Sampling_Assignment_Solution.ipynb

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      "source": [
        "# **Sampling Assignment**\n",
        "\n",
        "||||\n",
        "|---|----|----|\n",
        "|**NAME** | **ROLL NO** | **Email**  |\n",
        "| Arshpreet Singh |  102017045| asingh26_be20@thapar.edu |"
      ],
      "metadata": {
        "id": "2z1wLg1qyopn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Step 1 :**\n",
        "\n",
        "- Download the data-set from the given below link\n",
        "\n",
        "\n",
        "https://github.com/AnjulaMehto/Sampling_Assignment/blob/main/Creditcard_data.csv"
      ],
      "metadata": {
        "id": "R8TjrPjFzIrA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "6P-KKUyDr2Cp"
      },
      "outputs": [],
      "source": [
        "import urllib.request\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.over_sampling import RandomOverSampler"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the URL of the dataset\n",
        "url = 'https://raw.githubusercontent.com/AnjulaMehto/Sampling_Assignment/main/Creditcard_data.csv'\n",
        "\n",
        "# Set the filename to save the dataset\n",
        "filename = 'Creditcard_data.csv'\n",
        "\n",
        "# Download the dataset from the URL and save it to a local file\n",
        "urllib.request.urlretrieve(url, filename)\n",
        "\n",
        "print('Dataset downloaded successfully.')"
      ],
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        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset downloaded successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Step 2 :**\n",
        "\n",
        "- Convert this data-set into balanced class data-set."
      ],
      "metadata": {
        "id": "kREhlGn0zm-C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "                                                                # Load the credit card dataset\n",
        "data = pd.read_csv('Creditcard_data.csv')"
      ],
      "metadata": {
        "id": "IiTDxVB0ua3x"
      },
      "execution_count": 3,
      "outputs": []
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        "data.head()"
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              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
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              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
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              "          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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate the features and target variable\n",
        "X = data.drop('Class', axis=1)\n",
        "y = data['Class']"
      ],
      "metadata": {
        "id": "1DxGlHoFuQxE"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "YquJ_Oddui0F",
        "outputId": "fffa0584-21e5-48e2-efb5-2dce9bcff508"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
              "0     0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
              "1     0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
              "2     1 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
              "3     1 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
              "4     2 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
              "\n",
              "         V8        V9  ...       V20       V21       V22       V23       V24  \\\n",
              "0  0.098698  0.363787  ...  0.251412 -0.018307  0.277838 -0.110474  0.066928   \n",
              "1  0.085102 -0.255425  ... -0.069083 -0.225775 -0.638672  0.101288 -0.339846   \n",
              "2  0.247676 -1.514654  ...  0.524980  0.247998  0.771679  0.909412 -0.689281   \n",
              "3  0.377436 -1.387024  ... -0.208038 -0.108300  0.005274 -0.190321 -1.175575   \n",
              "4 -0.270533  0.817739  ...  0.408542 -0.009431  0.798278 -0.137458  0.141267   \n",
              "\n",
              "        V25       V26       V27       V28  Amount  \n",
              "0  0.128539 -0.189115  0.133558 -0.021053  149.62  \n",
              "1  0.167170  0.125895 -0.008983  0.014724    2.69  \n",
              "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66  \n",
              "3  0.647376 -0.221929  0.062723  0.061458  123.50  \n",
              "4 -0.206010  0.502292  0.219422  0.215153   69.99  \n",
              "\n",
              "[5 rows x 30 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>...</th>\n",
              "      <th>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>0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
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              "      <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>0</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>...</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</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>...</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>1</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>...</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>2</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>...</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",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 30 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27140ea3-f81d-4145-bd91-e665fe7fa70d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-27140ea3-f81d-4145-bd91-e665fe7fa70d 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-27140ea3-f81d-4145-bd91-e665fe7fa70d');\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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4dJwqkpmukfX",
