svm-kernels.ipynb

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    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "DnRjoUxE33TC"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from google.colab import drive\n",
        "import numpy as np\n",
        "import seaborn as sn\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, accuracy_score, log_loss\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import GridSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/gdrive')\n",
        "data = pd.read_csv('gdrive/My Drive/Datasetjadid.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WEXNrJZZ-Trj",
        "outputId": "b3ae33bf-e52e-44c9-c43a-a73494f19f72"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "JFOUYBrL_b_j",
        "outputId": "78277d1c-1042-4884-fcb0-4fef705d667e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Id   Income  Age  Experience Married/Single House_Ownership  \\\n",
              "0            1  1303834   23           3         single          rented   \n",
              "1            2  7574516   40          10         single          rented   \n",
              "2            3  3991815   66           4        married          rented   \n",
              "3            4  6256451   41           2         single          rented   \n",
              "4            5  5768871   47          11         single          rented   \n",
              "...        ...      ...  ...         ...            ...             ...   \n",
              "251995  251996  8154883   43          13         single          rented   \n",
              "251996  251997  2843572   26          10         single          rented   \n",
              "251997  251998  4522448   46           7         single          rented   \n",
              "251998  251999  6507128   45           0         single          rented   \n",
              "251999  252000  9070230   70          17         single          rented   \n",
              "\n",
              "       Car_Ownership           Profession                 CITY  \\\n",
              "0                 no  Mechanical_engineer                 Rewa   \n",
              "1                 no   Software_Developer             Parbhani   \n",
              "2                 no     Technical_writer            Alappuzha   \n",
              "3                yes   Software_Developer          Bhubaneswar   \n",
              "4                 no        Civil_servant  Tiruchirappalli[10]   \n",
              "...              ...                  ...                  ...   \n",
              "251995            no              Surgeon              Kolkata   \n",
              "251996            no         Army_officer                 Rewa   \n",
              "251997            no      Design_Engineer      Kalyan-Dombivli   \n",
              "251998            no     Graphic_Designer          Pondicherry   \n",
              "251999            no         Statistician                Avadi   \n",
              "\n",
              "                 STATE  CURRENT_JOB_YRS  CURRENT_HOUSE_YRS  Risk_Flag  \n",
              "0       Madhya_Pradesh                3                 13          0  \n",
              "1          Maharashtra                9                 13          0  \n",
              "2               Kerala                4                 10          0  \n",
              "3               Odisha                2                 12          1  \n",
              "4           Tamil_Nadu                3                 14          1  \n",
              "...                ...              ...                ...        ...  \n",
              "251995     West_Bengal                6                 11          0  \n",
              "251996  Madhya_Pradesh                6                 11          0  \n",
              "251997     Maharashtra                7                 12          0  \n",
              "251998      Puducherry                0                 10          0  \n",
              "251999      Tamil_Nadu                7                 11          0  \n",
              "\n",
              "[252000 rows x 13 columns]"
            ],
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              "      <td>1303834</td>\n",
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              "      <td>Mechanical_engineer</td>\n",
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              "      <td>Parbhani</td>\n",
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              "      <td>3991815</td>\n",
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              "      <td>no</td>\n",
              "      <td>Technical_writer</td>\n",
              "      <td>Alappuzha</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>4</td>\n",
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              "      <td>0</td>\n",
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              "      <td>6256451</td>\n",
              "      <td>41</td>\n",
              "      <td>2</td>\n",
              "      <td>single</td>\n",
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              "      <td>yes</td>\n",
              "      <td>Software_Developer</td>\n",
