Credit_Card_Fraud_Detection_assignment .ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "colab": {
      "name": "Credit Card Fraud Detection assignment.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GJQT0OoDE7C4"
      },
      "source": [
        "# Credit Card Fraud Detection::"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1U6mDTa7FF0R",
        "outputId": "b376aced-56d7-4c53-be85-0712d57d9d6a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.activity.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fexperimentsandconfigs%20https%3a%2f%2fwww.googleapis.com%2fauth%2fphotos.native&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "4/1AY0e-g7RwgEQI-pTwV_LGxz8mdcHEPxIB0v5qg6AvI_W1KA3H2VgV2MR3yo\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CPFEhRnRE7D6"
      },
      "source": [
        "Download dataset from this link:\n",
        "\n",
        "https://www.kaggle.com/mlg-ulb/creditcardfraud"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qq8i9cjAE7EA"
      },
      "source": [
        "# Description about dataset::"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oPMaqqhvE7EM"
      },
      "source": [
        "The datasets contains transactions made by credit cards in September 2013 by european cardholders.\n",
        "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, we cannot provide the original features and more background information about the data. 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'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. \n",
        "\n",
        "\n",
        "### Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "48D33k2LE7EP"
      },
      "source": [
        "# WORKFLOW :"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uchrHTffE7EQ"
      },
      "source": [
        "1.Load Data\n",
        "\n",
        "2.Check Missing Values ( If Exist ; Fill each record with mean of its feature )\n",
        "\n",
        "3.Standardized the Input Variables. \n",
        "\n",
        "4.Split into 50% Training(Samples,Labels) , 30% Test(Samples,Labels) and 20% Validation Data(Samples,Labels).\n",
        "\n",
        "5.Model : input Layer (No. of features ), 3 hidden layers including 10,8,6 unit & Output Layer with activation function relu/tanh (check by experiment).\n",
        "\n",
        "6.Compilation Step (Note : Its a Binary problem , select loss , metrics according to it)\n",
        "\n",
        "7.Train the Model with Epochs (100).\n",
        "\n",
        "8.If the model gets overfit tune your model by changing the units , No. of layers , epochs , add dropout layer or add Regularizer according to the need .\n",
        "\n",
        "9.Prediction should be > 92%\n",
        "10.Evaluation Step\n",
        "11Prediction\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gBA6gNvDE7EU"
      },
      "source": [
        "# Task::"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OvpjObYRE7EW"
      },
      "source": [
        "## Identify fraudulent credit card transactions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KL1iYMrWE7EX"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd "
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dz8zOlvQE7Es"
      },
      "source": [
        "df= pd.read_csv('/content/drive/MyDrive/creditcard.csv')"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "id": "c2hpib1fE7Eu",
        "outputId": "cf9a4335-17ed-443d-f468-cb9bb4a457db"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.991390</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>1.468177</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>149.62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Time        V1        V2        V3  ...       V27       V28  Amount  Class\n",
              "0   0.0 -1.359807 -0.072781  2.536347  ...  0.133558 -0.021053  149.62      0\n",
              "1   0.0  1.191857  0.266151  0.166480  ... -0.008983  0.014724    2.69      0\n",
              "2   1.0 -1.358354 -1.340163  1.773209  ... -0.055353 -0.059752  378.66      0\n",
              "3   1.0 -0.966272 -0.185226  1.792993  ...  0.062723  0.061458  123.50      0\n",
              "4   2.0 -1.158233  0.877737  1.548718  ...  0.219422  0.215153   69.99      0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2OtwBuEjOLsh"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wPKPV1fQE7Ey",
        "outputId": "9b4653c9-1670-4c95-eac4-b602a23aa5d4"
      },
      "source": [
        "df.isnull().any()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      False\n",
              "V1        False\n",
              "V2        False\n",
              "V3        False\n",
              "V4        False\n",
              "V5        False\n",
              "V6        False\n",
              "V7        False\n",
              "V8        False\n",
              "V9        False\n",
              "V10       False\n",
              "V11       False\n",
              "V12       False\n",
              "V13       False\n",
              "V14       False\n",
              "V15       False\n",
              "V16       False\n",
              "V17       False\n",
              "V18       False\n",
              "V19       False\n",
              "V20       False\n",
              "V21       False\n",
              "V22       False\n",
              "V23       False\n",
              "V24       False\n",
              "V25       False\n",
              "V26       False\n",
              "V27       False\n",
              "V28       False\n",
              "Amount    False\n",
              "Class     False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5tqjVJKsJNyY"
      },
      "source": [
        "X=df.drop(columns=\"Class\")\n",
        "y=df['Class']"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0KXfEftGE7E4"
      },
      "source": [
        "non_fraud=df[df[\"Class\"]==0]\n",
        "fraud=df[df[\"Class\"]==1]\n",
        "non_fraud=non_fraud.sample(3*fraud.shape[0])\n",
        "data = fraud.append(non_fraud, ignore_index=True)\n",
        "x_data= data.drop(columns=\"Class\", axis=0)\n",
        "label = data[\"Class\"]"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "haz0zJYVE7E9",
        "outputId": "8833eabb-a55a-43b8-b45a-281dbb56592b"
      },
      "source": [
        "data"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>406.0</td>\n",
              "      <td>-2.312227</td>\n",
              "      <td>1.951992</td>\n",
              "      <td>-1.609851</td>\n",
              "      <td>3.997906</td>\n",
              "      <td>-0.522188</td>\n",
              "      <td>-1.426545</td>\n",
              "      <td>-2.537387</td>\n",
              "      <td>1.391657</td>\n",
              "      <td>-2.770089</td>\n",
              "      <td>-2.772272</td>\n",
              "      <td>3.202033</td>\n",
              "      <td>-2.899907</td>\n",
              "      <td>-0.595222</td>\n",