        "outputId": "a2261f7c-8686-4ec1-fc84-2096401d1924"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    1\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the class balance\n",
        "class_counts = y.value_counts()\n",
        "print(f'Class counts:\\n{class_counts}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OtiJWmQ9ugL8",
        "outputId": "a44955f1-6342-4fda-c877-8ab35bd4310b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class counts:\n",
            "0    763\n",
            "1      9\n",
            "Name: Class, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a bar chart to visualize the class imbalance\n",
        "fig, ax = plt.subplots()\n",
        "class_counts.plot(kind='bar', ax=ax)\n",
        "ax.set_xlabel('Class')\n",
        "ax.set_ylabel('Count')\n",
        "ax.set_title('Class Imbalance')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "x3j_nEwiwMZ3",
        "outputId": "7a7b1d30-11a1-4f5e-deac-c6d70ab7d6bd"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the class imbalance ratio\n",
        "imbalance_ratio = class_counts[0] / class_counts[1]\n",
        "print(f'Class imbalance ratio: {imbalance_ratio:.2f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VzgRN45bum9N",
        "outputId": "d79d78f2-7d85-406b-de3d-08dbab8e5c2c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class imbalance ratio: 84.78\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Undersample the majority class\n",
        "undersampler = RandomUnderSampler(random_state=42)\n",
        "X_undersampled, y_undersampled = undersampler.fit_resample(X, y)\n",
        "\n",
        "# Oversample the minority class\n",
        "oversampler = RandomOverSampler(random_state=42)\n",
        "X_oversampled, y_oversampled = oversampler.fit_resample(X, y)\n",
        "\n",
        "# Concatenate the undersampled and oversampled data\n",
        "X_balanced = pd.concat([X_undersampled, X_oversampled])\n",
        "y_balanced = pd.concat([y_undersampled, y_oversampled])\n",
        "\n",
        "# Verify the class balance\n",
        "print(y_balanced.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kgYtgadwvigp",
        "outputId": "036b5419-5814-419e-d3be-6abf62288c92"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    772\n",
            "1    772\n",
            "Name: Class, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a bar chart to visualize the class balance\n",
        "fig, ax = plt.subplots()\n",
        "class_counts1 = y_balanced.value_counts()\n",
        "class_counts1.plot(kind='bar', ax=ax)\n",
        "ax.set_xlabel('Class')\n",
        "ax.set_ylabel('Count')\n",
        "ax.set_title('Class Imbalance')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "7jI2vDLyvsmb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "1d2ec578-5461-4eb3-c03b-16532c3821c4"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Step 3 :**\n",
        "\n",
        "Deciding 5 Models to be applied using Pycaret"
      ],
      "metadata": {
        "id": "olEYZ8YMYslu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DataSet = pd.concat([X_balanced , y_balanced] , axis = 1)"
      ],
      "metadata": {
        "id": "9-P7LvfvVLjb"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DataSet.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66Og8c_UcHIy",
        "outputId": "7da71fa3-5f0f-4b78-9998-864f622423ee"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1544 entries, 0 to 1525\n",
            "Data columns (total 31 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Time    1544 non-null   int64  \n",
            " 1   V1      1544 non-null   float64\n",
            " 2   V2      1544 non-null   float64\n",
            " 3   V3      1544 non-null   float64\n",
            " 4   V4      1544 non-null   float64\n",
            " 5   V5      1544 non-null   float64\n",
            " 6   V6      1544 non-null   float64\n",
            " 7   V7      1544 non-null   float64\n",
            " 8   V8      1544 non-null   float64\n",
            " 9   V9      1544 non-null   float64\n",
            " 10  V10     1544 non-null   float64\n",
            " 11  V11     1544 non-null   float64\n",
            " 12  V12     1544 non-null   float64\n",
            " 13  V13     1544 non-null   float64\n",
            " 14  V14     1544 non-null   float64\n",
            " 15  V15     1544 non-null   float64\n",
            " 16  V16     1544 non-null   float64\n",
            " 17  V17     1544 non-null   float64\n",
            " 18  V18     1544 non-null   float64\n",
            " 19  V19     1544 non-null   float64\n",
            " 20  V20     1544 non-null   float64\n",
            " 21  V21     1544 non-null   float64\n",
            " 22  V22     1544 non-null   float64\n",
            " 23  V23     1544 non-null   float64\n",
            " 24  V24     1544 non-null   float64\n",
            " 25  V25     1544 non-null   float64\n",
            " 26  V26     1544 non-null   float64\n",
            " 27  V27     1544 non-null   float64\n",
            " 28  V28     1544 non-null   float64\n",
            " 29  Amount  1544 non-null   float64\n",
            " 30  Class   1544 non-null   int64  \n",
            "dtypes: float64(29), int64(2)\n",
            "memory usage: 386.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DataSet.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "84JhUpK5XMPn",
        "outputId": "f96cd58b-a412-4e70-e046-9037e35faf0d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
              "0   265 -0.491003  0.906953  1.645423 -0.083531 -0.195560 -0.710165  0.559119   \n",
              "1   184 -0.143256  0.743649  1.534072  1.062170  0.208187 -0.838623  0.524151   \n",
