Handling_imbalanced_dataset_credit_card_fraud_DL.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "rrRYqII5gb_j"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-90kKsPiLDu",
        "outputId": "4ba2dfa1-13d3-4d65-c436-945127f0b221"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 70 Nov 25 06:19 kaggle.json\n"
          ]
        }
      ],
      "source": [
        "# Let's make sure the kaggle.json file is present.\n",
        "!ls -lha kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "xnPYclX3iNlO"
      },
      "outputs": [],
      "source": [
        "!pip install -q kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "X_KOsiLJiQZ_"
      },
      "outputs": [],
      "source": [
        "# The Kaggle API client expects this file to be in ~/.kaggle,\n",
        "# so move it there.\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "\n",
        "# This permissions change avoids a warning on Kaggle tool startup.\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G5W3SWaOiUWU",
        "outputId": "fcf1b5eb-a780-49de-e07d-d06017b5fc50"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (5.0.2)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2021.10.8)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.62.3)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n"
          ]
        }
      ],
      "source": [
        "!pip install kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-r84uoZRiZK-",
        "outputId": "28576601-5632-46ff-f951-227d90097847"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading creditcardfraud.zip to /content\n",
            " 76% 50.0M/66.0M [00:00<00:00, 139MB/s]\n",
            "100% 66.0M/66.0M [00:00<00:00, 167MB/s]\n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download -d mlg-ulb/creditcardfraud"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wBCngjRzidJ9",
        "outputId": "6fe8a14f-f230-4e77-8e76-acf148df22d9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  creditcardfraud.zip\n",
            "  inflating: Creditcardfraud/creditcard.csv  \n"
          ]
        }
      ],
      "source": [
        "!unzip creditcardfraud.zip -d Creditcardfraud"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "Che30XJGitj8",
        "outputId": "ab15d27a-8f18-4374-8c5b-282163ac66c4"
      },
      "outputs": [
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              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
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              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
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              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
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              "      <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",
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              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
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              "      <td>0.207971</td>\n",
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              "      <td>0.403993</td>\n",
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              "      <td>0.277838</td>\n",
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              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>149.62</td>\n",
              "      <td>0</td>\n",
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              "    <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",
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              "    <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",
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              "    <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",
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              "    <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",
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            ],
            "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]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv('/content/Creditcardfraud/creditcard.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "5n3fkerSi8D3",
        "outputId": "5d0cc43a-9d41-492d-aae5-280e15936968"
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      "outputs": [
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              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
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              "      <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>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>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>2.848070e+05</td>\n",
              "      <td>284807.000000</td>\n",
              "      <td>284807.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>94813.859575</td>\n",
              "      <td>3.919560e-15</td>\n",
              "      <td>5.688174e-16</td>\n",
              "      <td>-8.769071e-15</td>\n",
              "      <td>2.782312e-15</td>\n",
              "      <td>-1.552563e-15</td>\n",
              "      <td>2.010663e-15</td>\n",
              "      <td>-1.694249e-15</td>\n",
              "      <td>-1.927028e-16</td>\n",