              "      <td>-4.289254</td>\n",
              "      <td>0.389724</td>\n",
              "      <td>-1.140747</td>\n",
              "      <td>-2.830056</td>\n",
              "      <td>-0.016822</td>\n",
              "      <td>0.416956</td>\n",
              "      <td>0.126911</td>\n",
              "      <td>0.517232</td>\n",
              "      <td>-0.035049</td>\n",
              "      <td>-0.465211</td>\n",
              "      <td>0.320198</td>\n",
              "      <td>0.044519</td>\n",
              "      <td>0.177840</td>\n",
              "      <td>0.261145</td>\n",
              "      <td>-0.143276</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>472.0</td>\n",
              "      <td>-3.043541</td>\n",
              "      <td>-3.157307</td>\n",
              "      <td>1.088463</td>\n",
              "      <td>2.288644</td>\n",
              "      <td>1.359805</td>\n",
              "      <td>-1.064823</td>\n",
              "      <td>0.325574</td>\n",
              "      <td>-0.067794</td>\n",
              "      <td>-0.270953</td>\n",
              "      <td>-0.838587</td>\n",
              "      <td>-0.414575</td>\n",
              "      <td>-0.503141</td>\n",
              "      <td>0.676502</td>\n",
              "      <td>-1.692029</td>\n",
              "      <td>2.000635</td>\n",
              "      <td>0.666780</td>\n",
              "      <td>0.599717</td>\n",
              "      <td>1.725321</td>\n",
              "      <td>0.283345</td>\n",
              "      <td>2.102339</td>\n",
              "      <td>0.661696</td>\n",
              "      <td>0.435477</td>\n",
              "      <td>1.375966</td>\n",
              "      <td>-0.293803</td>\n",
              "      <td>0.279798</td>\n",
              "      <td>-0.145362</td>\n",
              "      <td>-0.252773</td>\n",
              "      <td>0.035764</td>\n",
              "      <td>529.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4462.0</td>\n",
              "      <td>-2.303350</td>\n",
              "      <td>1.759247</td>\n",
              "      <td>-0.359745</td>\n",
              "      <td>2.330243</td>\n",
              "      <td>-0.821628</td>\n",
              "      <td>-0.075788</td>\n",
              "      <td>0.562320</td>\n",
              "      <td>-0.399147</td>\n",
              "      <td>-0.238253</td>\n",
              "      <td>-1.525412</td>\n",
              "      <td>2.032912</td>\n",
              "      <td>-6.560124</td>\n",
              "      <td>0.022937</td>\n",
              "      <td>-1.470102</td>\n",
              "      <td>-0.698826</td>\n",
              "      <td>-2.282194</td>\n",
              "      <td>-4.781831</td>\n",
              "      <td>-2.615665</td>\n",
              "      <td>-1.334441</td>\n",
              "      <td>-0.430022</td>\n",
              "      <td>-0.294166</td>\n",
              "      <td>-0.932391</td>\n",
              "      <td>0.172726</td>\n",
              "      <td>-0.087330</td>\n",
              "      <td>-0.156114</td>\n",
              "      <td>-0.542628</td>\n",
              "      <td>0.039566</td>\n",
              "      <td>-0.153029</td>\n",
              "      <td>239.93</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6986.0</td>\n",
              "      <td>-4.397974</td>\n",
              "      <td>1.358367</td>\n",
              "      <td>-2.592844</td>\n",
              "      <td>2.679787</td>\n",
              "      <td>-1.128131</td>\n",
              "      <td>-1.706536</td>\n",
              "      <td>-3.496197</td>\n",
              "      <td>-0.248778</td>\n",
              "      <td>-0.247768</td>\n",
              "      <td>-4.801637</td>\n",
              "      <td>4.895844</td>\n",
              "      <td>-10.912819</td>\n",
              "      <td>0.184372</td>\n",
              "      <td>-6.771097</td>\n",
              "      <td>-0.007326</td>\n",
              "      <td>-7.358083</td>\n",
              "      <td>-12.598419</td>\n",
              "      <td>-5.131549</td>\n",
              "      <td>0.308334</td>\n",
              "      <td>-0.171608</td>\n",
              "      <td>0.573574</td>\n",
              "      <td>0.176968</td>\n",
              "      <td>-0.436207</td>\n",
              "      <td>-0.053502</td>\n",
              "      <td>0.252405</td>\n",
              "      <td>-0.657488</td>\n",
              "      <td>-0.827136</td>\n",
              "      <td>0.849573</td>\n",
              "      <td>59.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7519.0</td>\n",
              "      <td>1.234235</td>\n",
              "      <td>3.019740</td>\n",
              "      <td>-4.304597</td>\n",
              "      <td>4.732795</td>\n",
              "      <td>3.624201</td>\n",
              "      <td>-1.357746</td>\n",
              "      <td>1.713445</td>\n",
              "      <td>-0.496358</td>\n",
              "      <td>-1.282858</td>\n",
              "      <td>-2.447469</td>\n",
              "      <td>2.101344</td>\n",
              "      <td>-4.609628</td>\n",
              "      <td>1.464378</td>\n",
              "      <td>-6.079337</td>\n",
              "      <td>-0.339237</td>\n",
              "      <td>2.581851</td>\n",
              "      <td>6.739384</td>\n",
              "      <td>3.042493</td>\n",
              "      <td>-2.721853</td>\n",
              "      <td>0.009061</td>\n",
              "      <td>-0.379068</td>\n",
              "      <td>-0.704181</td>\n",
              "      <td>-0.656805</td>\n",
              "      <td>-1.632653</td>\n",
              "      <td>1.488901</td>\n",
              "      <td>0.566797</td>\n",
              "      <td>-0.010016</td>\n",
              "      <td>0.146793</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1</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",
              "      <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>1963</th>\n",
              "      <td>62345.0</td>\n",
              "      <td>-0.809681</td>\n",
              "      <td>1.224628</td>\n",
              "      <td>0.263897</td>\n",
              "      <td>0.950811</td>\n",
              "      <td>0.118756</td>\n",
              "      <td>0.068186</td>\n",
              "      <td>0.784965</td>\n",
              "      <td>0.195758</td>\n",
              "      <td>-0.651050</td>\n",
              "      <td>-0.354523</td>\n",
              "      <td>-0.794885</td>\n",
              "      <td>0.815042</td>\n",
              "      <td>1.276848</td>\n",
              "      <td>0.208578</td>\n",
              "      <td>0.201959</td>\n",
              "      <td>-0.900525</td>\n",
              "      <td>0.328319</td>\n",
              "      <td>-0.622770</td>\n",
              "      <td>0.462472</td>\n",
