DL_LAB2.ipynb

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        "<a href=\"https://colab.research.google.com/github/steffiangel/DL_AIML/blob/main/DL_LAB2.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": "code",
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      "metadata": {
        "id": "AKLLm1GsAuFA"
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      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import date, datetime, timedelta\n",
        "import os\n",
        "import math\n",
        "from imblearn.over_sampling import SMOTE\n",
        "import plotly.express as px\n",
        "import plotly.graph_objs as go\n",
        "from plotly.subplots import make_subplots\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import sklearn\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "JdTLWACeCCzy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/creditcard.csv\")"
      ],
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        "id": "g7ZgDJV2CDbv"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "df.head()"
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              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
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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",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 284807 entries, 0 to 284806\n",
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            " #   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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "dUAeNhULPp43",
        "outputId": "6136a1ef-481c-4c48-8d5f-afb5c8858849"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Time            V1            V2            V3            V4  \\\n",
              "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
              "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
              "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
              "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
              "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
              "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
              "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
              "\n",
              "                 V5            V6            V7            V8            V9  \\\n",
              "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
              "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
              "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
              "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
              "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
              "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
              "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
              "\n",
              "       ...           V21           V22           V23           V24  \\\n",
              "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
              "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
              "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
              "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
              "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
              "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
              "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
              "\n",
              "                V25           V26           V27           V28         Amount  \\\n",
              "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
              "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
              "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
              "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
              "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
              "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
              "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
              "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
              "\n",
              "               Class  \n",
              "count  284807.000000  \n",
              "mean        0.001727  \n",
              "std         0.041527  \n",
              "min         0.000000  \n",
              "25%         0.000000  \n",
              "50%         0.000000  \n",
              "75%         0.000000  \n",
              "max         1.000000  \n",
              "\n",
              "[8 rows x 31 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>...</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>count</th>\n",
              "      <td>284807.000000</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>...</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>284807.000000</td>\n",
              "      <td>284807.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>94813.859575</td>\n",
              "      <td>1.168375e-15</td>\n",
              "      <td>3.416908e-16</td>\n",
              "      <td>-1.379537e-15</td>\n",
              "      <td>2.074095e-15</td>\n",
              "      <td>9.604066e-16</td>\n",
              "      <td>1.487313e-15</td>\n",
              "      <td>-5.556467e-16</td>\n",
              "      <td>1.213481e-16</td>\n",
              "      <td>-2.406331e-15</td>\n",
              "      <td>...</td>\n",
              "      <td>1.654067e-16</td>\n",
              "      <td>-3.568593e-16</td>\n",
              "      <td>2.578648e-16</td>\n",
              "      <td>4.473266e-15</td>\n",
              "      <td>5.340915e-16</td>\n",
              "      <td>1.683437e-15</td>\n",
              "      <td>-3.660091e-16</td>\n",
              "      <td>-1.227390e-16</td>\n",
              "      <td>88.349619</td>\n",
