI8ivh7gaFTuVrF0dScE4eWjm4DrpzvvO4VAIx6QOyViZrkWJNzdRVX9ebkAWUFQGt913Y+IR1DVh0hJ3w84OU/8OUy1HTVb6Hm9BfAhzOu/3WZQgw2fmGWYccvojMI+YYBHt59VxMR2aboe+DzhUDNafHz6FRg2pu9+1d3SwNwfq8YcM5kA83azq+5+wC33MWJxvauXgMc+1hchl1e0HndzUucPzXmUXE40s1Duhr7ktEI1Jy5KuQcAiqPA8b2rm2D4jqHrCKSgNDR/f5zYKixot9Cja5CnOVeXy4u4dOVi5so6crF/zEEQ8/uo/S/qpcnrGuPj1+E+BegHXQFEjmNeg3w1TLxX+GA2Kvyi5VAwoy++X/N0AZc+kkMOKc+t+z250Rj+9KsFefHHN0mhlzBKF6XKYBhd4hBJv6XN+61cxZtTUDFkc6QU3bIco8dE4UHoB7bGXQik8XdkPsQQ40VksypMRrEJXe6MjH81Fdc9WdTCKoA2vQ9u5+bUlyaZ+7luabHxy9c7BWytu8BEXUytAMH/x/w7WsdkzxlwIS5wJ3L+q/3RBCAisLOgMOJxtJrbxFXLB35SFzBdPWwYeREcWhp1APAoMHS1WhP9DXinJyrg05TrWWbsETg6e/69Nsy1FhhtxOFBUH8F4Kpd+fqnh5deUf4qQAaa3p4QxkwKOSaXh4rw14OtJyPqE+VHAQ+/x2gOSp+Hj4emLZGXAkykDjRWBpGg9h7duQjcU+ZlquGB4OHA2MfAUY/DATGSlejoxAE4MoFy6ATOVGcWN+HGGqssNtQ01PtLR1hp6O3x9qf68utj4Fa4+lnZagrTOw6VI/lvxLJ+TReAb5eAeR/IH6uVAF3rQCSnpA+NHCicf8SBKDisDi0dGy7+HemiW84MOYhcXhJPYZ/99khhhorHD7U9ITRKPboWPTylHcd7rI2y/1qISOBhJni8kQH34mSCEYjUPBP4OuXgaYr4rXEWUDa7+1zWIETjfvOlQviXjJHt4kTYU2UKmDk/WKQGZIqfailbjHUWOESoaanmnVXTWa+ap5PXbE4Qc70r0SZHBh2F5A4Exg+DXBXSls3ka0qjgCfZ3WefhwyShxqGjJZ2rp6ihONbddQBRz/RBxeKsvrvO6mBG69W5wnE/dzwM1TuhrJJgw1VjDU9FBTrfgXQuGHQOmBzuueKmD0A0DCY+IYP7to+4/RKP5lfHq3+KY2MkMcFuTPvOeatcC3rwMHNomrWDwGAXcsBSY+5bgT6TnR+Ppa6oGTu8QemQv/6Vx1KpMDsVPFXuf4ewEl/+53RAw1VjDU9ELNOeDwh8DhLYCutPN64DBxeCphBuAfJV19zsTQBlz8QfyL+dTnQIPG8uuBQ4GxmeK/MoOGSVOjIxAEcbjhqxc7d3wd9QCQ/ro4b8yZuNJEY6NBXGLc3ixujNjWJD60JeIcmdNfiF8ziUjqWLn0IOAbKl3d1CcYaqxgqLkJRqP4hnv4Q+DEp+JfKgAAGRD7M7H3ZsR0rqiyVWujOHfi5Gdd5054+IpzJmQKcbt+888c4uqCsY8Cox8CvAMHvm57VX0a+Px58b9VQAzf094UN0lzdlJMNBaEjpDRZD1wtDWJhyu2NXVcb77Otcar7nPttY77XXuYqDVBt4hzZMY8zODvZBhqrGCo6SMtDcDJnUDh5s43D0CcwDjyfnH+zZApjrVl+EBqqhX3wzj5GXBur+WJut7B4r+uR0wHYv+rc8y/pUEMNke2dnStd2wKJncX5waMzRTnCrjqnKdWPfD9G8C+9YCxTZw78V8LgdTfuua8iZ5MNB52hzg0c3UAsQgX1167KpRc3e7agxIHgsJTDGbu3uJGeLekiUEmfJxrDbm5EIYaKxhq+kHtJfGNtnAzUFvUeV0VDSRkikNU/BeTOAn79OdikLn4o+Wye/9oIH46MOJeICrlxsME9RpxeOXIls59VgBxztOo+8WAE53qGqFSEMQ37uzF4jAEIIa7e/4IBMRIWprd6G6icV+Su4khw03ZGTjclTe45t1xveNh9ZqX5XPdvFzjv22ywFBjBUNNPxIEoCRXDDfHP+nYobVDVIoYbkY9IMlBaJK5fF7sXTn5WefKG5OQkeJQwIh7b25PoMoT4tk0R7ZZznlSRYnzCRJmAIOH9/412LMrRcAXi8TdYAExSN/zR3EVEFl39UTjsnyxF8siSHQXOLwsP7/2mqNOviaHwFBjBUPNAGlrEv/SPPwhcP6bzqESNyUw/JdA4mPA0DsAhZu0dfY1QRB7TkxB5tqVKZETOoLM9L7vvTIaO3ZI3dKxQ+pVoTIsARg7Q5x/4wwTJtuagX3rgB/WiEMgcnfgtt8CP1vIQ2GJnBRDjRUMNRLQVYg9CYUfAtUnO68PUosTXRMfE5eeOiqjASg5IIaYU5+J+/yYyBTiJGrTEtuBWnnT1iSuBDnyEXBuT+dQl+lQvrGZYj0ePgNTT186txfYvVDcUA0Ql+r+8k1g8K3S1kVE/YqhxgqGGgmZur0LPxT3kTDt6gqIe2skPiaeteITJFWFPdfeKm5QeHKnuJxWX935NTevq3Z7TZd+ZZK+RhwOPLzFchMydx+xxyghUwwG9r7cV1sGfLlEXHkHiKH47tfF5bqcGErk9BhqrGCosRPtrcDZL8WAc/bLzp4EubsYBBJmAnG/ANw8pK3zai0NwLmvxR6Zs19ZDu94qoDhd4tB5pa77LcH5PJ5cVL3ka1A7cXO64PU4sqRsZn2d+6NoQ3Y/1fgP38QT7KXKYCUp4Hbl3ATNSIXwlBjBUONHdLXiCt5Dm8WD5sz8Q4Se24SZ4o9OVK80TZeEYdxTn4mzg26et+PQaGdG5vF/My+AtiNCII4ZHZkK3D8Y3GJuUnISHFYcMyjgCpCuhoB4NI+YFdW57BlVIp4vIF6jLR1EdGAY6ixgqHGzlWeEMPNkY86d4IFgMEjxHAzNrP/D9fUloqTnE9+Jr6pmrZaB8QdWkfcKy6/jpzgHMtK21vFeTeHt4j7mZg3OJOJZwolzABG3DewvSINVcCe5eJEc0AMuD9/Rdzg0Rl+5kRkM4YaKxhqHIShHbjwrbg8/NTn1xyueac4PBU/re92Ra05K4aYk58B5fmWXwsd0xFk7gVCR9nX0Exfa6oV56wc+UhcSWViWrU2NlMcXuuvpbtGA5D3D2Dvq0CLFoAMSHoCuGu59HOTiEhSDDVWMNQ4oKa6jomuH4r74JjczOGapknLJzuWXtecvuqLMnGYwxRkAmP76IU4mNpL4oTuI1uBmjOd172DxKXhY2cAEeP7LuSVHRKHmioKxc/DEoBpfwIik/rm/kTk0BhqrGCocXCXz3cermnaPRa46nDNTHF3XmuMBqA4p+OwyF2Wz5e7i0cSjLgXGD7NOfZy6SumAHh4K3Ds35YrvYJu6Txgs7fhr6kW2PsKkPcuAEEMq3e9BCT/xv5XZBHRgGGosYKhxklYHK65U1wVYxLzM3F5+Ij7xG3bi74Te2NO7wYaL3e2c/cWz4sZcZ94dpIr7XTcW6ZhwSNbxXB49ZlVUZPECcajHujZUJHRKP7+9iwHGmvEa2NnAL94FRgU0j/1E5HDYqixgqHGCXV3uKZMBrQ2dF7zCgBuvUfcn2XYHX03J8cVtdSLwebIFuDCdzAfamhalj82U/xo7TDJyuPiSdrFOeLng+PFVU0xUwasfCJyLAw1VjDUOLm6YnGY5PDmzh1nfcM7T70ecpvzHc1gD3TlHQdsfgRUXnXAplIl9tyMzRR7ctr04n4z+/8qripz9wFuXwxMeobnBhFRt2x5/+7VGskNGzYgJiYGSqUSKSkpOHDgQLftt23bhvj4eCiVSowZMwa7d++2+LogCFi+fDnCwsLg5eWFtLQ0nD171vz1ixcvYu7cuYiNjYWXlxeGDRuGFStWoLW19dpvRa7KPxqY+gIwLx946j/i43fHgWlvAkOnMtD0F79w8eylZ34EntkH3DZfDJPNWuDQe8C79wDrEoD1E4Cc9WKgGXEf8NwB8XkMNETUh2wONVu3bkVWVhZWrFiB/Px8JCQkID09HVVVVVbb79u3DzNnzsTcuXNRUFCAjIwMZGRk4NixY+Y2q1evxrp167Bx40bk5ubCx8cH6enpaG5uBgCcOnUKRqMRf/vb33D8+HH86U9/wsaNG7F06dJevmxyWjIZED5OfHBfk4EVOkrcU+Z3x4DZO4HEWYCHr9iLVl8h7vUzazuQ+U9AFSl1tUTkhGwefkpJScGECROwfv16AIDRaERUVBTmzZuHxYsXd2mfmZkJvV6PXbt2ma9NmjQJiYmJ2LhxIwRBQHh4OJ5//nksXLgQAKDVahEaGor33nsPM2bMsFrHG2+8gb/+9a+4cOFCj+rm8BORBFobxY392lvE4Sh3pdQVEZGD6bfhp9bWVhw6dAhpaWmdN5DLkZaWhpycHKvPycnJsWgPAOnp6eb2RUVF0Gg0Fm1UKhVSUlKue09ADD6BgddfadHS0gKdTmfxIKIB5uENjH5Q3BWagYaI+plNoaampgYGgwGhoZZ7eYSGhkKj0Vh9jkaj6ba96aMt9zx37hzefvttPP3009etddWqVVCpVOZHVFRU9y+OiIiIHJrDTTooKyvD3XffjUceeQRPPvnkddstWbIEWq3W/CgpKbluWyIiInJ8NoWa4OBgKBQKVFZWWlyvrKyEWm39sEG1Wt1te9PHntyzvLwcd9xxB1JTU7Fp06Zua/X09ISfn5/Fg4iIiJyXTaHGw8MDSUlJ2Lt3r/ma0WjE3r17MXnyZKvPmTx5skV7ANizZ4+5fWxsLNRqtUUbnU6H3Nxci3uWlZXh9ttvR1JSEt59913IubKFiIiIrmLz5h1ZWVl4/PHHkZycjIkTJ2Lt2rXQ6/WYM2cOAGD27NmIiIjAqlWrAADz58/H1KlTsWbNGkybNg1btmxBXl6euadFJpNhwYIFWLlyJeLi4hAbG4uXXnoJ4eHhyMjIANAZaIYMGYI333wT1dWdZ9Bcr4eIiIiIXIvNoSYzMxPV1dVYvnw5NBoNEhMTkZ2dbZ7oW1xcbNGLkpqais2bN2PZsmVYunQp4uLisGPHDowePdrcZtGiRdDr9XjqqadQV1eHKVOmIDs7G0qluFpiz549OHfuHM6dO4fISMv9LVxkQ2QiIiK6AR6TQERERHar349JICIiIrI3DDVERETkFBhqiIiIyCkw1BAREZFTYKghIiIip8BQQ0RERE7B5n1qHJVp5TpP6yYiInIcpvftnuxA4zKhpr6+HgB4WjcREZEDqq+vh0ql6raNy2y+ZzQaUV5eDl9fX8hksj69t06nQ1RUFEpKSrixnx3g78O+8PdhX/j7sD/8nXRPEATU19cjPDz8huc+ukxPjVwu73LEQl/jaeD2hb8P+8Lfh33h78P+8HdyfTfqoTHhRGEiIiJyCgw1RERE5BQYavqAp6cnVqxYAU9PT6lLIfD3YW/4+7Av/H3YH/5O+o7LTBQmIiIi58aeGiIiInIKDDVERETkFBhqiIiIyCkw1BAREZFTYKi5SRs2bEBMTAyUSiVSUlJw4MABqUtyWatWrcKECRPg6+uLkJAQZGRk4PTp01KXRQD+8Ic/QCaTYcGCBVKX4tLKysrwq1/9CkFBQfDy8sKYMWOQl5cndVkuyWAw4KWXXkJsbCy8vLwwbNgwvPrqqz0634iuj6HmJmzduhVZWVlYsWIF8vPzkZCQgPT0dFRVVUldmkv67rvv8Oyzz2L//v3Ys2cP2tra8Itf/AJ6vV7q0lzawYMH8be//Q1jx46VuhSXVltbi9tuuw3u7u744osvcOLECaxZswYBAQFSl+aS/vjHP+Kvf/0r1q9fj5MnT+KPf/wjVq9ejbffflvq0hwal3TfhJSUFEyYMAHr168HIJ4vFRUVhXnz5mHx4sUSV0fV1dUICQnBd999h//6r/+SuhyX1NDQgPHjx+Mvf/kLVq5cicTERKxdu1bqslzS4sWL8dNPP+GHH36QuhQCcO+99yI0NBTvvPOO+dpDDz0ELy8v/N///Z+ElTk29tT0UmtrKw4dOoS0tDTzNblcjrS0NOTk5EhYGZlotVoAQGBgoMSVuK5nn30W06ZNs/j/hKSxc+dOJCcn45FHHkFISAjGjRuHv//971KX5bJSU1Oxd+9enDlzBgBw+PBh/Pjjj7jnnnskrsyxucyBln2tpqYGBoMBoaGhFtdDQ0Nx6tQpiaoiE6PRiAULFuC2227D6NGjpS7HJW3ZsgX5+fk4ePCg1KUQgAsXLuCvf/0rsrKysHTpUhw8eBC//e1v4eHhgccff1zq8lzO4sWLodPpEB8fD4VCAYPBgNdeew2zZs2SujSHxlBDTunZZ5/FsWPH8OOPP0pdiksqKSnB/PnzsWfPHiiVSqnLIYhBPzk5Ga+//joAYNy4cTh27Bg2btzIUCOBjz76CP/617+wefNmjBo1CoWFhViwYAHCw8P5+7gJDDW9FBwcDIVCgcrKSovrlZWVUKvVElVFAPDcc89h165d+P777xEZGSl1OS7p0KFDqKqqwvjx483XDAYDvv/+e6xfvx4tLS1QKBQSVuh6wsLCMHLkSItrI0aMwPbt2yWqyLW98MILWLx4MWbMmAEAGDNmDC5duoRVq1Yx1NwEzqnpJQ8PDyQlJWHv3r3ma0ajEXv37sXkyZMlrMx1CYKA5557Dp988gm++eYbxMbGSl2Sy7rrrrtw9OhRFBYWmh/JycmYNWsWCgsLGWgkcNttt3XZ4uDMmTMYMmSIRBW5tsbGRsjllm/BCoUCRqNRooqcA3tqbkJWVhYef/xxJCcnY+LEiVi7di30ej3mzJkjdWku6dlnn8XmzZvx6aefwtfXFxqNBgCgUqng5eUlcXWuxdfXt8tcJh8fHwQFBXGOk0R+97vfITU1Fa+//joeffRRHDhwAJs2bcKmTZukLs0lTZ8+Ha+99hqio6MxatQoFBQU4K233sJvfvMbqUtzbALdlLfffluIjo4WPDw8hIkTJwr79++XuiSXBcDq491335W6NBIEYerUqcL8+fOlLsOlffbZZ8Lo0aMFT09PIT4+Xti0aZPUJbksnU4nzJ8/X4iOjhaUSqUwdOhQ4cUXXxRaWlqkLs2hcZ8aIiIicgqcU0NEREROgaGGiIiInAJDDRERETkFhhoiIiJyCgw1RERE5BQYaoiIiMgpMNQQERGRU2CoISIiIqfAUENEREROgaGGiIiInAJDDRERETkFhhoiIiJyCv8fYnf5OvTgMGYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Capturing learning history per epoch\n",
        "hist  = pd.DataFrame(history.history)\n",
        "hist['epoch'] = history.epoch\n",
        "\n",
        "# Plotting accuracy at different epochs\n",
        "plt.plot(hist['loss'])\n",
        "plt.plot(hist['val_loss'])\n",
        "plt.legend((\"train\" , \"valid\") , loc =0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DL6Jtk6chF01"
      },
      "source": [
        "### Evaluation\n",
        "Keras model can be evaluated with evaluate() function\n",
        "\n",
        "Evaluation results are contained in a list\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-bE1P9FHd8n",
        "outputId": "99688eb1-098f-4791-d35c-ef62486863cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1781/1781 [==============================] - 5s 3ms/step - loss: 0.0038 - accuracy: 0.9994\n"
          ]
        }
      ],
      "source": [
        "score = model.evaluate(X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_9kLP8mGHd8n"
      },
      "source": [
        "- The model achieves an accuracy of 99.95% ! Is this a good performance ?\n",