              "      <td>Bhubaneswar</td>\n",
              "      <td>Odisha</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>5</td>\n",
              "      <td>5768871</td>\n",
              "      <td>47</td>\n",
              "      <td>11</td>\n",
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              "      <td>rented</td>\n",
              "      <td>no</td>\n",
              "      <td>Civil_servant</td>\n",
              "      <td>Tiruchirappalli[10]</td>\n",
              "      <td>Tamil_Nadu</td>\n",
              "      <td>3</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>251995</th>\n",
              "      <td>251996</td>\n",
              "      <td>8154883</td>\n",
              "      <td>43</td>\n",
              "      <td>13</td>\n",
              "      <td>single</td>\n",
              "      <td>rented</td>\n",
              "      <td>no</td>\n",
              "      <td>Surgeon</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>West_Bengal</td>\n",
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              "      <td>11</td>\n",
              "      <td>0</td>\n",
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              "      <th>251996</th>\n",
              "      <td>251997</td>\n",
              "      <td>2843572</td>\n",
              "      <td>26</td>\n",
              "      <td>10</td>\n",
              "      <td>single</td>\n",
              "      <td>rented</td>\n",
              "      <td>no</td>\n",
              "      <td>Army_officer</td>\n",
              "      <td>Rewa</td>\n",
              "      <td>Madhya_Pradesh</td>\n",
              "      <td>6</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>251997</th>\n",
              "      <td>251998</td>\n",
              "      <td>4522448</td>\n",
              "      <td>46</td>\n",
              "      <td>7</td>\n",
              "      <td>single</td>\n",
              "      <td>rented</td>\n",
              "      <td>no</td>\n",
              "      <td>Design_Engineer</td>\n",
              "      <td>Kalyan-Dombivli</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>251998</th>\n",
              "      <td>251999</td>\n",
              "      <td>6507128</td>\n",
              "      <td>45</td>\n",
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              "      <td>single</td>\n",
              "      <td>rented</td>\n",
              "      <td>no</td>\n",
              "      <td>Graphic_Designer</td>\n",
              "      <td>Pondicherry</td>\n",
              "      <td>Puducherry</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>251999</th>\n",
              "      <td>252000</td>\n",
              "      <td>9070230</td>\n",
              "      <td>70</td>\n",
              "      <td>17</td>\n",
              "      <td>single</td>\n",
              "      <td>rented</td>\n",
              "      <td>no</td>\n",
              "      <td>Statistician</td>\n",
              "      <td>Avadi</td>\n",
              "      <td>Tamil_Nadu</td>\n",
              "      <td>7</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
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              "<p>252000 rows × 13 columns</p>\n",
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if data.duplicated().any():\n",
        "    print(data.duplicated().sum(),\"has duplicate data.\")\n",
        "else:\n",
        "    print(\"no duplicate.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1fbsHF3rAdmf",
        "outputId": "d7682017-c835-4a14-bf61-1648faa1ee53"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "no duplicate.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop(['Risk_Flag','CITY','STATE'],axis=1)\n",
        "y = data['Risk_Flag']"
      ],
      "metadata": {
        "id": "vl3OloGPWedX"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = pd.get_dummies(X,drop_first=True)"
      ],
      "metadata": {
        "id": "Qi_AtO9nWwLf"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "FccDPqu0tzYF",
        "outputId": "ddbe1e9e-df79-4c95-b21c-96b0e0d7d5a7"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Id   Income  Age  Experience  CURRENT_JOB_YRS  CURRENT_HOUSE_YRS  \\\n",
              "0            1  1303834   23           3                3                 13   \n",
              "1            2  7574516   40          10                9                 13   \n",
              "2            3  3991815   66           4                4                 10   \n",
              "3            4  6256451   41           2                2                 12   \n",
              "4            5  5768871   47          11                3                 14   \n",
              "...        ...      ...  ...         ...              ...                ...   \n",
              "251995  251996  8154883   43          13                6                 11   \n",
              "251996  251997  2843572   26          10                6                 11   \n",
              "251997  251998  4522448   46           7                7                 12   \n",
              "251998  251999  6507128   45           0                0                 10   \n",
              "251999  252000  9070230   70          17                7                 11   \n",
