CreditCardFraudDetection.ipynb

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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "PlIZCXEEhMm1"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from matplotlib import gridspec"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv('/content/drive/MyDrive/creditcard.csv')\n",
        "data"
      ],
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          "height": 443
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        "id": "r3-kGUIyhXMB",
        "outputId": "31d8c07a-c840-4ceb-c2b9-b18bdd277fe0"
      },
      "execution_count": 2,
      "outputs": [
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              "            Time         V1         V2        V3        V4        V5  \\\n",
              "0            0.0  -1.359807  -0.072781  2.536347  1.378155 -0.338321   \n",
              "1            0.0   1.191857   0.266151  0.166480  0.448154  0.060018   \n",
              "2            1.0  -1.358354  -1.340163  1.773209  0.379780 -0.503198   \n",
              "3            1.0  -0.966272  -0.185226  1.792993 -0.863291 -0.010309   \n",
              "4            2.0  -1.158233   0.877737  1.548718  0.403034 -0.407193   \n",
              "...          ...        ...        ...       ...       ...       ...   \n",
              "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
              "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
              "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
              "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
              "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
              "\n",
              "              V6        V7        V8        V9  ...       V21       V22  \\\n",
              "0       0.462388  0.239599  0.098698  0.363787  ... -0.018307  0.277838   \n",
              "1      -0.082361 -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672   \n",
              "2       1.800499  0.791461  0.247676 -1.514654  ...  0.247998  0.771679   \n",
              "3       1.247203  0.237609  0.377436 -1.387024  ... -0.108300  0.005274   \n",
              "4       0.095921  0.592941 -0.270533  0.817739  ... -0.009431  0.798278   \n",
              "...          ...       ...       ...       ...  ...       ...       ...   \n",
              "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
              "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
              "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
              "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
              "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
              "\n",
              "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
              "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62   \n",
              "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69   \n",
              "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66   \n",
              "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50   \n",
              "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99   \n",
              "...          ...       ...       ...       ...       ...       ...     ...   \n",
              "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
              "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
              "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
              "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
              "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
              "\n",
              "        Class  \n",
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              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
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              "      <th>1</th>\n",
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              "      <td>0.266151</td>\n",
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              "      <td>0.060018</td>\n",
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              "      <th>2</th>\n",
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              "      <td>1.773209</td>\n",
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              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
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              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
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              "      <th>3</th>\n",
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              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d48f69a7-70bb-4e9a-b44a-c30ceb435e8c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d48f69a7-70bb-4e9a-b44a-c30ceb435e8c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e76866ac-3771-4004-9b79-50ede664acbb\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_e76866ac-3771-4004-9b79-50ede664acbb button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
              "      }\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prints information about the DataFrame. The information contains the number of columns, column labels, column data types\n",
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cTD3L80DlJUw",
        "outputId": "2339a97e-6ce5-44cd-f9a1-f28b7598bc7f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 284807 entries, 0 to 284806\n",
            "Data columns (total 31 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   Time    284807 non-null  float64\n",
            " 1   V1      284807 non-null  float64\n",
            " 2   V2      284807 non-null  float64\n",
            " 3   V3      284807 non-null  float64\n",
            " 4   V4      284807 non-null  float64\n",
            " 5   V5      284807 non-null  float64\n",
            " 6   V6      284807 non-null  float64\n",
            " 7   V7      284807 non-null  float64\n",
            " 8   V8      284807 non-null  float64\n",
            " 9   V9      284807 non-null  float64\n",
            " 10  V10     284807 non-null  float64\n",
            " 11  V11     284807 non-null  float64\n",
            " 12  V12     284807 non-null  float64\n",
            " 13  V13     284807 non-null  float64\n",
            " 14  V14     284807 non-null  float64\n",
            " 15  V15     284807 non-null  float64\n",
            " 16  V16     284807 non-null  float64\n",
            " 17  V17     284807 non-null  float64\n",
            " 18  V18     284807 non-null  float64\n",
            " 19  V19     284807 non-null  float64\n",
            " 20  V20     284807 non-null  float64\n",
            " 21  V21     284807 non-null  float64\n",
            " 22  V22     284807 non-null  float64\n",
            " 23  V23     284807 non-null  float64\n",
            " 24  V24     284807 non-null  float64\n",
            " 25  V25     284807 non-null  float64\n",
            " 26  V26     284807 non-null  float64\n",
            " 27  V27     284807 non-null  float64\n",
            " 28  V28     284807 non-null  float64\n",
            " 29  Amount  284807 non-null  float64\n",
            " 30  Class   284807 non-null  int64  \n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 67.4 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Identifying any NAN value in dataset\n",
        "data.isnull().sum(axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dw1jJRAPhh7K",
        "outputId": "f675c552-43d3-4da7-a768-a87fe39facf4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      0\n",
              "V1        0\n",
              "V2        0\n",
              "V3        0\n",
              "V4        0\n",
              "V5        0\n",
              "V6        0\n",
              "V7        0\n",
              "V8        0\n",
              "V9        0\n",
              "V10       0\n",
              "V11       0\n",
              "V12       0\n",
              "V13       0\n",
              "V14       0\n",
              "V15       0\n",
              "V16       0\n",
              "V17       0\n",
              "V18       0\n",
              "V19       0\n",
              "V20       0\n",
              "V21       0\n",
              "V22       0\n",
              "V23       0\n",
              "V24       0\n",
              "V25       0\n",
              "V26       0\n",
              "V27       0\n",
              "V28       0\n",
              "Amount    0\n",
              "Class     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check for duplicates\n",
        "duplicates = data[data.duplicated()]\n",
        "print(\"Number of duplicates: \" ,len (duplicates) )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J1VuFVP3ldOq",
        "outputId": "481849a1-ad2d-4484-f7fb-5819eae1e36c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicates:  1081\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for outliers\n",
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "_CNx5-D2lfKj",
        "outputId": "3ff3dadd-f6bc-4904-a672-75a950f6f166"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Time            V1            V2            V3            V4  \\\n",
              "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
              "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
              "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
              "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
              "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
              "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
              "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
              "\n",
              "                 V5            V6            V7            V8            V9  \\\n",
              "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
              "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
              "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
              "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
              "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
              "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
              "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
              "\n",
              "       ...           V21           V22           V23           V24  \\\n",
              "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
              "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
              "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
              "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
              "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
              "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
              "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
              "\n",
              "                V25           V26           V27           V28         Amount  \\\n",
              "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
              "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
              "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
              "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
              "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