        "- Remember that our dataset is significantly composed of non fraudulent samples with only 172 fraudulent transactions per 100,000. Consequently, a model predicting every transaction as 'non fraudulent' would achieve 99.83% accuracy despite being unable to detect a single fraudulent case !"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DSRFaU6_Hd8n",
        "outputId": "44173b41-fe7d-41dd-9b16-4ef04ae3a560"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.003810544265434146, 0.9993504285812378]\n"
          ]
        }
      ],
      "source": [
        "print(score)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EduZvXxIhKVl"
      },
      "source": [
        "Let's Print confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "dqbO565hHd8n",
        "outputId": "5bd5b22e-77f6-4c58-96b2-a0c52b9a4476"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1781/1781 [==============================] - 4s 2ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "## Confusion Matrix on unsee test set\n",
        "import seaborn as sn\n",
        "y_pred1 = model.predict(X_test)\n",
        "for i in range(len(y_test)):\n",
        "    if y_pred1[i]>0.5:\n",
        "        y_pred1[i]=1\n",
        "    else:\n",
        "        y_pred1[i]=0\n",
        "\n",
        "\n",
        "\n",
        "cm2=confusion_matrix(y_test, y_pred1)\n",
        "labels = ['True Negative','False Positive','False Negative','True Positive']\n",
        "#categories = [ 'Not_Fraud','Fraud']\n",
        "make_confusion_matrix(cm2,\n",
        "                      group_names=labels,\n",
        "                      #categories=categories,\n",
        "                      cmap='Blues')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H7PxiW8VHd8o"
      },
      "source": [
        "Detection of fraudulent transactions did not improve compared to the previous machine learning model ( Randomforest).\n",
        "\n",
        "- There are 100 fraudulent transactions in the test data and  yet 15 fraudulent transactions are not identified (false negative) which remains an issue. Our objective must be to detect as many fraudulent transactions as possible since these can have a huge negative impact.\n",
        "\n",
        "- 15 regular transactions are detected as potentially fraudulent by the model. These are false positive. This number is negligible.\n",
        "\n",
        "**Conclusion:**\n",
        "\n",
        "We must find ways to further reduce the number of false negative."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xczamwmqlv2Z"
      },
      "source": [
        "#Model-2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rXi21mjKhRmt"
      },
      "source": [
        "##Let's try another architecture to get the better Recall\n",
        "\n",
        "\n",
        "There are some basic Hyperparameters which can help to get the better model performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VEvElzWLjHNC"
      },
      "source": [
        "**Early stopping:**\n",
        "\n",
        "During training, the model is evaluated on a holdout validation dataset after each epoch. If the performance of the model on the validation dataset starts to degrade or no improvement (e.g. loss begins to increase or accuracy begins to decrease), then the training process is stopped after the certian interations.The model at the time that training is stopped is then used and is known to have good generalization performance.\n",
        "\n",
        "This procedure is called “early stopping” and is perhaps one of the oldest and most widely used forms of neural network regularization.\n",
        "\n",
        "**Weight Initialization**\n",
        "\n",
        "Weight initialization is an important consideration in the design of a neural network model.\n",
        "\n",
        "The nodes in neural networks are composed of parameters referred to as weights used to calculate a weighted sum of the inputs.\n",
        "\n",
        "Neural network models are fit using an optimization algorithm called stochastic gradient descent that incrementally changes the network weights to minimize a loss function, hopefully resulting in a set of weights for the mode that is capable of making useful predictions.\n",
        "\n",
        "This optimization algorithm requires a starting point in the space of possible weight values from which to begin the optimization process. Weight initialization is a procedure to set the weights of a neural network to small random values that define the starting point for the optimization (learning or training) of the neural network model.\n",
        "\n",
        "There are many WI techniques as follows:\n",
        "\n",
        "1) Random normal initialization\n",
        "\n",
        "2) Random Uniform initialization\n",
        "\n",
        "3) Xaviour Initialization\n",
        "\n",
        "4) He Initialization\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5UAI_84Dc8qm",
        "outputId": "d7006858-72ec-455b-99b9-1517a4176bd0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "261/261 [==============================] - 2s 5ms/step - loss: 0.0716 - accuracy: 0.9844 - val_loss: 0.0066 - val_accuracy: 0.9993\n",
            "Epoch 2/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0165 - accuracy: 0.9985 - val_loss: 0.0056 - val_accuracy: 0.9995\n",
            "Epoch 3/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0118 - accuracy: 0.9988 - val_loss: 0.0045 - val_accuracy: 0.9995\n",
            "Epoch 4/40\n",
            "261/261 [==============================] - 2s 6ms/step - loss: 0.0098 - accuracy: 0.9989 - val_loss: 0.0038 - val_accuracy: 0.9995\n",
            "Epoch 5/40\n",
            "261/261 [==============================] - 2s 6ms/step - loss: 0.0077 - accuracy: 0.9990 - val_loss: 0.0036 - val_accuracy: 0.9995\n",
            "Epoch 6/40\n",
            "261/261 [==============================] - 1s 5ms/step - loss: 0.0076 - accuracy: 0.9990 - val_loss: 0.0030 - val_accuracy: 0.9995\n",
            "Epoch 7/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0070 - accuracy: 0.9990 - val_loss: 0.0026 - val_accuracy: 0.9995\n",
            "Epoch 8/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0060 - accuracy: 0.9990 - val_loss: 0.0025 - val_accuracy: 0.9995\n",
            "Epoch 9/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0061 - accuracy: 0.9990 - val_loss: 0.0025 - val_accuracy: 0.9995\n",
            "Epoch 10/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0055 - accuracy: 0.9991 - val_loss: 0.0023 - val_accuracy: 0.9995\n",
            "Epoch 11/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0048 - accuracy: 0.9992 - val_loss: 0.0022 - val_accuracy: 0.9995\n",
            "Epoch 12/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0047 - accuracy: 0.9991 - val_loss: 0.0022 - val_accuracy: 0.9995\n",
            "Epoch 13/40\n",
            "261/261 [==============================] - 1s 5ms/step - loss: 0.0045 - accuracy: 0.9992 - val_loss: 0.0022 - val_accuracy: 0.9995\n",
            "Epoch 14/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0045 - accuracy: 0.9991 - val_loss: 0.0022 - val_accuracy: 0.9995\n",
            "Epoch 15/40\n",
            "261/261 [==============================] - 2s 6ms/step - loss: 0.0045 - accuracy: 0.9992 - val_loss: 0.0021 - val_accuracy: 0.9995\n",
            "Epoch 16/40\n",
            "261/261 [==============================] - 2s 6ms/step - loss: 0.0042 - accuracy: 0.9992 - val_loss: 0.0022 - val_accuracy: 0.9995\n",
            "Epoch 17/40\n",
            "261/261 [==============================] - 1s 5ms/step - loss: 0.0044 - accuracy: 0.9992 - val_loss: 0.0021 - val_accuracy: 0.9995\n",
            "Epoch 18/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0041 - accuracy: 0.9993 - val_loss: 0.0021 - val_accuracy: 0.9995\n",
            "Epoch 19/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0039 - accuracy: 0.9993 - val_loss: 0.0020 - val_accuracy: 0.9995\n",
            "Epoch 20/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0039 - accuracy: 0.9992 - val_loss: 0.0020 - val_accuracy: 0.9995\n",
            "Epoch 21/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0038 - accuracy: 0.9992 - val_loss: 0.0020 - val_accuracy: 0.9995\n",
            "Epoch 22/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0037 - accuracy: 0.9992 - val_loss: 0.0020 - val_accuracy: 0.9995\n",
            "Epoch 23/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0037 - accuracy: 0.9993 - val_loss: 0.0020 - val_accuracy: 0.9995\n",