              "2   566  1.166360  0.005061  0.497768  0.798920 -0.365524 -0.233421 -0.074210   \n",
              "3   128  1.239495 -0.182609  0.155058 -0.928892 -0.746227 -1.235608 -0.061695   \n",
              "4   246 -1.069200  1.239963  0.545157  1.005354 -0.025696 -0.910673  0.422442   \n",
              "\n",
              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
              "0  0.116340 -0.538190  ... -0.168067 -0.517387  0.018650  0.491652 -0.277795   \n",
              "1 -0.294661 -0.478856  ... -0.205014 -0.460893  0.047407  0.339243 -0.779439   \n",
              "2 -0.008325  0.437687  ... -0.195728 -0.365798  0.030729  0.123133  0.381749   \n",
              "3 -0.125223  0.984938  ...  0.146077  0.481119 -0.140019  0.538261  0.710720   \n",
              "4  0.049283 -0.564601  ...  0.146555  0.602990  0.132656  0.427113 -0.084030   \n",
              "\n",
              "        V26       V27       V28  Amount  Class  \n",
              "0  0.043841  0.253372  0.111749    9.03      0  \n",
              "1  0.234456 -0.024125 -0.049898    1.98      0  \n",
              "2  0.296735 -0.007175  0.011905   18.56      0  \n",
              "3 -0.621382  0.036867  0.010963    8.80      0  \n",
              "4 -0.417194 -0.897885 -0.462042    2.98      0  \n",
              "\n",
              "[5 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1cfb8ab8-3d2f-4edb-b220-ecdad5f8307d\">\n",
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>...</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>265</td>\n",
              "      <td>-0.491003</td>\n",
              "      <td>0.906953</td>\n",
              "      <td>1.645423</td>\n",
              "      <td>-0.083531</td>\n",
              "      <td>-0.195560</td>\n",
              "      <td>-0.710165</td>\n",
              "      <td>0.559119</td>\n",
              "      <td>0.116340</td>\n",
              "      <td>-0.538190</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.168067</td>\n",
              "      <td>-0.517387</td>\n",
              "      <td>0.018650</td>\n",
              "      <td>0.491652</td>\n",
              "      <td>-0.277795</td>\n",
              "      <td>0.043841</td>\n",
              "      <td>0.253372</td>\n",
              "      <td>0.111749</td>\n",
              "      <td>9.03</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>184</td>\n",
              "      <td>-0.143256</td>\n",
              "      <td>0.743649</td>\n",
              "      <td>1.534072</td>\n",
              "      <td>1.062170</td>\n",
              "      <td>0.208187</td>\n",
              "      <td>-0.838623</td>\n",
              "      <td>0.524151</td>\n",
              "      <td>-0.294661</td>\n",
              "      <td>-0.478856</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.205014</td>\n",
              "      <td>-0.460893</td>\n",
              "      <td>0.047407</td>\n",
              "      <td>0.339243</td>\n",
              "      <td>-0.779439</td>\n",
              "      <td>0.234456</td>\n",
              "      <td>-0.024125</td>\n",
              "      <td>-0.049898</td>\n",
              "      <td>1.98</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>566</td>\n",
              "      <td>1.166360</td>\n",
              "      <td>0.005061</td>\n",
              "      <td>0.497768</td>\n",
              "      <td>0.798920</td>\n",
              "      <td>-0.365524</td>\n",
              "      <td>-0.233421</td>\n",
              "      <td>-0.074210</td>\n",
              "      <td>-0.008325</td>\n",
              "      <td>0.437687</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.195728</td>\n",
              "      <td>-0.365798</td>\n",
              "      <td>0.030729</td>\n",
              "      <td>0.123133</td>\n",
              "      <td>0.381749</td>\n",
              "      <td>0.296735</td>\n",
              "      <td>-0.007175</td>\n",
              "      <td>0.011905</td>\n",
              "      <td>18.56</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>128</td>\n",
              "      <td>1.239495</td>\n",
              "      <td>-0.182609</td>\n",
              "      <td>0.155058</td>\n",
              "      <td>-0.928892</td>\n",
              "      <td>-0.746227</td>\n",
              "      <td>-1.235608</td>\n",
              "      <td>-0.061695</td>\n",
              "      <td>-0.125223</td>\n",
              "      <td>0.984938</td>\n",
              "      <td>...</td>\n",
              "      <td>0.146077</td>\n",
              "      <td>0.481119</td>\n",
              "      <td>-0.140019</td>\n",
              "      <td>0.538261</td>\n",
              "      <td>0.710720</td>\n",
              "      <td>-0.621382</td>\n",
              "      <td>0.036867</td>\n",
              "      <td>0.010963</td>\n",
              "      <td>8.80</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>246</td>\n",
              "      <td>-1.069200</td>\n",
              "      <td>1.239963</td>\n",
              "      <td>0.545157</td>\n",
              "      <td>1.005354</td>\n",
              "      <td>-0.025696</td>\n",
              "      <td>-0.910673</td>\n",
              "      <td>0.422442</td>\n",
              "      <td>0.049283</td>\n",
              "      <td>-0.564601</td>\n",
              "      <td>...</td>\n",
              "      <td>0.146555</td>\n",
              "      <td>0.602990</td>\n",
              "      <td>0.132656</td>\n",
              "      <td>0.427113</td>\n",
              "      <td>-0.084030</td>\n",
              "      <td>-0.417194</td>\n",