              "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
              "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
              "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
              "\n",
              "               Class  \n",
              "count  284807.000000  \n",
              "mean        0.001727  \n",
              "std         0.041527  \n",
              "min         0.000000  \n",
              "25%         0.000000  \n",
              "50%         0.000000  \n",
              "75%         0.000000  \n",
              "max         1.000000  \n",
              "\n",
              "[8 rows x 31 columns]"
            ],
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              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>...</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>284807.000000</td>\n",
              "      <td>284807.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>94813.859575</td>\n",
              "      <td>1.168375e-15</td>\n",
              "      <td>3.416908e-16</td>\n",
              "      <td>-1.379537e-15</td>\n",
              "      <td>2.074095e-15</td>\n",
              "      <td>9.604066e-16</td>\n",
              "      <td>1.487313e-15</td>\n",
              "      <td>-5.556467e-16</td>\n",
              "      <td>1.213481e-16</td>\n",
              "      <td>-2.406331e-15</td>\n",
              "      <td>...</td>\n",
              "      <td>1.654067e-16</td>\n",
              "      <td>-3.568593e-16</td>\n",
              "      <td>2.578648e-16</td>\n",
              "      <td>4.473266e-15</td>\n",
              "      <td>5.340915e-16</td>\n",
              "      <td>1.683437e-15</td>\n",
              "      <td>-3.660091e-16</td>\n",
              "      <td>-1.227390e-16</td>\n",
              "      <td>88.349619</td>\n",
              "      <td>0.001727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47488.145955</td>\n",
              "      <td>1.958696e+00</td>\n",
              "      <td>1.651309e+00</td>\n",
              "      <td>1.516255e+00</td>\n",
              "      <td>1.415869e+00</td>\n",
              "      <td>1.380247e+00</td>\n",
              "      <td>1.332271e+00</td>\n",
              "      <td>1.237094e+00</td>\n",
              "      <td>1.194353e+00</td>\n",
              "      <td>1.098632e+00</td>\n",
              "      <td>...</td>\n",
              "      <td>7.345240e-01</td>\n",
              "      <td>7.257016e-01</td>\n",
              "      <td>6.244603e-01</td>\n",
              "      <td>6.056471e-01</td>\n",
              "      <td>5.212781e-01</td>\n",
              "      <td>4.822270e-01</td>\n",
              "      <td>4.036325e-01</td>\n",
              "      <td>3.300833e-01</td>\n",
              "      <td>250.120109</td>\n",
              "      <td>0.041527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-5.640751e+01</td>\n",
              "      <td>-7.271573e+01</td>\n",
              "      <td>-4.832559e+01</td>\n",
              "      <td>-5.683171e+00</td>\n",
              "      <td>-1.137433e+02</td>\n",
              "      <td>-2.616051e+01</td>\n",
              "      <td>-4.355724e+01</td>\n",
              "      <td>-7.321672e+01</td>\n",
              "      <td>-1.343407e+01</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.483038e+01</td>\n",
              "      <td>-1.093314e+01</td>\n",
              "      <td>-4.480774e+01</td>\n",
              "      <td>-2.836627e+00</td>\n",
              "      <td>-1.029540e+01</td>\n",
              "      <td>-2.604551e+00</td>\n",
              "      <td>-2.256568e+01</td>\n",
              "      <td>-1.543008e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>54201.500000</td>\n",
              "      <td>-9.203734e-01</td>\n",
              "      <td>-5.985499e-01</td>\n",
              "      <td>-8.903648e-01</td>\n",
              "      <td>-8.486401e-01</td>\n",
              "      <td>-6.915971e-01</td>\n",
              "      <td>-7.682956e-01</td>\n",
              "      <td>-5.540759e-01</td>\n",
              "      <td>-2.086297e-01</td>\n",
              "      <td>-6.430976e-01</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.283949e-01</td>\n",
              "      <td>-5.423504e-01</td>\n",
              "      <td>-1.618463e-01</td>\n",
              "      <td>-3.545861e-01</td>\n",
              "      <td>-3.171451e-01</td>\n",
              "      <td>-3.269839e-01</td>\n",
              "      <td>-7.083953e-02</td>\n",
              "      <td>-5.295979e-02</td>\n",
              "      <td>5.600000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>84692.000000</td>\n",
              "      <td>1.810880e-02</td>\n",
              "      <td>6.548556e-02</td>\n",
              "      <td>1.798463e-01</td>\n",
              "      <td>-1.984653e-02</td>\n",
              "      <td>-5.433583e-02</td>\n",
              "      <td>-2.741871e-01</td>\n",
              "      <td>4.010308e-02</td>\n",
              "      <td>2.235804e-02</td>\n",
              "      <td>-5.142873e-02</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.945017e-02</td>\n",
              "      <td>6.781943e-03</td>\n",
              "      <td>-1.119293e-02</td>\n",
              "      <td>4.097606e-02</td>\n",
              "      <td>1.659350e-02</td>\n",
              "      <td>-5.213911e-02</td>\n",
              "      <td>1.342146e-03</td>\n",
              "      <td>1.124383e-02</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>139320.500000</td>\n",
              "      <td>1.315642e+00</td>\n",
              "      <td>8.037239e-01</td>\n",
              "      <td>1.027196e+00</td>\n",
              "      <td>7.433413e-01</td>\n",
              "      <td>6.119264e-01</td>\n",
              "      <td>3.985649e-01</td>\n",
              "      <td>5.704361e-01</td>\n",
              "      <td>3.273459e-01</td>\n",
              "      <td>5.971390e-01</td>\n",
              "      <td>...</td>\n",
              "      <td>1.863772e-01</td>\n",
              "      <td>5.285536e-01</td>\n",
              "      <td>1.476421e-01</td>\n",
              "      <td>4.395266e-01</td>\n",
              "      <td>3.507156e-01</td>\n",
              "      <td>2.409522e-01</td>\n",
              "      <td>9.104512e-02</td>\n",
              "      <td>7.827995e-02</td>\n",
              "      <td>77.165000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>172792.000000</td>\n",
              "      <td>2.454930e+00</td>\n",
              "      <td>2.205773e+01</td>\n",
              "      <td>9.382558e+00</td>\n",
              "      <td>1.687534e+01</td>\n",
              "      <td>3.480167e+01</td>\n",
              "      <td>7.330163e+01</td>\n",
              "      <td>1.205895e+02</td>\n",
              "      <td>2.000721e+01</td>\n",
              "      <td>1.559499e+01</td>\n",
              "      <td>...</td>\n",
              "      <td>2.720284e+01</td>\n",
              "      <td>1.050309e+01</td>\n",
              "      <td>2.252841e+01</td>\n",
              "      <td>4.584549e+00</td>\n",
              "      <td>7.519589e+00</td>\n",
              "      <td>3.517346e+00</td>\n",
              "      <td>3.161220e+01</td>\n",
              "      <td>3.384781e+01</td>\n",
              "      <td>25691.160000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-89a8b976-9e62-47eb-9044-f25a26e29f8d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-89a8b976-9e62-47eb-9044-f25a26e29f8d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-89a8b976-9e62-47eb-9044-f25a26e29f8d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-74ccd849-5612-43fa-8c92-ab4504965bc8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-74ccd849-5612-43fa-8c92-ab4504965bc8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-74ccd849-5612-43fa-8c92-ab4504965bc8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking dtypes\n",
        "data.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3De6NvfYlnj0",
        "outputId": "c3c0bde6-72c9-41c5-f90d-7da6e80cff57"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      float64\n",
              "V1        float64\n",
              "V2        float64\n",
              "V3        float64\n",
              "V4        float64\n",
              "V5        float64\n",
              "V6        float64\n",
              "V7        float64\n",
              "V8        float64\n",
              "V9        float64\n",
              "V10       float64\n",
              "V11       float64\n",
              "V12       float64\n",
              "V13       float64\n",
              "V14       float64\n",
              "V15       float64\n",
              "V16       float64\n",
              "V17       float64\n",
              "V18       float64\n",
              "V19       float64\n",
              "V20       float64\n",
              "V21       float64\n",
              "V22       float64\n",
              "V23       float64\n",
              "V24       float64\n",
              "V25       float64\n",
              "V26       float64\n",
              "V27       float64\n",
              "V28       float64\n",
              "Amount    float64\n",
              "Class       int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# show shape of data after removing duplicates\n",
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z181ja4klspX",
        "outputId": "0f988526-20c3-458c-8f87-71fb85c8eb2b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XVuwE73jlztt",
        "outputId": "5a36e290-2b4f-43b2-9861-0f9ede621a35"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Time            V1            V2            V3            V4  \\\n",
              "Time    1.000000  1.173963e-01 -1.059333e-02 -4.196182e-01 -1.052602e-01   \n",
              "V1      0.117396  1.000000e+00  4.135835e-16 -1.227819e-15 -9.215150e-16   \n",
              "V2     -0.010593  4.135835e-16  1.000000e+00  3.243764e-16 -1.121065e-15   \n",
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              "V11    -0.247689  2.125498e-16  1.975426e-16  1.576830e-15  3.459380e-16   \n",
              "V12     0.124348  2.053457e-16 -9.568710e-17  6.310231e-16 -5.625518e-16   \n",
              "V13    -0.065902 -2.425603e-17  6.295388e-16  2.807652e-16  1.303306e-16   \n",
              "V14    -0.098757 -5.020280e-16 -1.730566e-16  4.739859e-16  2.282280e-16   \n",
              "V15    -0.183453  3.547782e-16 -4.995814e-17  9.068793e-16  1.377649e-16   \n",
              "V16     0.011903  7.212815e-17  1.177316e-17  8.299445e-16 -9.614528e-16   \n",
              "V17    -0.073297 -3.879840e-16 -2.685296e-16  7.614712e-16 -2.699612e-16   \n",
              "V18     0.090438  3.230206e-17  3.284605e-16  1.509897e-16 -5.103644e-16   \n",
              "V19     0.028975  1.502024e-16 -7.118719e-18  3.463522e-16 -3.980557e-16   \n",
              "V20    -0.050866  4.654551e-16  2.506675e-16 -9.316409e-16 -1.857247e-16   \n",
              "V21     0.044736 -2.457409e-16 -8.480447e-17  5.706192e-17 -1.949553e-16   \n",
              "V22     0.144059 -4.290944e-16  1.526333e-16 -1.133902e-15 -6.276051e-17   \n",
              "V23     0.051142  6.168652e-16  1.634231e-16 -4.983035e-16  9.164206e-17   \n",