            "Epoch 24/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0035 - accuracy: 0.9994 - val_loss: 0.0020 - val_accuracy: 0.9995\n",
            "Epoch 25/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0036 - accuracy: 0.9993 - val_loss: 0.0019 - val_accuracy: 0.9995\n",
            "Epoch 26/40\n",
            "261/261 [==============================] - 2s 6ms/step - loss: 0.0036 - accuracy: 0.9993 - val_loss: 0.0019 - val_accuracy: 0.9995\n",
            "Epoch 27/40\n",
            "261/261 [==============================] - 2s 6ms/step - loss: 0.0035 - accuracy: 0.9993 - val_loss: 0.0018 - val_accuracy: 0.9995\n",
            "Epoch 28/40\n",
            "261/261 [==============================] - 1s 5ms/step - loss: 0.0034 - accuracy: 0.9993 - val_loss: 0.0019 - val_accuracy: 0.9995\n",
            "Epoch 29/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0033 - accuracy: 0.9994 - val_loss: 0.0019 - val_accuracy: 0.9995\n",
            "Epoch 30/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0031 - accuracy: 0.9994 - val_loss: 0.0019 - val_accuracy: 0.9995\n",
            "Epoch 31/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0031 - accuracy: 0.9993 - val_loss: 0.0019 - val_accuracy: 0.9995\n",
            "Epoch 32/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0032 - accuracy: 0.9993 - val_loss: 0.0020 - val_accuracy: 0.9995\n",
            "Epoch 33/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0031 - accuracy: 0.9994 - val_loss: 0.0019 - val_accuracy: 0.9995\n",
            "Epoch 34/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0030 - accuracy: 0.9994 - val_loss: 0.0019 - val_accuracy: 0.9995\n",
            "Epoch 35/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0031 - accuracy: 0.9993 - val_loss: 0.0019 - val_accuracy: 0.9995\n",
            "Epoch 36/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0032 - accuracy: 0.9993 - val_loss: 0.0017 - val_accuracy: 0.9995\n",
            "Epoch 37/40\n",
            "261/261 [==============================] - 2s 6ms/step - loss: 0.0032 - accuracy: 0.9993 - val_loss: 0.0018 - val_accuracy: 0.9996\n",
            "Epoch 38/40\n",
            "261/261 [==============================] - 2s 6ms/step - loss: 0.0030 - accuracy: 0.9993 - val_loss: 0.0018 - val_accuracy: 0.9995\n",
            "Epoch 39/40\n",
            "261/261 [==============================] - 1s 5ms/step - loss: 0.0030 - accuracy: 0.9993 - val_loss: 0.0018 - val_accuracy: 0.9995\n",
            "Epoch 40/40\n",
            "261/261 [==============================] - 1s 4ms/step - loss: 0.0027 - accuracy: 0.9994 - val_loss: 0.0019 - val_accuracy: 0.9995\n"
          ]
        }
      ],
      "source": [
        "#Training Multi-layer perceptron with 2 hidden layers\n",
        "\n",
        "#adding earlystopping callback\n",
        "es= keras.callbacks.EarlyStopping(monitor='val_loss',\n",
        "                              min_delta=0,\n",
        "                              patience=15,\n",
        "\n",
        "                             verbose=0, mode='min', restore_best_weights= True)\n",
        "\n",
        "n_inputs = X_train.shape[1]\n",
        "Model2 = Sequential()\n",
        "#Initializing the weights uisng hue_normal\n",
        "Model2.add(Dense(65, input_shape=(n_inputs, ), kernel_initializer='he_normal', activation='relu'))\n",
        "Model2.add(Dropout(0.5))\n",
        "Model2.add(Dense(65, kernel_initializer='he_normal', activation='relu'))\n",
        "Model2.add(Dropout(0.5))\n",
        "Model2.add(Dense(1, kernel_initializer='he_normal', activation='sigmoid'))\n",
        "\n",
        "Model2.compile(optimizers.Adam(lr=0.001), loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "his_mod2= Model2.fit(X_train, y_train, validation_split=0.2, batch_size=700, epochs=40, callbacks=[es], shuffle=True, verbose=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SzY17xlajoW-"
      },
      "source": [
        "Plotting the train and validation loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "1cmXlKUJdRdT",
        "outputId": "753078cb-6d95-4087-b9f5-457d07058ad7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7d179a3de680>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Capturing learning history per epoch\n",
        "hist  = pd.DataFrame(his_mod2.history)\n",
        "hist['epoch'] = his_mod2.epoch\n",
        "\n",
        "# Plotting accuracy at different epochs\n",
        "plt.plot(hist['loss'])\n",
        "plt.plot(hist['val_loss'])\n",
        "plt.legend((\"train\" , \"valid\") , loc =0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3PddIYF0jdpr"
      },
      "source": [
        "Plotting confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "NJCViKgZfT7N",
        "outputId": "f350e047-3c45-4be4-99e9-d0c0e2d51325"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1781/1781 [==============================] - 3s 2ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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UX65UcehqayJksexvShrqqrh889FX90/PeI9Ji/di9sj2WL71RLbtVSv8AqfqZeHfx1Vcp6oigbaWBrS11FHBwhSPEl7JzDW5cO1BtnbaudhhUOeGKFPSGHo6mlBTVcHrlOxVlC95/ioZh85EoWPzWjh16S4szIvBsXpZeE/J+sFb2cocamqquLJrnMx+mupqeJn4+WEDUj61ajtg9NgJ4mdtHW0AwJnw01i5fCliYu4hJTkZmZmZSEtLw7t376CtrZ2tne6evTBx/BiE7PkHDo514OLaFKVKlwYA3Lp5E7duRWNfyH//2AsQIJVK8fjRI5QtVy5bewDw5s0bONasAUGQIi0tDTXs7DF+0hQkJyfj2dOnsK1hJxNfo4YdoqNvAgBau7fB7317o5VbU9StVx8NGv6KOnW//ovFlzRv0RKTxo/F6LEToKGhgX1796BpMzeoqGT9Dnsr+iYiL13E8qVLxH2k0i9fNyJFY8JSCKS8kx2WkUoF4JNMXE1NVfyzno4mAKDN4MV48jRRJi49PXdj1H/vPY+h3RtjVN+mePDkhcw2PW1NTFmyD7sOR2bbLzUtd+07VCuD1VM9MXnJPhwKj0JS8ju0d7XHkM9Udb5k874LmDWyHfz+3IKOzWri6q3HuH7nSVZfdTTx/n0m6nT5E5mfjOmnvM3db8ykHLS1tbPdEfT48SP4DPodHTp2hs8QX+gbGODSxQhMGDsaGRkZOf7DO9DLB83cWuDEsWM4efI4Fi+cjz9nzUFj5yZ4++4t2nXohC5du2fbr0SJEp/tm66uLjZt3QkVFRUUNzaGlpYWACA5Ofmr51XJpjL2/RuGkyeO42z4aYwcNhQOjnXw19z5X933cxr++hsEYQyOHzuKKlWq4mLEBYzw/2/+ytu3bzHQyweNnV2y7aupqfnNx/2Z/ezVkfzAhKUQev4qGZWtZH94Vq/4CzL+f1dP1L14pKZloJSZ4Rfnq3yJIAgYt2A3Nv3VF8u3npTZFnnzIcpbmuDew+c57nvrQQJKmhrCxKgInr7MqrLYVy4tE+NYvQxi415ixsqD4rrSJWTv+EjPeA9Vla+Pau45egVBYzvDpY4NOjariY0hZz/q6yOoqanCxKgITl26+9W26McSdf06pFIBw0aOEqsH/x74/HDQB5aWZWBpWQbdPXvCf7gf/tm5HY2dm6BSJRvcu3snz7dKq6io5LiPnp4ejE1MEHnpImrWqi2uv3TpIqpUrSYT17RZczRt1hzOLq4Y9HtfJCUmZhvy+TBnRSrN/GJ/NDU18ZuzC/aF7MHD2AewLFMGlWwqi9srVbLB/fsxBX5LeKHCfEVuTFgKoaPnb8HXszG6tKiNs1di0Ll5LdiUM8fl6KzhnuS3aZi7NgwzhnlARUUFpy/dhYGeFpxsy+F1Sio27Dn7lSNkOXDyOs5fe4A+HnXFxAMApi07gB3zBuBh3CvsPHQJUkFAtQolYVOuBCYuCkHYmZu49+gZlk/qjtHzdqGIjhYmeLUEkFVeB4A7sc9QyswI7V3tceH6AzSrXxmtGlWXOX7sk5ew/KUYqlX4BY+fJuJNShrSM7JXcN6mpmPPkcsYN6gFrMuYYsuB/54hcyf2Kf7eew4rJnfHqNk7EXnzEYwN9fCrQ0Vcu/UYB05ez9vFJ6VSqrQF3r/PwN8b1qHhr7/h0qUIbP3CPIzU1FTMnjUDTVxc8UvJkkiIj8f1a1fRuElWpaFXn37o3qUjpk2ZhLYe7aGto417d+8g/PRp/DFm3Gfb/ZKevfpg8cIFKFmqNKytrbFr5w5E37yJwBmzAABrg1fD2NgY1pUqQaKigtB/D6B4cWMU0dfP1paRUTFoaWnh1MkTMDU1g4am5mdvaXZr0RI+g37H3bu34dailcy2/gO9MNhrAEqUMIeziytUJCqIjr6Ju7dvwXuI7zedJ5G8mLAUQofCoxC4/ACmDnGHlqYa1v5zBhv3nkNlK3MxZuKiEDx/lYwRvZqgzNjOSHzzDpFRDzFj1cEvtJzdmHn/4OiaYdmO33bIEvzRvymG9WyCjPeZuHU/Aat3ngaQNWTVwW85Fo/rgpPrRyDm0Qv8MXcXdswfIA4Z7T12FQs2HMZs//bQ1FDDgZPXMX35fowe0Fw8zs6wSLRuXB0Hlg+Bob4O+o1bh/WfSbY27b+Af4IG4UTEbTyMfyWzrf+E9RjVtymm+7WBuUlRvEhMwbkrMdh//FqergUpn4rW1hg+MgCrVy7H/LmzYWdfE4OH+mFMgH+O8aoqKkhKTMSYAH+8ePEcRQ0N0djZBYO8sybwVqhojZXB67Bg/lz06tEFggCUKlUKrs2a59hebnTp1gPJycn4a+Z0vHzxEuXKlcP8oEWwsLAEkDWctHrVCsQ+eABVVRVUrlIVQUuWiRWjj6mpqcE/YAyWLl6IRUHzYWdfEyuD1+V43NoOjjAwMMD9mBg0d2sps61uvfqYv3AJli1eiNUrl0NNTQ2WZcqirUf7bz7Pnx2HhOQnEQrh4w+1a/C2ux+NU/WyOBzsB5uWExDzKOehJJLfq/NffnIy0c9I6zv86l5y0K58aefRIvd8aedHxAoLFYhWjaoh+W067sQ+RbnSxpg1oh1O//+2YiKiwoYVFvkxYaECoaerhSlD3FHKzBAvEpNx+Gw0Rs3O/i4YIiIigAkLFZCNIeewMeRcQXeDiOj7YIFFbkxYiIiIFIxDQvLjo/mJiIhI6bHCQqLRvzfHmAGyt2dGx8TDtu0U8bNDtTKY4NUCtapaIjNTiiu3HqPloIXie3ysSptgmq87nKqXhYa6Kq7dfoKJi0Jw/MJtsY13l7LfqdJj1GpsPfjf81E6NasJ357OsCplgqTkd/j31A38MXcXXibxcflUOKxcvgzz5/6Frt16YGTA6ILuDikYKyzyY8JCMq7feSK+/RgA3mf+97h6h2pl8E/QIMxa/S/8/tyK95lSVKvwS9arAP5vx/wBuBP7FM1+n493aRnw7tIIO+YPQOWWE2TeHdRv3DqEnr4hfk588078s1P1slgxuQdG/rUde49dwy8mBpg/uhMWje2MTsNXKOrUib6ba1evYNvWTahQoWJBd4W+EyYs8mPCQjI+vP04JzOGtcWiTUcxa3WouO72g6fin4sV1UV5CxMMnLgB125nvatn7Px/MKBjA9hYmSPhRbQYm/Tm3WeP41CtDB48eYFFfx8DADx48gIrt5/CsJ7Ocp8fUUF7m5KCAP8RGD9xCpYvXVzQ3SH6YXAOC8mwKm2Me/9OxY09E7B6qidKmRkCAIwN9VC7Whk8e5mMI8F+uH9oGv5dMQR1bMuK+75ITEF0TDy6tKgNHS0NqKqqoK9HPSS8eI1LN2TfKD03oAMeHp6OE+uGo0drR5ltZ6/EoKSZIVzr2QAATIyKoI2zLQ6cvAGiH920KZPQoEFDODrVKeiu0HckkUjyZfmZFWiF5fnz