              "      <td>-3.137024e-15</td>\n",
              "      <td>1.768627e-15</td>\n",
              "      <td>9.170318e-16</td>\n",
              "      <td>-1.810658e-15</td>\n",
              "      <td>1.693438e-15</td>\n",
              "      <td>1.479045e-15</td>\n",
              "      <td>3.482336e-15</td>\n",
              "      <td>1.392007e-15</td>\n",
              "      <td>-7.528491e-16</td>\n",
              "      <td>4.328772e-16</td>\n",
              "      <td>9.049732e-16</td>\n",
              "      <td>5.085503e-16</td>\n",
              "      <td>1.537294e-16</td>\n",
              "      <td>7.959909e-16</td>\n",
              "      <td>5.367590e-16</td>\n",
              "      <td>4.458112e-15</td>\n",
              "      <td>1.453003e-15</td>\n",
              "      <td>1.699104e-15</td>\n",
              "      <td>-3.660161e-16</td>\n",
              "      <td>-1.206049e-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>1.088850e+00</td>\n",
              "      <td>1.020713e+00</td>\n",
              "      <td>9.992014e-01</td>\n",
              "      <td>9.952742e-01</td>\n",
              "      <td>9.585956e-01</td>\n",
              "      <td>9.153160e-01</td>\n",
              "      <td>8.762529e-01</td>\n",
              "      <td>8.493371e-01</td>\n",
              "      <td>8.381762e-01</td>\n",
              "      <td>8.140405e-01</td>\n",
              "      <td>7.709250e-01</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>-2.458826e+01</td>\n",
              "      <td>-4.797473e+00</td>\n",
              "      <td>-1.868371e+01</td>\n",
              "      <td>-5.791881e+00</td>\n",
              "      <td>-1.921433e+01</td>\n",
              "      <td>-4.498945e+00</td>\n",
              "      <td>-1.412985e+01</td>\n",
              "      <td>-2.516280e+01</td>\n",
              "      <td>-9.498746e+00</td>\n",
              "      <td>-7.213527e+00</td>\n",
              "      <td>-5.449772e+01</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>-5.354257e-01</td>\n",
              "      <td>-7.624942e-01</td>\n",
              "      <td>-4.055715e-01</td>\n",
              "      <td>-6.485393e-01</td>\n",
              "      <td>-4.255740e-01</td>\n",
              "      <td>-5.828843e-01</td>\n",
              "      <td>-4.680368e-01</td>\n",
              "      <td>-4.837483e-01</td>\n",
              "      <td>-4.988498e-01</td>\n",
              "      <td>-4.562989e-01</td>\n",
              "      <td>-2.117214e-01</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>-9.291738e-02</td>\n",
              "      <td>-3.275735e-02</td>\n",
              "      <td>1.400326e-01</td>\n",
              "      <td>-1.356806e-02</td>\n",
              "      <td>5.060132e-02</td>\n",
              "      <td>4.807155e-02</td>\n",
              "      <td>6.641332e-02</td>\n",
              "      <td>-6.567575e-02</td>\n",
              "      <td>-3.636312e-03</td>\n",
              "      <td>3.734823e-03</td>\n",
              "      <td>-6.248109e-02</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>4.539234e-01</td>\n",
              "      <td>7.395934e-01</td>\n",
              "      <td>6.182380e-01</td>\n",
              "      <td>6.625050e-01</td>\n",
              "      <td>4.931498e-01</td>\n",
              "      <td>6.488208e-01</td>\n",
              "      <td>5.232963e-01</td>\n",
              "      <td>3.996750e-01</td>\n",
              "      <td>5.008067e-01</td>\n",
              "      <td>4.589494e-01</td>\n",
              "      <td>1.330408e-01</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>2.374514e+01</td>\n",
              "      <td>1.201891e+01</td>\n",
              "      <td>7.848392e+00</td>\n",
              "      <td>7.126883e+00</td>\n",
              "      <td>1.052677e+01</td>\n",
              "      <td>8.877742e+00</td>\n",
              "      <td>1.731511e+01</td>\n",
              "      <td>9.253526e+00</td>\n",
              "      <td>5.041069e+00</td>\n",
              "      <td>5.591971e+00</td>\n",
              "      <td>3.942090e+01</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",
              "</div>"
            ],
            "text/plain": [
              "                Time            V1  ...         Amount          Class\n",
              "count  284807.000000  2.848070e+05  ...  284807.000000  284807.000000\n",
              "mean    94813.859575  3.919560e-15  ...      88.349619       0.001727\n",
              "std     47488.145955  1.958696e+00  ...     250.120109       0.041527\n",
              "min         0.000000 -5.640751e+01  ...       0.000000       0.000000\n",
              "25%     54201.500000 -9.203734e-01  ...       5.600000       0.000000\n",
              "50%     84692.000000  1.810880e-02  ...      22.000000       0.000000\n",
              "75%    139320.500000  1.315642e+00  ...      77.165000       0.000000\n",
              "max    172792.000000  2.454930e+00  ...   25691.160000       1.000000\n",
              "\n",
              "[8 rows x 31 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2lEXmuAdjFJE",