              "V24    -0.016182 -4.425156e-17  1.247925e-17  2.686834e-19  1.584638e-16   \n",
              "V25    -0.233083 -9.605737e-16 -4.478846e-16 -1.104734e-15  6.070716e-16   \n",
              "V26    -0.041407 -1.581290e-17  2.057310e-16 -1.238062e-16 -4.247268e-16   \n",
              "V27    -0.005135  1.198124e-16 -4.966953e-16  1.045747e-15  3.977061e-17   \n",
              "V28    -0.009413  2.083082e-15 -5.093836e-16  9.775546e-16 -2.761403e-18   \n",
              "Amount -0.010596 -2.277087e-01 -5.314089e-01 -2.108805e-01  9.873167e-02   \n",
              "Class  -0.012323 -1.013473e-01  9.128865e-02 -1.929608e-01  1.334475e-01   \n",
              "\n",
              "                  V5            V6            V7            V8            V9  \\\n",
              "Time    1.730721e-01 -6.301647e-02  8.471437e-02 -3.694943e-02 -8.660434e-03   \n",
              "V1      1.812612e-17 -6.506567e-16 -1.005191e-15 -2.433822e-16 -1.513678e-16   \n",
              "V2      5.157519e-16  2.787346e-16  2.055934e-16 -5.377041e-17  1.978488e-17   \n",
              "V3     -6.539009e-17  1.627627e-15  4.895305e-16 -1.268779e-15  5.568367e-16   \n",
              "V4     -1.719944e-15 -7.491959e-16 -4.104503e-16  5.697192e-16  6.923247e-16   \n",
              "V5      1.000000e+00  2.408382e-16  2.715541e-16  7.437229e-16  7.391702e-16   \n",
              "V6      2.408382e-16  1.000000e+00  1.191668e-16 -1.104219e-16  4.131207e-16   \n",
              "V7      2.715541e-16  1.191668e-16  1.000000e+00  3.344412e-16  1.122501e-15   \n",
              "V8      7.437229e-16 -1.104219e-16  3.344412e-16  1.000000e+00  4.356078e-16   \n",
              "V9      7.391702e-16  4.131207e-16  1.122501e-15  4.356078e-16  1.000000e+00   \n",
              "V10    -5.202306e-16  5.932243e-17 -7.492834e-17 -2.801370e-16 -4.642274e-16   \n",
              "V11     7.203963e-16  1.980503e-15  1.425248e-16  2.487043e-16  1.354680e-16   \n",
              "V12     7.412552e-16  2.375468e-16 -3.536655e-18  1.839891e-16 -1.079314e-15   \n",
              "V13     5.886991e-16 -1.211182e-16  1.266462e-17 -2.921856e-16  2.251072e-15   \n",
              "V14     6.565143e-16  2.621312e-16  2.607772e-16 -8.599156e-16  3.784757e-15   \n",
              "V15    -8.720275e-16 -1.531188e-15 -1.690540e-16  4.127777e-16 -1.051167e-15   \n",
              "V16     2.246261e-15  2.623672e-18  5.869302e-17 -5.254741e-16 -1.214086e-15   \n",
              "V17     1.281914e-16  2.015618e-16  2.177192e-16 -2.269549e-16  1.113695e-15   \n",
              "V18     5.308590e-16  1.223814e-16  7.604126e-17 -3.667974e-16  4.993240e-16   \n",
              "V19    -1.450421e-16 -1.865597e-16 -1.881008e-16 -3.875186e-16 -1.376135e-16   \n",
              "V20    -3.554057e-16 -1.858755e-16  9.379684e-16  2.033737e-16 -2.343720e-16   \n",
              "V21    -3.920976e-16  5.833316e-17 -2.027779e-16  3.892798e-16  1.936953e-16   \n",
              "V22     1.253751e-16 -4.705235e-19 -8.898922e-16  2.026927e-16 -7.071869e-16   \n",
              "V23    -8.428683e-18  1.046712e-16 -4.387401e-16  6.377260e-17 -5.214137e-16   \n",
              "V24    -1.149255e-15 -1.071589e-15  7.434913e-18 -1.047097e-16 -1.430343e-16   \n",
              "V25     4.808532e-16  4.562861e-16 -3.094082e-16 -4.653279e-16  6.757763e-16   \n",
              "V26     4.319541e-16 -1.357067e-16 -9.657637e-16 -1.727276e-16 -7.888853e-16   \n",
              "V27     6.590482e-16 -4.452461e-16 -1.782106e-15  1.299943e-16 -6.709655e-17   \n",
              "V28    -5.613951e-18  2.594754e-16 -2.776530e-16 -6.200930e-16  1.110541e-15   \n",
              "Amount -3.863563e-01  2.159812e-01  3.973113e-01 -1.030791e-01 -4.424560e-02   \n",
              "Class  -9.497430e-02 -4.364316e-02 -1.872566e-01  1.987512e-02 -9.773269e-02   \n",
              "\n",
              "        ...           V21           V22           V23           V24  \\\n",
              "Time    ...  4.473573e-02  1.440591e-01  5.114236e-02 -1.618187e-02   \n",
              "V1      ... -2.457409e-16 -4.290944e-16  6.168652e-16 -4.425156e-17   \n",
              "V2      ... -8.480447e-17  1.526333e-16  1.634231e-16  1.247925e-17   \n",
              "V3      ...  5.706192e-17 -1.133902e-15 -4.983035e-16  2.686834e-19   \n",
              "V4      ... -1.949553e-16 -6.276051e-17  9.164206e-17  1.584638e-16   \n",
              "V5      ... -3.920976e-16  1.253751e-16 -8.428683e-18 -1.149255e-15   \n",
              "V6      ...  5.833316e-17 -4.705235e-19  1.046712e-16 -1.071589e-15   \n",
              "V7      ... -2.027779e-16 -8.898922e-16 -4.387401e-16  7.434913e-18   \n",
              "V8      ...  3.892798e-16  2.026927e-16  6.377260e-17 -1.047097e-16   \n",
              "V9      ...  1.936953e-16 -7.071869e-16 -5.214137e-16 -1.430343e-16   \n",
              "V10     ...  1.177547e-15 -6.418202e-16  3.214491e-16 -1.355885e-16   \n",
              "V11     ... -5.658364e-16  7.772895e-16 -4.505332e-16  1.933267e-15   \n",
              "V12     ...  7.300527e-16  1.644699e-16  1.800885e-16  4.436512e-16   \n",
              "V13     ...  1.008461e-16  6.747721e-17 -7.132064e-16 -1.397470e-16   \n",
              "V14     ... -3.356561e-16  3.740383e-16  3.883204e-16  2.003482e-16   \n",
              "V15     ...  6.605263e-17 -4.208921e-16 -3.912243e-16 -4.478263e-16   \n",
              "V16     ... -4.715090e-16 -7.923387e-17  5.020770e-16 -3.005985e-16   \n",
              "V17     ... -8.230527e-16 -8.743398e-16  3.706214e-16 -2.403828e-16   \n",
              "V18     ... -9.408680e-16 -4.819365e-16 -1.912006e-16 -8.986916e-17   \n",
              "V19     ...  5.115885e-16 -1.163768e-15  7.032035e-16  2.587708e-17   \n",
              "V20     ... -7.614597e-16  1.009285e-15  2.712885e-16  1.277215e-16   \n",
              "V21     ...  1.000000e+00  3.649908e-15  8.119580e-16  1.761054e-16   \n",
              "V22     ...  3.649908e-15  1.000000e+00 -7.303916e-17  9.970809e-17   \n",
              "V23     ...  8.119580e-16 -7.303916e-17  1.000000e+00  2.130519e-17   \n",
              "V24     ...  1.761054e-16  9.970809e-17  2.130519e-17  1.000000e+00   \n",
              "V25     ... -1.686082e-16 -5.018575e-16 -8.232727e-17  1.015391e-15   \n",
              "V26     ... -5.557329e-16 -2.503187e-17  1.114524e-15  1.343722e-16   \n",
              "V27     ... -1.211281e-15  8.461337e-17  2.839721e-16 -2.274142e-16   \n",
              "V28     ...  5.278775e-16 -6.627203e-16  1.481903e-15 -2.819805e-16   \n",
              "Amount  ...  1.059989e-01 -6.480065e-02 -1.126326e-01  5.146217e-03   \n",
              "Class   ...  4.041338e-02  8.053175e-04 -2.685156e-03 -7.220907e-03   \n",
              "\n",
              "                 V25           V26           V27           V28    Amount  \\\n",
              "Time   -2.330828e-01 -4.140710e-02 -5.134591e-03 -9.412688e-03 -0.010596   \n",
              "V1     -9.605737e-16 -1.581290e-17  1.198124e-16  2.083082e-15 -0.227709   \n",
              "V2     -4.478846e-16  2.057310e-16 -4.966953e-16 -5.093836e-16 -0.531409   \n",
              "V3     -1.104734e-15 -1.238062e-16  1.045747e-15  9.775546e-16 -0.210880   \n",
              "V4      6.070716e-16 -4.247268e-16  3.977061e-17 -2.761403e-18  0.098732   \n",
              "V5      4.808532e-16  4.319541e-16  6.590482e-16 -5.613951e-18 -0.386356   \n",
              "V6      4.562861e-16 -1.357067e-16 -4.452461e-16  2.594754e-16  0.215981   \n",
              "V7     -3.094082e-16 -9.657637e-16 -1.782106e-15 -2.776530e-16  0.397311   \n",
              "V8     -4.653279e-16 -1.727276e-16  1.299943e-16 -6.200930e-16 -0.103079   \n",
              "V9      6.757763e-16 -7.888853e-16 -6.709655e-17  1.110541e-15 -0.044246   \n",
              "V10    -2.846052e-16 -3.028119e-16 -2.197977e-16  4.864782e-17 -0.101502   \n",
              "V11    -5.600475e-16 -1.003221e-16 -2.640281e-16 -3.792314e-16  0.000104   \n",
              "V12    -5.712973e-16 -2.359969e-16 -4.672391e-16  6.415167e-16 -0.009542   \n",
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              "V15     3.206423e-16  2.817791e-16 -1.143519e-15 -1.194130e-15 -0.002986   \n",
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              "V17     2.666806e-16  6.932833e-16  6.148525e-16 -5.534540e-17  0.007309   \n",
              "V18    -6.629212e-17  2.990167e-16  2.242791e-16  7.976796e-16  0.035650   \n",
              "V19     9.577163e-16  5.898033e-16 -2.959370e-16 -1.405379e-15 -0.056151   \n",
              "V20     1.410054e-16 -2.803504e-16 -1.138829e-15 -2.436795e-16  0.339403   \n",
              "V21    -1.686082e-16 -5.557329e-16 -1.211281e-15  5.278775e-16  0.105999   \n",
              "V22    -5.018575e-16 -2.503187e-17  8.461337e-17 -6.627203e-16 -0.064801   \n",
              "V23    -8.232727e-17  1.114524e-15  2.839721e-16  1.481903e-15 -0.112633   \n",
              "V24     1.015391e-15  1.343722e-16 -2.274142e-16 -2.819805e-16  0.005146   \n",
              "V25     1.000000e+00  2.646517e-15 -6.406679e-16 -7.008939e-16 -0.047837   \n",
              "V26     2.646517e-15  1.000000e+00 -3.667715e-16 -2.782204e-16 -0.003208   \n",
              "V27    -6.406679e-16 -3.667715e-16  1.000000e+00 -3.061287e-16  0.028825   \n",
              "V28    -7.008939e-16 -2.782204e-16 -3.061287e-16  1.000000e+00  0.010258   \n",
              "Amount -4.783686e-02 -3.208037e-03  2.882546e-02  1.025822e-02  1.000000   \n",
              "Class   3.307706e-03  4.455398e-03  1.757973e-02  9.536041e-03  0.005632   \n",
              "\n",
              "           Class  \n",
              "Time   -0.012323  \n",
              "V1     -0.101347  \n",
              "V2      0.091289  \n",
              "V3     -0.192961  \n",
              "V4      0.133447  \n",
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              "V26     0.004455  \n",
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              "Class   1.000000  \n",
              "\n",
              "[31 rows x 31 columns]"
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              "      <td>8.471437e-02</td>\n",
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              "      <th>V1</th>\n",
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              "      <td>-4.425156e-17</td>\n",
              "      <td>-9.605737e-16</td>\n",
              "      <td>-1.581290e-17</td>\n",
              "      <td>1.198124e-16</td>\n",
              "      <td>2.083082e-15</td>\n",
              "      <td>-0.227709</td>\n",
              "      <td>-0.101347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V2</th>\n",
              "      <td>-0.010593</td>\n",
              "      <td>4.135835e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.243764e-16</td>\n",
              "      <td>-1.121065e-15</td>\n",
              "      <td>5.157519e-16</td>\n",
              "      <td>2.787346e-16</td>\n",
              "      <td>2.055934e-16</td>\n",
              "      <td>-5.377041e-17</td>\n",
              "      <td>1.978488e-17</td>\n",
              "      <td>...</td>\n",
              "      <td>-8.480447e-17</td>\n",
              "      <td>1.526333e-16</td>\n",
              "      <td>1.634231e-16</td>\n",