              "      <td>-0.181896</td>\n",
              "      <td>0.194105</td>\n",
              "      <td>0.702406</td>\n",
              "      <td>-0.012002</td>\n",
              "      <td>-0.215192</td>\n",
              "      <td>-0.389075</td>\n",
              "      <td>-0.314491</td>\n",
              "      <td>-0.112717</td>\n",
              "      <td>0.118569</td>\n",
              "      <td>75.40</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964</th>\n",
              "      <td>66083.0</td>\n",
              "      <td>-1.088752</td>\n",
              "      <td>0.714853</td>\n",
              "      <td>2.335800</td>\n",
              "      <td>0.057212</td>\n",
              "      <td>-0.789869</td>\n",
              "      <td>0.592091</td>\n",
              "      <td>-0.544032</td>\n",
              "      <td>0.973873</td>\n",
              "      <td>-0.022969</td>\n",
              "      <td>-0.859478</td>\n",
              "      <td>0.623509</td>\n",
              "      <td>0.155127</td>\n",
              "      <td>-1.672149</td>\n",
              "      <td>0.350707</td>\n",
              "      <td>-0.260168</td>\n",
              "      <td>0.125973</td>\n",
              "      <td>0.176539</td>\n",
              "      <td>0.226258</td>\n",
              "      <td>0.152759</td>\n",
              "      <td>-0.242361</td>\n",
              "      <td>0.061390</td>\n",
              "      <td>0.109851</td>\n",
              "      <td>-0.182018</td>\n",
              "      <td>0.020963</td>\n",
              "      <td>0.032187</td>\n",
              "      <td>0.369641</td>\n",
              "      <td>-0.064960</td>\n",
              "      <td>-0.006019</td>\n",
              "      <td>2.49</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1965</th>\n",
              "      <td>156369.0</td>\n",
              "      <td>2.060854</td>\n",
              "      <td>-0.096996</td>\n",
              "      <td>-1.074450</td>\n",
              "      <td>0.424944</td>\n",
              "      <td>-0.205787</td>\n",
              "      <td>-1.234304</td>\n",
              "      <td>0.141588</td>\n",
              "      <td>-0.299167</td>\n",
              "      <td>0.638113</td>\n",
              "      <td>0.092058</td>\n",
              "      <td>-0.842634</td>\n",
              "      <td>0.100107</td>\n",
              "      <td>-0.579516</td>\n",
              "      <td>0.403768</td>\n",
              "      <td>0.109822</td>\n",
              "      <td>-0.077658</td>\n",
              "      <td>-0.248134</td>\n",
              "      <td>-0.793738</td>\n",
              "      <td>0.147041</td>\n",
              "      <td>-0.265458</td>\n",
              "      <td>-0.295951</td>\n",
              "      <td>-0.750128</td>\n",
              "      <td>0.348861</td>\n",
              "      <td>0.028741</td>\n",
              "      <td>-0.321365</td>\n",
              "      <td>0.197514</td>\n",
              "      <td>-0.074953</td>\n",
              "      <td>-0.061193</td>\n",
              "      <td>1.98</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1966</th>\n",
              "      <td>111093.0</td>\n",
              "      <td>2.095880</td>\n",
              "      <td>0.065258</td>\n",
              "      <td>-1.431455</td>\n",
              "      <td>0.312769</td>\n",
              "      <td>0.440517</td>\n",
              "      <td>-0.627421</td>\n",
              "      <td>0.048422</td>\n",
              "      <td>-0.327947</td>\n",
              "      <td>2.062684</td>\n",
              "      <td>-0.476309</td>\n",
              "      <td>-0.012147</td>\n",
              "      <td>-2.316512</td>\n",
              "      <td>1.694046</td>\n",
              "      <td>1.907661</td>\n",
              "      <td>0.236909</td>\n",
              "      <td>-0.266023</td>\n",
              "      <td>0.135802</td>\n",
              "      <td>0.397387</td>\n",
              "      <td>-0.349400</td>\n",
              "      <td>-0.270042</td>\n",
              "      <td>0.100207</td>\n",
              "      <td>0.690091</td>\n",
              "      <td>-0.003124</td>\n",
              "      <td>0.375189</td>\n",
              "      <td>0.351217</td>\n",
              "      <td>-0.469535</td>\n",
              "      <td>-0.020660</td>\n",
              "      <td>-0.056916</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1967</th>\n",
              "      <td>137415.0</td>\n",
              "      <td>2.257734</td>\n",
              "      <td>-1.293812</td>\n",
              "      <td>-0.896147</td>\n",
              "      <td>-1.488630</td>\n",
              "      <td>-1.209841</td>\n",
              "      <td>-0.783885</td>\n",
              "      <td>-0.987219</td>\n",
              "      <td>-0.255289</td>\n",
              "      <td>-1.372561</td>\n",
              "      <td>1.542124</td>\n",
              "      <td>-0.538718</td>\n",
              "      <td>-0.340350</td>\n",
              "      <td>0.996153</td>\n",
              "      <td>-0.501390</td>\n",
              "      <td>-0.227737</td>\n",
              "      <td>-0.796460</td>\n",
              "      <td>0.651233</td>\n",
              "      <td>-0.402524</td>\n",
              "      <td>-0.334389</td>\n",
              "      <td>-0.373288</td>\n",
              "      <td>-0.131877</td>\n",
              "      <td>0.126225</td>\n",
              "      <td>0.299616</td>\n",
              "      <td>1.087076</td>\n",
              "      <td>-0.193545</td>\n",
              "      <td>-0.217176</td>\n",
              "      <td>0.009927</td>\n",
              "      <td>-0.038101</td>\n",
              "      <td>25.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1968 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Time        V1        V2        V3  ...       V27       V28  Amount  Class\n",
              "0        406.0 -2.312227  1.951992 -1.609851  ...  0.261145 -0.143276    0.00      1\n",
              "1        472.0 -3.043541 -3.157307  1.088463  ... -0.252773  0.035764  529.00      1\n",
              "2       4462.0 -2.303350  1.759247 -0.359745  ...  0.039566 -0.153029  239.93      1\n",
              "3       6986.0 -4.397974  1.358367 -2.592844  ... -0.827136  0.849573   59.00      1\n",
              "4       7519.0  1.234235  3.019740 -4.304597  ... -0.010016  0.146793    1.00      1\n",
              "...        ...       ...       ...       ...  ...       ...       ...     ...    ...\n",
              "1963   62345.0 -0.809681  1.224628  0.263897  ... -0.112717  0.118569   75.40      0\n",
              "1964   66083.0 -1.088752  0.714853  2.335800  ... -0.064960 -0.006019    2.49      0\n",
              "1965  156369.0  2.060854 -0.096996 -1.074450  ... -0.074953 -0.061193    1.98      0\n",