51i1ahXCw8MRHx8PADAzM0OdOnXQs2dPGBsbF2T3fjrnr91H/3HrcetBAsyKG2D0781waJUv7NtNRZmSxQFkzXMJmLMTV6IfoWuL2ti31Af27afhbuwzAIDbgCBsntMfz07NglQq4NmrZLT2WiQz5DNxUQiOnbuFt6npcHayxryAjtDT0RQrKuGX76HXH2uwbnpvaGmoQ11dFSHHrmLo9M3f/6IQ5aP9+/YiKuoGNm7eVtBdoe/t58418kWBJSznz5+Hq6srdHR04OzsjAoVKgAAEhISMH/+fEyfPh0HDx5EzZo1v9hOWloa0tLSZNYJ0kxIVFQV1vfC6t9T/1Uwrt1+gvNX7yN63yR4uNghOiYroVy5/STW7T4DALgc/Qi/1q4Iz9ZOGLdgNwBgTkAHPHv5Bs695+JdWjp6tqmD7fN+R71uMxH//DUAYPryA+JxLkc/go62Jnx7OIsJi3VZM8wa2Q6By/YjNDwKZsUNMG2oOxaM7oSBEzd+l2tBlN/i4+IwY/pULF2+CpqamgXdHaIfToElLD4+Pmjfvj2WLFmSrcwlCAIGDBgAHx8fhIeHf7GdwMBATJw4UWadqmktqJeo/Zk9KLeSkt9lPTq/lDGOnrsFAIi6Fy8TEx0TLw4b/Vq7AprXr4ISDUfiTUoqAGBo4BY0drRGt5YOMnNfPnb+6n380b8ZNNTVkJ7xHiN6uSA88i7mrA0DkJU8vX2XhrDVfpi4MERMfIh+JDduXMfLFy/QqX1bcV1mZiYiLpzHpr834Pylq1BV5S9ahdXPPpyTHwosYbl8+TKCg4Nz/I8okUjg6+uLGjVqfLWdgIAA+Pn5yawzqZ/zm1gpb3S1NVCmZHHE7z2HB09e4MnTRFSwNJGJsbIwESszOloaAACpVCoTI5UKX/yyVqtYEi+TUpCekfWmZh1tDbx/nykTk/n/O5H4pacflYOjI7bt2iOzbvzoAFiWLYteffoxWSnk+LNLfgWWsJiZmeHcuXOwtrbOcfu5c+dgamr61XY0NTWzlVc5HPRtAn3bYO/xq4h98hLmJgYYM8ANmVIpthzIej7KnDWHMGaAG67eeozL0Y/QraUDKlqaosuIlQCyJsu+ev0WKyb3wLRl+/EuNQO929aB5S/FcODkdQBA8wZVYFKsCM5duY/U9Aw0drTGyD4umPv/agoA7D12FYvGdkG/9vUQejoKJYobYOYID5y/eh9xz5K+/4Uhyge6unooX76CzDptHR0UNSiabT0VPsxX5FdgCcvw4cPRv39/REREoHHjxmJykpCQgLCwMCxfvhyzZs0qqO79lH4xLYq1gb1gZKCD56+ScTryHhr2+AvPXyUDAII2HoWWpjpmDPOAoYEOrt56jBYDg8Q3LL9ITEFr70WY4NUS+5cOhrqaCqLuxaO97zJcvfUYAJDxPhO/d2iAGcM8IJFIcPfhM/j/tQOrdpwW+7F+z1kU0dXCgI4NMd23LZKS3+HouWiMmffP978oRESkFCSCIAhfD1OMzZs3Y86cOYiIiEBmZtYQgKqqKuzt7eHn54cOHTp8U7vaNbzzs5tEhcar89mfMkz0s9P6Dr+6lx9x4OtBuXB7ZtN8aedHVKC3NXfs2BEdO3ZERkYGnj/P+i29ePHiUFdXL8huERER5SsOCclPKZ50q66ujhIlShR0N4iIiEhJKUXCQkREVJjxLiH5MWEhIiJSMOYr8uO7hIiIiEjpscJCRESkYCoqLLHIixWWn5yejiZmDvdA9L5JeBk+G0eC/WBvU1rcbmJUBMsmdsO9f6fixenZ+CdoEMqV/vpLKb27/IrLO8fiZfhs3N4/GTOGtYWmxn/58fDeLji5fgSenpyFB2GB2DK7H8pbyD5F989hbfH46J+4vX8yOjWTfadUW+ca2Db3dznPnihvNm3cgGZNfkOtGlXRtVN7XL1y5Yvx/x7cj9YtmqJWjarwcG+JE8ePyWw/FPovfu/XGw3qOKB65Yq4GRWVrY2ZfwaivlNtuDRuiL0hu7O17zNogPwnRgonkeTP8jNjwvKTWzyuC35ztEbvMWtQs8M0HAq/ib1LfGBubAAA2DKnP8qULI72Q5fCsfN0xMa9xL4lPuJj+HPSsWlNTB7cGtOW7odt2ykYMHED2rnaY5JPKzGmvp0Vlmw+joY9ZqHFwCCoqakiZLG32G7zBlXQoWlNtBy0EKPn7cKicV1QrKguAEBfTwsTvFvCd/oWBV4ZIlkH9u/DrBmB+H2QFzZt3YmKFa0x8Pc+ePHiRY7xkZcuYtSIYWjTth02b9uFRr81xlAfL9y+fUuMeffuLWrUsMNQv+E5tnH0yGHs3xuCJctXwtdvBCaOG4NXr14CAN68eYMF8+bijzHj8v9kiZQQE5afmJamOtwb22L03F04dfEu7j18jqlL9+Huw2fo174+rEqbwKFaGQyeugkRN2Jx+8FTDJ62GVqa6ujQzP6z7TpWL4PwyHvYfOACYuNeIuzMTWw5cAE1K1uIMa29F2H9nrOIuhePq7ceo//49Shdwgg1bEoBAKzLmOFExG1cvBGLLQci8DolFZbmxQAAU4e4Y/nWE3gY/0qxF4joI+vWrEbbdh3g3sYD5aysMGb8RGhpaWHXju05xm9YvxZ16tVHz959UbZcOXgPHopKNjbYtHG9GNOylTsGDPKGg5NTjm3E3LuLmrVro3KVqmjm1gK6enp4/OgRAGDOXzPRoWNnlDA3z/+TpXwnkUjyZZHH9OnTIZFIMHToUHFdamoqvLy8UKxYMejp6cHDwwMJCQky+8XGxsLNzQ06OjowMTHBiBEj8P79e5mYo0ePws7ODpqamrCyskJwcHC24y9cuBCWlpbQ0tKCg4MDzp07l6f+M2H5iampqkBNTRWp6Rky61PTMlCnRjlxCCc1/b+/mIIgID39PerYlvtsu2cux6CGTSkxQbH8pRhc61YW3yeUE309LQDAq6S3AIArtx7DrlJpFC2ijRqVSkFbUx13Hz5DHduyqFGpFBb+ffSbzpnoW2SkpyPqxnU4OtUR16moqMDRsQ6uXL6U4z5XIiPh6CibiNSpWw9XIiNzfdwKFa1x49o1vE5Kwo3r15CWmorSpS1wMeICbt64ji7dun/T+dD3V9BDQufPn8fSpUtRrVo1mfW+vr7Ys2cPtm7dimPHjuHJkydo21b2jeJubm5IT0/H6dOnsWbNGgQHB2PcuP8qezExMXBzc0OjRo0QGRmJoUOHom/fvjh48KAYs3nzZvj5+WH8+PG4ePEiqlevDldXVzx9+jTX58CE5SeW/DYNZy7fQ0C/ZihhbAAVFQk6Na8Fh2plYFZcH9H34xEb9xKTfVqhaBFtqKupYlhPZ5Q0M4RZcYPPtrv5wAVMXrwXYat98frcPESFTMTxiNuYuerfHOMlEglmDm+H05fu4sbdOADAofAo/L3vPE6uH4llE7uj37h1SHmXjnl/dILP1E3o374+Lu8ci8OrfVGprJlCrg/RB68SXyEzMxPFihWTWV+sWDHxKd2fev78OYoVK549/kXO8TmpW68+3Fq2QpeO7TB2dAAmT/sT2tramDp5IsaMn4gtm/5GKzdXeHbthDt3buf9xOi7KcgKS3JyMrp27Yrly5fD0NBQXJ+UlISVK1di9uzZ+O2332Bvb4/Vq1fj9OnTOHPmDADg33//xY0bN7B+/XrY2tqiWbNmmDx5MhYuXIj09HQAwJIlS1CmTBn89ddfqFSpEry9vdGuXTvMmTNHPNbs2bPRr18/9OrVCzY2NliyZAl0dHSwatWqXJ8HE5afXO8xayGRAPf+nYqks3Ph1bkhthy4AKlUwPv3UnQathxWFiaIOz4TL8Nno0HNCjhw8jqkgvSzbda3L48RvV0xJHAznLr8iY5+y9CsXmWM6pfzOzDmBnRAZasS6DFqtcz6qUv3oUrriajVYRp2H7mCEb1dcOTsTWS8z4R/36Zo3HsOVu8Mx4rJPfL1mhApk4FePgg5EIrtu/agsXMTrFyxDI6OTlBTU8PypYsRvO5vtGnXHmMC/Au6q/QdpKWl4fXr1zJLWlraF/fx8vKCm5sbnJ2dZdZHREQgIyNDZr21tTVKly6N8PBwAEB4eDiqVq0qvqAYAFxdXfH69Wtcv35djPm0bVdXV7GN9PR0REREyMSoqKjA2dlZjMkNJiw/uZhHz+HSdx6KOfmhfLOxqN99FtTVVBHzOOu3wEtRD+HYaTpM6w9HGZfRaO29CMUMdBHzKOeJhgAwfpAb/t57DsE7w3H9zhPsPnIF44L2YEQvl2y/Iczxb4/m9avAtd98PH6a+Nk2K1iaorNbLUxcFIIGNcvj1MU7eP4qGdv/vQg7m9LQ09HMl+tBlBPDooZQVVXNNsH2xYsXKF68eI77FC9eHC8+qaa8ePECxYvlHJ8bMffuYu+e3fDyGYLz58/BvmZNGBkZwcW1GaJuXEdKSvI3t02KlV8VlsDAQBgYGMgsgYGBnz3upk2bcPHixRxj4uPjoaGhgaJFi8qsNzU1RXx8vBjzcbLyYfuHbV+Kef36Nd69e4fnz58jMzMzx5gPbeQGExYCALxNTUf889coWkQbznUqIeToVZntr5NT8fxVMsqVNoadTWmEHP387ZzaWhqQSmVfAi6VZlVkPs5X5vi3R6vfqqPp7/Px4MnnEyAACBrTCf5/7UDKu3SoqqhAXU0VAMT/V1XhX2VSHHUNDVSyqYyzZ/77bVAqleLs2XBUq14jx32q2dri7P/L6h+cCT+Nara239QHQRAweeJ4DBs5Cjq6upBmSpHx/4mPHyZAZmZ+vvJJBSu/5rAEBAQgKSlJZgkICMjxmA8fPsSQIUOwYcMGaGlpfeczzn98cNxPztmpEiQS4Nb9pyhXyhjTfN1xKyYBa3dn/WBu61wDz14l42H8S1Qpb45ZI9phz9ErCDtzU2xjxeTuePI0CeMWZD0jYt/xaxjcrREuRz/Cuav3Ua6UMcYNbIF9x6+KiczcgA7o2Kwm2vsuQ3JKKkyLFQEAJCWnIjVNdhJwrzZ18PxVMvYdvwYACI+8h9G/N0ftqpZwqWuDG3fjkJT8TuHXin5u3T17Yewf/qhcuQqqVK2G9evW4N27d3BvkzVBcXTASJiYmGKI7zAAQNduPdCnZ3esCV6FBg0a4sD+fbh+7RrGTpgktpmUmIi4uDg8e5Y18fD+/RgAWdWZ4sayzzvasW0rDA2N8Guj3wAAtjXssGTRAly5HImTJ46jbDkr6OvrK/w6UMHS1NSEpmbuKsoRERF4+vQp7OzsxHWZmZk4fvw4goKCcPDgQaSnpyMxMVGmypKQkAAzs6y5gWZmZtnu5vlwF9HHMZ/eWZSQkAB9fX1oa2tDVVUVqqqqOcZ8aCM3mLD85Az0tDDJpxV+MS2Kl0lv8U9YJMYv3IP377N+UzMz1sefw9rCpFgRxD9/jQ0hZxG47IBMG6XMjGQqKtNXHIAgCBg/qAXMTQzw/FUy9h6/hglBe8SY3zs0AACErhgq01a/ceuwfs9Z8bOJURH493VFo56zxXUXrj/AvPVh2DF/IJ69fIN+49bl2/Ug+pymzZrj1cuXWBQ0H8+fP0NF60pYtHQFiv1/SCg+Lg4qkv8qfbY17BA4YxaC5s/FgrmzUdrCEnMXLET58hXEmKNHDmPcmP9+O/Yf7gsAGDDIGwO9fMT1L54/x4plS7Bmw9/iuqrVqqG7Zy94D/wdRsWMMHnqnwo7d5JfQbz8sHHjxrh6VbZa3qtXL1hbW8Pf3x+lSpWCuro6wsLC4OHhAQCIjo5GbGwsnP5/q72TkxOmTp2Kp0+fwsQk6+GeoaGh0NfXh42NjRizb98+meOEhoaKbWhoaMDe3h5hYWFwd3cHkFWhDAsLg7e3d67PRyIIgvD1sB+Ldo3cXwCin8mr80EF3QUipaP1HX51t5t0OF/auTjuN7n2//XXX2Fra4u5c+cCAAYOHIh9+/YhODgY+vr68PHJSpRPnz4NIKsiY2trC3Nzc8yYMQPx8fHo3r07+vbti2nTpgHIuq25SpUq8PLyQu/evXH48GEMHjwYe/fuhaurK4Cs25o9PT2xdOlS1K5dG3PnzsWWLVtw8+bNbHNbPocVFiIiop/UnDlzoKKiAg8PD6SlpcHV1RWLFi0St6uqqiIkJAQDBw6Ek5MTdHV14enpiUmT/hvaLFOmDPbu3QtfX1/MmzcPJUuWxIoVK8RkBQA6duyIZ8+eYdy4cYiPj4etrS0OHDiQ62QFYIWF6KfCCgtRdt+jwmI/+Ui+tBMxtlG+tPMjYoWFiIhIwX72FxfmB94LSkREREqPFRYiIiIFK4i7hAobJixEREQKxnxFfkxYiIiIFIwVFvlxDgsREREpPVZYiIiIFIwFFvkxYSEiIlIwDgnJj0NCREREpPRYYSEiIlIwFljkx4SFiIhIwTgkJD8OCREREZHSY4WFiIhIwVhgkR8TFiIiIgXjkJD8OCRERERESo8VFiIiIgVjhUV+TFiIiIgUjPmK/JiwEBERKRgrLPLjHBYiIiJSeqywEBERKRgLLPJjwkJERKRgHBKSH4eEiIiISOmxwkJERKRgLLDIjwkLERGRgqkwY5Ebh4SIiIhI6bHCQkREpGAssMiPCQsREZGC8S4h+TFhISIiUjAV5ity4xwWIiIiUnqssBARESkYh4Tkx4SFiIhIwZivyI9DQkRERKT0WGEhIiJSMAlYYpEXExYiIiIF411C8uOQEBERESk9VliIiIgUjHcJyY8JCxERkYIxX5FfrhKW3bt357rBVq1afXNniIiIiHKSq4TF3d09V41JJBJkZmbK0x8iIqJCR4UlFrnlKmGRSqWK7gcREVGhxXxFfnLNYUlNTYWWllZ+9YWIiKhQ4qRb+eX5tubMzExMnjwZv/zyC/T09HDv3j0AwNixY7Fy5cp87yARERFRnhOWqVOnIjg4GDNmzICGhoa4vkqVKlixYkW+do6IiKgwkEjyZ/mZ5TlhWbt2LZYtW4auXbtCVVVVXF+9enXcvHkzXztHRERUGKhIJPmy/MzynLA8fvwYVlZW2dZLpVJkZGTkS6eIiIiIPpbnhMXGxgYnTpzItn7btm2oUaNGvnSKiIioMJHk0/Izy/NdQuPGjYOnpyceP34MqVSKHTt2IDo6GmvXrkVISIgi+khERPRD411C8stzhaV169bYs2cPDh06BF1dXYwbNw5RUVHYs2cPmjRpoog+EhER0U/um57DUr9+fYSGhuZ3X4iIiAolFRZY5PbND467cOECoqKiAGTNa7G3t8+3ThERERUmHBKSX54TlkePHqFz5844deoUihYtCgBITExEnTp1sGnTJpQsWTK/+0hEREQ/uTzPYenbty8yMjIQFRWFly9f4uXLl4iKioJUKkXfvn0V0UciIqIfGh8cJ788V1iOHTuG06dPo2LFiuK6ihUrYsGCBahfv36+do6IiKgw4JCQ/PKcsJQqVSrHB8RlZmbC3Nw8XzpFRERUmHDSrfzyPCQ0c+ZM+Pj44MKFC+K6CxcuYMiQIZg1a1a+do6IiIi+zeLFi1GtWjXo6+tDX18fTk5O2L9/v7g9NTUVXl5eKFasGPT09ODh4YGEhASZNmJjY+Hm5gYdHR2YmJhgxIgReP/+vUzM0aNHYWdnB01NTVhZWSE4ODhbXxYuXAhLS0toaWnBwcEB586dy/P55KrCYmhoKFPOSklJgYODA9TUsnZ///491NTU0Lt3b7i7u+e5E0RERIVZQQwJlSxZEtOnT0f58uUhCALWrFmD1q1b49KlS6hcuTJ8fX2xd+9ebN26FQYGBvD29kbbtm1x6tQpAFkjJ25ubjAzM8Pp06cRFxeHHj16QF1dHdOmTQMAxMTEwM3NDQMGDMCGDRsQFhaGvn37okSJEnB1dQUAbN68GX5+fliyZAkcHBwwd+5cuLq6Ijo6GiYmJrk+H4kgCMLXgtasWZPrBj09PXMdqyjaNbwLugtESunV+aCC7gKR0tH65gd85F7vTVfzpZ1VnarKtb+RkRFmzpyJdu3awdjYGBs3bkS7du0AADdv3kSlSpUQHh4OR0dH7N+/Hy1atMCTJ09gamoKAFiyZAn8/f3x7NkzaGhowN/fH3v37sW1a9fEY3Tq1AmJiYk4cOAAAMDBwQG1atVCUFDWzx+pVIpSpUrBx8cHo0aNynXfc/WfSRmSECIiop9dWloa0tLSZNZpampCU1Pzi/tlZmZi69atSElJgZOTEyIiIpCRkQFnZ2cxxtraGqVLlxYTlvDwcFStWlVMVgDA1dUVAwcOxPXr11GjRg2Eh4fLtPEhZujQoQCA9PR0REREICAgQNyuoqICZ2dnhIeH5+nc8zyH5WOpqal4/fq1zEJERESyVCSSfFkCAwNhYGAgswQGBn72uFevXoWenh40NTUxYMAA7Ny5EzY2NoiPj4eGhob4PLUPTE1NER8fDwCIj4+XSVY+bP+w7Usxr1+/xrt37/D8+XNkZmbmGPOhjdzKcyEsJSUF/v7+2LJlC168eJFte2ZmZl6bJCIiKtTyawpLQEAA/Pz8ZNZ9qbpSsWJFREZGIikpCdu2bYOnpyeOHTuWP535zvJcYRk5ciQOHz6MxYsXQ1NTEytWrMDEiRNhbm6OtWvXKqKPREREhKzk5MNdPx+WLyUsGhoasLKygr29PQIDA1G9enXMmzcPZmZmSE9PR2Jiokx8QkICzMzMAABmZmbZ7hr68PlrMfr6+tDW1kbx4sWhqqqaY8yHNnIrzwnLnj17sGjRInh4eEBNTQ3169fHmDFjMG3aNGzYsCGvzRERERV6EokkXxZ5SaVSpKWlwd7eHurq6ggLCxO3RUdHIzY2Fk5OTgAAJycnXL16FU+fPhVjQkNDoa+vDxsbGzHm4zY+xHxoQ0NDA/b29jIxUqkUYWFhYkxu5XlI6OXLlyhbtiwAQF9fHy9fvgQA1KtXDwMHDsxrc0RERIVeQTzoNiAgAM2aNUPp0qXx5s0bbNy4EUePHsXBgwdhYGCAPn36wM/PD0ZGRtDX14ePjw+cnJzg6OgIAHBxcYGNjQ26d++OGTNmID4+HmPGjIGXl5dY1RkwYACCgoIwcuRI9O7dG4cPH8aWLVuwd+9esR9+fn7w9PREzZo1Ubt2bcydOxcpKSno1atXns4nzwlL2bJlERMTg9KlS8Pa2hpbtmxB7dq1sWfPnmyTd4iIiKhgPH36FD169EBcXBwMDAxQrVo1HDx4EE2aNAEAzJkzByoqKvDw8EBaWhpcXV2xaNEicX9VVVWEhIRg4MCBcHJygq6uLjw9PTFp0iQxpkyZMti7dy98fX0xb948lCxZEitWrBCfwQIAHTt2xLNnzzBu3DjEx8fD1tYWBw4cyDYR92ty9RyWj82ZMweqqqoYPHgwDh06hJYtW0IQBGRkZGD27NkYMmRInjqgCHwOC1HO+BwWouy+x3NYBm6/kS/tLPawyZd2fkR5/s/k6+sr/tnZ2Rk3b95EREQErKysUK1atXztHBERUWHAdx/KT+680sLCAhYWFvnRFyIiokKJb2uWX64Slvnz5+e6wcGDB39zZ4iIiIhykqs5LGXKlMldYxIJ7t27J3en5JX6/usxREREwPeZw+KzMypf2lnQplK+tPMjytV/ppiYGEX3g4iIqNDikJD85HqXEBEREdH38B0KYURERD83FRZY5MaEhYiISMGYsMiPQ0JERESk9FhhISIiUjBOupXfN1VYTpw4gW7dusHJyQmPHz8GAKxbtw4nT57M184REREVBiqS/Fl+ZnlOWLZv3w5XV1doa2vj0qVLSEtLAwAkJSVh2rRp+d5BIiIiojwnLFOmTMGSJUuwfPlyqKuri+vr1q2Lixcv5mvniIiICgOJJH+Wn1me57BER0ejQYMG2dYbGBggMTExP/pERERUqKj87NlGPshzhcXMzAx37tzJtv7kyZMoW7ZsvnSKiIioMFHJp+Vnlufz79evH4YMGYKzZ89CIpHgyZMn2LBhA4YPH46BAwcqoo9ERET0k8vzkNCoUaMglUrRuHFjvH37Fg0aNICmpiaGDx8OHx8fRfSRiIjoh8YRIfnl6m3NOUlPT8edO3eQnJwMGxsb6Onp5Xffvhnf1kxERLn1Pd7WPPbA7XxpZ3LT8vnSzo/om/8zaWhowMbGJj/7QkRERJSjPCcsjRo1+uIT+w4fPixXh4iIiAobDgnJL88Ji62trcznjIwMREZG4tq1a/D09MyvfhERERUaP/tTavNDnhOWOXPm5Lh+woQJSE5OlrtDRERERJ/Kt9u6u3XrhlWrVuVXc0RERIWGikSSL8vPLN/mRoeHh0NLSyu/miMiIio0fvJcI1/kOWFp27atzGdBEBAXF4cLFy5g7Nix+dYxIiIiog/ynLAYGBjIfFZRUUHFihUxadIkuLi45FvHiIiICgtOupVfnhKWzMxM9OrVC1WrVoWhoaGi+kRERFSoSMCMRV55mnSrqqoKFxcXvpWZiIgoD1Qk+bP8zPJ8l1CVKlVw7949RfSFiIiIKEd5TlimTJmC4cOHIyQkBHFxcXj9+rXMQkRERLJYYZFfruewTJo0CcOGDUPz5s0BAK1atZJ5RL8gCJBIJMjMzMz/XhIREf3AvvRKG8qdXL+tWVVVFXFxcYiKivpiXMOGDfOlY/Lg25qJiCi3vsfbmmcezZ+pFCN+LZsv7fyIcv2f6UNeowwJCRER0Y/kZx/OyQ95yitZ0iIiIso7/vMpvzwlLBUqVPhq0vLy5Uu5OkRERET0qTwlLBMnTsz2pFsiIiL6sp/9xYX5IU8JS6dOnWBiYqKovhARERVKnMMiv1w/h4XzV4iIiKig5PkuISIiIsob/s4vv1wnLFKpVJH9ICIiKrRU+PJDuX2Hx+UQERH93FhhkV+e3yVERERE9L2xwkJERKRgvEtIfkxYiIiIFIzPYZEfh4SIiIhI6bHCQkREpGAssMiPCQsREZGCcUhIfhwSIiIiIqXHCgsREZGCscAiPyYsRERECsbhDPnxGhIREZHSY4WFiIhIwSQcE5IbExYiIiIFY7oiPyYsRERECsbbmuXHOSxERESFUGBgIGrVqoUiRYrAxMQE7u7uiI6OlolJTU2Fl5cXihUrBj09PXh4eCAhIUEmJjY2Fm5ubtDR0YGJiQlGjBiB9+/fy8QcPXoUdnZ20NTUhJWVFYKDg7P1Z+HChbC0tISWlhYcHBxw7ty5PJ0PExYiIiIFk+TTkhfHjh2Dl5cXzpw5g9DQUGRkZMDFxQUpKSlijK+vL/bs2YOtW7fi2LFjePLkCdq2bStuz8zMhJubG9LT03H69GmsWbMGwcHBGDdunBgTExMDNzc3NGrUCJGRkRg6dCj69u2LgwcPijGbN2+Gn58fxo8fj4sXL6J69epwdXXF06dPc30+EkEQhDxeA6WX+v7rMURERACg9R0mR2y8+Chf2uliV/Kb93327BlMTExw7NgxNGjQAElJSTA2NsbGjRvRrl07AMDNmzdRqVIlhIeHw9HREfv370eLFi3w5MkTmJqaAgCWLFkCf39/PHv2DBoaGvD398fevXtx7do18VidOnVCYmIiDhw4AABwcHBArVq1EBQUBACQSqUoVaoUfHx8MGrUqFz1nxUWIiKiH0RaWhpev34ts6SlpeVq36SkJACAkZERACAiIgIZGRlwdnYWY6ytrVG6dGmEh4cDAMLDw1G1alUxWQEAV1dXvH79GtevXxdjPm7jQ8yHNtLT0xERESETo6KiAmdnZzEmN5iwEBERKZhEIsmXJTAwEAYGBjJLYGDgV48vlUoxdOhQ1K1bF1WqVAEAxMfHQ0NDA0WLFpWJNTU1RXx8vBjzcbLyYfuHbV+Kef36Nd69e4fnz58jMzMzx5gPbeQG7xIiIiJSsPyqDgQEBMDPz09mnaam5lf38/LywrVr13Dy5Ml86sn3x4SFiIjoB6GpqZmrBOVj3t7eCAkJwfHjx1Gy5H9zYMzMzJCeno7ExESZKktCQgLMzMzEmE/v5vlwF9HHMZ/eWZSQkAB9fX1oa2tDVVUVqqqqOcZ8aCM3OCRERESkYPk1JJQXgiDA29sbO3fuxOHDh1GmTBmZ7fb29lBXV0dYWJi4Ljo6GrGxsXBycgIAODk54erVqzJ384SGhkJfXx82NjZizMdtfIj50IaGhgbs7e1lYqRSKcLCwsSY3GCFhYiISMEK4rFxXl5e2LhxI/755x8UKVJEnC9iYGAAbW1tGBgYoE+fPvDz84ORkRH09fXh4+MDJycnODo6AgBcXFxgY2OD7t27Y8aMGYiPj8eYMWPg5eUlVnoGDBiAoKAgjBw5Er1798bhw4exZcsW7N27V+yLn58fPD09UbNmTdSuXRtz585FSkoKevXqlevz4W3NRET0U/setzVvjXySL+20tzXPdeznKjKrV69Gz549AWQ9OG7YsGH4+++/kZaWBldXVyxatEhmqObBgwcYOHAgjh49Cl1dXXh6emL69OlQU/vvwh09ehS+vr64ceMGSpYsibFjx4rH+CAoKAgzZ85EfHw8bG1tMX/+fDg4OOT+fJiwEBHRz+x7JCzbLsflSzvtqpfIl3Z+RBwSIiIiUjBOGJUfExYiIiIFy+uEWcqOSR8REREpPVZYiIiIFIz1FfkxYSEiIlIwjgjJj0NCREREpPRYYSEiIlIwFQ4KyY0JCxERkYJxSEh+HBIqpP7ZuQP1HGsWdDcK3Ng/RmGoz6CC7gZRvsntd7t65Yo4HHboO/SI6PtghUWJjf1jFHb/szPb+j37/kVpC4sC6NF//tm5A+PGBKBO3XpYvGyluP7169eo71QLK1avRa3auX/ksrweP36E5i6NsXnbLlhXqiSuHxkwGoXwYc6Ug+qVK35x+4BB3hjo5fNd+tKnZ3dcOJ/1hlsNDQ2ULFkKnbp0RcfOXeVu27VZc9Rr0FD8vHjhAhwJO4QtO/6RiQs7ehL6BgZyH4/yh4RDQnJjwqLk6tarj0lTAmXWGRoZFVBvZKmpqeHsmXCcO3sGtR0cC7o7OSpSpEhBd4G+k7CjJ8U/HzywD4uC5uOfkAPiOh0dHfHPgiAgMzNT5l0o+c2jXQcM8h6M1NRU7Nm9C9OmTIK+vgGaubWQq10tLS1oaWl9Na64sbFcx6H8xSEh+XFISMlpaGiguLGxzKKqqoq1wavh4d4SDjVt4dK4IaZOmoC3KSmfbSf65k306dkdTrVqoE5tO3Rq3xbXr10Vt1+MuICe3bugtl01uDRuiOnTpuDt27df7Ju2tjbc23hg3py/vhgXHxeHEX5DUM+xJuo71cYQ74F4/PiRuP39+/eYPm0K6jnWRIM6Dpjz10yMCfCXGco5deI4PLt1FmO8B/2Oh7Gx4vbmLo0BAB3buaN65Yro07M7ANkhoW1bNsP513qQSqUy/RviPRDjxgSIn48cPoSO7dqgVo2qaO7aGEsWBeH9e76gStl9/B3R0ysCiUQifo6JuQen2nY4eeIYOrVvi5q2VXHpYkSOQ4YzAqeKf38AQCqVYuXypWjm8htq21VD+zatEHrwwKeHz0ZLSwvFjY1RslQpDPTyQWkLSxw9chgAEPfkCYZ4D4Rjzazv4wi/IXjx/Lm475e+rx8PCf2zcweWLApCdPRNVK9cEdUrV8Q/O3cAkB0S6tG1E+b8NVOmfy9fvoR99cqIuHAeAJCeno6/Zv4J50b14VDTFl07tcf5c2fz9N+ASJGYsPygVFQk8A8YjR3/hGDy1Ok4d+5Mth9IHwvwHw5TMzNs3LwNf2/dgd59+0FNTR0A8DA2FoN+7wfnJi7YunM3Zsyag0sXIxA4dfJX+zHAyxt3bt/67A/wjIwMDOzfBzq6uli9dgPWrP8bOjo6GPR7X2SkpwMAVq9cjn0hezBxSiDWrN+IlJRkHDksO/b+7t07dPfshY2bt2PZymCoSCTwHeIlJh8bNm0FACxbGYywoycxe+6CbH1xcW2KxMREmR/CSYmJOHXyBNxatAKQlbiNCfBH1249sHP3PowdPwn/7NqBFcuWfPVakPKbN/svDPEdhl179qFChS8PIX2wcvlS7Nm9C2PGTcSOf/aiW4+e+GPUCHHIJ7e0NDWRkZEBqVSKIT6DkJSUhFVr1mHJitV49OghRg73FWO/9H39mGuz5ujRszfKWZVH2NGTCDt6Eq7NmmeLa+7WEgf375MZHj24fx+MjU1gZ5+V/AROmYQrkZcwY9YcbNuxGy6uTTHo97548OB+ns6TcqYCSb4sPzMmLEru+LGjcKxZQ1yG+w4GAHTr0RO1HRzxyy8l4eDoBG+fofj34P7PthMf9wSOjnVQpmw5WFhYwsW1GSpaWwMAVq5YiuYtWqJbj56wsLCEbQ07+AeMRsjuXUhLS/ti/0xMTNGlWw8smD8nxyrEwQP7IBWkmDBpKspXqIiy5cph0pRAxMfF4fz/f+D/vWE9+vTrj8bOTVCmbDkEjB6HIkX0ZdpxdnGFcxMXlLawgHWlSpg4ZRpu37qFu3fvAPhvmMzAoCiKGxvDoGjRbH3RNzBAvfoNsG/vHnFd6L8HUdTQUJxvs2RREHr37Y9W7m1QslQpONWpCy+fIdi2ZdMXrwP9GAb5DIZTnbooVbp0jn9HPpWeno4Vy5di4uRpqFuvPkqWKoXWbdrCrWUrbNuyOVfHzMzMRMief3DrVjRqOzji7Jlw3Ll9C9Nn/AWbylVQrVp1TJk2AxfOn8O1q1cAfPn7+jEtLS3o6OhATVVVrCblNFzk0rQZnj17iksXI8R1+/eFoGlzN0gkEsQ9eYJ/du3AzDnzYGdfE6VKl4Znrz6oYWcvVmxIPhJJ/iw/M85hUXK1ajtg9NgJ4mdtHW0AwJnw01i5fCliYu4hJTkZmZmZSEtLw7t376CtrZ2tne6evTBx/BiE7PkHDo514OLaFKVKlwYA3Lp5E7duRWNfyH//kAsQIJVK8fjRI5QtV+6LfezVpx+2bdmMXTu2w6VpM5ltt6Jv4mFsLJxq2cmsT0tLw6OHsXjz5g1evHiOKlWridtUVVVRqXJlCB8N3Tx4cB+LFszH1auXkfjqFaTSrN8U4+PiUL58hS/272PNW7TEpPFjMXrsBGhoaGDf3j1o2swNKioqYn8jL13E8qX/VVSk0i9fW/px2FSumqf42NgHSH33Dr/37S2zPiMjQ2Zyd042b/obO7ZvQ0ZGBlRVVdCtR0906NQZf29cD1MzM5iVKCHGlrOyQhF9fcTcu4cqVat98fv6LYyMjOBUpy72huyGnX1NPHr0EJcjL2Hs+IkAgNu3byEzMxOtmjf95DzTc5XY0df97MlGfmDCouS0tbWz3RH0+PEj+Az6HR06dobPEF/oGxjg0sUITBg7GhkZGTn+ozrQywfN3FrgxLFjOHnyOBYvnI8/Z81BY+cmePvuLdp16IQuXbtn26/ERz9UP0dfXx99+vXHksVBaPDrrzLb3r59i0o2lRH456xs++Vl8vBgrwEoUeIXjJ84BcbGJpAKUni0boGMjIxctwEADX/9DYIwBsePHUWVKlVxMeICRvj/N3/l7du3GOjlg8bOLtn21dTUzNOxSPl8+t2QqEiy3UX2caXwwzyuoMVLYWJiKhOnoaHxxWM1b9ES/foPgKaWFoyNjcWkODe+9H39Vs3dWuLPwCkY9cdY7N8bgvIVKqD8/4fF3r59C1VVVWzauh0qKqoy+308WZmoIDFh+QFFXb8OqVTAsJGjxB+C/x74/HDQB5aWZWBpWQbdPXvCf7gf/tm5HY2dm6BSJRvcu3tHrlulO3ftjo0b1mHDurUy6ytVqoyD+/fDqFgx6Onp5bhvsWLFce3aVdjXrAUgq4R+88YNsQSemPgK92NiMH7iFHG8/WLEBZk21NWzxvel0swv9lNTUxO/ObtgX8gePIx9AMsyZVDJpvJH/bXB/fsxBX7bOH0fhoZGuHP7tsy66JtRUPv/36dy5cpBQ0MDcXFPULNW7Ty1XURPL8e/R2XLlkNCfDzi4+LEKsvdO3fw5vVrmWrm576vn1JXV0fmJxPJc/Lrb40xacI4nDp5Avv2hqBlq9biNutKlZCZmYmXL1+K3zHKX7ytWX6cw/IDKlXaAu/fZ+DvDevw6OFD7Nm9C1u/MMciNTUV06ZMwvlzZ/HkyWNcuhiB69euokzZrB+Ovfr0w+XIS5g2ZRJuRkXhwYP7OHL4EKZNmZTrPmlqamKglw/+3rBOZn3zFi1R1NAQQ7wH4mLEBTx69BDnz53F9GlTkBAfDwDo3LUbVi1fiiOHD+F+zD38GTgVr18niTVUfX0DFC1aFNu2bkbsgwc4eyYcs2ZMlzmOkVExaGlp4dTJE3jx/DnevHnz2b66tWiJE8ePYtfO7Wju1lJmW/+BXgjZ/Q+WLArCnTu3ce/uXezftxdB8+bk+lrQj6O2gyNuXL+GPf/syhp2DJqPO3f+S2B0dfXg2bM3Zv0ZiN27duJhbCyiblzHxg3rsHtX9mck5YajUx1Yla+AAP/hiLpxHVevXMGYP0aiZq3aqFyl6le/r58yN/8Fjx89ws2oKLx69RLp/5/M/ikdHR00atwYixbMQ8y9u2jW/L/bqy0ty6B5i5YYHTASh0L/xaNHD3H1yhWsXL4Ux48d/abzJFkqkvxZfmassPyAKlpbY/jIAKxeuRzz586GnX1NDB7qhzEB/jnGq6qoICkxEWMC/PHixXMUNTREY2cXDPLOmsBboaI1Vgavw4L5c9GrRxcIAlCqVKkc7zb4klat22Bt8Grc+/9EWCCrBL96zXrMnT0LfkO8kZKSAhNTUzg4OEH3/xWXXn364fnz5xgT4A8VFVV4tO+AOnXriaVpFRUV/DlrDv6cNgUe7i1gaVkG/n+Mkbn1VE1NDf4BY7B08UIsCpoPO/uaWBksmzx9UNvBEQYGBrgfE5MtYalbrz7mL1yCZYsXYvXK5VBTU4NlmbJo69E+T9eCfgx169VH/wGDMOevmUhPT0PrNh5o0codd27fEmO8Bg+FoZERVq5YikcPH6GIfhFUqmSDvv0HfNMxJRIJ5i1YhOnTJqNXj25QUZGgbr36GPXHWABf/75+ytnFFWGHQtG3dw+8ef0ak6YEonWbtjnGurVoCa8B/WFfsxZKmJvLbJs0JRDLly7GXzOn42nCUxgaFkXV6rZo0PDXbzpPovwmEQrhY0BT+ciMH5pUKoV7y2ZwcW0G78FDC7o7RFTIaX2HX90P33yRL+38Zl0sX9r5EbHCQgXuyZPHCD91Cva1aiEjPR1/b9yAx48eZ6t+EBH9qHiXkPyYsFCBU5GoYPeuHZg9608IggCr8hWwbOXqr95OTUREPw8OCRER0U/tewwJHY1+mS/t/FpROd4lVxBYYSEiIlKwn/0On/zA25qJiIhI6bHCQnJbuXwpwkL/RUzMPWhqacHWtgaG+g2HZZmyYsykCeNw9sxpPHv6FDo6Oqj+/5jPPVuC6EfXrMlvePLkcbb1HTt1gWfvPuIbxj81c/ZcuLg2y3Eb/bj44Dj5cQ4LyW1g/z5o2swNlatWReb7TCyYNxt3bt/Gjt17xcd6b9uyGWXKloVZiRJ4nZSExQsXIPrmTez7NwyqqqpfOQLRj+fly5eQZv735OU7d27j9769sGL1WtjZ18Srl7JzGrZt3Yw1q1ci7OhJ6Ojqfu/u/tS+xxyWk7df5Us79cob5ks7PyImLJTvXr58iUb1nbBqzXrxcfufuhV9E+3btkbI/lC5XupG9KOYETgVx48dxZ79/0KSwz2uHTzcUcnGBhMnTyuA3v3cvkfCciqfEpa6P3HCwjkslO+S//9YfH0Dgxy3v337Fv/s3IFfSpaEmZnZ9+waUYHISE/H3pDdcG/rkWOycuP6NUTfjEKbtu0KoHdEP4Yffg5LWloa0tLSZNYJqpp8s24BkUqlmPHnNNjWsEP58hVktm3+ewPm/DUL7969hWWZMli6fDXUv/LGW6LC4PDhQ3jz5g1aubfJcfvO7dtQtmw52Naw+849o+9FhU+Ok5tSV1gePnyI3r17fzEmMDAQBgYGMsvMPwO/Uw/pU9OmTMTd27cxY1b2lwU2b9EKm7fvxKo162FhYYkRw4ZmSzaJCqOd27ejbr0GMDExzbYtNTUV+/eFwN2D1ZXCTJJPy89MqeewXL58GXZ2dsj8aOLap1hhUR7TpkzC0SNhWLVmPUqWLPXF2Iz0dNSrUxsTJk5BM7cWX4wl+pE9efIYbq7OmD1vARr95pxt+57duzBh7BiEHjkOI6Of96FgBel7zGE5cycxX9pxtCqaL+38iAp0SGj37t1f3H7v3r2vtqGpmT054aTb70sQBAROnYzDYaFYGbzuq8kKAAhZOyI9PV3h/SMqSP/s3AEjo2Ko3+DXHLfv2rEdvzb6jclKYfezl0fyQYEmLO7u7pBIJPhSkSenCWqkXKZNnoj9+0Iwd8Ei6Oro4vmzZwAAvSJFoKWlhUcPH+LggX1wqlMXhoZGSEiIx6oVy6CpqYV6DRoWcO+JFEcqleKfnTvQsrU71NSy/7iNffAAERfOY+HiZQXQO/qe+BwW+RVowlKiRAksWrQIrVu3znF7ZGQk7O3tv3OvKK+2bP4bANCnZ3eZ9ZOmBKJ1m7bQ0NTAxYgLWL9uDV4nvUax4sVgb18Tazf8jWLFft5XpVPhdyb8NOLinsC9rUeO23ft3A5TUzM41a33nXtG9OMp0DksrVq1gq2tLSZNmpTj9suXL6NGjRqQSqV5apdDQkRElFvfYw7LuXtJ+dJO7bI5Py7iZ1CgFZYRI0YgJSXls9utrKxw5MiR79gjIiKi/McBIfkp9V1C34oVFiIiyq3vUWE5n08VllqssBAREZHCsMQiNyYsRERECsa7hOTHhIWIiEjB+IQO+Sn1o/mpYGzauAHNmvyGWjWqomun9rh65coX4/89uB+tWzRFrRpV4eHeEieOH/ts7OSJ41C9ckWsXxssrktPT8cfo0agTm07tGzuijPhp2X2CV61AoFTJ8t1TkT5IS/fjTt3bsNviA+aNfkt29/5nKxcvgzVK1fEjMCpMutn/hmI+k614dK4IfaGyD5s89+D++EzaMA3nw/Rj4QJC8k4sH8fZs0IxO+DvLBp605UrGiNgb/3wYsXL3KMj7x0EaNGDEObtu2wedsuNPqtMYb6eOH27VvZYsMOheLq5cswNjGRWb9t62ZEXb+OtRs3o137Dhg1cpj4MMFHjx5i+7at8Bnim/8nS5QHef1upL57h5KlSmKw7zAUL278xbavXb2CbVs3oUKFijLrjx45jP17Q7Bk+Ur4+o3AxHFj8OrVSwDAmzdvsGDeXPwxZlz+nCApFN8lJD8mLCRj3ZrVaNuuA9zbeKCclRXGjJ8ILS0t7NqxPcf4DevXok69+ujZuy/KlisH78FDUcnGBps2rpeJS0hIwPRpkzFtxiyoq6nLbIu5excNG/0GK6vy6Ni5K169fIlXr14BAKZOmoChfsOhp6enkPMlyq28fjeqVK0Gv+H+aNbcDRpfeCv525QUBPiPwPiJU6BvIHsHSMy9u6hZuzYqV6mKZm4toKunh8ePHgEA5vw1Ex06dkYJc/P8O0lSHGYscmPCQqKM9HRE3bgOR6c64joVFRU4OtbBlcuXctznSmQkHB2dZNbVqVsPVyIjxc9SqRSjR41Az159YGVVPlsbFaytceliBFJTU3H61EkYGxvD0NAQe0N2Q1NTE42dm+TPCRJ9o2/5buTWtCmT0KBBQ5m2P6hQ0Ro3rl3D66Qk3Lh+DWmpqShd2gIXIy7g5o3r6NKtew4tEhVOnHRLoleJr5CZmZntcfnFihVDTEzOL6J8/vw5ihUrni3++Yvn4ufVK5dDVU0NXbr1yLEN9zYeuB0djTatmsOwqCFm/DUXr5OSsChoPlauXoegeXNwYP8+lCxVGhOnTIOpqamcZ0qUN9/y3ciN/fv2IirqBjZu3pbj9rr16sOtZSt06dgOmlpamDztT2hra2Pq5ImYPDUQWzb9jb83roNhUUOMnTg5x18ISDnwLiH5MWEhhbpx/Ro2rFuLTdt2fPZFlurq6vhj7HiZdWNHB6BL1+64GXUDhw+HYcuOfxC8agX+nDYFs+ct+B5dJ1Ko+Lg4zJg+FUuXr8r2xvmPDfTywUAvH/HzkkVBcHR0gpqaGpYvXYxtu/bg+LEjGBPgj01bd3yPrtM34F1C8uOQEIkMixpCVVU12yTCFy9eoHjx4jnuU7x4cbz4qJoixv+/6nIx4gJevnyBps6NYFfNBnbVbPDkyWP8NfNPNGvyW45tnjt7Bnfv3EanLt1w/vw51K/fADo6OnBp2gwXzp/LhzMlyptv+W58zY0b1/HyxQt0at9W/G5cOH8OGzesg101G2RmZmbbJ+beXezdsxtePkNw/vw52NesCSMjI7i4NkPUjetISUn+pr4Q/QhYYSGRuoYGKtlUxtkz4fitsTOArPknZ8+Go1PnbjnuU83WFmfPnEG3Hj3FdWfCT6OarS0AoEWr1nD4ZGx+YP8+aNGyNdzbtM3WXlpaGgKnTMK0GbOgqqoKqTQT7/9/x9D7jPeQSrP/ECdStG/5bnyNg6Mjtu3aI7Nu/OgAWJYti159+kFVVVVmmyAImDxxPIaNHAUdXV1IM6XIeJ/1HpL3////zMy8vSiWvh8WWOTHhIVkdPfshbF/+KNy5SqoUrUa1q9bg3fv3onJxeiAkTAxMcUQ32EAgK7deqBPz+5YE7wKDRo0xIH9+3D92jWMnZD1Bu6iRQ1RtKihzDHU1dRRvHhxWJYpm+34y5YsQr0GDVGpkg0AwLaGHebMmonWbdpi09/rYVvDTpGnT/RZef1uZKSn4+7du1l/zkjH06cJuBkVBR0dHZS2sICurh7Kl68gcwxtHR0UNSiabT0A7Ni2FYaGRvi1UVZl0raGHZYsWoArlyNx8sRxlC1nBX19fUVeApIHMxa5MWEhGU2bNcerly+xKGg+nj9/horWlbBo6QoU+3/ZOz4uDiqS/0YSbWvYIXDGLATNn4sFc2ejtIUl5i5YmOMP3K+5ffsW/j2wH5u37xLXNXFpigvnzqFXj66wsCyD6TP+kvscib5FXr8bT589Rcd27uLnNatXYc3qVahZqzZWBq/L07FfPH+OFcuWYM2Gv8V1VatVQ3fPXvAe+DuMihlh8tQ/5TtBIiXHtzUTEdFP7Xu8rfnKw/yZX1St1M/7TCpWWIiIiBSMdwnJjwkLERGRgjFfkR9vayYiIiqkjh8/jpYtW8Lc3BwSiQS7du2S2S4IAsaNG4cSJUpAW1sbzs7OuH37tkzMy5cv0bVrV+jr66No0aLo06cPkpNlh7iuXLmC+vXrQ0tLC6VKlcKMGTOy9WXr1q2wtraGlpYWqlatin379uXpXJiwEBERKVoBvUsoJSUF1atXx8KFC3PcPmPGDMyfPx9LlizB2bNnoaurC1dXV6SmpooxXbt2xfXr1xEaGoqQkBAcP34c/fv3F7e/fv0aLi4usLCwQEREBGbOnIkJEyZg2bJlYszp06fRuXNn9OnTB5cuXYK7uzvc3d1x7dq1XJ8LJ90SEdFP7XtMur3+OCVf2qn8i+437yuRSLBz5064u7sDyKqumJubY9iwYRg+fDgAICkpCaampggODkanTp0QFRUFGxsbnD9/HjVr1gQAHDhwAM2bN8ejR49gbm6OxYsXY/To0YiPjxdf9Dlq1Cjs2rULN2/eBAB07NgRKSkpCAkJEfvj6OgIW1tbLFmyJFf9Z4WFiIjoB5GWlobXr1/LLGlpad/UVkxMDOLj4+Hs7CyuMzAwgIODA8LDwwEA4eHhKFq0qJisAICzszNUVFRw9uxZMaZBgwYybyV3dXVFdHQ0Xr16JcZ8fJwPMR+OkxtMWIiIiBRMIsmfJTAwEAYGBjJLYGDgN/UpPj4eALK9UNbU1FTcFh8fDxMTE5ntampqMDIykonJqY2Pj/G5mA/bc4N3CRERESlYft0lFBAQAD8/P5l1X3p5ZmHChIWIiOgHoampmW8JipmZGQAgISEBJUqUENcnJCTA9v/vgzMzM8PTp09l9nv//j1evnwp7m9mZoaEhASZmA+fvxbzYXtucEiIiIhI0QroLqEvKVOmDMzMzBAWFiaue/36Nc6ePQsnJycAgJOTExITExERESHGHD58GFKpFA4ODmLM8ePHkZGRIcaEhoaiYsWKMDQ0FGM+Ps6HmA/HyQ0mLERERAomyaf/5VVycjIiIyMRGRkJIGuibWRkJGJjYyGRSDB06FBMmTIFu3fvxtWrV9GjRw+Ym5uLdxJVqlQJTZs2Rb9+/XDu3DmcOnUK3t7e6NSpE8zNzQEAXbp0gYaGBvr06YPr169j8+bNmDdvnszQ1ZAhQ3DgwAH89ddfuHnzJiZMmIALFy7A29s799eQtzUTEdHP7Hvc1nwz7m2+tGNdQidP8UePHkWjRo2yrff09ERwcDAEQcD48eOxbNkyJCYmol69eli0aBEqVPjvBbYvX76Et7c39uzZAxUVFXh4eGD+/PnQ0/vvvUZXrlyBl5cXzp8/j+LFi8PHxwf+/v4yx9y6dSvGjBmD+/fvo3z58pgxYwaaN2+e63NhwkJERD+175GwRMfnT8JS0SxvCUthwkm3RERECsZ3CcmPCQsREZGiMWORGyfdEhERkdJjhYWIiEjBvuUOH5LFhIWIiEjBJMxX5MYhISIiIlJ6rLAQEREpGAss8mPCQkREpGjMWOTGISEiIiJSeqywEBERKRjvEpIfExYiIiIF411C8uOQEBERESk9VliIiIgUjAUW+TFhISIiUjRmLHJjwkJERKRgnHQrP85hISIiIqXHCgsREZGC8S4h+TFhISIiUjDmK/LjkBAREREpPVZYiIiIFIxDQvJjwkJERKRwzFjkxSEhIiIiUnqssBARESkYh4Tkx4SFiIhIwZivyI9DQkRERKT0WGEhIiJSMA4JyY8JCxERkYLxXULyY8JCRESkaMxX5MY5LERERKT0WGEhIiJSMBZY5MeEhYiISME46VZ+HBIiIiIipccKCxERkYLxLiH5MWEhIiJSNOYrcuOQEBERESk9VliIiIgUjAUW+TFhISIiUjDeJSQ/DgkRERGR0mOFhYiISMF4l5D8mLAQEREpGIeE5MchISIiIlJ6TFiIiIhI6XFIiIiISME4JCQ/JixEREQKxkm38uOQEBERESk9VliIiIgUjENC8mPCQkREpGDMV+THISEiIiJSeqywEBERKRpLLHJjwkJERKRgvEtIfhwSIiIiIqXHCgsREZGC8S4h+TFhISIiUjDmK/JjwkJERKRozFjkxjksREREpPRYYSEiIlIw3iUkPyYsRERECsZJt/LjkBAREREpPYkgCEJBd4IKp7S0NAQGBiIgIACampoF3R0ipcHvBlHeMWEhhXn9+jUMDAyQlJQEfX39gu4OkdLgd4Mo7zgkREREREqPCQsREREpPSYsREREpPSYsJDCaGpqYvz48ZxUSPQJfjeI8o6TbomIiEjpscJCRERESo8JCxERESk9JixERESk9JiwEBERkdJjwkIKs3DhQlhaWkJLSwsODg44d+5cQXeJqEAdP34cLVu2hLm5OSQSCXbt2lXQXSL6YTBhIYXYvHkz/Pz8MH78eFy8eBHVq1eHq6srnj59WtBdIyowKSkpqF69OhYuXFjQXSH64fC2ZlIIBwcH1KpVC0FBQQAAqVSKUqVKwcfHB6NGjSrg3hEVPIlEgp07d8Ld3b2gu0L0Q2CFhfJdeno6IiIi4OzsLK5TUVGBs7MzwsPDC7BnRET0o2LCQvnu+fPnyMzMhKmpqcx6U1NTxMfHF1CviIjoR8aEhYiIiJQeExbKd8WLF4eqqioSEhJk1ickJMDMzKyAekVERD8yJiyU7zQ0NGBvb4+wsDBxnVQqRVhYGJycnAqwZ0RE9KNSK+gOUOHk5+cHT09P1KxZE7Vr18bcuXORkpKCXr16FXTXiApMcnIy7ty5I36OiYlBZGQkjIyMULp06QLsGZHy423NpDBBQUGYOXMm4uPjYWtri/nz58PBwaGgu0VUYI4ePYpGjRplW+/p6Yng4ODv3yGiHwgTFiIiIlJ6nMNCRERESo8JCxERESk9JixERESk9JiwEBERkdJjwkJERERKjwkLERERKT0mLERERKT0mLAQERGR0mPCQqREevbsCXd3d/Hzr7/+iqFDh373fhw9ehQSiQSJiYmfjZFIJNi1a1eu25wwYQJsbW3l6tf9+/chkUgQGRkpVztE9ONhwkL0FT179oREIoFEIoGGhgasrKwwadIkvH//XuHH3rFjByZPnpyr2NwkGUREPyq+/JAoF5o2bYrVq1cjLS0N+/btg5eXF9TV1REQEJAtNj09HRoaGvlyXCMjo3xph4joR8cKC1EuaGpqwszMDBYWFhg4cCCcnZ2xe/duAP8N40ydOhXm5uaoWLEiAODhw4fo0KEDihYtCiMjI7Ru3Rr3798X28zMzISfnx+KFi2KYsWKYeTIkfj01V6fDgmlpaXB398fpUqVgqamJqysrLBy5Urcv39ffKmeoaEhJBIJevbsCQCQSqUIDAxEmTJloK2tjerVq2Pbtm0yx9m3bx8qVKgAbW1tNGrUSKafueXv748KFSpAR0cHZcuWxdixY5GRkZEtbunSpShVqhR0dHTQoUMHJCUlyWxfsWIFKlWqBC0tLVhbW2PRokV57gsRFT5MWIi+gba2NtLT08XPYWFhiI6ORmhoKEJCQpCRkQFXV1cUKVIEJ06cwKlTp6Cnp4emTZuK+/31118IDg7GqlWrcPLkSbx8+RI7d+784nF79OiBv//+G/Pnz0dUVBSWLl0KPT09lCpVCtu3bwcAREdHIy4uDvPmzQMABAYGYu3atViyZAmuX78OX19fdOvWDceOHQOQlVi1bdsWLVu2RGRkJPr27YtRo0bl+ZoUKVIEwcHBuHHjBubNm4fly5djzpw5MjF37tzBli1bsGfPHhw4cACXLl3CoEGDxO0bNmzAuHHjMHXqVERFRWHatGkYO3Ys1qxZk+f+EFEhIxDRF3l6egqtW7cWBEEQpFKpEBoaKmhqagrDhw8Xt5uamgppaWniPuvWrRMqVqwoSKVScV1aWpqgra0tHDx4UBAEQShRooQwY8YMcXtGRoZQsmRJ8ViCIAgNGzYUhgwZIgiCIERHRwsAhNDQ0Bz7eeTIEQGA8OrVK3FdamqqoKOjI5w+fVomtk+fPkLnzp0FQRCEgIAAwcbGRma7v79/trY+BUDYuXPnZ7fPnDlTsLe3Fz+PHz9eUFVVFR49eiSu279/v6CioiLExcUJgiAI5cqVEzZu3CjTzuTJkwUnJydBEAQhJiZGACBcunTps8closKJc1iIciEkJAR6enrIyMiAVCpFly5dMGHCBHF71apVZeatXL58GXfu3EGRIkVk2klNTcXdu3eRlJSEuLg4ODg4iNvU1NRQs2bNbMNCH0RGRkJVVRUNGzbMdb/v3LmDt2/fokmTJjLr09PTUaNGDQBAVFSUTD8AwMnJKdfH+GDz5s2YP38+7t69i+TkZLx//x76+voyMaVLl8Yvv/wicxypVIro6GgUKVIEd+/eRZ8+fdCvXz8x5v379zAwMMhzf4iocGHCQpQLjRo1wuLFi6GhoQFzc3Ooqcl+dXR1dWU+Jycnw97eHhs2bMjWlrGx8Tf1QVtbO8/7JCcnAwD27t