              "      <td>1.247925e-17</td>\n",
              "      <td>-4.478846e-16</td>\n",
              "      <td>2.057310e-16</td>\n",
              "      <td>-4.966953e-16</td>\n",
              "      <td>-5.093836e-16</td>\n",
              "      <td>-0.531409</td>\n",
              "      <td>0.091289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V3</th>\n",
              "      <td>-0.419618</td>\n",
              "      <td>-1.227819e-15</td>\n",
              "      <td>3.243764e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>4.711293e-16</td>\n",
              "      <td>-6.539009e-17</td>\n",
              "      <td>1.627627e-15</td>\n",
              "      <td>4.895305e-16</td>\n",
              "      <td>-1.268779e-15</td>\n",
              "      <td>5.568367e-16</td>\n",
              "      <td>...</td>\n",
              "      <td>5.706192e-17</td>\n",
              "      <td>-1.133902e-15</td>\n",
              "      <td>-4.983035e-16</td>\n",
              "      <td>2.686834e-19</td>\n",
              "      <td>-1.104734e-15</td>\n",
              "      <td>-1.238062e-16</td>\n",
              "      <td>1.045747e-15</td>\n",
              "      <td>9.775546e-16</td>\n",
              "      <td>-0.210880</td>\n",
              "      <td>-0.192961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V4</th>\n",
              "      <td>-0.105260</td>\n",
              "      <td>-9.215150e-16</td>\n",
              "      <td>-1.121065e-15</td>\n",
              "      <td>4.711293e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-1.719944e-15</td>\n",
              "      <td>-7.491959e-16</td>\n",
              "      <td>-4.104503e-16</td>\n",
              "      <td>5.697192e-16</td>\n",
              "      <td>6.923247e-16</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.949553e-16</td>\n",
              "      <td>-6.276051e-17</td>\n",
              "      <td>9.164206e-17</td>\n",
              "      <td>1.584638e-16</td>\n",
              "      <td>6.070716e-16</td>\n",
              "      <td>-4.247268e-16</td>\n",
              "      <td>3.977061e-17</td>\n",
              "      <td>-2.761403e-18</td>\n",
              "      <td>0.098732</td>\n",
              "      <td>0.133447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V5</th>\n",
              "      <td>0.173072</td>\n",
              "      <td>1.812612e-17</td>\n",
              "      <td>5.157519e-16</td>\n",
              "      <td>-6.539009e-17</td>\n",
              "      <td>-1.719944e-15</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.408382e-16</td>\n",
              "      <td>2.715541e-16</td>\n",
              "      <td>7.437229e-16</td>\n",
              "      <td>7.391702e-16</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.920976e-16</td>\n",
              "      <td>1.253751e-16</td>\n",
              "      <td>-8.428683e-18</td>\n",
              "      <td>-1.149255e-15</td>\n",
              "      <td>4.808532e-16</td>\n",
              "      <td>4.319541e-16</td>\n",
              "      <td>6.590482e-16</td>\n",
              "      <td>-5.613951e-18</td>\n",
              "      <td>-0.386356</td>\n",
              "      <td>-0.094974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V6</th>\n",
              "      <td>-0.063016</td>\n",
              "      <td>-6.506567e-16</td>\n",
              "      <td>2.787346e-16</td>\n",
              "      <td>1.627627e-15</td>\n",
              "      <td>-7.491959e-16</td>\n",
              "      <td>2.408382e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.191668e-16</td>\n",
              "      <td>-1.104219e-16</td>\n",
              "      <td>4.131207e-16</td>\n",
              "      <td>...</td>\n",
              "      <td>5.833316e-17</td>\n",
              "      <td>-4.705235e-19</td>\n",
              "      <td>1.046712e-16</td>\n",
              "      <td>-1.071589e-15</td>\n",
              "      <td>4.562861e-16</td>\n",
              "      <td>-1.357067e-16</td>\n",
              "      <td>-4.452461e-16</td>\n",
              "      <td>2.594754e-16</td>\n",
              "      <td>0.215981</td>\n",
              "      <td>-0.043643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V7</th>\n",
              "      <td>0.084714</td>\n",
              "      <td>-1.005191e-15</td>\n",
              "      <td>2.055934e-16</td>\n",
              "      <td>4.895305e-16</td>\n",
              "      <td>-4.104503e-16</td>\n",
              "      <td>2.715541e-16</td>\n",
              "      <td>1.191668e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.344412e-16</td>\n",
              "      <td>1.122501e-15</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.027779e-16</td>\n",
              "      <td>-8.898922e-16</td>\n",
              "      <td>-4.387401e-16</td>\n",
              "      <td>7.434913e-18</td>\n",
              "      <td>-3.094082e-16</td>\n",
              "      <td>-9.657637e-16</td>\n",
              "      <td>-1.782106e-15</td>\n",
              "      <td>-2.776530e-16</td>\n",
              "      <td>0.397311</td>\n",
              "      <td>-0.187257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V8</th>\n",
              "      <td>-0.036949</td>\n",
              "      <td>-2.433822e-16</td>\n",
              "      <td>-5.377041e-17</td>\n",
              "      <td>-1.268779e-15</td>\n",
              "      <td>5.697192e-16</td>\n",
              "      <td>7.437229e-16</td>\n",
              "      <td>-1.104219e-16</td>\n",
              "      <td>3.344412e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>4.356078e-16</td>\n",
              "      <td>...</td>\n",
              "      <td>3.892798e-16</td>\n",
              "      <td>2.026927e-16</td>\n",
              "      <td>6.377260e-17</td>\n",
              "      <td>-1.047097e-16</td>\n",
              "      <td>-4.653279e-16</td>\n",
              "      <td>-1.727276e-16</td>\n",
              "      <td>1.299943e-16</td>\n",
              "      <td>-6.200930e-16</td>\n",
              "      <td>-0.103079</td>\n",
              "      <td>0.019875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V9</th>\n",
              "      <td>-0.008660</td>\n",
              "      <td>-1.513678e-16</td>\n",
              "      <td>1.978488e-17</td>\n",
              "      <td>5.568367e-16</td>\n",
              "      <td>6.923247e-16</td>\n",
              "      <td>7.391702e-16</td>\n",
              "      <td>4.131207e-16</td>\n",
              "      <td>1.122501e-15</td>\n",
              "      <td>4.356078e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>...</td>\n",
              "      <td>1.936953e-16</td>\n",
              "      <td>-7.071869e-16</td>\n",
              "      <td>-5.214137e-16</td>\n",
              "      <td>-1.430343e-16</td>\n",
              "      <td>6.757763e-16</td>\n",
              "      <td>-7.888853e-16</td>\n",
              "      <td>-6.709655e-17</td>\n",
              "      <td>1.110541e-15</td>\n",
              "      <td>-0.044246</td>\n",
              "      <td>-0.097733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V10</th>\n",
              "      <td>0.030617</td>\n",
              "      <td>7.388135e-17</td>\n",
              "      <td>-3.991394e-16</td>\n",
              "      <td>1.156587e-15</td>\n",
              "      <td>2.232685e-16</td>\n",
              "      <td>-5.202306e-16</td>\n",
              "      <td>5.932243e-17</td>\n",
              "      <td>-7.492834e-17</td>\n",
              "      <td>-2.801370e-16</td>\n",
              "      <td>-4.642274e-16</td>\n",
              "      <td>...</td>\n",
              "      <td>1.177547e-15</td>\n",
              "      <td>-6.418202e-16</td>\n",
              "      <td>3.214491e-16</td>\n",
              "      <td>-1.355885e-16</td>\n",
              "      <td>-2.846052e-16</td>\n",
              "      <td>-3.028119e-16</td>\n",
              "      <td>-2.197977e-16</td>\n",
              "      <td>4.864782e-17</td>\n",
              "      <td>-0.101502</td>\n",
              "      <td>-0.216883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V11</th>\n",
              "      <td>-0.247689</td>\n",
              "      <td>2.125498e-16</td>\n",
              "      <td>1.975426e-16</td>\n",
              "      <td>1.576830e-15</td>\n",
              "      <td>3.459380e-16</td>\n",
              "      <td>7.203963e-16</td>\n",
              "      <td>1.980503e-15</td>\n",
              "      <td>1.425248e-16</td>\n",
              "      <td>2.487043e-16</td>\n",
              "      <td>1.354680e-16</td>\n",
              "      <td>...</td>\n",
              "      <td>-5.658364e-16</td>\n",
              "      <td>7.772895e-16</td>\n",
              "      <td>-4.505332e-16</td>\n",
              "      <td>1.933267e-15</td>\n",
              "      <td>-5.600475e-16</td>\n",
              "      <td>-1.003221e-16</td>\n",
              "      <td>-2.640281e-16</td>\n",
              "      <td>-3.792314e-16</td>\n",
              "      <td>0.000104</td>\n",
              "      <td>0.154876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V12</th>\n",
              "      <td>0.124348</td>\n",
              "      <td>2.053457e-16</td>\n",
              "      <td>-9.568710e-17</td>\n",
              "      <td>6.310231e-16</td>\n",
              "      <td>-5.625518e-16</td>\n",
              "      <td>7.412552e-16</td>\n",
              "      <td>2.375468e-16</td>\n",
              "      <td>-3.536655e-18</td>\n",
              "      <td>1.839891e-16</td>\n",
              "      <td>-1.079314e-15</td>\n",
              "      <td>...</td>\n",
              "      <td>7.300527e-16</td>\n",
              "      <td>1.644699e-16</td>\n",
              "      <td>1.800885e-16</td>\n",
              "      <td>4.436512e-16</td>\n",
              "      <td>-5.712973e-16</td>\n",
              "      <td>-2.359969e-16</td>\n",
              "      <td>-4.672391e-16</td>\n",
              "      <td>6.415167e-16</td>\n",
              "      <td>-0.009542</td>\n",
              "      <td>-0.260593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V13</th>\n",
              "      <td>-0.065902</td>\n",
              "      <td>-2.425603e-17</td>\n",
              "      <td>6.295388e-16</td>\n",
              "      <td>2.807652e-16</td>\n",
              "      <td>1.303306e-16</td>\n",
              "      <td>5.886991e-16</td>\n",
              "      <td>-1.211182e-16</td>\n",
              "      <td>1.266462e-17</td>\n",
              "      <td>-2.921856e-16</td>\n",
              "      <td>2.251072e-15</td>\n",
              "      <td>...</td>\n",
              "      <td>1.008461e-16</td>\n",
              "      <td>6.747721e-17</td>\n",
              "      <td>-7.132064e-16</td>\n",
              "      <td>-1.397470e-16</td>\n",
              "      <td>-5.497612e-16</td>\n",
              "      <td>-1.769255e-16</td>\n",
              "      <td>-4.720898e-16</td>\n",
              "      <td>1.144372e-15</td>\n",
              "      <td>0.005293</td>\n",
              "      <td>-0.004570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V14</th>\n",