              "1966  111093.0  2.095880  0.065258 -1.431455  ... -0.020660 -0.056916    1.00      0\n",
              "1967  137415.0  2.257734 -1.293812 -0.896147  ...  0.009927 -0.038101   25.00      0\n",
              "\n",
              "[1968 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HxhwJtifE7E_"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJq7KvuxE7FB"
      },
      "source": [
        "train_data, test_data, train_labels, test_labels=train_test_split(x_data, label, test_size=0.3, random_state=1, stratify = label)"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "EuHTaRJzE7FC",
        "outputId": "5b94d6a2-62d1-4fb1-f56a-ee91dbf72d70"
      },
      "source": [
        "X_train"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>769</th>\n",
              "      <td>0.280801</td>\n",
              "      <td>-0.104481</td>\n",
              "      <td>0.460743</td>\n",
              "      <td>0.790942</td>\n",
              "      <td>0.846403</td>\n",
              "      <td>0.706699</td>\n",
              "      <td>2.380803</td>\n",
              "      <td>0.424808</td>\n",
              "      <td>-0.008318</td>\n",
              "      <td>1.030531</td>\n",
              "      <td>1.552438</td>\n",
              "      <td>0.435938</td>\n",
              "      <td>-0.575877</td>\n",
              "      <td>0.424903</td>\n",
              "      <td>0.613968</td>\n",
              "      <td>1.203871</td>\n",
              "      <td>0.157214</td>\n",
              "      <td>0.489013</td>\n",
              "      <td>0.223821</td>\n",
              "      <td>0.069879</td>\n",
              "      <td>1.416415</td>\n",
              "      <td>-0.457485</td>\n",
              "      <td>-0.400583</td>\n",
              "      <td>-0.175188</td>\n",
              "      <td>-1.865036</td>\n",
              "      <td>-0.225453</td>\n",
              "      <td>0.268438</td>\n",
              "      <td>-0.219943</td>\n",
              "      <td>-2.595949</td>\n",
              "      <td>-0.391678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1877</th>\n",
              "      <td>-0.115502</td>\n",
              "      <td>0.220460</td>\n",
              "      <td>-0.343713</td>\n",
              "      <td>0.693908</td>\n",
              "      <td>-0.800754</td>\n",
              "      <td>0.018544</td>\n",
              "      <td>-0.070884</td>\n",
              "      <td>0.301040</td>\n",
              "      <td>-0.018841</td>\n",
              "      <td>-0.182184</td>\n",
              "      <td>0.472262</td>\n",
              "      <td>-0.048481</td>\n",
              "      <td>0.379727</td>\n",
              "      <td>-0.732404</td>\n",
              "      <td>0.372234</td>\n",
              "      <td>-1.734193</td>\n",
              "      <td>0.567918</td>\n",
              "      <td>0.490743</td>\n",
              "      <td>-0.200544</td>\n",
              "      <td>1.009456</td>\n",
              "      <td>0.057955</td>\n",
              "      <td>0.047531</td>\n",
              "      <td>1.025507</td>\n",
              "      <td>-0.122556</td>\n",
              "      <td>1.025484</td>\n",
              "      <td>-0.466080</td>\n",
              "      <td>-0.444450</td>\n",
              "      <td>0.132995</td>\n",
              "      <td>0.369597</td>\n",
              "      <td>-0.330542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1468</th>\n",
              "      <td>-1.038362</td>\n",
              "      <td>0.538320</td>\n",
              "      <td>-0.280280</td>\n",
              "      <td>0.350076</td>\n",
              "      <td>-0.266055</td>\n",
              "      <td>0.254035</td>\n",
              "      <td>-0.048714</td>\n",
              "      <td>0.410171</td>\n",
              "      <td>-0.064442</td>\n",
              "      <td>0.111742</td>\n",
              "      <td>0.412922</td>\n",
              "      <td>0.334500</td>\n",
              "      <td>0.743822</td>\n",
              "      <td>0.094539</td>\n",
              "      <td>0.641497</td>\n",
              "      <td>0.324776</td>\n",
              "      <td>0.350678</td>\n",
              "      <td>0.311293</td>\n",
              "      <td>-0.157099</td>\n",
              "      <td>-0.134651</td>\n",
              "      <td>-0.045933</td>\n",
              "      <td>-0.243021</td>\n",
              "      <td>-0.919953</td>\n",
              "      <td>0.122059</td>\n",
              "      <td>0.096074</td>\n",
              "      <td>0.263839</td>\n",
              "      <td>0.296298</td>\n",
              "      <td>-0.152786</td>\n",
              "      <td>-0.023646</td>\n",
              "      <td>-0.137563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>922</th>\n",
              "      <td>1.246234</td>\n",
              "      <td>0.318750</td>\n",
              "      <td>-1.876652</td>\n",
              "      <td>-0.464277</td>\n",
              "      <td>-0.683803</td>\n",
              "      <td>-0.088722</td>\n",
              "      <td>-0.326433</td>\n",
              "      <td>0.717613</td>\n",
              "      <td>-0.324543</td>\n",
              "      <td>-0.868350</td>\n",
              "      <td>0.738724</td>\n",
              "      <td>-0.909246</td>\n",
              "      <td>0.220746</td>\n",
              "      <td>0.989846</td>\n",
              "      <td>0.569653</td>\n",
              "      <td>-0.579958</td>\n",
              "      <td>-0.061859</td>\n",
              "      <td>0.509562</td>\n",
              "      <td>0.227205</td>\n",
              "      <td>-0.396601</td>\n",
              "      <td>2.177701</td>\n",
              "      <td>0.291450</td>\n",
              "      <td>0.251128</td>\n",
              "      <td>-1.090952</td>\n",
              "      <td>1.089352</td>\n",
              "      <td>0.475102</td>\n",
              "      <td>0.321840</td>\n",
              "      <td>-0.465845</td>\n",
              "      <td>0.308005</td>\n",
              "      <td>4.737632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1059</th>\n",
              "      <td>-1.793729</td>\n",
              "      <td>0.139442</td>\n",
              "      <td>0.046921</td>\n",
              "      <td>0.683206</td>\n",
              "      <td>-0.367135</td>\n",
              "      <td>0.283779</td>\n",
              "      <td>-0.027843</td>\n",
              "      <td>0.445716</td>\n",
              "      <td>-0.047992</td>\n",
              "      <td>0.941769</td>\n",
              "      <td>0.231437</td>\n",
              "      <td>-0.143032</td>\n",
              "      <td>-0.357884</td>\n",
              "      <td>0.840250</td>\n",
              "      <td>0.915176</td>\n",
              "      <td>0.496496</td>\n",
              "      <td>0.341408</td>\n",
              "      <td>0.504040</td>\n",
