Dummy_variable_trap.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
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      "source": [
        "data = pd.read_csv('/content/titanic.csv')"
      ],
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        "data.head()"
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              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
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              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  "
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        "Features = data[['Sex','Age','Fare']]"
      ],
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        "id": "NZBaOoYfDjXu"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor"
      ],
      "metadata": {
        "id": "D2p2WOgjD6jm"
      },
      "execution_count": null,
      "outputs": []
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        "Features.head()"
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        "id": "MKKQFm6WEW4p",
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              "      Sex   Age     Fare\n",
              "0    male  22.0   7.2500\n",
              "1  female  38.0  71.2833\n",
              "2  female  26.0   7.9250\n",
              "3  female  35.0  53.1000\n",
              "4    male  35.0   8.0500"
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6ab77fa0-71bb-4425-9ab7-5cfbc41157d0 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-6ab77fa0-71bb-4425-9ab7-5cfbc41157d0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Features_dummied = pd.get_dummies(Features)"
      ],
      "metadata": {
        "id": "hY7jiXJFEZ3X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Features_dummied.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t133iHm-Etog",
        "outputId": "a6b65f17-afd5-4c9b-de6b-fcee15b71f4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age           177\n",
              "Fare            0\n",
              "Sex_female      0\n",
              "Sex_male        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Features_dummied.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "tMwxWoqiEyHL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vif_data = pd.DataFrame()\n",
        "vif_data[\"feature\"] = Features_dummied.columns\n",
        "\n",
        "# calculating VIF for each feature\n",
        "vif_data[\"VIF\"] = [variance_inflation_factor(Features_dummied.values, i)\n",
        "                          for i in range(len(Features_dummied.columns))]\n",
        "\n",
        "print(vif_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1j3yawqjEA93",
        "outputId": "971d6ad7-610e-46de-da24-4f04a922fd96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      feature       VIF\n",
            "0         Age  1.022487\n",
            "1        Fare  1.049512\n",
            "2  Sex_female  2.541082\n",
            "3    Sex_male  3.917424\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Features_dummied.drop(['Sex_male'],inplace=True,axis=1)"
      ],
      "metadata": {
        "id": "S9F0uB9KE4H_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vif_data = pd.DataFrame()\n",
        "vif_data[\"feature\"] = Features_dummied.columns\n",
        "\n",
        "# calculating VIF for each feature\n",
        "vif_data[\"VIF\"] = [variance_inflation_factor(Features_dummied.values, i)\n",
        "                          for i in range(len(Features_dummied.columns))]\n",
        "\n",
        "print(vif_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CsRe0nCbFGVv",
        "outputId": "416274ed-08f6-41a0-ef5f-221f4e50eeef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      feature       VIF\n",
            "0         Age  1.589969\n",
            "1        Fare  1.482145\n",
            "2  Sex_female  1.445634\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Features = data[['Fare','Age','Embarked']]"
      ],
      "metadata": {
        "id": "9Shx-6G-Ffdz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Features_dummied = pd.get_dummies(Features)"
      ],
      "metadata": {
        "id": "urwtgXkLF1UR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Features_dummied.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cP_l9wkbFmU9",
        "outputId": "5de3295a-8a61-480d-e145-8bf534f0bde4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Fare            0\n",
              "Age           177\n",
              "Embarked_C      0\n",
              "Embarked_Q      0\n",
              "Embarked_S      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Features_dummied.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "JYXyVUOQFpAd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vif_data = pd.DataFrame()\n",
        "vif_data[\"feature\"] = Features_dummied.columns\n",
        "\n",
        "# calculating VIF for each feature\n",
        "vif_data[\"VIF\"] = [variance_inflation_factor(Features_dummied.values, i)\n",
        "                          for i in range(len(Features_dummied.columns))]\n",
        "\n",
        "print(vif_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PBRsMwJ-FsgE",
        "outputId": "4a382e1c-b2a7-4266-d67f-f3e66d4e31f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      feature       VIF\n",
            "0        Fare  1.582754\n",
            "1         Age  5.103963\n",
            "2  Embarked_C  2.041021\n",
            "3  Embarked_Q  1.144313\n",
            "4  Embarked_S  4.205990\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Features_dummied.drop(['Embarked_S'],inplace=True,axis=1)"
      ],
      "metadata": {
        "id": "w8wLMreeGFGL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vif_data = pd.DataFrame()\n",
        "vif_data[\"feature\"] = Features_dummied.columns\n",
        "\n",
        "# calculating VIF for each feature\n",
        "vif_data[\"VIF\"] = [variance_inflation_factor(Features_dummied.values, i)\n",
        "                          for i in range(len(Features_dummied.columns))]\n",
        "\n",
        "print(vif_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dfxznveZGewh",
        "outputId": "ef8d2916-8a2a-42a2-c821-f428fc2c1f2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      feature       VIF\n",
            "0        Fare  1.547287\n",
            "1         Age  1.492029\n",
            "2  Embarked_C  1.330847\n",
            "3  Embarked_Q  1.035036\n"
          ]
        }
      ]
    }
  ]
}