              "      <td>-0.098757</td>\n",
              "      <td>-5.020280e-16</td>\n",
              "      <td>-1.730566e-16</td>\n",
              "      <td>4.739859e-16</td>\n",
              "      <td>2.282280e-16</td>\n",
              "      <td>6.565143e-16</td>\n",
              "      <td>2.621312e-16</td>\n",
              "      <td>2.607772e-16</td>\n",
              "      <td>-8.599156e-16</td>\n",
              "      <td>3.784757e-15</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.356561e-16</td>\n",
              "      <td>3.740383e-16</td>\n",
              "      <td>3.883204e-16</td>\n",
              "      <td>2.003482e-16</td>\n",
              "      <td>-8.547932e-16</td>\n",
              "      <td>-1.660327e-16</td>\n",
              "      <td>1.044274e-16</td>\n",
              "      <td>2.289427e-15</td>\n",
              "      <td>0.033751</td>\n",
              "      <td>-0.302544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V15</th>\n",
              "      <td>-0.183453</td>\n",
              "      <td>3.547782e-16</td>\n",
              "      <td>-4.995814e-17</td>\n",
              "      <td>9.068793e-16</td>\n",
              "      <td>1.377649e-16</td>\n",
              "      <td>-8.720275e-16</td>\n",
              "      <td>-1.531188e-15</td>\n",
              "      <td>-1.690540e-16</td>\n",
              "      <td>4.127777e-16</td>\n",
              "      <td>-1.051167e-15</td>\n",
              "      <td>...</td>\n",
              "      <td>6.605263e-17</td>\n",
              "      <td>-4.208921e-16</td>\n",
              "      <td>-3.912243e-16</td>\n",
              "      <td>-4.478263e-16</td>\n",
              "      <td>3.206423e-16</td>\n",
              "      <td>2.817791e-16</td>\n",
              "      <td>-1.143519e-15</td>\n",
              "      <td>-1.194130e-15</td>\n",
              "      <td>-0.002986</td>\n",
              "      <td>-0.004223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V16</th>\n",
              "      <td>0.011903</td>\n",
              "      <td>7.212815e-17</td>\n",
              "      <td>1.177316e-17</td>\n",
              "      <td>8.299445e-16</td>\n",
              "      <td>-9.614528e-16</td>\n",
              "      <td>2.246261e-15</td>\n",
              "      <td>2.623672e-18</td>\n",
              "      <td>5.869302e-17</td>\n",
              "      <td>-5.254741e-16</td>\n",
              "      <td>-1.214086e-15</td>\n",
              "      <td>...</td>\n",
              "      <td>-4.715090e-16</td>\n",
              "      <td>-7.923387e-17</td>\n",
              "      <td>5.020770e-16</td>\n",
              "      <td>-3.005985e-16</td>\n",
              "      <td>-1.345418e-15</td>\n",
              "      <td>-7.290010e-16</td>\n",
              "      <td>6.789513e-16</td>\n",
              "      <td>7.588849e-16</td>\n",
              "      <td>-0.003910</td>\n",
              "      <td>-0.196539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V17</th>\n",
              "      <td>-0.073297</td>\n",
              "      <td>-3.879840e-16</td>\n",
              "      <td>-2.685296e-16</td>\n",
              "      <td>7.614712e-16</td>\n",
              "      <td>-2.699612e-16</td>\n",
              "      <td>1.281914e-16</td>\n",
              "      <td>2.015618e-16</td>\n",
              "      <td>2.177192e-16</td>\n",
              "      <td>-2.269549e-16</td>\n",
              "      <td>1.113695e-15</td>\n",
              "      <td>...</td>\n",
              "      <td>-8.230527e-16</td>\n",
              "      <td>-8.743398e-16</td>\n",
              "      <td>3.706214e-16</td>\n",
              "      <td>-2.403828e-16</td>\n",
              "      <td>2.666806e-16</td>\n",
              "      <td>6.932833e-16</td>\n",
              "      <td>6.148525e-16</td>\n",
              "      <td>-5.534540e-17</td>\n",
              "      <td>0.007309</td>\n",
              "      <td>-0.326481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V18</th>\n",
              "      <td>0.090438</td>\n",
              "      <td>3.230206e-17</td>\n",
              "      <td>3.284605e-16</td>\n",
              "      <td>1.509897e-16</td>\n",
              "      <td>-5.103644e-16</td>\n",
              "      <td>5.308590e-16</td>\n",
              "      <td>1.223814e-16</td>\n",
              "      <td>7.604126e-17</td>\n",
              "      <td>-3.667974e-16</td>\n",
              "      <td>4.993240e-16</td>\n",
              "      <td>...</td>\n",
              "      <td>-9.408680e-16</td>\n",
              "      <td>-4.819365e-16</td>\n",
              "      <td>-1.912006e-16</td>\n",
              "      <td>-8.986916e-17</td>\n",
              "      <td>-6.629212e-17</td>\n",
              "      <td>2.990167e-16</td>\n",
              "      <td>2.242791e-16</td>\n",
              "      <td>7.976796e-16</td>\n",
              "      <td>0.035650</td>\n",
              "      <td>-0.111485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V19</th>\n",
              "      <td>0.028975</td>\n",
              "      <td>1.502024e-16</td>\n",
              "      <td>-7.118719e-18</td>\n",
              "      <td>3.463522e-16</td>\n",
              "      <td>-3.980557e-16</td>\n",
              "      <td>-1.450421e-16</td>\n",
              "      <td>-1.865597e-16</td>\n",
              "      <td>-1.881008e-16</td>\n",
              "      <td>-3.875186e-16</td>\n",
              "      <td>-1.376135e-16</td>\n",
              "      <td>...</td>\n",
              "      <td>5.115885e-16</td>\n",
              "      <td>-1.163768e-15</td>\n",
              "      <td>7.032035e-16</td>\n",
              "      <td>2.587708e-17</td>\n",
              "      <td>9.577163e-16</td>\n",
              "      <td>5.898033e-16</td>\n",
              "      <td>-2.959370e-16</td>\n",
              "      <td>-1.405379e-15</td>\n",
              "      <td>-0.056151</td>\n",
              "      <td>0.034783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V20</th>\n",
              "      <td>-0.050866</td>\n",
              "      <td>4.654551e-16</td>\n",
              "      <td>2.506675e-16</td>\n",
              "      <td>-9.316409e-16</td>\n",
              "      <td>-1.857247e-16</td>\n",
              "      <td>-3.554057e-16</td>\n",
              "      <td>-1.858755e-16</td>\n",
              "      <td>9.379684e-16</td>\n",
              "      <td>2.033737e-16</td>\n",
              "      <td>-2.343720e-16</td>\n",
              "      <td>...</td>\n",
              "      <td>-7.614597e-16</td>\n",
              "      <td>1.009285e-15</td>\n",
              "      <td>2.712885e-16</td>\n",
              "      <td>1.277215e-16</td>\n",
              "      <td>1.410054e-16</td>\n",
              "      <td>-2.803504e-16</td>\n",
              "      <td>-1.138829e-15</td>\n",
              "      <td>-2.436795e-16</td>\n",
              "      <td>0.339403</td>\n",
              "      <td>0.020090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V21</th>\n",
              "      <td>0.044736</td>\n",
              "      <td>-2.457409e-16</td>\n",
              "      <td>-8.480447e-17</td>\n",
              "      <td>5.706192e-17</td>\n",
              "      <td>-1.949553e-16</td>\n",
              "      <td>-3.920976e-16</td>\n",
              "      <td>5.833316e-17</td>\n",
              "      <td>-2.027779e-16</td>\n",
              "      <td>3.892798e-16</td>\n",
              "      <td>1.936953e-16</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.649908e-15</td>\n",
              "      <td>8.119580e-16</td>\n",
              "      <td>1.761054e-16</td>\n",
              "      <td>-1.686082e-16</td>\n",
              "      <td>-5.557329e-16</td>\n",
              "      <td>-1.211281e-15</td>\n",
              "      <td>5.278775e-16</td>\n",
              "      <td>0.105999</td>\n",
              "      <td>0.040413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V22</th>\n",
              "      <td>0.144059</td>\n",
              "      <td>-4.290944e-16</td>\n",
              "      <td>1.526333e-16</td>\n",
              "      <td>-1.133902e-15</td>\n",
              "      <td>-6.276051e-17</td>\n",
              "      <td>1.253751e-16</td>\n",
              "      <td>-4.705235e-19</td>\n",
              "      <td>-8.898922e-16</td>\n",
              "      <td>2.026927e-16</td>\n",
              "      <td>-7.071869e-16</td>\n",
              "      <td>...</td>\n",
              "      <td>3.649908e-15</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-7.303916e-17</td>\n",
              "      <td>9.970809e-17</td>\n",
              "      <td>-5.018575e-16</td>\n",
              "      <td>-2.503187e-17</td>\n",
              "      <td>8.461337e-17</td>\n",
              "      <td>-6.627203e-16</td>\n",
              "      <td>-0.064801</td>\n",
              "      <td>0.000805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V23</th>\n",
              "      <td>0.051142</td>\n",
              "      <td>6.168652e-16</td>\n",
              "      <td>1.634231e-16</td>\n",
              "      <td>-4.983035e-16</td>\n",
              "      <td>9.164206e-17</td>\n",
              "      <td>-8.428683e-18</td>\n",
              "      <td>1.046712e-16</td>\n",
              "      <td>-4.387401e-16</td>\n",
              "      <td>6.377260e-17</td>\n",
              "      <td>-5.214137e-16</td>\n",
              "      <td>...</td>\n",
              "      <td>8.119580e-16</td>\n",
              "      <td>-7.303916e-17</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.130519e-17</td>\n",
              "      <td>-8.232727e-17</td>\n",
              "      <td>1.114524e-15</td>\n",
              "      <td>2.839721e-16</td>\n",
              "      <td>1.481903e-15</td>\n",
              "      <td>-0.112633</td>\n",
              "      <td>-0.002685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V24</th>\n",
              "      <td>-0.016182</td>\n",
              "      <td>-4.425156e-17</td>\n",
              "      <td>1.247925e-17</td>\n",
              "      <td>2.686834e-19</td>\n",
              "      <td>1.584638e-16</td>\n",
              "      <td>-1.149255e-15</td>\n",
              "      <td>-1.071589e-15</td>\n",
              "      <td>7.434913e-18</td>\n",
              "      <td>-1.047097e-16</td>\n",
              "      <td>-1.430343e-16</td>\n",
              "      <td>...</td>\n",
              "      <td>1.761054e-16</td>\n",
              "      <td>9.970809e-17</td>\n",
              "      <td>2.130519e-17</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.015391e-15</td>\n",
              "      <td>1.343722e-16</td>\n",
              "      <td>-2.274142e-16</td>\n",
              "      <td>-2.819805e-16</td>\n",
              "      <td>0.005146</td>\n",
              "      <td>-0.007221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V25</th>\n",
              "      <td>-0.233083</td>\n",
              "      <td>-9.605737e-16</td>\n",
              "      <td>-4.478846e-16</td>\n",
              "      <td>-1.104734e-15</td>\n",
              "      <td>6.070716e-16</td>\n",