              "      <td>-0.897885</td>\n",
              "      <td>-0.462042</td>\n",
              "      <td>2.98</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pycaret &> /dev/null\n",
        "print (\"Pycaret installed sucessfully!!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U3EeQ7kCYFfw",
        "outputId": "057e2c1e-067f-4c94-a0ea-8d09f1dafaad"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pycaret installed sucessfully!!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.utils import version\n",
        "version()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "8LyP7qy6YH7D",
        "outputId": "57d1066e-799d-45e9-c2be-2b8805481f04"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.3.10'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.classification import *\n",
        "setup(data=DataSet, target='Class',\n",
        "      remove_outliers = True, outliers_threshold = 0.05, \n",
        "      normalize = True, normalize_method = 'zscore', \n",
        "      transformation = True, transformation_method = 'yeo-johnson', silent=True)\n",
        "cm = compare_models()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696,
          "referenced_widgets": [
            "0afc033e05ec48e2b9e499efdc98aeef",
            "07680751d7074b04a23738ed47f331e0",
            "ed65ee33ccc34f97835223b77d074c57",
            "1820048f169b4accb2db558b0fb79de1",
            "1086b70e5b0f438089b7378b953e6d18",
            "f500794bc2e84e1f9fa89df742b037fc"
          ]
        },
        "id": "TPP0Nx5wTvbv",
        "outputId": "997da4d6-d8a2-4d90-db29-6d9e5777bcef"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                    Model  Accuracy     AUC  Recall   Prec.  \\\n",
              "rf               Random Forest Classifier    1.0000  1.0000  1.0000  1.0000   \n",
              "et                 Extra Trees Classifier    1.0000  1.0000  1.0000  1.0000   \n",
              "gbc          Gradient Boosting Classifier    0.9990  1.0000  1.0000  0.9982   \n",
              "ada                  Ada Boost Classifier    0.9971  1.0000  1.0000  0.9946   \n",
              "lightgbm  Light Gradient Boosting Machine    0.9952  1.0000  1.0000  0.9913   \n",
              "dt               Decision Tree Classifier    0.9943  0.9940  1.0000  0.9894   \n",
              "knn                K Neighbors Classifier    0.9789  0.9909  1.0000  0.9622   \n",
              "svm                   SVM - Linear Kernel    0.9560  0.0000  0.9670  0.9519   \n",
              "lr                    Logistic Regression    0.9551  0.9798  1.0000  0.9225   \n",
              "ridge                    Ridge Classifier    0.9321  0.0000  1.0000  0.8865   \n",
              "lda          Linear Discriminant Analysis    0.9321  0.9756  1.0000  0.8865   \n",
              "nb                            Naive Bayes    0.6435  0.8510  0.3914  0.8420   \n",
              "dummy                    Dummy Classifier    0.5229  0.5000  1.0000  0.5229   \n",
              "qda       Quadratic Discriminant Analysis    0.4771  0.5000  0.0000  0.0000   \n",
              "\n",
              "              F1   Kappa     MCC  TT (Sec)  \n",
              "rf        1.0000  1.0000  1.0000     0.555  \n",
              "et        1.0000  1.0000  1.0000     0.515  \n",
              "gbc       0.9991  0.9981  0.9981     0.844  \n",
              "ada       0.9973  0.9943  0.9943     0.559  \n",
              "lightgbm  0.9955  0.9904  0.9906     0.725  \n",
              "dt        0.9946  0.9885  0.9887     0.025  \n",
              "knn       0.9805  0.9576  0.9590     0.028  \n",
              "svm       0.9573  0.9119  0.9160     0.030  \n",
              "lr        0.9593  0.9094  0.9140     0.355  \n",
              "ridge     0.9395  0.8629  0.8719     0.028  \n",
              "lda       0.9395  0.8629  0.8719     0.026  \n",
              "nb        0.5307  0.3038  0.3622     0.014  \n",
              "dummy     0.6867  0.0000  0.0000     0.017  \n",
              "qda       0.0000  0.0000  0.0000     0.048  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1cb286b3-0fe5-400e-8d99-a326b68f679a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "      <th>TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Classifier</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gbc</th>\n",
              "      <td>Gradient Boosting Classifier</td>\n",
              "      <td>0.9990</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9982</td>\n",
              "      <td>0.9991</td>\n",
              "      <td>0.9981</td>\n",
              "      <td>0.9981</td>\n",
              "      <td>0.844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>Ada Boost Classifier</td>\n",
              "      <td>0.9971</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9946</td>\n",
              "      <td>0.9973</td>\n",
              "      <td>0.9943</td>\n",
              "      <td>0.9943</td>\n",
              "      <td>0.559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>0.9952</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9913</td>\n",
              "      <td>0.9955</td>\n",
              "      <td>0.9904</td>\n",
              "      <td>0.9906</td>\n",
              "      <td>0.725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.9943</td>\n",
              "      <td>0.9940</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9894</td>\n",
              "      <td>0.9946</td>\n",
              "      <td>0.9885</td>\n",
              "      <td>0.9887</td>\n",
              "      <td>0.025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Classifier</td>\n",
              "      <td>0.9789</td>\n",