              "      <td>0.057272</td>\n",
              "      <td>-0.643505</td>\n",
              "      <td>-0.082356</td>\n",
              "      <td>-0.278075</td>\n",
              "      <td>-0.720204</td>\n",
              "      <td>0.054058</td>\n",
              "      <td>-0.004039</td>\n",
              "      <td>-0.546775</td>\n",
              "      <td>0.107933</td>\n",
              "      <td>0.080142</td>\n",
              "      <td>0.282997</td>\n",
              "      <td>-0.345654</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",
              "      <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>377</th>\n",
              "      <td>0.842671</td>\n",
              "      <td>0.458618</td>\n",
              "      <td>0.460237</td>\n",
              "      <td>-0.710339</td>\n",
              "      <td>0.985044</td>\n",
              "      <td>0.223999</td>\n",
              "      <td>-0.608533</td>\n",
              "      <td>-0.099986</td>\n",
              "      <td>0.192528</td>\n",
              "      <td>-0.465341</td>\n",
              "      <td>-0.521633</td>\n",
              "      <td>1.041806</td>\n",
              "      <td>-0.516629</td>\n",
              "      <td>-1.362499</td>\n",
              "      <td>-1.118684</td>\n",
              "      <td>0.335290</td>\n",
              "      <td>-0.576729</td>\n",
              "      <td>0.076162</td>\n",
              "      <td>0.502848</td>\n",
              "      <td>1.989597</td>\n",
              "      <td>0.274285</td>\n",
              "      <td>0.078787</td>\n",
              "      <td>0.071654</td>\n",
              "      <td>0.189220</td>\n",
              "      <td>0.663062</td>\n",
              "      <td>-0.814446</td>\n",
              "      <td>-0.531203</td>\n",
              "      <td>0.323179</td>\n",
              "      <td>-0.172859</td>\n",
              "      <td>-0.386354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1625</th>\n",
              "      <td>-0.035647</td>\n",
              "      <td>0.756907</td>\n",
              "      <td>-0.317351</td>\n",
              "      <td>0.057537</td>\n",
              "      <td>-0.364944</td>\n",
              "      <td>0.355317</td>\n",
              "      <td>-0.116231</td>\n",
              "      <td>0.362845</td>\n",
              "      <td>-0.096194</td>\n",
              "      <td>0.495079</td>\n",
              "      <td>0.445062</td>\n",
              "      <td>0.080317</td>\n",
              "      <td>0.776490</td>\n",
              "      <td>0.526193</td>\n",
              "      <td>0.610505</td>\n",
              "      <td>0.075744</td>\n",
              "      <td>0.345002</td>\n",
              "      <td>0.199578</td>\n",
              "      <td>0.445302</td>\n",
              "      <td>-0.169590</td>\n",
              "      <td>-0.235595</td>\n",
              "      <td>0.040869</td>\n",
              "      <td>1.072238</td>\n",
              "      <td>0.040235</td>\n",
              "      <td>1.340660</td>\n",
              "      <td>0.406880</td>\n",
              "      <td>-0.329728</td>\n",
              "      <td>-0.101114</td>\n",
              "      <td>-0.211431</td>\n",
              "      <td>-0.387256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1143</th>\n",
              "      <td>-0.182010</td>\n",
              "      <td>0.239711</td>\n",
              "      <td>-0.566240</td>\n",
              "      <td>0.675125</td>\n",
              "      <td>-1.212956</td>\n",
              "      <td>-0.163544</td>\n",
              "      <td>-0.446564</td>\n",
              "      <td>0.414500</td>\n",
              "      <td>-0.167869</td>\n",
              "      <td>-0.775448</td>\n",
              "      <td>0.679229</td>\n",
              "      <td>-0.597092</td>\n",
              "      <td>0.053754</td>\n",
              "      <td>-0.129517</td>\n",
              "      <td>0.316546</td>\n",
              "      <td>0.000542</td>\n",
              "      <td>0.242521</td>\n",
              "      <td>0.424658</td>\n",
              "      <td>0.291323</td>\n",
              "      <td>-1.007338</td>\n",
              "      <td>-0.131263</td>\n",
              "      <td>-0.158974</td>\n",
              "      <td>0.016323</td>\n",
              "      <td>0.209516</td>\n",
              "      <td>1.175252</td>\n",
              "      <td>-0.207764</td>\n",
              "      <td>-0.875764</td>\n",
              "      <td>-0.277939</td>\n",
              "      <td>-0.534365</td>\n",
              "      <td>0.128015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1794</th>\n",
              "      <td>-1.230666</td>\n",
              "      <td>-0.462821</td>\n",
              "      <td>-0.248628</td>\n",
              "      <td>0.834342</td>\n",
              "      <td>-0.572546</td>\n",
              "      <td>0.421737</td>\n",
              "      <td>0.890907</td>\n",
              "      <td>0.192407</td>\n",
              "      <td>-0.571110</td>\n",
              "      <td>1.037485</td>\n",
              "      <td>0.635965</td>\n",
              "      <td>0.210710</td>\n",
              "      <td>0.646449</td>\n",
              "      <td>-0.525067</td>\n",
              "      <td>0.201130</td>\n",
              "      <td>-0.324031</td>\n",
              "      <td>0.284202</td>\n",
              "      <td>0.311043</td>\n",
              "      <td>-0.073725</td>\n",
              "      <td>-0.905796</td>\n",
              "      <td>-1.339566</td>\n",
              "      <td>0.575655</td>\n",
              "      <td>-0.152103</td>\n",
              "      <td>-0.193812</td>\n",
              "      <td>-0.384740</td>\n",
              "      <td>-0.041620</td>\n",
              "      <td>0.284199</td>\n",
              "      <td>-1.994981</td>\n",
              "      <td>0.497723</td>\n",
              "      <td>-0.365317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>917</th>\n",
              "      <td>1.487430</td>\n",
              "      <td>0.747925</td>\n",
              "      <td>-0.272151</td>\n",
              "      <td>0.032328</td>\n",
              "      <td>-0.240960</td>\n",
              "      <td>0.282669</td>\n",
              "      <td>-0.537465</td>\n",
              "      <td>0.380379</td>\n",
              "      <td>-0.141174</td>\n",
              "      <td>0.830355</td>\n",
              "      <td>0.149778</td>\n",
              "      <td>-0.532655</td>\n",
              "      <td>0.672272</td>\n",
              "      <td>0.862364</td>\n",
              "      <td>0.029902</td>\n",
              "      <td>-0.211286</td>\n",
              "      <td>0.328719</td>\n",
              "      <td>0.596818</td>\n",
              "      <td>0.179205</td>\n",
              "      <td>-0.117862</td>\n",