              "      <td>0.001727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47488.145955</td>\n",
              "      <td>1.958696e+00</td>\n",
              "      <td>1.651309e+00</td>\n",
              "      <td>1.516255e+00</td>\n",
              "      <td>1.415869e+00</td>\n",
              "      <td>1.380247e+00</td>\n",
              "      <td>1.332271e+00</td>\n",
              "      <td>1.237094e+00</td>\n",
              "      <td>1.194353e+00</td>\n",
              "      <td>1.098632e+00</td>\n",
              "      <td>...</td>\n",
              "      <td>7.345240e-01</td>\n",
              "      <td>7.257016e-01</td>\n",
              "      <td>6.244603e-01</td>\n",
              "      <td>6.056471e-01</td>\n",
              "      <td>5.212781e-01</td>\n",
              "      <td>4.822270e-01</td>\n",
              "      <td>4.036325e-01</td>\n",
              "      <td>3.300833e-01</td>\n",
              "      <td>250.120109</td>\n",
              "      <td>0.041527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-5.640751e+01</td>\n",
              "      <td>-7.271573e+01</td>\n",
              "      <td>-4.832559e+01</td>\n",
              "      <td>-5.683171e+00</td>\n",
              "      <td>-1.137433e+02</td>\n",
              "      <td>-2.616051e+01</td>\n",
              "      <td>-4.355724e+01</td>\n",
              "      <td>-7.321672e+01</td>\n",
              "      <td>-1.343407e+01</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.483038e+01</td>\n",
              "      <td>-1.093314e+01</td>\n",
              "      <td>-4.480774e+01</td>\n",
              "      <td>-2.836627e+00</td>\n",
              "      <td>-1.029540e+01</td>\n",
              "      <td>-2.604551e+00</td>\n",
              "      <td>-2.256568e+01</td>\n",
              "      <td>-1.543008e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>54201.500000</td>\n",
              "      <td>-9.203734e-01</td>\n",
              "      <td>-5.985499e-01</td>\n",
              "      <td>-8.903648e-01</td>\n",
              "      <td>-8.486401e-01</td>\n",
              "      <td>-6.915971e-01</td>\n",
              "      <td>-7.682956e-01</td>\n",
              "      <td>-5.540759e-01</td>\n",
              "      <td>-2.086297e-01</td>\n",
              "      <td>-6.430976e-01</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.283949e-01</td>\n",
              "      <td>-5.423504e-01</td>\n",
              "      <td>-1.618463e-01</td>\n",
              "      <td>-3.545861e-01</td>\n",
              "      <td>-3.171451e-01</td>\n",
              "      <td>-3.269839e-01</td>\n",
              "      <td>-7.083953e-02</td>\n",
              "      <td>-5.295979e-02</td>\n",
              "      <td>5.600000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>84692.000000</td>\n",
              "      <td>1.810880e-02</td>\n",
              "      <td>6.548556e-02</td>\n",
              "      <td>1.798463e-01</td>\n",
              "      <td>-1.984653e-02</td>\n",
              "      <td>-5.433583e-02</td>\n",
              "      <td>-2.741871e-01</td>\n",
              "      <td>4.010308e-02</td>\n",
              "      <td>2.235804e-02</td>\n",
              "      <td>-5.142873e-02</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.945017e-02</td>\n",
              "      <td>6.781943e-03</td>\n",
              "      <td>-1.119293e-02</td>\n",
              "      <td>4.097606e-02</td>\n",
              "      <td>1.659350e-02</td>\n",
              "      <td>-5.213911e-02</td>\n",
              "      <td>1.342146e-03</td>\n",
              "      <td>1.124383e-02</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>139320.500000</td>\n",
              "      <td>1.315642e+00</td>\n",
              "      <td>8.037239e-01</td>\n",
              "      <td>1.027196e+00</td>\n",
              "      <td>7.433413e-01</td>\n",
              "      <td>6.119264e-01</td>\n",
              "      <td>3.985649e-01</td>\n",
              "      <td>5.704361e-01</td>\n",
              "      <td>3.273459e-01</td>\n",
              "      <td>5.971390e-01</td>\n",
              "      <td>...</td>\n",
              "      <td>1.863772e-01</td>\n",
              "      <td>5.285536e-01</td>\n",
              "      <td>1.476421e-01</td>\n",
              "      <td>4.395266e-01</td>\n",
              "      <td>3.507156e-01</td>\n",
              "      <td>2.409522e-01</td>\n",
              "      <td>9.104512e-02</td>\n",
              "      <td>7.827995e-02</td>\n",
              "      <td>77.165000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>172792.000000</td>\n",
              "      <td>2.454930e+00</td>\n",
              "      <td>2.205773e+01</td>\n",
              "      <td>9.382558e+00</td>\n",
              "      <td>1.687534e+01</td>\n",
              "      <td>3.480167e+01</td>\n",
              "      <td>7.330163e+01</td>\n",
              "      <td>1.205895e+02</td>\n",
              "      <td>2.000721e+01</td>\n",
              "      <td>1.559499e+01</td>\n",
              "      <td>...</td>\n",
              "      <td>2.720284e+01</td>\n",
              "      <td>1.050309e+01</td>\n",
              "      <td>2.252841e+01</td>\n",
              "      <td>4.584549e+00</td>\n",
              "      <td>7.519589e+00</td>\n",
              "      <td>3.517346e+00</td>\n",
              "      <td>3.161220e+01</td>\n",
              "      <td>3.384781e+01</td>\n",
              "      <td>25691.160000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