              "      <td>0.9909</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9622</td>\n",
              "      <td>0.9805</td>\n",
              "      <td>0.9576</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>SVM - Linear Kernel</td>\n",
              "      <td>0.9560</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.9670</td>\n",
              "      <td>0.9519</td>\n",
              "      <td>0.9573</td>\n",
              "      <td>0.9119</td>\n",
              "      <td>0.9160</td>\n",
              "      <td>0.030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.9551</td>\n",
              "      <td>0.9798</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9225</td>\n",
              "      <td>0.9593</td>\n",
              "      <td>0.9094</td>\n",
              "      <td>0.9140</td>\n",
              "      <td>0.355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Classifier</td>\n",
              "      <td>0.9321</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.8865</td>\n",
              "      <td>0.9395</td>\n",
              "      <td>0.8629</td>\n",
              "      <td>0.8719</td>\n",
              "      <td>0.028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lda</th>\n",
              "      <td>Linear Discriminant Analysis</td>\n",
              "      <td>0.9321</td>\n",
              "      <td>0.9756</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.8865</td>\n",
              "      <td>0.9395</td>\n",
              "      <td>0.8629</td>\n",
              "      <td>0.8719</td>\n",
              "      <td>0.026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nb</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.6435</td>\n",
              "      <td>0.8510</td>\n",
              "      <td>0.3914</td>\n",
              "      <td>0.8420</td>\n",
              "      <td>0.5307</td>\n",
              "      <td>0.3038</td>\n",
              "      <td>0.3622</td>\n",
              "      <td>0.014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dummy</th>\n",
              "      <td>Dummy Classifier</td>\n",
              "      <td>0.5229</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.5229</td>\n",
              "      <td>0.6867</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>qda</th>\n",
              "      <td>Quadratic Discriminant Analysis</td>\n",
              "      <td>0.4771</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.048</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:create_model_container: 14\n",
            "INFO:logs:master_model_container: 14\n",
            "INFO:logs:display_container: 2\n",
            "INFO:logs:RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
            "                       criterion='gini', max_depth=None, max_features='auto',\n",
            "                       max_leaf_nodes=None, max_samples=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=1, min_samples_split=2,\n",
            "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
            "                       n_jobs=-1, oob_score=False, random_state=1413, verbose=0,\n",
            "                       warm_start=False)\n",
            "INFO:logs:compare_models() succesfully completed......................................\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **5 Models** \n",
        "\n",
        "\n",
        "- Random Forest Classifier\n",
        "\n",
        "- Extra Trees Classifier\n",
        "\n",
        "- Gradient Boosting Classifier\n",
        "\n",
        "- Decision Tree Classifier\n",
        "\n",
        "- Ada Boost Classifier\n"
      ],
      "metadata": {
        "id": "YyojpakKZzrx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4 Sampling Techniques**\n",
        "\n",
        "- simple random sampling\n",
        "- systematic sampling\n",
        "- stratified sampling\n",
        "- cluster sampling"
      ],
      "metadata": {
        "id": "toAloCHJfMl4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n"
      ],
      "metadata": {
        "id": "tIJCn2w6l74Y"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load credit card dataset from CSV file\n",
        "creditcard_df = DataSet\n",
        "creditcard_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "dNZY8ZoRl9GF",
        "outputId": "cc1c22ba-462c-4dc0-b832-a81bf982390d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
              "0   265 -0.491003  0.906953  1.645423 -0.083531 -0.195560 -0.710165  0.559119   \n",
              "1   184 -0.143256  0.743649  1.534072  1.062170  0.208187 -0.838623  0.524151   \n",
              "2   566  1.166360  0.005061  0.497768  0.798920 -0.365524 -0.233421 -0.074210   \n",
              "3   128  1.239495 -0.182609  0.155058 -0.928892 -0.746227 -1.235608 -0.061695   \n",
              "4   246 -1.069200  1.239963  0.545157  1.005354 -0.025696 -0.910673  0.422442   \n",
              "\n",
              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
              "0  0.116340 -0.538190  ... -0.168067 -0.517387  0.018650  0.491652 -0.277795   \n",
              "1 -0.294661 -0.478856  ... -0.205014 -0.460893  0.047407  0.339243 -0.779439   \n",
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              "3 -0.125223  0.984938  ...  0.146077  0.481119 -0.140019  0.538261  0.710720   \n",
              "4  0.049283 -0.564601  ...  0.146555  0.602990  0.132656  0.427113 -0.084030   \n",
              "\n",
              "        V26       V27       V28  Amount  Class  \n",
              "0  0.043841  0.253372  0.111749    9.03      0  \n",
              "1  0.234456 -0.024125 -0.049898    1.98      0  \n",