0rkygAWfNy8kt4eDi6du2KiRMnwtXVFQYGBti0aRP++uuvPPd1+fLl2RIoVVXVfOsrEf2YmLAQ5YKuri6srKxyHW9nZ4fNmzfDxMQkW5XhgxIlSuDs2bNo0KABgKxKQkREBOzs7HKMr1q1KqRSKY4dOwZnZ+ds2z9UeDIzM8V1NjY20NTURGxs7GcrM5UqVRInEH9w5syZr5/kR06fPg0LCwuMHj1aXPfgwYNscbGxsXjy5AnMzc3F46ioqKBixYowNTWFubk57t27h65du+bp+ERU+HHSLZECdO3aFcWLF0fr1q1x4sQJxMTE4OjRoxg8eDAePXoEABgyZAimT5+OXbt24ebNmxg0aNAXn6FiaWkJT09P9O7dG7t27RLb3LJlCwDAwsICEokEISEhePbsGZKTk1GkSBEMHz4cvr6+WLNmDe7evYuLFy9iwYIF4kTWAQMG4Pbt2xgxYgSio6OxceNGBAcH5+l8y5cvj9jYWGzatAl3797F/Pnzc5xArKWlBU9PT1y+fBknTpzA4MGD0aFDB5iZmQEAJk6ciMDAQMyfPx+3bt3C1atXsXr1asyePTtP/SGiwocJC5EC6Ojo4Pjx4yhdujTatm2LSpUqoU+fPkhNTRUrLsOGDUP37t3h6ekJJycnFClSBG3atPliu4sXL0a7du0waNAgWFtbo1+/fkhJSQEA/PLLL5g4cSJGjRoFU1NTeHt7AwAmT56MsWPHIjAwEJUqVULTpk2xd+9elClTBkDWvJLt27dj165dqF69OpYsWYJp06bl6XxbtWoFX19feHt7w9bWFqdPn8bYsWOzxVlZWaFt27Zo3rw5XFxcUK1aNZnblvv27YsVK1Zg9erVqFq1Kho2bIjg4GCxr0T085IIn5vhR0RERKQkWGEhIiIipceEhYiIiJQeExYiIiJSekxYiIiISOkxYSEiIiKlx4SFiIiIlB4TFiIiIlJ6TFiIiIhI6TFhISIiIqXHhIWIiIiUHhMWIiIiUnpMWIiIiEjpMWEhIiIipceEhYiIiJQeExYiIiJSekxYiIiISOkxYSEiIiKlx4SFiIiIlB4TFiIiIlJ6TFiIiIhI6TFhISIiIqXHhIXoE+Hh4VBVVYWbm1tBd0UpXb9+HR4eHrC0tIREIsHcuXNztd+VK1dQv359aGlpoVSpUpgxY0a2mK1bt8La2hpaWlqoWrUq9u3bJ7NdEASMGzcOJUqUgLa2NpydnXH79u38OC0iUnJMWIg+sXLlSvj4+OD48eN48uRJgfUjPT29wI79JW/fvkXZsmUxffp0mJmZ5Wqf169fw8XFBRYWFoiIiMDMmTMxYcIELFu2TIw5ffo0OnfujD59+uDSpUtwd3eHu7s7rl27JsbMmDED8+fPx5IlS3D27Fno6urC1dUVqamp+X6eRKRkBCISvXnzRtDT0xNu3rwpdOzYUZg6darM9t27dws1a9YUNDU1hWLFignu7u7ittTUVGHkyJFCyZIlBQ0NDaFcuXLCihUrBEEQhNWrVwsGBgYybe3cuVP4+Cs4fvx4oXr16sLy5csFS0tLQSKRCIIgCPv37xfq1q0rGBgYCEZGRoKbm5tw584dmbYePnwodOrUSTA0NBR0dHQEe3t74cyZM0JMTIwgkUiE8+fPy8TPmTNHKF26tJCZmSnX9bKwsBDmzJnz1bhFixYJhoaGQlpamrjO399fqFixovi5Q4cOgpubm8x+Dg4Owu+//y4IgiBIpVLBzMxMmDlzprg9MTFR0NTUFP7++2+5zoOIlB8rLEQf2bJlC6ytrVGxYkV069YNq1atgiAIAIC9e/eiTZs2aN68OS5duoSwsDDUrl1b3LdHjx74+++/MX/+fERFRWHp0qXQ09PL0/Hv3LmD7du3Y8eOHYiMjAQApKSkwM/PDxcuXEBYWBhUVFTQpk0bSKVSAEBycjIaNmyIx48fY/fu3bh8+TJGjhwJqVQKS0tLODs7Y/Xq1TLHWb16NXr27AkVlawfAXp6el9cBgwY8K2XFEDWMFuDBg2goaEhrnN1dUV0dDRevXolxjg7O8vs5+rqivDwcABATEwM4uPjZWIMDAzg4OAgxhBR4aVW0B0gUiYrV65Et27dAABNmzZFUlISjh07hl9//RVTp05Fp06dMHHiRDG+evXqAIBbt25hy5YtCA0NFf9BLVu2bJ6Pn56ejrVr18LY2Fhc5+HhIROzatUqGBsb48aNG6hSpQo2btyIZ8+e4fz58zAyMgIAWFlZifF9+/bFgAEDMHv2bGhqauLixYu4evUq/vnnHzHmQ3L0Ofr6+nk+l4/Fx8ejTJkyMutMTU3FbYaGhoiPjxfXfRwTHx8vxn28X04xRFR4scJC9H/R0dE4d+4cOnfuDABQU1NDx44dsXLlSgBZ/6g3btw4x30jIyOhqqqKhg0bytUHCwsLmWQFAG7fvo3OnTujbNmy0NfXh6WlJQAgNjZWPHaNGjXEZOVT7u7uUFVVxc6dOwEAwcHBaNSokdgOkJXgfGkxMTGR67yIiOTFhIXo/1auXIn379/D3NwcampqUFNTw+LFi7F9+3YkJSVBW1v7s/t+aRsAqKioiENLH2RkZGSL09XVzbauZcuWePnyJZYvX46zZ8/i7NmzAP6blPu1Y2toaKBHjx5YvXo10tPTsXHjRvTu3VsmRtFDQmZmZkhISJBZ9+Hzh4m7n4v5ePvH++UUQ0SFF4eEiAC8f/8ea9euxV9//QUXFxeZbe7u7vj7779RrVo1hIWFoVevXtn2r1q1KqRSKY4dO5ZtHgYAGBsb482bN0hJSRGTkq8NwwDAixcvEB0djeXLl6N+/foAgJMnT8rEVKtWDStWrMDLly8/W2Xp27cvqlSpgkWLFuH9+/do27atzHZFDwk5OTlh9OjRyMjIgLq6OgAgNDQUFStWhKGhoRgTFhaGoUOHivuFhobCyckJAFCmTBmYmZkhLCwMtra2ALLuPjp79iwGDhwoV/+I6AdQ0LN+iZTBzp07BQ0NDSExMTHbtpEjRwo1a9YUjhw5IqioqAjjxo0Tbty4IVy5ckWYPn26GNezZ0+hVKlSws6dO4V79+4JR44cETZv3iwIgiC8ePFC0NXVFQYPHizcuXNH2LBhg2Bubp7jXUIfy8zMFIoVKyZ069ZNuH37thAWFibUqlVLACDs3LlTEARBSEtLEypUqCDUr19fOHnypHD37l1h27ZtwunTp2XaqlOnjqChoSEMGDBArmuVlpYmXLp0Sbh06ZJQokQJYfjw4cKlS5eE27dvizELFiwQfvvtN/FzYmKiYGpqKnTv3l24du2asGnTJkFHR0dYunSpGHPq1ClBTU1NmDVrlhAVFSWMHz9eUFdXF65evSrGTJ8+XShatKjwzz//CFeuXBFat24tlClTRnj37p1c50REyo8JC5EgCC1atBCaN2+e47azZ88KAITLly8L27dvF2xtbQUNDQ2hePHiQtu2bcW4d+/eCb6+vkKJEiUEDQ0NwcrKSli1apW4fefOnYKVlZWgra0ttGjRQli2bNlXExZBEITQ0FChUqVKgqamplCtWjXh6NGjMgmLIAjC/fv3BQ8PD0FfX1/Q0dERatasKZw9e1amnZUrVwoAhHPnzn3jVcoSExMjAMi2NGzYUOZcLCwsZPa7fPmyUK9ePUFTU1P45ZdfZJK9D7Zs2SJUqFBB0NDQECpXrizs3btXZrtUKhXGjh0rmJqaCpqamkLjxo2F6Ohouc6HiH4MEkH4ZGCdiAqlyZMnY+vWrbhy5UpBd4WIKM846ZaokEtOTsa1a9cQFBQEHx+fgu4OEdE3YcJCVMh5e3vD3t4ev/76a7a7g4iIfhQcEiIiIiKlxwoLERERKT0mLEQFTCKRYNeuXfkeS0RUmDBhIfpIz549IZFIIJFIoKGhASsrK0yaNAnv379X2DHj4uLQrFmzfI9VlNjYWLi5uUFHRwcmJiYYMWLEV6/PxYsX0aRJExQtWhTFihVD//79kZycLBPz4bp/vGzatEncHhcXhy5duqBChQpQUVGRecAcERV+TFiIPtG0aVPExcXh9u3bGDZsGCZMmICZM2dmi/vwaHx5mZmZQVNTM99jFSEzMxNubm5IT0/H6dOnsWbNGgQHB2PcuHGf3efJkydwdnaGlZUVzp49iwMHDuD69evo2bNnttjVq1cjLi5OXNzd3cVtaWlpMDY2xpgxY8SXThLRT6RgHwNDpFw8PT2F1q1by6xr0qSJ4OjoKG6bMmWKUKJECcHS0lIQBEGIjY0V2rdvLxgYGAiGhoZCq1athJiYGJk2Vq5cKdjY2AgaGhqCmZmZ4OXlJW7DJ0+t9fLyEszMzARNTU2hdOnSwrRp03KMFQRBuHLlitCoUSNBS0tLMDIyEvr16ye8efMm2/nMnDlTMDMzE4yMjIRBgwYJ6enp33R99u3bJ6ioqAjx8fHiusWLFwv6+vpCWlpajvssXbpUMDExETIzM2X6DUDm6bifntuXNGzYUBgyZMg3nQMR/ZhYYSH6Cm1tbbGaEhYWhujoaISGhiIkJAQZGRlwdXVFkSJFcOLECZw6dQp6enpo2rSpuM/ixYvh5eWF/v374+rVq9i9ezesrKxyPNb8+fOxe/dubNmyBdHR0diwYYPMW5U/lpKSAldXVxgaGuL8+fPYunUrDh06BG9vb5m4I0eO4O7duzhy5IhYEQkODha3Dxgw4KsvP/wgPDwcVatWhampqbjO1dUVr1+/xvXr13PsZ1paGjQ0NKCi8t+Pmw8vbPz0vUheXl4oXrw4ateujVWrVmV7YSQR/bz48kOizxAEAWFhYTh48CB8fHzw7Nkz6OrqYsWKFdDQ0AAArF+/HlKpFCtWrIBEIgGQNaxRtGhRHD16FC4uLpgyZQqGDRuGIUOGiG3XqlUrx2PGxsaifPnyqFevHiQSCSwsLD7bv40bNyI1NRVr164VX6gYFBSEli1b4s8//xSTCkNDQwQFBUFVVRXW1tZwc3NDWFgY+vXrBwCYNGkShg8fnqtrEh8fL5OsABA/x8fH57jPb7/9Bj8/P8ycORNDhgxBSkoKRo0aBSBrXsoHkyZNwm+//QYdHR38+++/GDRoEJKTkzF48OBc9Y2ICjcmLESfCAkJgZ6eHjIyMiCVStGlSxdMmDABXl5eqFq1qpisAMDly5dx584dFClSRKaN1NRU3L17F0+fPsWTJ0/QuHHjXB27Z8+eaNKkCSpWrIimTZuiRYsW2d4e/UFUVBSqV68uJisAULduXUilUkRHR4uJROXKlaGqqirGlChRAlevXhU/m5iYwMTEJFf9+xaVK1fGmjVr4Ofnh4CAAKiqqmLw4MEwNTWVqbqMHTtW/HONGjWQkpKCmTNnMmEhIgBMWIiyadSoERYvXgwNDQ2Ym5tDTe2/r8nHyQGQ9dh7e3t7bNiwIVs7xsbGMv8g54adnR1iYmKwf/9+HDp0CB06dICzszO2bdv2bScDQF1dXeazRCKBVCoVPw8YMADr16//Yhsf7ugxMzPDuXPnZLYlJCSI2z6nS5cu6NKlCxISEqCrqwuJRILZs2ejbNmyn93HwcEBkydPRlpaWoFONCYi5cCEhegTurq6n51j8ik7Ozts3rwZJiYm0NfXzzHG0tISYWFhaNSoUa7a1NfXR8eOHdGxY0e0a9cOTZs2xcuXL2FkZCQTV6lSJQQHByMlJUVMpE6dOgUVFRVUrFgxV8cC8jYk5OTkhKlTp+Lp06diVSY0NBT6+vqwsbH56v4fqj6rVq2ClpYWmjRp8tnYyMhIGBoaMlkhIgBMWIjk0rVrV8ycOROtW7fGpEmTULJkSTx48AA7duzAyJEjUbJkSUyYMAEDBgyAiYkJmjVrhjdv3uDUqVM5vohw9uzZKFGiBGrUqAEVFRVs3boVZmZmKFq0aI7HHj9+PDw9PTFhwgQ8e/YMPj4+6N69e7Z5Jl+SlyEhFxcX2NjYoHv37pgxYwbi4+MxZswYeHl5iYnFuXPn0KNHD4SFheGXX34BkDW3pk6dOtDT00NoaChGjBiB6dOni+e1Z88eJCQkwNHREVpaWggNDcW0adOyJVKRkZEAsio+z549Q2RkJDQ0NHKVLBHRj40JC5EcdHR0cPz4cfj7+6Nt27Z48+YNfvnlFzRu3FisuHh6eiI1NRVz5szB8OHDUbx4cbRr1y7H9ooUKYIZM2bg9u3bUFVVRa1atbBv374ch5Z0dHRw8OBBDBkyBLVq1YKOjg48PDwwe/ZshZ2vqqoqQkJCMHDgQDg5OUFXVxeenp6YNGmSGPP27VtER0cjIyNDXHfu3DmMHz8eycnJsLa2xtKlS9G9e3dxu7q6OhYuXAhfX18IggArKyvMnj1bnBj8QY0aNcQ/R0REYOPGjbCwsMD9+/cVds5EpBz48kMiIiJSenwOCxERESk9JixERESk9JiwEBERkdJjwkJERERKjwkLERERKT0mLEQ/MIlEgl27dgEA7t+/D4lEIj6rhIioMGHCQvSNevbsCYlEAolEAnV1dZQpUwYjR45EampqQXftm23duhXW1tbQ0tJC1apVsW/fvi/Gf3wNPl4qV64sxlhaWuYY4+XlJcakpqbCy8sLxYoVg56eHjw8PMRH/hMRAUxYiOTStGlTxMXF4d69e5gzZw6WLl2K8ePHF3S3vsnp06fRuXNn9OnTB5cuXYK7uzvc3d1x7dq1z+4zb948xMXFicvDhw9hZGSE9u3bizHnz5+XiQkNDQUAmRhfX1/s2bMHW7duxbFjx/DkyRO0bdtWcSdLRD8egYi+iaenp9C6dWuZdW3bthVq1KghCIIgZGZmCtOmTRMsLS0FLS0toVq1asLWrVtl4q9duya4ubkJRYoUEfT09IR69eoJd+7cEQRBEM6dOyc4OzsLxYoVE/T19YUGDRoIERERMvsDEHbu3CkIgiDExMQIAIRLly590/l06NBBcHNzk1nn4OAg/P7777luY+fOnYJEIhHu37//2ZghQ4YI5cqVE6RSqSAIgpCYmCioq6vLXJuoqCgBgBAeHp7HsyCiwooVFqJ8cu3aNZw+fRoaGhoAgMDAQKxduxZLlizB9evX4evri27duuHYsWMAgMePH6NBgwbQ1NTE4cOHERERgd69e+P9+/cAgDdv3sDT0xMnT57EmTNnUL58eTRv3hxv3rzJdZ/09PS+uAwYMECMDQ8Ph7Ozs8z+rq6uCA8Pz/XxVq5cCWdnZ1hYWOS4PT09HevXr0fv3r0hkUgAZD1iPyMjQ+bY1tbWKF26dJ6OTUSFG98lRCSHkJAQ6Onp4f3790hLS4OKigqCgoKQlpaGadOm4dChQ3BycgIAlC1bFidPnsTSpUvRsGFDLFy4EAYGBti0aRPU1dUBABUqVBDb/u2332SOtWzZMhQtWhTHjh1DixYtctW/r03A/fgN0/Hx8dlemmhqaor4+PhcHevJkyfYv38/Nm7c+NmYXbt2ITExET179pQ5roaGRrYXPObl2ERU+DFhIZJDo0aNsHjxYqSkpGDOnDlQU1ODh4cHrl+/jrdv36JJkyYy8enp6eIL/CIjI1G/fn0xWflUQkICxowZg6NHj+Lp06fIzMzE27dvERsbm+v+WVlZffvJ5dGaNWtQtGhRuLu7fzZm5cqVaNasGczNzb9bv4iocGDCQiQHXV1dMSlYtWoVqlevjpUrV6JKlSoAgL179+KXX36R2UdTUxMAoK2t/cW2PT098eLFC8ybNw8WFhbQ1NSEk5MT0tPTc90/PT29L27v1q0blixZAgAwMzPLdmdOQkICzMzMvnocQRCwatUqdO/eXRwS+9SDBw9w6NAh7NixQ2a9mZkZ0tPTkZiYKFNlye2xiejnwISFKJ+oqKjgjz/+gJ+fH27dugVNTU3ExsaiYcOGOcZXq1YNa9asQUZGRo5VllOnTmHRokVo3rw5AODhw4d4/vx5nvqUlyEhJycnhIWFYejQoeK60NBQcUjrS44dO4Y7d+6gT58+n41ZvXo1TExM4ObmJrPe3t4e6urqCAsLg4eHBwAgOjoasbGxuTo2Ef0cmLAQ5aP27dtjxIgRWLp0KYYPHw5fX19IpVLUq1cPSUlJOHXqFPT19eHp6Qlvb28sWLAAnTp1QkBAAAwMDHDmzBnUrl0bFStWRPny5bFu3TrUrFkTr1+/xogRI75alflUXoaEhgwZgoYNG+Kvv/6Cm5sbNm3ahAsXLmDZsmViTEBAAB4/foy1a9fK7Lty5Uo4ODiIlaVPSaVSrF69Gp6enlBTk/2xY2BggD59+sDPzw9GRkbQ19eHj48PnJyc4OjomIezJaLCjAkLUT5SU1ODt7c3ZsyYgZiYGBgbGyMwMBD37t1D0aJFYWdnhz/++AMAUKxYMRw+fBgjRoxAw4YNoaqqCltbW9StWxdAVhLQv39/2NnZoVSpUpg2bRqGDx+usL7XqVMHGzduxJgxY/DHH3+gfPny2LVrl0wSEhcXl20OTVJSErZv34558+Z9tu1Dhw4hNjYWvXv3znH7nDlzoKKiAg8PD6SlpcHV1RWLFi3KnxMjokJBIgiCUNCdICIiIvoSPoeFiIiIlB4TFiIiIlJ6TFiIiIhI6TFhISIiIqXHhIWIiIiUHhMWok/07NkTEokk23Lnzh0AwPHjx9GyZUuYm5tDIpFg165dX20zMzMT06dPh7W1NbS1tWFkZAQHBwesWLFCwWeT/1JTU+Hl5YVixYpBT08PHh4e2Z6Q+6nk5GR4e3ujZMmS0NbWho2NjfiE3Q9+/fXXbNf845czAsDgwYNhb28PTU1N2Nra5vepEZES43NYiHLQtGlTrF69WmadsbExACAlJQXVq1dH79690bZt21y1N3HiRCxduhRBQUHig+AuXLiAV69e5XvfP0hPT//sY/Ll4evri71792Lr1q0wMDCAt7c32rZti1OnTn12Hz8/Pxw+fBjr16+HpaUl/v33XwwaNAjm5uZo1aqVGNevXz9MmjRJ/Kyjo5Otrd69e+Ps2bO4cuVK/p4YESk3gYhkeHp6Cq1bt85VLABh586dX42rXr26MGHChC/GZGZmCn/++adQrlw5QUNDQyhVqpQwZcoUcfuVK1eERo0aCVpaWoKRkZHQr18/4c2bN9n6PWXKFKFEiRKCpaWlIAiCEBsbK7Rv314wMDAQDA0NhVatWgkxMTG5Or9PJSYmCurq6sLWrVvFdVFRUQIAITw8/LP7Va5cWZg0aZLMOjs7O2H06NHi54YNGwpDhgzJVT/Gjx8vVK9ePU99J6IfG4eEiL4DMzMzHD58GM+ePftsTEBAAKZPn46xY8fixo0b2LhxI0xNTQFkVXVcXV1haGiI8+fPY+vWrTh06BC8vb1l2ggLC0N0dDRCQ0MREhKCjIwMuLq6okiRIjhx4gROnToFPT09NG3aVHyJ4oYNG6Cnp/fF5cSJEwCAiIgIZGRkwNnZWTymtbU1SpcujfDw8M+eW506dbB79248fvwYgiDgyJEjuHXrFlxcXGTiNmzYgOLFi6NKlSoICAjA27dv83ahiajQ4pAQUQ5CQkJk3nTcrFkzbN269Zvbmz17Ntq1awczMzNUrlwZderUQevWrdGsWTMAwJs3bzBv3jwEBQXB09MTAFCuXDnUq1cPALBx40akpqZi7dq10NXVBQAEBQWhZcuW+PPPP8XERldXFytWrBCHgtavXw+pVIoVK1ZAIpEAyHoJYdGiRXH06FG4uLigVatWcHBw+GL/P7xxOj4+HhoaGjJvVQYAU1NTxMfHf3b/BQsWoH///ihZsiTU1NSgoqKC5cuXo0GDBmJMly5dYGFhAXNzc1y5cgX+/v6Ijo7O9nZnIvo5MWEhykGjRo2wePFi8fOHJOFb2djY4Nq1a4iIiMCpU6fEibs9e/bEihUrEBUVhbS0NDRu3DjH/aOiolC9enWZftStWxdSqRTR0dFiwlK1alWZeSuXL1/GnTt3UKRIEZn2UlNTcffuXQBAkSJFsm3PbwsWLMCZM2ewe/duWFhY4Pjx4/Dy8oK5ublYrenfv78YX7VqVZQoUQKNGzfG3bt3Ua5cOYX2j4iUHxMWohzo6urm6U3HuaGiooJatWqhVq1aGDp0KNavX4/u3btj9OjReX4L8+d8mlglJyfD3t4eGzZsyBb7YRLxhg0b8Pvvv3+x3f3796N+/fowMzNDeno6EhMTZaosCQkJMDMzy3Hfd+/e4Y8//sDOnTvh5uYGAKhWrRoiIyMxa9YsmeGlj32o+ty5c4cJCxExYSEqKDY2NgCy5qeUL18e2traCAsLQ9++fbPFVqpUCcHBwUhJSRGTklOnTkFFRQUVK1b87DHs7OywefNmmJiYQF9fP8eYvAwJ2dvbQ11dHWFhYfDw8AAAREdHIzY2Fk5OTjnum5GRgYyMDKioyE6ZU1VVhVQq/ewxIyMjAQAlSpT4Yt+I6OfAhIUoj5KTk8VnsgBATEwMIiMjYWRkhNKlS+e4T7t27VC3bl3UqVMHZmZmiImJQUBAACpUqABra2uoqanB398fI0eOhIaGBurWrYtnz57h+vXr6NOnD7p27Yrx48fD09MTEyZMwLNnz+Dj44Pu3buLw0E56dq1K2bOnInWrVtj0qRJKFmyJB48eIAdO3Zg5MiRKFmyZJ6GhAwMDNCnTx/4+fnByMgI+vr68PHxgZOTExwdHcU4a2trBAYGok2bNtDX10fDhg0xYsQIaGtrw8LCAseOHcPatWsxe/ZsAMDdu3exceNGNG/eHMWKFcOVK1fg6+uLBg0aoFq1amK7d+7cQXJyMuLj4/Hu3TsxqbGxsVHILdxEpEQK+jYlImXztduajxw5IgDItnh6en52n2XLlgmNGjUSjI2NBQ0NDaF06dJCz549hfv374sxmZmZwpQpUwQLCwtBXV1dKF26tDBt2jRxe25va/5UXFyc0KNHD6F48eKCpqamULZsWaFfv35CUlJSnq7LB+/evRMGDRokGBoaCjo6OkKbNm2EuLg4mRjgf+3csQ2DUAxF0aSCmpIa6W8Bq7AEYzADm/IoUkVJEaWJI50zgeXqNvYtx3E8zbCua8ZxTN/3aa1l3/ec55nkcXo9z3OGYUjXdZmmKdu2vcy4LMvb3X97pg38j3uS/KiVAAA+4g8LAFCeYAEAyhMsAEB5ggUAKE+wAADlCRYAoDzBAgCUJ1gAgPIECwBQnmABAMoTLABAeRcYly2QcuF4VQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "## Confusion Matrix on unsee test set\n",
        "import seaborn as sn\n",
        "y_pred1 = Model2.predict(X_test)\n",
        "for i in range(len(y_test)):\n",
        "    if y_pred1[i]>0.5:\n",
        "        y_pred1[i]=1\n",
        "    else:\n",
        "        y_pred1[i]=0\n",
        "\n",
        "\n",
        "\n",
        "cm2=confusion_matrix(y_test, y_pred1)\n",
        "labels = ['True Negative','False Positive','False Negative','True Positive']\n",
        "categories = [ 'Not_Fraud','Fraud']\n",
        "make_confusion_matrix(cm2,\n",
        "                      group_names=labels,\n",
        "                      #categories=categories,\n",
        "                      cmap='Blues')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NIJhD-FdkCey"
      },
      "source": [
        "**Conclusion:**\n",
        "\n",
        "As you can see here the Recall of the model is not improved and it is worse than the Previous ANN model as well as the RandomForest but the precision is changed.\n",
        "\n",
        "Let's try weighted loss for imbalance dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rd0Oron8Hd8q"
      },
      "source": [
        "### Weighted loss to account for large class imbalance in train dataset\n",
        "- we will adjust the class imbalance by giving additional weight to the loss associated to errors made on fraudulent transaction detection.\n",
        "\n",
        "We will use our first ANN model and apply weighted loss\n",
        "\n",
        "\n",
        " Let's review the process:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "id": "YOOpPUs2Hd8q",
        "outputId": "1f8058fc-f237-4e58-8376-198f08899797"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-41-9aa2fd39f857>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mclass_weights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_class_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'balanced'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mclass_weights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_class_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_weight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"balanced\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mclasses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mclass_weights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mclass_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: compute_class_weight() takes 1 positional argument but 3 were given"
          ]
        }
      ],
      "source": [
        "from sklearn.utils import class_weight\n",
        "class_weights = class_weight.compute_class_weight('balanced', np.unique(y_train), np.array([y_train.iloc[i] for i in range(len(y_train))]))\n",
        "class_weights = class_weight.compute_class_weight(class_weight = \"balanced\",classes = np.unique(y_train), y = y_train)\n",
        "class_weights = dict(enumerate(class_weights))\n",
        "class_weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IzhC-vmHHd8q"
      },
      "source": [
        "- The class 'Fraudulent' (y=1) is assigned a weight of 290 vs 0.5 for the class 'not fraudulent' due to the very low prevalence we detected during data exploration. This allows the model to give more importance to the errors made on fraudulent cases during training."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qIqdG2SjR5Tp"
      },
      "source": [
        "Training the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pdO-qfNuHd8q"
      },
      "outputs": [],
      "source": [
        "model.fit(X_train,y_train,batch_size=15,epochs=5, class_weight=class_weights, shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tCxSuodPHd8q"
      },
      "outputs": [],
      "source": [
        "score_weighted = model.evaluate(X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J2llngAA9w7r"
      },
      "source": [
        "Plotting confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6y0arPk172IK"
      },
      "outputs": [],
      "source": [
        "## Confusion Matrix on unsee test set\n",
        "import seaborn as sn\n",
        "y_pred1 = model.predict(X_test)\n",
        "for i in range(len(y_test)):\n",
        "    if y_pred1[i]>0.5:\n",
        "        y_pred1[i]=1\n",
        "    else:\n",
        "        y_pred1[i]=0\n",
        "\n",
        "\n",
        "\n",
        "cm2=confusion_matrix(y_test, y_pred1)\n",
        "labels = ['True Negative','False Positive','False Negative','True Positive']\n",
        "categories = [ 'Not_Fraud','Fraud']\n",
        "make_confusion_matrix(cm2,\n",
        "                      group_names=labels,\n",
        "                      #categories=categories,\n",
        "                      cmap='Blues')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4OKECUXK4xGM"
      },
      "source": [
        "\n",
        " **Conclusion:**\n",
        "\n",
        "\n",
        "\n",
        " As you can see here the Recall is increased but the precision is very bad.  There is still  lot of scope of  improvements  as follows:\n",
        "\n",
        "1) Threshold can be tuned to get the optimal value\n",
        "\n",
        "2) Resampling techniques can be applied to balanced the data and then train the model\n",
        "\n",
        "3) Hyperparameter tuning can be applied to tune the different Hyperparameters\n",
        "\n",
        "\n",
        "We can select the Model-1 as our final model based on the above analysis"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "include_colab_link": true
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    "kernelspec": {
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      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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