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              "\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-3e0afcaa-0b8d-45b1-81b7-1ce1b771b2a3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3e0afcaa-0b8d-45b1-81b7-1ce1b771b2a3')\"\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",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-3e0afcaa-0b8d-45b1-81b7-1ce1b771b2a3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SAzjvZGGPrUJ",
        "outputId": "9d470d3d-81c6-45e4-c17f-25aaae157bd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      0\n",
              "V1        0\n",
              "V2        0\n",
              "V3        0\n",
              "V4        0\n",
              "V5        0\n",
              "V6        0\n",
              "V7        0\n",
              "V8        0\n",
              "V9        0\n",
              "V10       0\n",
              "V11       0\n",
              "V12       0\n",
              "V13       0\n",
              "V14       0\n",
              "V15       0\n",
              "V16       0\n",
              "V17       0\n",
              "V18       0\n",
              "V19       0\n",
              "V20       0\n",
              "V21       0\n",
              "V22       0\n",
              "V23       0\n",
              "V24       0\n",
              "V25       0\n",
              "V26       0\n",
              "V27       0\n",
              "V28       0\n",
              "Amount    0\n",
              "Class     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.isnull(),cmap='viridis')\n",
        "plt.grid()\n",
        "plt.title(\"NO OF MISSING VALUES\")\n",
        "plt.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "zClpqSchPs7V",
        "outputId": "7b3639ae-d610-473f-c8ba-bcef5724d467"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4e-AsV1ZP2HE",
        "outputId": "ac2e101f-aab1-4e76-b8be-060246e53f76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1081"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Class'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RwO4yv9kRGb7",
        "outputId": "db9e82e9-95ce-4b25-bb8b-fbfa86b452f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.boxplot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "w9_HaZkIoEvO",
        "outputId": "798f1a97-74ed-4a6f-cd13-6d716938020d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oWGO2mJboEy1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=df,x='Class',hue='Class',color='pink')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "Qg-wuy3DQLum",
        "outputId": "0192b824-8710-4a4a-e458-ea00f781e57a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Class', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count=df['Class'].value_counts()\n",
        "plt.pie(count,labels=count.index,autopct='%1.1f%%',startangle=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "CN1oYka5SbP5",
        "outputId": "30265447-f533-4734-ec6c-1e5d80219201"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yBiqVJdPTzFQ",
        "outputId": "a9daa726-979f-4b3e-d508-7c3457b0c46e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    284315\n",
              "1       492\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.scatter(x=df['Class'], y=df['Time'], color='pink')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "5pidJtUqQoit",
        "outputId": "8aa9479f-8963-4770-fce9-d114273edc08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x78c35d056e90>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eps = 1e-8\n",
        "df['Log Amount'] = np.log(df['Amount'] + eps)\n",
        "\n",
        "df.drop('Amount', axis=1, inplace=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "bg4FZoIjpF7v",
        "outputId": "0f8f5717-9fac-42ff-910f-dfce4e452082"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "'Amount'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\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   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Amount'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-126-c0f208031fd7>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0meps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1e-8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Log Amount'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Amount'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0meps\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Drop the original 'Amount' column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Amount'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\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   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Amount'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from tensorflow.keras import models, layers, optimizers, losses"
      ],
      "metadata": {
        "id": "qeQH2te6Roys"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.drop(['Class','Time'],axis=1)\n",
        "\n",
        "Y=df['Class']\n"
      ],
      "metadata": {
        "id": "QSPXvWMVatZB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_C428pd_noY2",
        "outputId": "5cac23c7-e3ad-4d2b-ec61-4d8efd48c8b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DLa2rY0HnoU0",
        "outputId": "b7a77bdf-5dbe-4a27-cb79-53b7280c8518"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807,)"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "#smote = SMOTE(random_state=42)\n",
        "#X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "3LW01N0zkngZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "model = Sequential([\n",
        "    Dense(64, input_dim=X_train.shape[1], activation='relu'),\n",
        "    Dropout(0.2),\n",
        "    Dense(32, activation='relu'),\n",
        "    Dropout(0.2),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "metadata": {
        "id": "d5QUgvhwlQAx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cQA2saenaFgS"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}