        "outputId": "0702f779-556d-46c6-e7f5-07ec42b5a33c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    284315\n",
              "1       492\n",
              "Name: Class, dtype: int64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Class'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "D1g3CkccjNJc"
      },
      "outputs": [],
      "source": [
        "X = df.iloc[:, 0:30]\n",
        "y = df.iloc[:, 30]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4-HZxEWskpQN",
        "outputId": "00b11048-3ccd-4505-d8e3-f97c88ab871c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "Name: Class, dtype: int64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "CbVYegcCksfc"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0, test_size = 0.33)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "Tr7h2cV5pHMe",
        "outputId": "213d6d93-7348-4a9b-ff7f-39acebb26837"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>22827</th>\n",
              "      <td>32448.0</td>\n",
              "      <td>-0.544234</td>\n",
              "      <td>-0.448641</td>\n",
              "      <td>0.657485</td>\n",
              "      <td>-3.670843</td>\n",
              "      <td>1.281379</td>\n",
              "      <td>3.493796</td>\n",
              "      <td>-0.992897</td>\n",
              "      <td>1.001550</td>\n",
              "      <td>-2.289931</td>\n",
              "      <td>0.899774</td>\n",
              "      <td>-0.490759</td>\n",
              "      <td>-1.402153</td>\n",
              "      <td>0.405993</td>\n",
              "      <td>-0.542794</td>\n",
              "      <td>0.452350</td>\n",
              "      <td>0.315830</td>\n",
              "      <td>-0.344234</td>\n",
              "      <td>0.836372</td>\n",
              "      <td>-0.592480</td>\n",
              "      <td>-0.043908</td>\n",
              "      <td>-0.112491</td>\n",
              "      <td>-0.040475</td>\n",
              "      <td>-0.327215</td>\n",
              "      <td>0.981274</td>\n",
              "      <td>0.578130</td>\n",
              "      <td>-0.154278</td>\n",
              "      <td>0.313498</td>\n",
              "      <td>0.122708</td>\n",
              "      <td>8.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104281</th>\n",
              "      <td>68996.0</td>\n",
              "      <td>1.099397</td>\n",
              "      <td>-0.201076</td>\n",
              "      <td>0.886129</td>\n",
              "      <td>0.406299</td>\n",
              "      <td>-0.747247</td>\n",
              "      <td>-0.115210</td>\n",
              "      <td>-0.479978</td>\n",
              "      <td>0.092256</td>\n",
              "      <td>0.166606</td>\n",
              "      <td>0.025720</td>\n",
              "      <td>1.169334</td>\n",
              "      <td>1.090135</td>\n",
              "      <td>0.760660</td>\n",
              "      <td>0.046660</td>\n",
              "      <td>0.668173</td>\n",
              "      <td>0.929527</td>\n",
              "      <td>-0.973930</td>\n",
              "      <td>0.543510</td>\n",
              "      <td>0.019095</td>\n",
              "      <td>0.088499</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.078674</td>\n",
              "      <td>-0.044063</td>\n",
              "      <td>0.058322</td>\n",
              "      <td>0.165440</td>\n",
              "      <td>0.386638</td>\n",
              "      <td>-0.017080</td>\n",
              "      <td>0.023510</td>\n",
              "      <td>58.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55205</th>\n",
              "      <td>46834.0</td>\n",
              "      <td>1.062558</td>\n",
              "      <td>0.145901</td>\n",
              "      <td>1.531429</td>\n",
              "      <td>2.608796</td>\n",
              "      <td>-0.789044</td>\n",
              "      <td>0.463536</td>\n",
              "      <td>-0.672808</td>\n",
              "      <td>0.330517</td>\n",
              "      <td>-0.089507</td>\n",
              "      <td>0.669475</td>\n",
              "      <td>0.674933</td>\n",
              "      <td>0.642812</td>\n",
              "      <td>-0.444117</td>\n",
              "      <td>-0.135842</td>\n",
              "      <td>-0.959016</td>\n",
              "      <td>0.837336</td>\n",
              "      <td>-0.642885</td>\n",
              "      <td>0.345980</td>\n",
              "      <td>-0.552866</td>\n",
              "      <td>-0.157424</td>\n",
              "      <td>-0.033093</td>\n",
              "      <td>-0.019463</td>\n",
              "      <td>0.028234</td>\n",
              "      <td>0.307752</td>\n",
              "      <td>0.250185</td>\n",
              "      <td>-0.060125</td>\n",
              "      <td>0.034603</td>\n",
              "      <td>0.026393</td>\n",
              "      <td>10.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124479</th>\n",
              "      <td>77304.0</td>\n",
              "      <td>0.480369</td>\n",
              "      <td>-2.776255</td>\n",
              "      <td>-0.548201</td>\n",
              "      <td>-0.754582</td>\n",
              "      <td>-1.972704</td>\n",