              "      <td>-0.157556</td>\n",
              "      <td>-0.219125</td>\n",
              "      <td>-0.310247</td>\n",
              "      <td>0.174191</td>\n",
              "      <td>-0.116929</td>\n",
              "      <td>-0.092467</td>\n",
              "      <td>-0.184931</td>\n",
              "      <td>-0.070448</td>\n",
              "      <td>-0.089453</td>\n",
              "      <td>-0.307402</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1377 rows × 30 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Time        V1        V2  ...       V27       V28    Amount\n",
              "769   0.280801 -0.104481  0.460743  ... -0.219943 -2.595949 -0.391678\n",
              "1877 -0.115502  0.220460 -0.343713  ...  0.132995  0.369597 -0.330542\n",
              "1468 -1.038362  0.538320 -0.280280  ... -0.152786 -0.023646 -0.137563\n",
              "922   1.246234  0.318750 -1.876652  ... -0.465845  0.308005  4.737632\n",
              "1059 -1.793729  0.139442  0.046921  ...  0.080142  0.282997 -0.345654\n",
              "...        ...       ...       ...  ...       ...       ...       ...\n",
              "377   0.842671  0.458618  0.460237  ...  0.323179 -0.172859 -0.386354\n",
              "1625 -0.035647  0.756907 -0.317351  ... -0.101114 -0.211431 -0.387256\n",
              "1143 -0.182010  0.239711 -0.566240  ... -0.277939 -0.534365  0.128015\n",
              "1794 -1.230666 -0.462821 -0.248628  ... -1.994981  0.497723 -0.365317\n",
              "917   1.487430  0.747925 -0.272151  ... -0.070448 -0.089453 -0.307402\n",
              "\n",
              "[1377 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3eUBLmOtE7FD"
      },
      "source": [
        "train_mean = train_data.mean(axis=0) # taking the mean of \n",
        "train_data -= train_mean\n",
        "train_std = train_data.std(axis=0)\n",
        "train_data /= train_std\n",
        "test_data -= train_mean\n",
        "test_data /= train_std"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PZttotpqE7FK"
      },
      "source": [
        "from keras import models , layers\n",
        "from keras import regularizers"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RjY3oqeJE7FL"
      },
      "source": [
        "model = models.Sequential()\n",
        "model.add(layers.Dense(20, activation='relu',kernel_regularizer=regularizers.l2(0.001), input_shape=(train_data.shape[1],)))\n",
        "model.add(layers.Dropout(0.5))\n",
        "model.add(layers.Dense(10, activation='relu',kernel_regularizer=regularizers.l2(0.001)))\n",
        "model.add(layers.Dropout(0.5))\n",
        "model.add(layers.Dense(8, activation='relu',kernel_regularizer=regularizers.l2(0.001)))\n",
        "model.add(layers.Dropout(0.5))\n",
        "model.add(layers.Dense(6, activation='relu',kernel_regularizer=regularizers.l2(0.001)))\n",
        "model.add(layers.Dropout(0.5))\n",
        "model.add(layers.Dense(1, activation='sigmoid'))"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGDeaQ02E7FN"
      },
      "source": [
        "model.compile(loss= 'binary_crossentropy', optimizer= 'rmsprop', metrics=['accuracy'])"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n1u-SyZsE7FY",
        "outputId": "0de1d248-3126-42e4-cc3b-623632acf6ea"
      },
      "source": [
        "model.fit(train_data, train_labels, epochs=100 , validation_split=0.3)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 1.1103 - accuracy: 0.4746 - val_loss: 0.7468 - val_accuracy: 0.5386\n",
            "Epoch 2/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.8723 - accuracy: 0.5763 - val_loss: 0.7143 - val_accuracy: 0.7826\n",
            "Epoch 3/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.8148 - accuracy: 0.6260 - val_loss: 0.6986 - val_accuracy: 0.7681\n",
            "Epoch 4/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.7705 - accuracy: 0.6846 - val_loss: 0.6876 - val_accuracy: 0.7415\n",
            "Epoch 5/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.7370 - accuracy: 0.7185 - val_loss: 0.6713 - val_accuracy: 0.7415\n",
            "Epoch 6/100\n",
            "31/31 [==============================] - 0s 4ms/step - loss: 0.6821 - accuracy: 0.7436 - val_loss: 0.6572 - val_accuracy: 0.7415\n",
            "Epoch 7/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.6614 - accuracy: 0.7826 - val_loss: 0.6432 - val_accuracy: 0.7415\n",
            "Epoch 8/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.6645 - accuracy: 0.7762 - val_loss: 0.6324 - val_accuracy: 0.7440\n",
            "Epoch 9/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.6664 - accuracy: 0.7686 - val_loss: 0.6187 - val_accuracy: 0.7464\n",
            "Epoch 10/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.6448 - accuracy: 0.7692 - val_loss: 0.6056 - val_accuracy: 0.7488\n",
            "Epoch 11/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.6600 - accuracy: 0.7687 - val_loss: 0.5932 - val_accuracy: 0.7512\n",
            "Epoch 12/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.6220 - accuracy: 0.7859 - val_loss: 0.5758 - val_accuracy: 0.7778\n",
            "Epoch 13/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.5964 - accuracy: 0.7662 - val_loss: 0.5538 - val_accuracy: 0.8043\n",
            "Epoch 14/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.5930 - accuracy: 0.8008 - val_loss: 0.5371 - val_accuracy: 0.8068\n",
            "Epoch 15/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.5663 - accuracy: 0.8203 - val_loss: 0.5160 - val_accuracy: 0.8237\n",
            "Epoch 16/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.5882 - accuracy: 0.7832 - val_loss: 0.4952 - val_accuracy: 0.8309\n",
            "Epoch 17/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.5841 - accuracy: 0.8166 - val_loss: 0.4729 - val_accuracy: 0.8454\n",
            "Epoch 18/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.5607 - accuracy: 0.8252 - val_loss: 0.4489 - val_accuracy: 0.8599\n",
            "Epoch 19/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.5369 - accuracy: 0.8145 - val_loss: 0.4347 - val_accuracy: 0.8671\n",
            "Epoch 20/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.4728 - accuracy: 0.8611 - val_loss: 0.4182 - val_accuracy: 0.8720\n",
            "Epoch 21/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.5209 - accuracy: 0.8288 - val_loss: 0.4063 - val_accuracy: 0.8816\n",
            "Epoch 22/100\n",
            "31/31 [==============================] - 0s 4ms/step - loss: 0.4782 - accuracy: 0.8462 - val_loss: 0.3865 - val_accuracy: 0.8913\n",
            "Epoch 23/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.4811 - accuracy: 0.8454 - val_loss: 0.3625 - val_accuracy: 0.9034\n",
            "Epoch 24/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.4533 - accuracy: 0.8523 - val_loss: 0.3417 - val_accuracy: 0.9203\n",