              "      <td>4.808532e-16</td>\n",
              "      <td>4.562861e-16</td>\n",
              "      <td>-3.094082e-16</td>\n",
              "      <td>-4.653279e-16</td>\n",
              "      <td>6.757763e-16</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.686082e-16</td>\n",
              "      <td>-5.018575e-16</td>\n",
              "      <td>-8.232727e-17</td>\n",
              "      <td>1.015391e-15</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.646517e-15</td>\n",
              "      <td>-6.406679e-16</td>\n",
              "      <td>-7.008939e-16</td>\n",
              "      <td>-0.047837</td>\n",
              "      <td>0.003308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V26</th>\n",
              "      <td>-0.041407</td>\n",
              "      <td>-1.581290e-17</td>\n",
              "      <td>2.057310e-16</td>\n",
              "      <td>-1.238062e-16</td>\n",
              "      <td>-4.247268e-16</td>\n",
              "      <td>4.319541e-16</td>\n",
              "      <td>-1.357067e-16</td>\n",
              "      <td>-9.657637e-16</td>\n",
              "      <td>-1.727276e-16</td>\n",
              "      <td>-7.888853e-16</td>\n",
              "      <td>...</td>\n",
              "      <td>-5.557329e-16</td>\n",
              "      <td>-2.503187e-17</td>\n",
              "      <td>1.114524e-15</td>\n",
              "      <td>1.343722e-16</td>\n",
              "      <td>2.646517e-15</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-3.667715e-16</td>\n",
              "      <td>-2.782204e-16</td>\n",
              "      <td>-0.003208</td>\n",
              "      <td>0.004455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V27</th>\n",
              "      <td>-0.005135</td>\n",
              "      <td>1.198124e-16</td>\n",
              "      <td>-4.966953e-16</td>\n",
              "      <td>1.045747e-15</td>\n",
              "      <td>3.977061e-17</td>\n",
              "      <td>6.590482e-16</td>\n",
              "      <td>-4.452461e-16</td>\n",
              "      <td>-1.782106e-15</td>\n",
              "      <td>1.299943e-16</td>\n",
              "      <td>-6.709655e-17</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.211281e-15</td>\n",
              "      <td>8.461337e-17</td>\n",
              "      <td>2.839721e-16</td>\n",
              "      <td>-2.274142e-16</td>\n",
              "      <td>-6.406679e-16</td>\n",
              "      <td>-3.667715e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-3.061287e-16</td>\n",
              "      <td>0.028825</td>\n",
              "      <td>0.017580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V28</th>\n",
              "      <td>-0.009413</td>\n",
              "      <td>2.083082e-15</td>\n",
              "      <td>-5.093836e-16</td>\n",
              "      <td>9.775546e-16</td>\n",
              "      <td>-2.761403e-18</td>\n",
              "      <td>-5.613951e-18</td>\n",
              "      <td>2.594754e-16</td>\n",
              "      <td>-2.776530e-16</td>\n",
              "      <td>-6.200930e-16</td>\n",
              "      <td>1.110541e-15</td>\n",
              "      <td>...</td>\n",
              "      <td>5.278775e-16</td>\n",
              "      <td>-6.627203e-16</td>\n",
              "      <td>1.481903e-15</td>\n",
              "      <td>-2.819805e-16</td>\n",
              "      <td>-7.008939e-16</td>\n",
              "      <td>-2.782204e-16</td>\n",
              "      <td>-3.061287e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.010258</td>\n",
              "      <td>0.009536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Amount</th>\n",
              "      <td>-0.010596</td>\n",
              "      <td>-2.277087e-01</td>\n",
              "      <td>-5.314089e-01</td>\n",
              "      <td>-2.108805e-01</td>\n",
              "      <td>9.873167e-02</td>\n",
              "      <td>-3.863563e-01</td>\n",
              "      <td>2.159812e-01</td>\n",
              "      <td>3.973113e-01</td>\n",
              "      <td>-1.030791e-01</td>\n",
              "      <td>-4.424560e-02</td>\n",
              "      <td>...</td>\n",
              "      <td>1.059989e-01</td>\n",
              "      <td>-6.480065e-02</td>\n",
              "      <td>-1.126326e-01</td>\n",
              "      <td>5.146217e-03</td>\n",
              "      <td>-4.783686e-02</td>\n",
              "      <td>-3.208037e-03</td>\n",
              "      <td>2.882546e-02</td>\n",
              "      <td>1.025822e-02</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.005632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class</th>\n",
              "      <td>-0.012323</td>\n",
              "      <td>-1.013473e-01</td>\n",
              "      <td>9.128865e-02</td>\n",
              "      <td>-1.929608e-01</td>\n",
              "      <td>1.334475e-01</td>\n",
              "      <td>-9.497430e-02</td>\n",
              "      <td>-4.364316e-02</td>\n",
              "      <td>-1.872566e-01</td>\n",
              "      <td>1.987512e-02</td>\n",
              "      <td>-9.773269e-02</td>\n",
              "      <td>...</td>\n",
              "      <td>4.041338e-02</td>\n",
              "      <td>8.053175e-04</td>\n",
              "      <td>-2.685156e-03</td>\n",
              "      <td>-7.220907e-03</td>\n",
              "      <td>3.307706e-03</td>\n",
              "      <td>4.455398e-03</td>\n",
              "      <td>1.757973e-02</td>\n",
              "      <td>9.536041e-03</td>\n",
              "      <td>0.005632</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>31 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-69b197b5-efc2-452d-9e57-4a069d2664cd')\"\n",
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              "\n",
              "  <style>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-69b197b5-efc2-452d-9e57-4a069d2664cd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1861cac8-4383-4fbf-b223-ac8627904849 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.skew()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7tZW9hgBl4Dd",
        "outputId": "00d33045-ceb6-4d20-f36d-9ff8a0e762fb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      -0.035568\n",
              "V1        -3.280667\n",
              "V2        -4.624866\n",
              "V3        -2.240155\n",
              "V4         0.676292\n",
              "V5        -2.425901\n",
              "V6         1.826581\n",
              "V7         2.553907\n",
              "V8        -8.521944\n",
              "V9         0.554680\n",
              "V10        1.187141\n",
              "V11        0.356506\n",
              "V12       -2.278401\n",
              "V13        0.065233\n",
              "V14       -1.995176\n",
              "V15       -0.308423\n",
              "V16       -1.100966\n",
              "V17       -3.844914\n",
              "V18       -0.259880\n",
              "V19        0.109192\n",
              "V20       -2.037155\n",
              "V21        3.592991\n",
              "V22       -0.213258\n",
              "V23       -5.875140\n",
              "V24       -0.552499\n",
              "V25       -0.415793\n",
              "V26        0.576693\n",
              "V27       -1.170209\n",
              "V28       11.192091\n",
              "Amount    16.977724\n",
              "Class     23.997579\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# returns the number of unique values for each column\n",
        "data.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kI09hoHwl7cs",
        "outputId": "4d0263e2-9176-483a-96e9-3f82e095c302"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      124592\n",
              "V1        275663\n",
              "V2        275663\n",
              "V3        275663\n",
              "V4        275663\n",
              "V5        275663\n",
              "V6        275663\n",
              "V7        275663\n",
              "V8        275663\n",
              "V9        275663\n",
              "V10       275663\n",
              "V11       275663\n",
              "V12       275663\n",
              "V13       275663\n",
              "V14       275663\n",
              "V15       275663\n",
              "V16       275663\n",
              "V17       275663\n",
              "V18       275663\n",
              "V19       275663\n",
              "V20       275663\n",
              "V21       275663\n",
              "V22       275663\n",
              "V23       275663\n",
              "V24       275663\n",
              "V25       275663\n",
              "V26       275663\n",
              "V27       275663\n",
              "V28       275663\n",
              "Amount     32767\n",
              "Class          2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ugPh0mp7l-ma",
        "outputId": "43f58273-feec-470d-8358-4484f6337481"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
              "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
              "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
              "       'Class'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for the value counts\n",
        "data[\"Amount\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-E4wkO9imgjF",
        "outputId": "05b77f47-68e8-4442-ff86-3fc3a83c6fb0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.00       13688\n",
              "1.98        6044\n",
              "0.89        4872\n",
              "9.99        4747\n",
              "15.00       3280\n",
              "           ...  \n",
              "62.09          1\n",
              "552.05         1\n",
              "1467.75        1\n",
              "196.68         1\n",
              "95.63          1\n",
              "Name: Amount, Length: 32767, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Describing the data\n",
        "print(\"Shape of the Data:\", data.shape)\n",
        "print(\"Description of the data:\")\n",
        "print(data.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dhtgryDbjUmD",
        "outputId": "ca9671f7-17de-400b-9e55-53008c306f7d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the Data: (284807, 31)\n",
            "Description of the data:\n",
            "                Time            V1            V2            V3            V4  \\\n",
            "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
            "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
            "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
            "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
            "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
            "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
            "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
            "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
            "\n",
            "                 V5            V6            V7            V8            V9  \\\n",
            "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
            "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
            "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