              "      <td>-0.851128</td>\n",
              "      <td>-0.054217</td>\n",
              "      <td>-0.371061</td>\n",
              "      <td>-1.661668</td>\n",
              "      <td>1.176395</td>\n",
              "      <td>-0.999535</td>\n",
              "      <td>-1.517889</td>\n",
              "      <td>-0.977793</td>\n",
              "      <td>0.042723</td>\n",
              "      <td>-0.159776</td>\n",
              "      <td>-0.826886</td>\n",
              "      <td>0.952616</td>\n",
              "      <td>-0.020062</td>\n",
              "      <td>-0.091200</td>\n",
              "      <td>0.669371</td>\n",
              "      <td>0.100809</td>\n",
              "      <td>-0.496867</td>\n",
              "      <td>-0.555391</td>\n",
              "      <td>0.340263</td>\n",
              "      <td>0.437977</td>\n",
              "      <td>-0.114189</td>\n",
              "      <td>-0.096010</td>\n",
              "      <td>0.100662</td>\n",
              "      <td>559.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75718</th>\n",
              "      <td>56219.0</td>\n",
              "      <td>-1.644622</td>\n",
              "      <td>0.999933</td>\n",
              "      <td>0.927572</td>\n",
              "      <td>1.088490</td>\n",
              "      <td>0.041400</td>\n",
              "      <td>0.861214</td>\n",
              "      <td>0.234369</td>\n",
              "      <td>0.563300</td>\n",
              "      <td>-0.476943</td>\n",
              "      <td>-0.010884</td>\n",
              "      <td>0.199895</td>\n",
              "      <td>0.092168</td>\n",
              "      <td>-1.197975</td>\n",
              "      <td>0.589714</td>\n",
              "      <td>-0.175797</td>\n",
              "      <td>-0.647336</td>\n",
              "      <td>0.375651</td>\n",
              "      <td>0.185812</td>\n",
              "      <td>1.397366</td>\n",
              "      <td>-0.275715</td>\n",
              "      <td>0.017683</td>\n",
              "      <td>-0.046211</td>\n",
              "      <td>-0.421555</td>\n",
              "      <td>-0.657974</td>\n",
              "      <td>0.443550</td>\n",
              "      <td>-0.152744</td>\n",
              "      <td>-0.560838</td>\n",
              "      <td>0.067824</td>\n",
              "      <td>61.90</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Time        V1        V2  ...       V27       V28  Amount\n",
              "22827   32448.0 -0.544234 -0.448641  ...  0.313498  0.122708    8.40\n",
              "104281  68996.0  1.099397 -0.201076  ... -0.017080  0.023510   58.88\n",
              "55205   46834.0  1.062558  0.145901  ...  0.034603  0.026393   10.56\n",
              "124479  77304.0  0.480369 -2.776255  ... -0.096010  0.100662  559.12\n",
              "75718   56219.0 -1.644622  0.999933  ... -0.560838  0.067824   61.90\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "ftoQeptKlDku"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.layers import Dense, Flatten\n",
        "from tensorflow.keras.models import Sequential\n",
        "import tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "puTMb0u7m1X1"
      },
      "outputs": [],
      "source": [
        "n_inputs = 30"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0A2X8Z48lro3",
        "outputId": "6715c6b3-c725-48d7-b016-5b19a09dfcf6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 256)               7936      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 257       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8,193\n",
            "Trainable params: 8,193\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(256, input_dim = n_inputs, activation= 'relu', kernel_initializer='he_uniform'))\n",
        "model.add(Dense(1, activation = 'sigmoid'))\n",
        "\n",
        "model.compile(optimizer = 'adam',\n",
        "              loss = 'binary_crossentropy')\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "na81zEj5ow-m",
        "outputId": "fbe0b7aa-5daa-4934-d501-e7a1cd0aba15"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "5964/5964 [==============================] - 9s 1ms/step - loss: 56.4997\n",
            "Epoch 2/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 30.3867\n",
            "Epoch 3/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 20.4899\n",
            "Epoch 4/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 17.4170\n",
            "Epoch 5/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 16.3949\n",
            "Epoch 6/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 12.9017\n",
            "Epoch 7/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 11.4606\n",
            "Epoch 8/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 12.7486\n",
            "Epoch 9/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 11.9499\n",
            "Epoch 10/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 10.8628\n",
            "Epoch 11/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 10.4844\n",
            "Epoch 12/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 10.7576\n",