              "2  0.296735 -0.007175  0.011905   18.56      0  \n",
              "3 -0.621382  0.036867  0.010963    8.80      0  \n",
              "4 -0.417194 -0.897885 -0.462042    2.98      0  \n",
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              "      <th>Time</th>\n",
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              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
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              "      <td>0.497768</td>\n",
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              "      <td>-0.365524</td>\n",
              "      <td>-0.233421</td>\n",
              "      <td>-0.074210</td>\n",
              "      <td>-0.008325</td>\n",
              "      <td>0.437687</td>\n",
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              "      <td>-0.365798</td>\n",
              "      <td>0.030729</td>\n",
              "      <td>0.123133</td>\n",
              "      <td>0.381749</td>\n",
              "      <td>0.296735</td>\n",
              "      <td>-0.007175</td>\n",
              "      <td>0.011905</td>\n",
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              "      <td>0.155058</td>\n",
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              "      <td>-0.746227</td>\n",
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              "      <td>0.146077</td>\n",
              "      <td>0.481119</td>\n",
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              "      <td>0.710720</td>\n",
              "      <td>-0.621382</td>\n",
              "      <td>0.036867</td>\n",
              "      <td>0.010963</td>\n",
              "      <td>8.80</td>\n",
              "      <td>0</td>\n",
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              "          const element = document.querySelector('#df-24e465b0-dd26-43b8-a83a-891d7de4750d');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(creditcard_df.drop('Class', axis=1), creditcard_df['Class'], test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "WhluQ7z5m4G0"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create clusters using k-means clustering\n",
        "kmeans = KMeans(n_clusters=5, random_state=42)\n",
        "creditcard_df['Cluster'] = kmeans.fit_predict(creditcard_df.drop('Class', axis=1))\n",
        "# Perform cluster sampling\n",
        "cluster_sample = pd.concat([creditcard_df[creditcard_df['Cluster'] == i].sample(n=10, random_state=42 , replace = True) for i in range(5)], ignore_index=True)\n",
        "cluster_sample = cluster_sample.drop('Cluster', axis = 1)\n",
        "cluster_sample.head()\n",
        "\n"
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        "outputId": "cce7d966-af95-4a6b-aded-cb8aa2d5ec87"
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      "execution_count": 22,
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              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
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              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "# Perform simple random sampling\n",
        "srs_sample = creditcard_df.sample(n=500, random_state=42)\n",
        "srs_sample = srs_sample.drop('Cluster', axis = 1)\n",
        "srs_sample.head()"
      ],
      "metadata": {
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      "execution_count": 23,
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              "      Time        V1        V2        V3        V4        V5        V6  \\\n",
              "1476   164  0.073497  0.551033  0.451890  0.114964  0.822947  0.251480   \n",
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              "           V24       V25       V26       V27       V28  Amount  Class  \n",
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              "      <td>-0.511518</td>\n",
              "      <td>-0.729446</td>\n",
              "      <td>-0.029177</td>\n",
              "      <td>-0.430328</td>\n",
              "      <td>-0.012086</td>\n",
              "      <td>0.080318</td>\n",
              "      <td>0.540107</td>\n",
              "      <td>0.034934</td>\n",
              "      <td>8.93</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1250</th>\n",
              "      <td>574</td>\n",
              "      <td>1.257719</td>\n",
              "      <td>0.364739</td>\n",
              "      <td>0.306923</td>\n",
              "      <td>0.690638</td>\n",
              "      <td>-0.357792</td>\n",
              "      <td>-1.067481</td>\n",
              "      <td>0.094272</td>\n",
              "      <td>-0.210300</td>\n",
              "      <td>0.014455</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.286856</td>\n",
              "      <td>-0.820658</td>\n",
              "      <td>0.127663</td>\n",
              "      <td>0.343128</td>\n",
              "      <td>0.221120</td>\n",
              "      <td>0.094391</td>\n",
              "      <td>-0.022189</td>\n",
              "      <td>0.030944</td>\n",
              "      <td>1.29</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>510</th>\n",
              "      <td>376</td>\n",
              "      <td>0.894056</td>\n",
              "      <td>-0.031001</td>\n",
              "      <td>1.514665</td>\n",
              "      <td>2.587276</td>\n",
              "      <td>-0.222693</td>\n",
              "      <td>1.987772</td>\n",
              "      <td>-0.922106</td>\n",
              "      <td>0.782940</td>\n",
              "      <td>0.181510</td>\n",
              "      <td>...</td>\n",
              "      <td>0.052819</td>\n",