              "\n",
              "        Married/Single_single  House_Ownership_owned  House_Ownership_rented  \\\n",
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              "251995                      1                      0                       1   \n",
              "251996                      1                      0                       1   \n",
              "251997                      1                      0                       1   \n",
              "251998                      1                      0                       1   \n",
              "251999                      1                      0                       1   \n",
              "\n",
              "        Car_Ownership_yes  ...  Profession_Scientist  Profession_Secretary  \\\n",
              "0                       0  ...                     0                     0   \n",
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              "2                       0  ...                     0                     0   \n",
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              "...                   ...  ...                   ...                   ...   \n",
              "251995                  0  ...                     0                     0   \n",
              "251996                  0  ...                     0                     0   \n",
              "251997                  0  ...                     0                     0   \n",
              "251998                  0  ...                     0                     0   \n",
              "251999                  0  ...                     0                     0   \n",
              "\n",
              "        Profession_Software_Developer  Profession_Statistician  \\\n",
              "0                                   0                        0   \n",
              "1                                   1                        0   \n",
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              "3                                   1                        0   \n",
              "4                                   0                        0   \n",
              "...                               ...                      ...   \n",
              "251995                              0                        0   \n",
              "251996                              0                        0   \n",
              "251997                              0                        0   \n",
              "251998                              0                        0   \n",
              "251999                              0                        1   \n",
              "\n",
              "        Profession_Surgeon  Profession_Surveyor  Profession_Technical_writer  \\\n",
              "0                        0                    0                            0   \n",
              "1                        0                    0                            0   \n",
              "2                        0                    0                            1   \n",
              "3                        0                    0                            0   \n",
              "4                        0                    0                            0   \n",
              "...                    ...                  ...                          ...   \n",
              "251995                   1                    0                            0   \n",
              "251996                   0                    0                            0   \n",
              "251997                   0                    0                            0   \n",
              "251998                   0                    0                            0   \n",
              "251999                   0                    0                            0   \n",
              "\n",
              "        Profession_Technician  Profession_Technology_specialist  \\\n",
              "0                           0                                 0   \n",
              "1                           0                                 0   \n",
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              "3                           0                                 0   \n",
              "4                           0                                 0   \n",
              "...                       ...                               ...   \n",
              "251995                      0                                 0   \n",
              "251996                      0                                 0   \n",
              "251997                      0                                 0   \n",
              "251998                      0                                 0   \n",
              "251999                      0                                 0   \n",
              "\n",
              "        Profession_Web_designer  \n",
              "0                             0  \n",
              "1                             0  \n",
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              "3                             0  \n",
              "4                             0  \n",