            "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
            "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
            "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
            "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
            "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
            "\n",
            "       ...           V21           V22           V23           V24  \\\n",
            "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
            "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
            "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
            "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
            "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
            "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
            "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
            "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
            "\n",
            "                V25           V26           V27           V28         Amount  \\\n",
            "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
            "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
            "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
            "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
            "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
            "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
            "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
            "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
            "\n",
            "               Class  \n",
            "count  284807.000000  \n",
            "mean        0.001727  \n",
            "std         0.041527  \n",
            "min         0.000000  \n",
            "25%         0.000000  \n",
            "50%         0.000000  \n",
            "75%         0.000000  \n",
            "max         1.000000  \n",
            "\n",
            "[8 rows x 31 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Determine the number of fraud cases\n",
        "fraud=data[data['Class']==1]\n",
        "valid=data[data['Class']==0]\n",
        "OutlierFraction=len(fraud)/float(len(valid))\n",
        "print(\"Number of fraud cases as compared to valid cases:\", OutlierFraction)\n",
        "print('Fraud Cases: {}'.format(len(fraud)))\n",
        "print('Valid Cases: {}'.format(len(valid)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3b95FIisjX6O",
        "outputId": "024dc127-fc24-44e9-fa89-c5bbc7728361"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of fraud cases as compared to valid cases: 0.0017304750013189597\n",
            "Fraud Cases: 492\n",
            "Valid Cases: 284315\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Details of Fraudulent transaction\n",
        "print(\"Amount details of Fraudulent Transaction:\")\n",
        "fraud.Amount.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cLOfs3r-jbmp",
        "outputId": "c613ead1-12e2-4525-afbc-8bbd2364414d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Amount details of Fraudulent Transaction:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     492.000000\n",
              "mean      122.211321\n",
              "std       256.683288\n",
              "min         0.000000\n",
              "25%         1.000000\n",
              "50%         9.250000\n",
              "75%       105.890000\n",
              "max      2125.870000\n",
              "Name: Amount, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Details of Valid Transaction\n",
        "print(\"Amount details of Valid Transaction:\")\n",
        "valid.Amount.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xRT9debWjezn",
        "outputId": "9abad040-3fc7-48c8-87ae-46f507216c7e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Amount details of Valid Transaction:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    284315.000000\n",
              "mean         88.291022\n",
              "std         250.105092\n",
              "min           0.000000\n",
              "25%           5.650000\n",
              "50%          22.000000\n",
              "75%          77.050000\n",
              "max       25691.160000\n",
              "Name: Amount, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Correlation matrix for given dataset\n",
        "corrmat=data.corr()\n",
        "fig=plt.figure(figsize=(12,9))\n",
        "sns.heatmap(corrmat,vmax=0.8,square=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        },
        "id": "crsh6_aXjh9C",
        "outputId": "350d1fa8-2787-4917-f2d6-e3edd53a3922"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating X and y labels using the dataset\n",
        "X=data.drop(['Class'],axis=1)\n",
        "Y=data['Class']\n",
        "print(\"Shape of X:\", X.shape)\n",
        "print(\"Shape of Y:\" , Y.shape)\n",
        "\n",
        "xData=X.values\n",
        "yData=Y.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R7jt7FjsjlMj",
        "outputId": "64e4bd4d-43be-4a54-bef7-dde72c195fa0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X: (284807, 30)\n",
            "Shape of Y: (284807,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Using Skicit-learn to split data into training and testing data\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "#Spliting the data into training and testing data\n",
        "xTrain,xTest,yTrain,yTest=train_test_split(xData,yData,test_size=0.2,random_state=42)\n",
        "\n",
        "print(\"Shape of Training Data:\",xTrain.shape)\n",
        "print(\"Shape of Testing Data:\",xTest.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f47qOChRjohc",
        "outputId": "8771e04c-82e6-4783-e9dc-7fb3e896a4d6"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of Training Data: (227845, 30)\n",
            "Shape of Testing Data: (56962, 30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Building a Random Forest Classifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "#Random forest model creation\n",
        "rfc=RandomForestClassifier()\n",
        "rfc.fit(xTrain,yTrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "PLG2QWyRjrnP",
        "outputId": "c4108ba6-0635-4034-9978-c68ce8c988ac"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Predicting on Test set using above trained models\n",
        "yPred=rfc.predict(xTest)"
      ],
      "metadata": {
        "id": "F4BTPpjijuWA"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluating the Random Forest Classifier\n",
        "from sklearn.metrics import classification_report,accuracy_score\n",
        "from sklearn.metrics import precision_score,recall_score\n",
        "from sklearn.metrics import f1_score,matthews_corrcoef\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "n_outliers = len(fraud)\n",
        "n_errors = (yPred != yTest).sum()\n",
        "print(\"The model used is Random Forest classifier\")\n",
        "\n",
        "acc = accuracy_score(yTest, yPred)\n",
        "print(\"The accuracy is {}\".format(acc))\n",
        "\n",
        "prec = precision_score(yTest, yPred)\n",
        "print(\"The precision is {}\".format(prec))\n",
        "\n",
        "rec = recall_score(yTest, yPred)\n",
        "print(\"The recall is {}\".format(rec))\n",
        "\n",
        "f1 = f1_score(yTest, yPred)\n",
        "print(\"The F1-Score is {}\".format(f1))\n",
        "\n",
        "MCC = matthews_corrcoef(yTest, yPred)\n",
        "print(\"The Matthews correlation coefficient is{}\".format(MCC))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KqRUC7iZjw_c",
        "outputId": "6c5018b1-b4d2-473f-8078-0a08f045d926"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The model used is Random Forest classifier\n",
            "The accuracy is 0.9995786664794073\n",
            "The precision is 0.9868421052631579\n",
            "The recall is 0.7653061224489796\n",
            "The F1-Score is 0.8620689655172413\n",
            "The Matthews correlation coefficient is0.8688552993136148\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop(['Class'],axis=1, inplace = True)"
      ],
      "metadata": {
        "id": "4rTCeeuzzE29"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "gJDQ3Hs5zlbO",
        "outputId": "0ac1b869-08bb-40b5-cc97-2fd01ee17374"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               V1         V2        V3        V4        V5        V6  \\\n",
              "0       -1.359807  -0.072781  2.536347  1.378155 -0.338321  0.462388   \n",
              "1        1.191857   0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
              "2       -1.358354  -1.340163  1.773209  0.379780 -0.503198  1.800499   \n",
              "3       -0.966272  -0.185226  1.792993 -0.863291 -0.010309  1.247203   \n",
              "4       -1.158233   0.877737  1.548718  0.403034 -0.407193  0.095921   \n",
              "...           ...        ...       ...       ...       ...       ...   \n",
              "284802 -11.881118  10.071785 -9.834783 -2.066656 -5.364473 -2.606837   \n",
              "284803  -0.732789  -0.055080  2.035030 -0.738589  0.868229  1.058415   \n",
              "284804   1.919565  -0.301254 -3.249640 -0.557828  2.630515  3.031260   \n",
              "284805  -0.240440   0.530483  0.702510  0.689799 -0.377961  0.623708   \n",
              "284806  -0.533413  -0.189733  0.703337 -0.506271 -0.012546 -0.649617   \n",
              "\n",
              "              V7        V8        V9       V10  ...       V20       V21  \\\n",
              "0       0.239599  0.098698  0.363787  0.090794  ...  0.251412 -0.018307   \n",
              "1      -0.078803  0.085102 -0.255425 -0.166974  ... -0.069083 -0.225775   \n",
              "2       0.791461  0.247676 -1.514654  0.207643  ...  0.524980  0.247998   \n",
              "3       0.237609  0.377436 -1.387024 -0.054952  ... -0.208038 -0.108300   \n",
              "4       0.592941 -0.270533  0.817739  0.753074  ...  0.408542 -0.009431   \n",
              "...          ...       ...       ...       ...  ...       ...       ...   \n",
              "284802 -4.918215  7.305334  1.914428  4.356170  ...  1.475829  0.213454   \n",
              "284803  0.024330  0.294869  0.584800 -0.975926  ...  0.059616  0.214205   \n",
              "284804 -0.296827  0.708417  0.432454 -0.484782  ...  0.001396  0.232045   \n",
              "284805 -0.686180  0.679145  0.392087 -0.399126  ...  0.127434  0.265245   \n",
              "284806  1.577006 -0.414650  0.486180 -0.915427  ...  0.382948  0.261057   \n",
              "\n",
              "             V22       V23       V24       V25       V26       V27       V28  \\\n",
              "0       0.277838 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053   \n",
              "1      -0.638672  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724   \n",
              "2       0.771679  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752   \n",
              "3       0.005274 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458   \n",
              "4       0.798278 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "284802  0.111864  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731   \n",