              "      <td>0.394703</td>\n",
              "      <td>0.212676</td>\n",
              "      <td>-0.654428</td>\n",
              "      <td>-0.089633</td>\n",
              "      <td>0.066054</td>\n",
              "      <td>0.107787</td>\n",
              "      <td>0.024676</td>\n",
              "      <td>11.39</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1076</th>\n",
              "      <td>484</td>\n",
              "      <td>-0.928088</td>\n",
              "      <td>0.398194</td>\n",
              "      <td>1.741131</td>\n",
              "      <td>0.182673</td>\n",
              "      <td>0.966387</td>\n",
              "      <td>-0.901004</td>\n",
              "      <td>0.879016</td>\n",
              "      <td>-0.156590</td>\n",
              "      <td>-0.142117</td>\n",
              "      <td>...</td>\n",
              "      <td>0.066353</td>\n",
              "      <td>0.281378</td>\n",
              "      <td>-0.257966</td>\n",
              "      <td>0.385384</td>\n",
              "      <td>0.391117</td>\n",
              "      <td>-0.453853</td>\n",
              "      <td>-0.104448</td>\n",
              "      <td>-0.125765</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1</td>\n",
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              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform systematic sampling\n",
        "systematic_sample = creditcard_df.iloc[::3, :]\n",
        "systematic_sample = systematic_sample.drop('Cluster', axis = 1)\n",
        "systematic_sample.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "XRPv7NkCn51p",
        "outputId": "d5fb814c-77aa-4274-9a0c-b2bbf0865853"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Time        V1        V2        V3        V4        V5        V6  \\\n",
              "0    265 -0.491003  0.906953  1.645423 -0.083531 -0.195560 -0.710165   \n",
              "3    128  1.239495 -0.182609  0.155058 -0.928892 -0.746227 -1.235608   \n",
              "6    574 -0.402057  0.584300  2.474227  0.929684  0.014314  0.297490   \n",
              "9      0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
              "12   406 -2.312227  1.951992 -1.609851  3.997906 -0.522188 -1.426545   \n",
              "\n",
              "          V7        V8        V9  ...       V21       V22       V23       V24  \\\n",
              "0   0.559119  0.116340 -0.538190  ... -0.168067 -0.517387  0.018650  0.491652   \n",
              "3  -0.061695 -0.125223  0.984938  ...  0.146077  0.481119 -0.140019  0.538261   \n",
              "6   0.715195 -0.257153  0.593868  ... -0.072812  0.445733 -0.245103  0.421234   \n",
              "9  -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846   \n",
              "12 -2.537387  1.391657 -2.770089  ...  0.517232 -0.035049 -0.465211  0.320198   \n",
              "\n",
              "         V25       V26       V27       V28  Amount  Class  \n",
              "0  -0.277795  0.043841  0.253372  0.111749    9.03      0  \n",
              "3   0.710720 -0.621382  0.036867  0.010963    8.80      0  \n",
              "6   0.049280 -0.388323 -0.329333 -0.386747   12.00      0  \n",
              "9   0.167170  0.125895 -0.008983  0.014724    2.69      1  \n",
              "12  0.044519  0.177840  0.261145 -0.143276    0.00      1  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "      <td>-0.072812</td>\n",
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              "      <td>-0.386747</td>\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform stratified sampling\n",
        "stratified_sample = creditcard_df.groupby('Class').apply(lambda x: x.sample(n=250, random_state=42)).reset_index(drop=True)\n",
        "stratified_sample = stratified_sample.drop('Cluster', axis = 1)\n",
        "stratified_sample.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "RRXFfeuUoE3H",
        "outputId": "2a04a7ef-7685-4195-83f2-b5f864f2d1bc"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
              "0   399  1.091666  0.068682  1.230334  2.554474 -0.454169  0.915781 -0.701016   \n",
              "1   137  0.753308 -1.131593  0.878730  0.297871 -1.327250  0.066596 -0.535276   \n",
              "2   260  1.075229 -0.335339  1.680468  1.192098 -1.270531  0.313498 -0.871025   \n",
              "3   297 -1.148038  0.367626  2.769717 -0.356562 -0.268456  0.323423 -0.108718   \n",
              "4   430  1.331282 -0.460775  0.201621 -0.582848 -0.815358 -0.863857 -0.272425   \n",
              "\n",
              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
              "0  0.396866  0.307571  ... -0.098440 -0.178428  0.007277 -0.504025  0.255974   \n",
              "1  0.171133  1.184602  ...  0.031912 -0.213298 -0.091660  0.151837 -0.067673   \n",
              "2  0.235402  1.315261  ...  0.092930  0.815227 -0.054551  0.731149  0.431465   \n",
              "3  0.452755  0.166990  ...  0.063334  0.168210 -0.194521  0.228655  0.107744   \n",
              "4 -0.228814 -1.194789  ...  0.239532  0.718030 -0.181652  0.617561  0.760131   \n",
              "\n",
              "        V26       V27       V28  Amount  Class  \n",
              "0  0.022213  0.048714  0.027057   15.19      0  \n",
              "1  0.999073 -0.063985  0.049174  226.07      0  \n",
              "2  0.580189  0.062528  0.025628    5.46      0  \n",
              "3  0.222331 -0.109160  0.089767    6.20      0  \n",
              "4 -0.100635 -0.009469  0.001407   30.00      0  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
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              "      <th>V21</th>\n",