              "...                         ...  \n",
              "251995                        0  \n",
              "251996                        0  \n",
              "251997                        0  \n",
              "251998                        0  \n",
              "251999                        0  \n",
              "\n",
              "[252000 rows x 60 columns]"
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              "      <th>251996</th>\n",
              "      <td>251997</td>\n",
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              "      <th>251997</th>\n",
              "      <td>251998</td>\n",
              "      <td>4522448</td>\n",
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              "    <tr>\n",
              "      <th>251999</th>\n",
              "      <td>252000</td>\n",
              "      <td>9070230</td>\n",
              "      <td>70</td>\n",
              "      <td>17</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>252000 rows × 60 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "    <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-eb3b20c8-d74c-4cde-a883-aa389059465d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)"
      ],
      "metadata": {
        "id": "-LOTUp5WWyo9"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oeSytQ7vXGbn",
        "outputId": "d85a1be2-d121-4048-eb87-4fc33cd199ce"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                                       0\n",
              "Income                                   0\n",
              "Age                                      0\n",
              "Experience                               0\n",
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              "CURRENT_HOUSE_YRS                        0\n",
              "Married/Single_single                    0\n",
              "House_Ownership_owned                    0\n",
              "House_Ownership_rented                   0\n",
              "Car_Ownership_yes                        0\n",
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              "Profession_Computer_operator             0\n",
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              "Profession_Dentist                       0\n",
              "Profession_Design_Engineer               0\n",
              "Profession_Designer                      0\n",
              "Profession_Drafter                       0\n",
              "Profession_Economist                     0\n",
              "Profession_Engineer                      0\n",
              "Profession_Fashion_Designer              0\n",
              "Profession_Financial_Analyst             0\n",
              "Profession_Firefighter                   0\n",
              "Profession_Flight_attendant              0\n",
              "Profession_Geologist                     0\n",
              "Profession_Graphic_Designer              0\n",
              "Profession_Hotel_Manager                 0\n",
              "Profession_Industrial_Engineer           0\n",
              "Profession_Lawyer                        0\n",
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              "Profession_Magistrate                    0\n",
              "Profession_Mechanical_engineer           0\n",
              "Profession_Microbiologist                0\n",
              "Profession_Official                      0\n",
              "Profession_Petroleum_Engineer            0\n",
              "Profession_Physician                     0\n",
              "Profession_Police_officer                0\n",
              "Profession_Politician                    0\n",
              "Profession_Psychologist                  0\n",
              "Profession_Scientist                     0\n",
              "Profession_Secretary                     0\n",
              "Profession_Software_Developer            0\n",
              "Profession_Statistician                  0\n",
              "Profession_Surgeon                       0\n",
              "Profession_Surveyor                      0\n",
              "Profession_Technical_writer              0\n",
              "Profession_Technician                    0\n",
              "Profession_Technology_specialist         0\n",
              "Profession_Web_designer                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zE_uqoX7YM0b",
        "outputId": "2a6f5d6b-f837-4f9a-e7fe-06a9484f254a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                                       0\n",
              "Income                                   0\n",
              "Age                                      0\n",
              "Experience                               0\n",
              "CURRENT_JOB_YRS                          0\n",
              "CURRENT_HOUSE_YRS                        0\n",
              "Married/Single_single                    0\n",
              "House_Ownership_owned                    0\n",
              "House_Ownership_rented                   0\n",
              "Car_Ownership_yes                        0\n",
              "Profession_Analyst                       0\n",
              "Profession_Architect                     0\n",