              "284803  0.924384  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   \n",
              "284804  0.578229 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   \n",
              "284805  0.800049 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   \n",
              "284806  0.643078  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649   \n",
              "\n",
              "        Amount  \n",
              "0       149.62  \n",
              "1         2.69  \n",
              "2       378.66  \n",
              "3       123.50  \n",
              "4        69.99  \n",
              "...        ...  \n",
              "284802    0.77  \n",
              "284803   24.79  \n",
              "284804   67.88  \n",
              "284805   10.00  \n",
              "284806  217.00  \n",
              "\n",
              "[284807 rows x 29 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-72261a9d-1a67-4fdf-baa9-17e85feb971e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>...</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>0.090794</td>\n",
              "      <td>...</td>\n",
              "      <td>0.251412</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>149.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>-0.166974</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.069083</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>0.207643</td>\n",
              "      <td>...</td>\n",
              "      <td>0.524980</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>-0.054952</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.208038</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>0.753074</td>\n",
              "      <td>...</td>\n",
              "      <td>0.408542</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <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",
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              "    <tr>\n",
              "      <th>284802</th>\n",
              "      <td>-11.881118</td>\n",
              "      <td>10.071785</td>\n",
              "      <td>-9.834783</td>\n",
              "      <td>-2.066656</td>\n",
              "      <td>-5.364473</td>\n",
              "      <td>-2.606837</td>\n",
              "      <td>-4.918215</td>\n",
              "      <td>7.305334</td>\n",
              "      <td>1.914428</td>\n",
              "      <td>4.356170</td>\n",
              "      <td>...</td>\n",
              "      <td>1.475829</td>\n",
              "      <td>0.213454</td>\n",
              "      <td>0.111864</td>\n",
              "      <td>1.014480</td>\n",
              "      <td>-0.509348</td>\n",
              "      <td>1.436807</td>\n",
              "      <td>0.250034</td>\n",
              "      <td>0.943651</td>\n",
              "      <td>0.823731</td>\n",
              "      <td>0.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284803</th>\n",
              "      <td>-0.732789</td>\n",
              "      <td>-0.055080</td>\n",
              "      <td>2.035030</td>\n",
              "      <td>-0.738589</td>\n",
              "      <td>0.868229</td>\n",
              "      <td>1.058415</td>\n",
              "      <td>0.024330</td>\n",
              "      <td>0.294869</td>\n",
              "      <td>0.584800</td>\n",
              "      <td>-0.975926</td>\n",
              "      <td>...</td>\n",
              "      <td>0.059616</td>\n",
              "      <td>0.214205</td>\n",
              "      <td>0.924384</td>\n",
              "      <td>0.012463</td>\n",
              "      <td>-1.016226</td>\n",
              "      <td>-0.606624</td>\n",
              "      <td>-0.395255</td>\n",
              "      <td>0.068472</td>\n",
              "      <td>-0.053527</td>\n",
              "      <td>24.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284804</th>\n",
              "      <td>1.919565</td>\n",
              "      <td>-0.301254</td>\n",
              "      <td>-3.249640</td>\n",
              "      <td>-0.557828</td>\n",
              "      <td>2.630515</td>\n",
              "      <td>3.031260</td>\n",
              "      <td>-0.296827</td>\n",
              "      <td>0.708417</td>\n",
              "      <td>0.432454</td>\n",
              "      <td>-0.484782</td>\n",
              "      <td>...</td>\n",
              "      <td>0.001396</td>\n",
              "      <td>0.232045</td>\n",
              "      <td>0.578229</td>\n",
              "      <td>-0.037501</td>\n",
              "      <td>0.640134</td>\n",
              "      <td>0.265745</td>\n",
              "      <td>-0.087371</td>\n",
              "      <td>0.004455</td>\n",
              "      <td>-0.026561</td>\n",
              "      <td>67.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284805</th>\n",
              "      <td>-0.240440</td>\n",
              "      <td>0.530483</td>\n",
              "      <td>0.702510</td>\n",
              "      <td>0.689799</td>\n",
              "      <td>-0.377961</td>\n",
              "      <td>0.623708</td>\n",
              "      <td>-0.686180</td>\n",
              "      <td>0.679145</td>\n",
              "      <td>0.392087</td>\n",
              "      <td>-0.399126</td>\n",
              "      <td>...</td>\n",
              "      <td>0.127434</td>\n",
              "      <td>0.265245</td>\n",
              "      <td>0.800049</td>\n",
              "      <td>-0.163298</td>\n",
              "      <td>0.123205</td>\n",
              "      <td>-0.569159</td>\n",
              "      <td>0.546668</td>\n",
              "      <td>0.108821</td>\n",
              "      <td>0.104533</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284806</th>\n",
              "      <td>-0.533413</td>\n",
              "      <td>-0.189733</td>\n",
              "      <td>0.703337</td>\n",
              "      <td>-0.506271</td>\n",
              "      <td>-0.012546</td>\n",
              "      <td>-0.649617</td>\n",
              "      <td>1.577006</td>\n",
              "      <td>-0.414650</td>\n",
              "      <td>0.486180</td>\n",
              "      <td>-0.915427</td>\n",
              "      <td>...</td>\n",
              "      <td>0.382948</td>\n",
              "      <td>0.261057</td>\n",
              "      <td>0.643078</td>\n",
              "      <td>0.376777</td>\n",
              "      <td>0.008797</td>\n",
              "      <td>-0.473649</td>\n",
              "      <td>-0.818267</td>\n",
              "      <td>-0.002415</td>\n",
              "      <td>0.013649</td>\n",
              "      <td>217.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>284807 rows × 29 columns</p>\n",
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              "    0% {\n",
              "      border-color: transparent;\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <script>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Time=float(input(\"Enter value of 'Time': \"))\n",
        "v1=float(input(\"Enter value of 'v1': \"))\n",
        "v2=float(input(\"Enter value of 'v2': \"))\n",
        "v3=float(input(\"Enter value of 'v3': \"))\n",
        "v4=float(input(\"Enter value of 'v4': \"))\n",
        "v5=float(input(\"Enter value of 'v5': \"))\n",
        "v6=float(input(\"Enter value of 'v6': \"))\n",
        "v7=float(input(\"Enter value of 'v7': \"))\n",
        "v8=float(input(\"Enter value of 'v8': \"))\n",
        "v9=float(input(\"Enter value of 'v9': \"))\n",
        "v10=float(input(\"Enter value of 'v10': \"))\n",
        "v11=float(input(\"Enter value of 'v11': \"))\n",
        "v12=float(input(\"Enter value of 'v12': \"))\n",
        "v13=float(input(\"Enter value of 'v13': \"))\n",
        "v14=float(input(\"Enter value of 'v14': \"))\n",
        "v15=float(input(\"Enter value of 'v15': \"))\n",
        "v16=float(input(\"Enter value of 'v16': \"))\n",
        "v17=float(input(\"Enter value of 'v17': \"))\n",
        "v18=float(input(\"Enter value of 'v18': \"))\n",
        "v19=float(input(\"Enter value of 'v19': \"))\n",
        "v20=float(input(\"Enter value of 'v20': \"))\n",
        "v21=float(input(\"Enter value of 'v21': \"))\n",
        "v22=float(input(\"Enter value of 'v22': \"))\n",
        "v23=float(input(\"Enter value of 'v23': \"))\n",
        "v24=float(input(\"Enter value of 'v24': \"))\n",
        "v25=float(input(\"Enter value of 'v25': \"))\n",
        "v26=float(input(\"Enter value of 'v26': \"))\n",
        "v27=float(input(\"Enter value of 'v27': \"))\n",
        "v28=float(input(\"Enter value of 'v28': \"))\n",
        "Amount=float(input(\"Enter value of 'Amount': \"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-0JBP_3iISX",
        "outputId": "1fb1a1a8-e13c-4b02-e3c1-f88e6c5434ce"
      },
      "execution_count": 32,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter value of 'Time': 45678\n",
            "Enter value of 'v1': 0.000234\n",
            "Enter value of 'v2': 0.000087\n",
            "Enter value of 'v3': -0.00034\n",
            "Enter value of 'v4': 0.00234\n",
            "Enter value of 'v5': -0.000345\n",
            "Enter value of 'v6': 0.0034\n",
            "Enter value of 'v7': -0.0000034\n",
            "Enter value of 'v8': 0.0087\n",
            "Enter value of 'v9': 0.000034\n",
            "Enter value of 'v10': -0.0000234\n",
            "Enter value of 'v11': 0.000876\n",
            "Enter value of 'v12': -0.00096\n",
            "Enter value of 'v13': 0.0008734\n",
            "Enter value of 'v14': 0.0000873\n",
            "Enter value of 'v15': 0.00564\n",
            "Enter value of 'v16': -0.007634\n",
            "Enter value of 'v17': 0.007654\n",
            "Enter value of 'v18': 0.00965\n",
            "Enter value of 'v19': 0.000234\n",
            "Enter value of 'v20': -0.0098654\n",
            "Enter value of 'v21': -0.00003675\n",
            "Enter value of 'v22': 0.0000534\n",
            "Enter value of 'v23': 0.0087654\n",
            "Enter value of 'v24': -0.00765\n",
            "Enter value of 'v25': -0.0000009876\n",
            "Enter value of 'v26': 0.00000765\n",
            "Enter value of 'v27': 0.000345\n",
            "Enter value of 'v28': 0.0000876\n",
            "Enter value of 'Amount': 76543456\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "model.fit(xTrain, yTrain)\n",
        "\n",
        "test_list = [Time, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10,\n",
        "             v11, v12, v13, v14, v15, v16, v17, v18, v19, v20,\n",
        "             v21, v22, v23, v24, v25, v26, v27, v28, Amount]\n",
        "\n",
        "test_df = pd.DataFrame(test_list)\n",
        "test = test_df.transpose()\n",
        "\n",
        "test_pred = model.predict(test)\n",
        "\n",
        "if test_pred == 1:\n",
        "    print(\"Fraudulent Transaction Detected.\")\n",
        "else:\n",
        "    print(\"Non-Fraudulent Transaction.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2JnMWHgmo0cS",
        "outputId": "80e15791-567b-446b-b7ab-07199b54169c"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Non-Fraudulent Transaction.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C2vYwM3khbCL",
        "outputId": "636671db-c98f-4b65-95f8-578549c4ecfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    }
  ]
}