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              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
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              "      <th>V26</th>\n",
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              "      <th>Amount</th>\n",
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              "      <td>0.255974</td>\n",
              "      <td>0.022213</td>\n",
              "      <td>0.048714</td>\n",
              "      <td>0.027057</td>\n",
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              "      <th>1</th>\n",
              "      <td>137</td>\n",
              "      <td>0.753308</td>\n",
              "      <td>-1.131593</td>\n",
              "      <td>0.878730</td>\n",
              "      <td>0.297871</td>\n",
              "      <td>-1.327250</td>\n",
              "      <td>0.066596</td>\n",
              "      <td>-0.535276</td>\n",
              "      <td>0.171133</td>\n",
              "      <td>1.184602</td>\n",
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              "      <td>0.031912</td>\n",
              "      <td>-0.213298</td>\n",
              "      <td>-0.091660</td>\n",
              "      <td>0.151837</td>\n",
              "      <td>-0.067673</td>\n",
              "      <td>0.999073</td>\n",
              "      <td>-0.063985</td>\n",
              "      <td>0.049174</td>\n",
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              "      <td>-0.335339</td>\n",
              "      <td>1.680468</td>\n",
              "      <td>1.192098</td>\n",
              "      <td>-1.270531</td>\n",
              "      <td>0.313498</td>\n",
              "      <td>-0.871025</td>\n",
              "      <td>0.235402</td>\n",
              "      <td>1.315261</td>\n",
              "      <td>...</td>\n",
              "      <td>0.092930</td>\n",
              "      <td>0.815227</td>\n",
              "      <td>-0.054551</td>\n",
              "      <td>0.731149</td>\n",
              "      <td>0.431465</td>\n",
              "      <td>0.580189</td>\n",
              "      <td>0.062528</td>\n",
              "      <td>0.025628</td>\n",
              "      <td>5.46</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>-1.148038</td>\n",
              "      <td>0.367626</td>\n",
              "      <td>2.769717</td>\n",
              "      <td>-0.356562</td>\n",
              "      <td>-0.268456</td>\n",
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              "      <td>-0.108718</td>\n",
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              "      <td>0.228655</td>\n",
              "      <td>0.107744</td>\n",
              "      <td>0.222331</td>\n",
              "      <td>-0.109160</td>\n",
              "      <td>0.089767</td>\n",
              "      <td>6.20</td>\n",
              "      <td>0</td>\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the models to be tested\n",
        "models = {\n",
        "    'Random Forest': RandomForestClassifier(random_state=42),\n",
        "    'Extra Trees': ExtraTreesClassifier(random_state=42),\n",
        "    'Gradient Boosting': GradientBoostingClassifier(random_state=42),\n",
        "    'Decision Tree': DecisionTreeClassifier(random_state=42),\n",
        "    'Ada Boost': AdaBoostClassifier(random_state=42)\n",
        "}"
      ],
      "metadata": {
        "id": "mSQfxRJzoTB3"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a function to fit a model and return its accuracy score\n",
        "def fit_model(model, X_train, y_train, X_test, y_test):\n",
        "    model.fit(X_train, y_train)\n",
        "    return model.score(X_test, y_test)"
      ],
      "metadata": {
        "id": "VZQ5eG1LoWBV"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Test each model on each sampling technique\n",
        "results = {}\n",
        "for model_name, model in models.items():\n",
        "    for sample_name, sample in zip(['Simple Random', 'Systematic', 'Stratified', 'Cluster'], [srs_sample, systematic_sample, stratified_sample, cluster_sample]):\n",
        "        accuracy = fit_model(model, sample.drop(['Class'], axis=1), sample['Class'], X_test, y_test)\n",
        "        results.setdefault(model_name, {})[sample_name] = accuracy"
      ],
      "metadata": {
        "id": "DyWz1cg2oZ8p"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the results as a table\n",
        "results_df = pd.DataFrame(results)\n",
        "results_df.index.name = 'Sampling Technique'\n",
        "print(results_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O6ER2DRMon02",
        "outputId": "1eaf5afa-b8ff-49c9-8a70-99049d8db765"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                    Random Forest  Extra Trees  Gradient Boosting  \\\n",
            "Sampling Technique                                                  \n",
            "Simple Random            1.000000     1.000000           1.000000   \n",
            "Systematic               1.000000     1.000000           0.996764   \n",
            "Stratified               1.000000     1.000000           1.000000   \n",
            "Cluster                  0.779935     0.841424           0.870550   \n",
            "\n",
            "                    Decision Tree  Ada Boost  \n",
            "Sampling Technique                            \n",
            "Simple Random            1.000000   1.000000  \n",
            "Systematic               0.980583   1.000000  \n",
            "Stratified               0.993528   1.000000  \n",
            "Cluster                  0.737864   0.864078  \n"
          ]
        }
      ]
    }
  ]
}