              "Profession_Army_officer                  0\n",
              "Profession_Artist                        0\n",
              "Profession_Aviator                       0\n",
              "Profession_Biomedical_Engineer           0\n",
              "Profession_Chartered_Accountant          0\n",
              "Profession_Chef                          0\n",
              "Profession_Chemical_engineer             0\n",
              "Profession_Civil_engineer                0\n",
              "Profession_Civil_servant                 0\n",
              "Profession_Comedian                      0\n",
              "Profession_Computer_hardware_engineer    0\n",
              "Profession_Computer_operator             0\n",
              "Profession_Consultant                    0\n",
              "Profession_Dentist                       0\n",
              "Profession_Design_Engineer               0\n",
              "Profession_Designer                      0\n",
              "Profession_Drafter                       0\n",
              "Profession_Economist                     0\n",
              "Profession_Engineer                      0\n",
              "Profession_Fashion_Designer              0\n",
              "Profession_Financial_Analyst             0\n",
              "Profession_Firefighter                   0\n",
              "Profession_Flight_attendant              0\n",
              "Profession_Geologist                     0\n",
              "Profession_Graphic_Designer              0\n",
              "Profession_Hotel_Manager                 0\n",
              "Profession_Industrial_Engineer           0\n",
              "Profession_Lawyer                        0\n",
              "Profession_Librarian                     0\n",
              "Profession_Magistrate                    0\n",
              "Profession_Mechanical_engineer           0\n",
              "Profession_Microbiologist                0\n",
              "Profession_Official                      0\n",
              "Profession_Petroleum_Engineer            0\n",
              "Profession_Physician                     0\n",
              "Profession_Police_officer                0\n",
              "Profession_Politician                    0\n",
              "Profession_Psychologist                  0\n",
              "Profession_Scientist                     0\n",
              "Profession_Secretary                     0\n",
              "Profession_Software_Developer            0\n",
              "Profession_Statistician                  0\n",
              "Profession_Surgeon                       0\n",
              "Profession_Surveyor                      0\n",
              "Profession_Technical_writer              0\n",
              "Profession_Technician                    0\n",
              "Profession_Technology_specialist         0\n",
              "Profession_Web_designer                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "92jIMYPJYhQL"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rus = RandomUnderSampler(random_state=42)\n",
        "X_resampled, y_resampled = rus.fit_resample(X_train_scaled, y_train)"
      ],
      "metadata": {
        "id": "Z4OP39u7YxRj"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dt_gini = DecisionTreeClassifier(criterion='gini', random_state=42)\n",
        "dt_gini.fit(X_resampled, y_resampled)\n",
        "y_pred_gini = dt_gini.predict(X_test_scaled)\n",
        "y_pred_proba_gini = dt_gini.predict_proba(X_test_scaled)\n",
        "print(\"\\n'gini':\")\n",
        "cm_gini = confusion_matrix(y_test, y_pred_gini)\n",
        "print(f\"Confusion Matrix:\\n{cm_gini}\")\n",
        "precision_gini = precision_score(y_test, y_pred_gini)\n",
        "recall_gini = recall_score(y_test, y_pred_gini)\n",
        "f1_gini = f1_score(y_test, y_pred_gini)\n",
        "print(f\"Precision: {precision_gini:.4f}\")\n",
        "print(f\"Recall: {recall_gini:.4f}\")\n",
        "print(f\"F1 Score: {f1_gini:.4f}\")\n",
        "accuracy_gini = accuracy_score(y_test, y_pred_gini)\n",
        "print(f\"Accuracy: {accuracy_gini:.4f}\")\n",
        "logloss_gini = log_loss(y_test, y_pred_proba_gini)\n",
        "print(f\"Log Loss: {logloss_gini:.4f}\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E1y7kaqQZAiY",
        "outputId": "b8eeca5c-472e-48d3-ffd9-ceae34f361a1"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "'gini':\n",
            "Confusion Matrix:\n",
            "[[37503 17721]\n",
            " [ 2120  5656]]\n",
            "Precision: 0.2419\n",
            "Recall: 0.7274\n",
            "F1 Score: 0.3631\n",
            "Accuracy: 0.6851\n",
            "Log Loss: 11.3515\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt_entropy = DecisionTreeClassifier(criterion='entropy', random_state=42)\n",
        "dt_entropy.fit(X_resampled, y_resampled)\n",
        "y_pred_entropy = dt_entropy.predict(X_test_scaled)\n",
        "y_pred_proba_entropy = dt_entropy.predict_proba(X_test_scaled)\n",
        "print(\"\\n'entropy':\")\n",
        "cm_entropy = confusion_matrix(y_test, y_pred_entropy)\n",
        "print(f\"Confusion Matrix:\\n{cm_entropy}\")\n",
        "precision_entropy = precision_score(y_test, y_pred_entropy)\n",
