credit_card_dataset_preprocessing.ipynb

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    "colab": {
      "name": "credit_card_dataset_preprocessing.ipynb",
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    "kernelspec": {
      "name": "python3",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "WQH2T9tC8jvZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "outputId": "d467fcab-88fb-4420-9e4b-d03b37540a0e"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import sklearn\n",
        "missing_values = [\"n/a\", \"na\", \"--\", \" \", \"  \", \"   \", \"    \"]\n",
        "df = pd.read_csv('/content/creditcard.csv', na_values = missing_values)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>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": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uKgMXlk49ZjX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532
        },
        "outputId": "14c8b436-11fd-4a7c-b8a3-1c567b544ede"
      },
      "source": [
        "x = df.iloc[:, 1:len(list(df))-1]\n",
        "print(x)\n",
        "y = df.iloc[:, len(list(df))-1:len(list(df))]\n",
        "print(y)\n",
        "\n",
        "#print(df.isnull().sum())\n",
        "print (df.isnull().values.any())\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "               V1         V2        V3  ...       V27       V28  Amount\n",
            "0       -1.359807  -0.072781  2.536347  ...  0.133558 -0.021053  149.62\n",
            "1        1.191857   0.266151  0.166480  ... -0.008983  0.014724    2.69\n",
            "2       -1.358354  -1.340163  1.773209  ... -0.055353 -0.059752  378.66\n",
            "3       -0.966272  -0.185226  1.792993  ...  0.062723  0.061458  123.50\n",
            "4       -1.158233   0.877737  1.548718  ...  0.219422  0.215153   69.99\n",
            "...           ...        ...       ...  ...       ...       ...     ...\n",
            "284802 -11.881118  10.071785 -9.834783  ...  0.943651  0.823731    0.77\n",
            "284803  -0.732789  -0.055080  2.035030  ...  0.068472 -0.053527   24.79\n",
            "284804   1.919565  -0.301254 -3.249640  ...  0.004455 -0.026561   67.88\n",
            "284805  -0.240440   0.530483  0.702510  ...  0.108821  0.104533   10.00\n",
            "284806  -0.533413  -0.189733  0.703337  ... -0.002415  0.013649  217.00\n",
            "\n",
            "[284807 rows x 29 columns]\n",
            "        Class\n",
            "0           0\n",
            "1           0\n",
            "2           0\n",
            "3           0\n",
            "4           0\n",
            "...       ...\n",
            "284802      0\n",
            "284803      0\n",
            "284804      0\n",
            "284805      0\n",
            "284806      0\n",
            "\n",
            "[284807 rows x 1 columns]\n",
            "False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UvLq7jiF-JcM"
      },
      "source": [
        "#median_totalcharges = df['TotalCharges'].median()\n",
        "#df['TotalCharges'].fillna(median_totalcharges, inplace=True)\n",
        "#print(df.isnull().sum())\n",
        "#import sklearn\n",
        "#from sklearn.utils import shuffle\n",
        "#df = shuffle(df).reset_index(drop=True)\n",
        "#df.head(10)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UZhZcwgPBg-N"
      },
      "source": [
        "for i in range(28):\n",
        "  col_num = i+1\n",
        "  col = \"V\" + str(col_num)\n",
        "  cnt=0\n",
        "  for row in df[col]:\n",
        "      try:\n",
        "          float(row)\n",
        "          #df.loc[cnt, 'OWN_OCCUPIED']=np.nan\n",
        "      except ValueError:\n",
        "          print(str(row))\n",
        "      cnt+=1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hEkGsj8M8tr_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "627d7022-6769-43f1-d0bf-ba34a58b1409"
      },
      "source": [
        "#df['tenure'] = pd.qcut(df['tenure'], 10, labels=False)\n",
        "#df['MonthlyCharges'] = pd.qcut(df['MonthlyCharges'], 10, labels=False)\n",
        "#df['TotalCharges'] = pd.qcut(df['TotalCharges'], 100, labels=False)\n",
        "#df.head()\n",
        "\n",
        "#from sklearn.preprocessing import StandardScaler\n",
        "#scaler = StandardScaler() \n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler() \n",
        "\n",
        "x_scaled = scaler.fit_transform(x)\n",
        "x = pd.DataFrame(data=x_scaled, columns=[\"V1\", \"V2\", \"V3\", \"V4\", \"V5\", \"V6\", \"V7\", \"V8\", \"V9\", \"V10\", \"V11\", \"V12\", \"V13\", \"V14\", \"V15\", \"V16\", \"V17\", \"V18\", \"V19\", \"V20\", \"V21\", \"V22\", \"V23\", \"V24\", \"V25\", \"V26\", \"V27\", \"V28\", \"Amount\"])\n",
        "\n",
        "from sklearn.preprocessing import KBinsDiscretizer\n",
        "enc = KBinsDiscretizer(n_bins=20, encode='ordinal')\n",
        "#V1_np = np.array(x)\n",
        "#V1 = V1_np.reshape(-1, 1)\n",
        "x_binned = enc.fit_transform(x)\n",
        "print(x_binned)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 3.  8. 19. ... 15.  7. 17.]\n",
            " [13. 11.  9. ...  9. 10.  3.]\n",
            " [ 3.  2. 18. ...  5.  4. 19.]\n",
            " ...\n",
            " [16.  6.  0. ... 10.  7. 14.]\n",
            " [ 9. 13. 13. ... 15. 16.  7.]\n",
            " [ 7.  7. 13. ...  9. 10. 18.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zj5A0EBsNOLo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7d76e02f-8f35-4416-cb2d-15c38a82e91f"
      },
      "source": [
        "print(type(x_binned))\n",
        "x = pd.DataFrame(x_binned, columns=[\"V1\", \"V2\", \"V3\", \"V4\", \"V5\", \"V6\", \"V7\", \"V8\", \"V9\", \"V10\", \"V11\", \"V12\", \"V13\", \"V14\", \"V15\", \"V16\", \"V17\", \"V18\", \"V19\", \"V20\", \"V21\", \"V22\", \"V23\", \"V24\", \"V25\", \"V26\", \"V27\", \"V28\", \"Amount\"])\n",
        "#print(x.shape)\n",
        "#print(y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F3xdJUCiOD6Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "outputId": "93312b1e-3fca-4f08-b83d-9ceed0202ecc"
      },
      "source": [
        "df = pd.concat([x,y], axis=1)\n",
        "df.rename(columns = {'Class':'Target'}, inplace = True) \n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>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>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     V1    V2    V3    V4    V5    V6  ...   V25   V26   V27   V28  Amount  Target\n",
              "0   3.0   8.0  19.0  17.0   7.0  15.0  ...  11.0   7.0  15.0   7.0    17.0       0\n",
              "1  13.0  11.0   9.0  13.0  10.0  11.0  ...  12.0  13.0   9.0  10.0     3.0       0\n",
              "2   3.0   2.0  18.0  12.0   6.0  18.0  ...   4.0   8.0   5.0   4.0    19.0       0\n",
              "3   4.0   7.0  18.0   4.0  10.0  17.0  ...  18.0   6.0  13.0  14.0    16.0       0\n",
              "4   3.0  15.0  17.0  12.0   7.0  13.0  ...   6.0  16.0  17.0  18.0    14.0       0\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1yMFYXrjOFSJ"
      },
      "source": [
        "df.to_csv ('/content/creditcard_preprocessed.csv', index = False, header=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EjYPEeizOm19"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nywtHKa1Kqp9"
      },
      "source": [
        "# New Section"
      ]
    }
  ]
}