            "Epoch 25/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.4837 - accuracy: 0.8686 - val_loss: 0.3267 - val_accuracy: 0.9324\n",
            "Epoch 26/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.5269 - accuracy: 0.8593 - val_loss: 0.3149 - val_accuracy: 0.9324\n",
            "Epoch 27/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.3925 - accuracy: 0.8853 - val_loss: 0.3000 - val_accuracy: 0.9348\n",
            "Epoch 28/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.4572 - accuracy: 0.8635 - val_loss: 0.2886 - val_accuracy: 0.9348\n",
            "Epoch 29/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.4253 - accuracy: 0.8672 - val_loss: 0.2759 - val_accuracy: 0.9444\n",
            "Epoch 30/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.4097 - accuracy: 0.8770 - val_loss: 0.2656 - val_accuracy: 0.9517\n",
            "Epoch 31/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.3714 - accuracy: 0.8876 - val_loss: 0.2545 - val_accuracy: 0.9517\n",
            "Epoch 32/100\n",
            "31/31 [==============================] - 0s 4ms/step - loss: 0.3771 - accuracy: 0.8898 - val_loss: 0.2464 - val_accuracy: 0.9541\n",
            "Epoch 33/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.3946 - accuracy: 0.8901 - val_loss: 0.2383 - val_accuracy: 0.9541\n",
            "Epoch 34/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.3659 - accuracy: 0.8869 - val_loss: 0.2331 - val_accuracy: 0.9541\n",
            "Epoch 35/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.3417 - accuracy: 0.9204 - val_loss: 0.2283 - val_accuracy: 0.9541\n",
            "Epoch 36/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.3728 - accuracy: 0.8755 - val_loss: 0.2241 - val_accuracy: 0.9541\n",
            "Epoch 37/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.3627 - accuracy: 0.8954 - val_loss: 0.2208 - val_accuracy: 0.9541\n",
            "Epoch 38/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.3177 - accuracy: 0.9137 - val_loss: 0.2168 - val_accuracy: 0.9541\n",
            "Epoch 39/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.3613 - accuracy: 0.9046 - val_loss: 0.2149 - val_accuracy: 0.9541\n",
            "Epoch 40/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.3519 - accuracy: 0.8977 - val_loss: 0.2137 - val_accuracy: 0.9565\n",
            "Epoch 41/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.3166 - accuracy: 0.9089 - val_loss: 0.2124 - val_accuracy: 0.9565\n",
            "Epoch 42/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.3407 - accuracy: 0.8997 - val_loss: 0.2106 - val_accuracy: 0.9565\n",
            "Epoch 43/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.3432 - accuracy: 0.9017 - val_loss: 0.2109 - val_accuracy: 0.9565\n",
            "Epoch 44/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2890 - accuracy: 0.9130 - val_loss: 0.2084 - val_accuracy: 0.9589\n",
            "Epoch 45/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2895 - accuracy: 0.9261 - val_loss: 0.2079 - val_accuracy: 0.9589\n",
            "Epoch 46/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2977 - accuracy: 0.9160 - val_loss: 0.2097 - val_accuracy: 0.9589\n",
            "Epoch 47/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.3237 - accuracy: 0.9171 - val_loss: 0.2115 - val_accuracy: 0.9589\n",
            "Epoch 48/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.3085 - accuracy: 0.9173 - val_loss: 0.2115 - val_accuracy: 0.9589\n",
            "Epoch 49/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2656 - accuracy: 0.9321 - val_loss: 0.2111 - val_accuracy: 0.9589\n",
            "Epoch 50/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2583 - accuracy: 0.9342 - val_loss: 0.2100 - val_accuracy: 0.9589\n",
            "Epoch 51/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.3273 - accuracy: 0.9054 - val_loss: 0.2109 - val_accuracy: 0.9589\n",
            "Epoch 52/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2688 - accuracy: 0.9249 - val_loss: 0.2113 - val_accuracy: 0.9565\n",
            "Epoch 53/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2837 - accuracy: 0.9132 - val_loss: 0.2120 - val_accuracy: 0.9565\n",
            "Epoch 54/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2596 - accuracy: 0.9410 - val_loss: 0.2131 - val_accuracy: 0.9565\n",
            "Epoch 55/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2983 - accuracy: 0.9304 - val_loss: 0.2144 - val_accuracy: 0.9565\n",
            "Epoch 56/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2517 - accuracy: 0.9359 - val_loss: 0.2148 - val_accuracy: 0.9565\n",
            "Epoch 57/100\n",
            "31/31 [==============================] - 0s 4ms/step - loss: 0.2858 - accuracy: 0.9251 - val_loss: 0.2146 - val_accuracy: 0.9565\n",
            "Epoch 58/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2580 - accuracy: 0.9347 - val_loss: 0.2131 - val_accuracy: 0.9614\n",
            "Epoch 59/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2368 - accuracy: 0.9489 - val_loss: 0.2137 - val_accuracy: 0.9565\n",
            "Epoch 60/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2771 - accuracy: 0.9396 - val_loss: 0.2153 - val_accuracy: 0.9589\n",
            "Epoch 61/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2341 - accuracy: 0.9418 - val_loss: 0.2171 - val_accuracy: 0.9614\n",
            "Epoch 62/100\n",
            "31/31 [==============================] - 0s 4ms/step - loss: 0.2153 - accuracy: 0.9546 - val_loss: 0.2187 - val_accuracy: 0.9589\n",
            "Epoch 63/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2478 - accuracy: 0.9284 - val_loss: 0.2189 - val_accuracy: 0.9589\n",
            "Epoch 64/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2429 - accuracy: 0.9362 - val_loss: 0.2215 - val_accuracy: 0.9589\n",
            "Epoch 65/100\n",
            "31/31 [==============================] - 0s 4ms/step - loss: 0.1941 - accuracy: 0.9643 - val_loss: 0.2204 - val_accuracy: 0.9589\n",
            "Epoch 66/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2698 - accuracy: 0.9299 - val_loss: 0.2205 - val_accuracy: 0.9589\n",
            "Epoch 67/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2328 - accuracy: 0.9427 - val_loss: 0.2225 - val_accuracy: 0.9589\n",
            "Epoch 68/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2212 - accuracy: 0.9424 - val_loss: 0.2222 - val_accuracy: 0.9589\n",
            "Epoch 69/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2343 - accuracy: 0.9447 - val_loss: 0.2233 - val_accuracy: 0.9614\n",
            "Epoch 70/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2155 - accuracy: 0.9499 - val_loss: 0.2221 - val_accuracy: 0.9589\n",
            "Epoch 71/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2508 - accuracy: 0.9458 - val_loss: 0.2222 - val_accuracy: 0.9589\n",