            "Epoch 13/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 9.8837\n",
            "Epoch 14/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 9.9290\n",
            "Epoch 15/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 9.3132\n",
            "Epoch 16/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 7.9881\n",
            "Epoch 17/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 8.4130\n",
            "Epoch 18/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 6.7893\n",
            "Epoch 19/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 5.7314\n",
            "Epoch 20/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 5.8315\n",
            "Epoch 21/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 4.5902\n",
            "Epoch 22/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 4.1536\n",
            "Epoch 23/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 3.4708\n",
            "Epoch 24/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 3.7062\n",
            "Epoch 25/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 3.5378\n",
            "Epoch 26/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 3.5063\n",
            "Epoch 27/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 5.9115\n",
            "Epoch 28/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 4.5512\n",
            "Epoch 29/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 6.2405\n",
            "Epoch 30/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 5.8437\n",
            "Epoch 31/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 5.1281\n",
            "Epoch 32/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 6.6670\n",
            "Epoch 33/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 6.5964\n",
            "Epoch 34/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 5.8560\n",
            "Epoch 35/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 4.7746\n",
            "Epoch 36/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 5.2978\n",
            "Epoch 37/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 7.0915\n",
            "Epoch 38/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 6.4295\n",
            "Epoch 39/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 6.8862\n",
            "Epoch 40/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 6.7234\n",
            "Epoch 41/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 7.4032\n",
            "Epoch 42/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 4.6163\n",
            "Epoch 43/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 5.7570\n",
            "Epoch 44/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 5.5492\n",
            "Epoch 45/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 5.2205\n",
            "Epoch 46/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 5.1979\n",
            "Epoch 47/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 5.7121\n",
            "Epoch 48/50\n",
            "5964/5964 [==============================] - 8s 1ms/step - loss: 5.9172\n",
            "Epoch 49/50\n",
            "5964/5964 [==============================] - 9s 1ms/step - loss: 8.4744\n",
            "Epoch 50/50\n",
            "5964/5964 [==============================] - 9s 1ms/step - loss: 6.9309\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6f48b32dd0>"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.fit(X_train, y_train, epochs = 50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IlKsFG4gpBzU",
        "outputId": "c3ee4009-469f-4370-c5ff-5c358e23db0b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.9046165536706438\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "print(roc_auc_score(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l7trTFaFr308",
        "outputId": "a6a5b8dd-7a7f-49ca-d18d-bf3e2fbc52dc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    190490\n",
              "1       330\n",
              "Name: Class, dtype: int64"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UXp8mrFdsJ2n"
      },
      "source": [
        "# **Weight assigning**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "QiviHlWDr9jD"
      },
      "outputs": [],
      "source": [
        "weights_assigned={0:1, 1:570}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JjT6AznssZGk",
        "outputId": "f7955874-4352-4af6-8573-5562eb1095ad"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "5964/5964 [==============================] - 9s 1ms/step - loss: 4890.0801\n",
            "Epoch 2/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 3518.9890\n",
            "Epoch 3/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 2671.9954\n",
            "Epoch 4/50\n",
            "5964/5964 [==============================] - 9s 1ms/step - loss: 3035.6304\n",
            "Epoch 5/50\n",
            "5964/5964 [==============================] - 9s 1ms/step - loss: 2624.1641\n",
            "Epoch 6/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 2363.5930\n",
            "Epoch 7/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 2490.6570\n",
            "Epoch 8/50\n",