        "recall_entropy = recall_score(y_test, y_pred_entropy)\n",
        "f1_entropy = f1_score(y_test, y_pred_entropy)\n",
        "print(f\"Precision: {precision_entropy:.4f}\")\n",
        "print(f\"Recall: {recall_entropy:.4f}\")\n",
        "print(f\"F1 Score: {f1_entropy:.4f}\")\n",
        "accuracy_entropy = accuracy_score(y_test, y_pred_entropy)\n",
        "print(f\"Accuracy: {accuracy_entropy:.4f}\")\n",
        "logloss_entropy = log_loss(y_test, y_pred_proba_entropy)\n",
        "print(f\"Log Loss: {logloss_entropy:.4f}\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_922SmQuadNQ",
        "outputId": "26862968-26f9-4668-b392-45160a6294e0"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "'entropy':\n",
            "Confusion Matrix:\n",
            "[[38038 17186]\n",
            " [ 2104  5672]]\n",
            "Precision: 0.2481\n",
            "Recall: 0.7294\n",
            "F1 Score: 0.3703\n",
            "Accuracy: 0.6938\n",
            "Log Loss: 11.0362\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {\n",
        "    'criterion': ['gini', 'entropy'],\n",
        "    'max_depth': [ None, 5, 10, 15, 20, 25,30]\n",
        "}\n",
        "dt = DecisionTreeClassifier(random_state=42)\n",
        "grid_search = GridSearchCV(dt, param_grid, cv=5, scoring='accuracy')\n",
        "grid_search.fit(X_train, y_train)\n",
        "best_params = grid_search.best_params_\n",
        "best_model = grid_search.best_estimator_\n",
        "y_pred = best_model.predict(X_test)\n",
        "y_pred_proba = best_model.predict_proba(X_test)\n",
        "print(\"\\nbestDecision Tree :\")\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(f\"Confusion Matrix:\\n{cm}\")\n",
        "precision = precision_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "f1 = f1_score(y_test, y_pred)\n",
        "print(f\"Precision: {precision:.4f}\")\n",
        "print(f\"Recall: {recall:.4f}\")\n",
        "print(f\"F1 Score: {f1:.4f}\")\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Accuracy: {accuracy:.4f}\")\n",
        "logloss = log_loss(y_test, y_pred_proba)\n",
        "print(f\"Log Loss: {logloss:.4f}\")\n",
        "print(\"\\nBest Hyperparameters:\", best_params)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V7XYKSb1l1i9",
        "outputId": "099f3fa9-f710-4e8e-9e96-9050a375f17d"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "bestDecision Tree :\n",
            "Confusion Matrix:\n",
            "[[55222     2]\n",
            " [ 7775     1]]\n",
            "Precision: 0.3333\n",
            "Recall: 0.0001\n",
            "F1 Score: 0.0003\n",
            "Accuracy: 0.8766\n",
            "Log Loss: 0.3538\n",
            "\n",
            "Best Hyperparameters: {'criterion': 'entropy', 'max_depth': 5}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_0_weight = 1 / (len(y_resampled[y_resampled == 0]) / len(y_resampled))\n",
        "class_1_weight = 1 / (len(y_resampled[y_resampled == 1]) / len(y_resampled))\n",
        "linear_svc = LinearSVC(class_weight={0: class_0_weight, 1: class_1_weight}, random_state=42)\n",
        "linear_svc.fit(X_resampled, y_resampled)\n",
        "y_pred_linear = linear_svc.predict(X_test_scaled)\n",
        "confusion_linear = confusion_matrix(y_test, y_pred_linear)\n",
        "report_linear = classification_report(y_test, y_pred_linear, target_names=['No Risk', 'Risk'])\n",
        "print(\"Kernel: Linear\\n\")\n",
        "print(\"Confusion Matrix:\\n\", confusion_linear)\n",
        "print(\"\\nClassification Report:\\n\", report_linear)\n",
        "print(\"\\n-------------------------------------------\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ju_cN7MUbGUJ",
        "outputId": "c8244970-237e-4217-ed95-59a62fbf3203"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kernel: Linear\n",
            "\n",
            "Confusion Matrix:\n",
            " [[29680 25544]\n",
            " [ 3456  4320]]\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "     No Risk       0.90      0.54      0.67     55224\n",
            "        Risk       0.14      0.56      0.23      7776\n",
            "\n",
            "    accuracy                           0.54     63000\n",
            "   macro avg       0.52      0.55      0.45     63000\n",
            "weighted avg       0.80      0.54      0.62     63000\n",
            "\n",
            "\n",
            "-------------------------------------------\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svc_rbf = SVC(kernel='rbf', class_weight={0: 1/len(y_resampled[y_resampled == 0]), 1: 1/len(y_resampled[y_resampled == 1])}, random_state=42)\n",
        "svc_rbf.fit(X_resampled, y_resampled)\n",
        "y_pred_rbf = svc_rbf.predict(X_test_scaled)\n",
        "confusion_rbf = confusion_matrix(y_test, y_pred_rbf)\n",
        "report_rbf = classification_report(y_test, y_pred_rbf, target_names=['No Risk', 'Risk'])\n",
        "print(\"Kernel: RBF\\n\")\n",
        "print(\"Confusion Matrix:\\n\", confusion_rbf)\n",
        "print(\"\\nClassification Report:\\n\", report_rbf)\n",
        "print(\"\\n-------------------------------------------\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jEymDs_XcGpl",