            "Epoch 72/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.1831 - accuracy: 0.9546 - val_loss: 0.2234 - val_accuracy: 0.9614\n",
            "Epoch 73/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2297 - accuracy: 0.9440 - val_loss: 0.2242 - val_accuracy: 0.9614\n",
            "Epoch 74/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2036 - accuracy: 0.9556 - val_loss: 0.2261 - val_accuracy: 0.9614\n",
            "Epoch 75/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2041 - accuracy: 0.9490 - val_loss: 0.2256 - val_accuracy: 0.9614\n",
            "Epoch 76/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2303 - accuracy: 0.9328 - val_loss: 0.2265 - val_accuracy: 0.9614\n",
            "Epoch 77/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2391 - accuracy: 0.9407 - val_loss: 0.2271 - val_accuracy: 0.9614\n",
            "Epoch 78/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2191 - accuracy: 0.9406 - val_loss: 0.2277 - val_accuracy: 0.9614\n",
            "Epoch 79/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2228 - accuracy: 0.9412 - val_loss: 0.2260 - val_accuracy: 0.9614\n",
            "Epoch 80/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2131 - accuracy: 0.9425 - val_loss: 0.2254 - val_accuracy: 0.9614\n",
            "Epoch 81/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2165 - accuracy: 0.9362 - val_loss: 0.2288 - val_accuracy: 0.9614\n",
            "Epoch 82/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2008 - accuracy: 0.9528 - val_loss: 0.2286 - val_accuracy: 0.9614\n",
            "Epoch 83/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.1960 - accuracy: 0.9484 - val_loss: 0.2295 - val_accuracy: 0.9614\n",
            "Epoch 84/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2027 - accuracy: 0.9561 - val_loss: 0.2310 - val_accuracy: 0.9589\n",
            "Epoch 85/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2476 - accuracy: 0.9336 - val_loss: 0.2298 - val_accuracy: 0.9589\n",
            "Epoch 86/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2163 - accuracy: 0.9513 - val_loss: 0.2284 - val_accuracy: 0.9589\n",
            "Epoch 87/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2406 - accuracy: 0.9383 - val_loss: 0.2322 - val_accuracy: 0.9589\n",
            "Epoch 88/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2775 - accuracy: 0.9273 - val_loss: 0.2246 - val_accuracy: 0.9614\n",
            "Epoch 89/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2314 - accuracy: 0.9383 - val_loss: 0.2279 - val_accuracy: 0.9614\n",
            "Epoch 90/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.1843 - accuracy: 0.9582 - val_loss: 0.2271 - val_accuracy: 0.9589\n",
            "Epoch 91/100\n",
            "31/31 [==============================] - 0s 4ms/step - loss: 0.2143 - accuracy: 0.9376 - val_loss: 0.2281 - val_accuracy: 0.9589\n",
            "Epoch 92/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.1566 - accuracy: 0.9683 - val_loss: 0.2272 - val_accuracy: 0.9589\n",
            "Epoch 93/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2210 - accuracy: 0.9371 - val_loss: 0.2257 - val_accuracy: 0.9589\n",
            "Epoch 94/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.1658 - accuracy: 0.9650 - val_loss: 0.2245 - val_accuracy: 0.9589\n",
            "Epoch 95/100\n",
            "31/31 [==============================] - 0s 4ms/step - loss: 0.2131 - accuracy: 0.9439 - val_loss: 0.2257 - val_accuracy: 0.9589\n",
            "Epoch 96/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.1906 - accuracy: 0.9521 - val_loss: 0.2277 - val_accuracy: 0.9589\n",
            "Epoch 97/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.1776 - accuracy: 0.9567 - val_loss: 0.2243 - val_accuracy: 0.9589\n",
            "Epoch 98/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.1817 - accuracy: 0.9514 - val_loss: 0.2270 - val_accuracy: 0.9589\n",
            "Epoch 99/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.1981 - accuracy: 0.9469 - val_loss: 0.2241 - val_accuracy: 0.9589\n",
            "Epoch 100/100\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2139 - accuracy: 0.9398 - val_loss: 0.2248 - val_accuracy: 0.9589\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fd1dd375e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WDltmOrdE7FZ",
        "outputId": "eb4dc397-08ab-491c-8e62-d2540e382cc6"
      },
      "source": [
        "model.evaluate(test_data,test_labels)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "19/19 [==============================] - 0s 1ms/step - loss: 0.3491 - accuracy: 0.9492\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.34911996126174927, 0.9492385983467102]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRUdWiTsOS46"
      },
      "source": [
        "prediction=model.predict(test_data)"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "adrksojvPRiO",
        "outputId": "e26b2fba-6848-4f26-c4f1-dcbc41da5281"
      },
      "source": [
        "for i in prediction:\n",
        "  if i>0.9:\n",
        "    print(\"fraud cases area \",i)\n",
        "  "
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fraud cases area  [1.]\n",
            "fraud cases area  [0.99999785]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [0.9697614]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [0.9999994]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [0.9867792]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [0.99999976]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [0.9999992]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [0.9899063]\n",
            "fraud cases area  [0.9997853]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [0.99999976]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [0.9999813]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [0.9999585]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [0.99999774]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [0.9764143]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [0.99995387]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [0.98928535]\n",
            "fraud cases area  [0.9999995]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [0.9999994]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [0.9840997]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [1.]\n",
            "fraud cases area  [0.9259627]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-x0r5K9kNu5G",
        "outputId": "18c2f6db-caaa-47e8-99e5-644476c01ef0"
      },
      "source": [
        "import tensorflow as tf\n",
        "tf.math.confusion_matrix(labels= test_labels, predictions=prediction )"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 2), dtype=int32, numpy=\n",
              "array([[442,   1],\n",
              "       [ 41, 107]], dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zd_TrxzNN8xc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}