            "5964/5964 [==============================] - 9s 1ms/step - loss: 2574.8215\n",
            "Epoch 9/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 2359.8691\n",
            "Epoch 10/50\n",
            "5964/5964 [==============================] - 9s 1ms/step - loss: 2510.9771\n",
            "Epoch 11/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 2410.9514\n",
            "Epoch 12/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 2364.0383\n",
            "Epoch 13/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 2570.4539\n",
            "Epoch 14/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 2198.5371\n",
            "Epoch 15/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 2218.8555\n",
            "Epoch 16/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 2406.1799\n",
            "Epoch 17/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 2085.6626\n",
            "Epoch 18/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 2296.7485\n",
            "Epoch 19/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 2230.4487\n",
            "Epoch 20/50\n",
            "5964/5964 [==============================] - 9s 1ms/step - loss: 1841.7925\n",
            "Epoch 21/50\n",
            "5964/5964 [==============================] - 9s 1ms/step - loss: 2049.7759\n",
            "Epoch 22/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 2025.1962\n",
            "Epoch 23/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1729.6929\n",
            "Epoch 24/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1777.6886\n",
            "Epoch 25/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1811.8809\n",
            "Epoch 26/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1803.7883\n",
            "Epoch 27/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 2215.9541\n",
            "Epoch 28/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1854.7173\n",
            "Epoch 29/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1843.5426\n",
            "Epoch 30/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1663.3387\n",
            "Epoch 31/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1552.4028\n",
            "Epoch 32/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1460.2498\n",
            "Epoch 33/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1646.7109\n",
            "Epoch 34/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1859.0037\n",
            "Epoch 35/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1748.5775\n",
            "Epoch 36/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1651.2473\n",
            "Epoch 37/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1591.0192\n",
            "Epoch 38/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1813.6442\n",
            "Epoch 39/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1558.6064\n",
            "Epoch 40/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1695.3224\n",
            "Epoch 41/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1543.9574\n",
            "Epoch 42/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1818.8925\n",
            "Epoch 43/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1970.3129\n",
            "Epoch 44/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1789.1967\n",
            "Epoch 45/50\n",
            "5964/5964 [==============================] - 9s 1ms/step - loss: 1897.5892\n",
            "Epoch 46/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1844.7786\n",
            "Epoch 47/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1905.4574\n",
            "Epoch 48/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 1907.0543\n",
            "Epoch 49/50\n",
            "5964/5964 [==============================] - 9s 1ms/step - loss: 1938.7003\n",
            "Epoch 50/50\n",
            "5964/5964 [==============================] - 9s 2ms/step - loss: 2202.5696\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6f48a2c6d0>"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(256, input_dim = n_inputs, activation = 'relu', kernel_initializer = 'he_uniform'))\n",
        "model.add(Dense(1, activation = 'sigmoid'))\n",
        "model.compile(loss = 'binary_crossentropy',\n",
        "              optimizer = 'adam')\n",
        "\n",
        "model.fit(X_train, y_train, epochs = 50, class_weight=weights_assigned)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dG3EdbBnvPEr",
        "outputId": "53f9a362-3f4e-4df7-d542-5696adca4ed3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.9184252926876606\n"
          ]
        }
      ],
      "source": [
        "y_pred=model.predict(X_test)\n",
        "\n",
        "from sklearn.metrics import roc_auc_score\n",
        "print(roc_auc_score(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PA5trbCKva7d"
      },
      "source": [
        "**We can see that the accuracy has been developed after weight assigning. This way we can improve the accuracy even if the dataset is imbalanced.**"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Handling_imbalanced_dataset_credit_card_fraud_DL",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}