        "outputId": "f21e417f-e00a-49a4-b329-2adcb3f28eb0"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kernel: RBF\n",
            "\n",
            "Confusion Matrix:\n",
            " [[35045 20179]\n",
            " [ 4140  3636]]\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "     No Risk       0.89      0.63      0.74     55224\n",
            "        Risk       0.15      0.47      0.23      7776\n",
            "\n",
            "    accuracy                           0.61     63000\n",
            "   macro avg       0.52      0.55      0.49     63000\n",
            "weighted avg       0.80      0.61      0.68     63000\n",
            "\n",
            "\n",
            "-------------------------------------------\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svc_sigmoid = SVC(kernel='sigmoid', class_weight={0: 1/len(y_resampled[y_resampled == 0]), 1: 1/len(y_resampled[y_resampled == 1])}, random_state=42)\n",
        "svc_sigmoid.fit(X_resampled, y_resampled)\n",
        "y_pred_sigmoid = svc_sigmoid.predict(X_test_scaled)\n",
        "confusion_sigmoid = confusion_matrix(y_test, y_pred_sigmoid)\n",
        "report_sigmoid = classification_report(y_test, y_pred_sigmoid, target_names=['No Risk', 'Risk'])\n",
        "print(\"Kernel: Sigmoid\\n\")\n",
        "print(\"Confusion Matrix:\\n\", confusion_sigmoid)\n",
        "print(\"\\nClassification Report:\\n\", report_sigmoid)\n",
        "print(\"\\n-------------------------------------------\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXfqmTv5fd6P",
        "outputId": "f13b6c42-bf03-46ad-e3e2-27ed62f9d566"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kernel: Sigmoid\n",
            "\n",
            "Confusion Matrix:\n",
            " [[10560 44664]\n",
            " [ 1007  6769]]\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "     No Risk       0.91      0.19      0.32     55224\n",
            "        Risk       0.13      0.87      0.23      7776\n",
            "\n",
            "    accuracy                           0.28     63000\n",
            "   macro avg       0.52      0.53      0.27     63000\n",
            "weighted avg       0.82      0.28      0.31     63000\n",
            "\n",
            "\n",
            "-------------------------------------------\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svc_poly = SVC(kernel='poly', class_weight={0: 1/len(y_resampled[y_resampled == 0]), 1: 1/len(y_resampled[y_resampled == 1])}, random_state=42)\n",
        "svc_poly.fit(X_resampled, y_resampled)\n",
        "y_pred_poly = svc_poly.predict(X_test_scaled)\n",
        "confusion_poly = confusion_matrix(y_test, y_pred_poly)\n",
        "report_poly = classification_report(y_test, y_pred_poly, target_names=['No Risk', 'Risk'])\n",
        "print(\"Kernel: Polynomial\\n\")\n",
        "print(\"Confusion Matrix:\\n\", confusion_poly)\n",
        "print(\"\\nClassification Report:\\n\", report_poly)\n",
        "print(\"\\n-------------------------------------------\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ZFS2oJBikuB",
        "outputId": "c546df88-4e1b-4a95-cdf6-1eb62ec615b9"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kernel: Polynomial\n",
            "\n",
            "Confusion Matrix:\n",
            " [[25131 30093]\n",
            " [ 2671  5105]]\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "     No Risk       0.90      0.46      0.61     55224\n",
            "        Risk       0.15      0.66      0.24      7776\n",
            "\n",
            "    accuracy                           0.48     63000\n",
            "   macro avg       0.52      0.56      0.42     63000\n",
            "weighted avg       0.81      0.48      0.56     63000\n",
            "\n",
            "\n",
            "-------------------------------------------\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
        "linear_svc = LinearSVC(class_weight={0: 1/len(y_resampled[y_resampled == 0]), 1: 1/len(y_resampled[y_resampled == 1])}, random_state=42)\n",
        "grid_search = GridSearchCV(linear_svc, param_grid, cv=5, scoring='accuracy')\n",
        "grid_search.fit(X_resampled, y_resampled)\n",
        "best_C = grid_search.best_params_['C']"
      ],
      "metadata": {
        "id": "di1jcNgklLev"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_linear_svc = LinearSVC(C=best_C, class_weight={0: 1/len(y_resampled[y_resampled == 0]), 1: 1/len(y_resampled[y_resampled == 1])}, random_state=42)\n",
        "final_linear_svc.fit(X_resampled, y_resampled)\n",
        "y_pred_linear_svc = final_linear_svc.predict(X_test_scaled)\n",
        "report_linear_svc = classification_report(y_test, y_pred_linear_svc, target_names=['No Risk', 'Risk'])\n",
        "print(f\"Best C: {best_C}\\n\")\n",
        "print(\"Classification Report:\\n\", report_linear_svc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ltOOOzlloer",
        "outputId": "6adb265a-dbbb-4c5a-828c-4f869412a92e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best C: 10\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "     No Risk       0.90      0.53      0.67     55224\n",
            "        Risk       0.14      0.56      0.23      7776\n",
            "\n",
            "    accuracy                           0.54     63000\n",
            "   macro avg       0.52      0.55      0.45     63000\n",
            "weighted avg       0.80      0.54      0.61     63000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt_model = DecisionTreeClassifier(criterion='gini', random_state=42)\n",
        "dt_model.fit(X_resampled, y_resampled)\n",
        "y_pred_dt = dt_model.predict(X_test_scaled)\n",
        "linear_svm_model = LinearSVC(class_weight={0: 1/len(y_resampled[y_resampled == 0]), 1: 1/len(y_resampled[y_resampled == 1])}, random_state=42)\n",
        "linear_svm_model.fit(X_resampled, y_resampled)\n",
        "y_pred_linear_svm = linear_svm_model.predict(X_test_scaled)\n",
        "results_df = pd.DataFrame({'Actual': y_test, 'LinearSVC_Predicted': y_pred_linear_svm, 'DT_Predicted': y_pred_dt})\n",
        "linear_svm_wrong_dt_right = results_df[(results_df['LinearSVC_Predicted'] != results_df['Actual']) & (results_df['DT_Predicted'] == results_df['Actual'])].head(10)\n",
        "linear_svm_right_dt_wrong = results_df[(results_df['LinearSVC_Predicted'] == results_df['Actual']) & (results_df['DT_Predicted'] != results_df['Actual'])].head(10)\n",
        "linear_svm_wrong_dt_wrong = results_df[(results_df['LinearSVC_Predicted'] != results_df['Actual']) & (results_df['DT_Predicted'] != results_df['Actual'])].head(10)\n",
        "linear_svm_right_dt_right = results_df[(results_df['LinearSVC_Predicted'] == results_df['Actual']) & (results_df['DT_Predicted'] == results_df['Actual'])].head(10)\n",
        "print(\"LinearSVC=wrong,Decision Tree = right:\\n\", linear_svm_wrong_dt_right)\n",
        "print(\"\\nLinearSVC=right, Decision Tree= wrong:\\n\", linear_svm_right_dt_wrong)\n",
        "print(\"\\n LinearSVC = wrong, Decision Tree = wrong:\\n\", linear_svm_wrong_dt_wrong)\n",
        "print(\"\\nLinearSVC = right, Decision Tree =right:\\n\", linear_svm_right_dt_right)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKNfzTBqpIc2",
        "outputId": "5dde8f48-0f86-43de-ad8e-11f30aeef4f9"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearSVC=wrong,Decision Tree = right:\n",
            "         Actual  LinearSVC_Predicted  DT_Predicted\n",
            "187455       0                    1             0\n",
            "135104       0                    1             0\n",
            "167774       0                    1             0\n",
            "152514       0                    1             0\n",
            "233330       0                    1             0\n",
            "243723       0                    1             0\n",
            "137288       0                    1             0\n",
            "112563       0                    1             0\n",
            "53439        0                    1             0\n",
            "58726        0                    1             0\n",
            "\n",
            "LinearSVC=right, Decision Tree= wrong:\n",
            "         Actual  LinearSVC_Predicted  DT_Predicted\n",
            "111633       0                    0             1\n",
            "210439       0                    0             1\n",
            "33857        0                    0             1\n",
            "73307        0                    0             1\n",
            "149805       0                    0             1\n",
            "225880       0                    0             1\n",
            "59364        0                    0             1\n",
            "225330       0                    0             1\n",
            "82574        0                    0             1\n",
            "162910       0                    0             1\n",
            "\n",
            " LinearSVC = wrong, Decision Tree = wrong:\n",
            "         Actual  LinearSVC_Predicted  DT_Predicted\n",
            "219986       0                    1             1\n",
            "144990       0                    1             1\n",
            "69582        0                    1             1\n",
            "42239        0                    1             1\n",
            "249706       0                    1             1\n",
            "236342       0                    1             1\n",
            "244730       0                    1             1\n",
            "233389       0                    1             1\n",
            "209393       0                    1             1\n",
            "150486       0                    1             1\n",
            "\n",
            "LinearSVC = right, Decision Tree =right:\n",
            "         Actual  LinearSVC_Predicted  DT_Predicted\n",
            "8369         0                    0             0\n",
            "62408        0                    0             0\n",
            "18360        0                    0             0\n",
            "78326        0                    0             0\n",
            "188555       1                    1             1\n",
            "157989       1                    1             1\n",
            "180846       0                    0             0\n",
            "8128         0                    0             0\n",
            "17648        1                    1             1\n",
            "7604         0                    0             0\n"
          ]
        }
      ]
    }
  ]
}