notebooks/feature_engineering.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZCNdrk6jBrtj"
      },
      "source": [
        "##3.FEATURE ENGINEERING"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p6FT3B-K7nWt"
      },
      "source": [
        "####3.1 Correlation Analysis"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.clustermap(\n",
        "        df.corr(),\n",
        "        vmin=-1.0,\n",
        "        vmax=1.0,\n",
        "        cmap=\"icefire\",\n",
        "        method=\"complete\"\n",
        "    );"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "8dxUb-XsKa1v",
        "outputId": "5f8661db-51db-485f-a1b8-fea5fefae60a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pghHmMeg2npf"
      },
      "source": [
        "NOTE : No highly correlated variables with each other"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "El7Wka9YHLnd"
      },
      "source": [
        "### 3.2. Feature Encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-g7GT1xKbbbM"
      },
      "outputs": [],
      "source": [
        "# to visualize categorical columns\n",
        "def cat_val_graph(df, col_name):\n",
        "  plt.figure(figsize = (8, 6))\n",
        "  sns.countplot(x = df[col_name], palette = 'rocket_r')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sUSnFPSpUIbz"
      },
      "source": [
        "##### Education"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "xBoRHv6GOVfU",
        "outputId": "626ee336-fe4f-4835-af47-091a8801b260"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cat_val_graph(X_train, 'Education')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IwUgtl_1KLX8"
      },
      "outputs": [],
      "source": [
        "#Education levels are divided into three groups\n",
        "X_train[\"Education\"] = X_train[\"Education\"].replace({'Basic':'Undergraduate', \n",
        "                                                     '2n Cycle':'Undergraduate', \n",
        "                                                     'Graduation':'Graduate', \n",
        "                                                     'Master' :'Postgraduate', \n",
        "                                                     'PhD' :'Postgraduate'})\n",
        "\n",
        "X_test[\"Education\"] = X_test[\"Education\"].replace({'Basic':'Undergraduate', \n",
        "                                                   '2n Cycle':'Undergraduate', \n",
        "                                                   'Graduation':'Graduate', \n",
        "                                                   'Master' :'Postgraduate', \n",
        "                                                   'PhD' :'Postgraduate'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cbe560lQPcVP",
        "outputId": "d5f3a16a-7931-4f6c-f7ad-10868492b8a0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Graduate         913\n",
              "Postgraduate     675\n",
              "Undergraduate    203\n",
              "Name: Education, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "# new distribution\n",
        "X_train[\"Education\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2o36gY8vPf4A",
        "outputId": "3c4d9549-7da3-4327-f922-de4c0e72d4f8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Graduate         214\n",
              "Postgraduate     180\n",
              "Undergraduate     52\n",
              "Name: Education, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "# Does the test data contain data from all new categories? Checking before doing one-hot-encoding\n",
        "X_test[\"Education\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AnqPLBhqMrEB"
      },
      "outputs": [],
      "source": [
        "# to do one-hot-encoding\n",
        "# don't forget, 'one-hot-encoding' automatically drop the original variable columns\n",
        "X_train = pd.get_dummies(X_train, columns = [\"Education\"], prefix = [\"Education\"])\n",
        "X_test = pd.get_dummies(X_test, columns = [\"Education\"], prefix = [\"Education\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "crqMfg2dTqV9"
      },
      "source": [
        "#####'Marital Status'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "Ny1lbNBZFeFq",
        "outputId": "eca8f34e-ac87-404a-ff4b-2b8118249027"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cat_val_graph(X_train, 'Marital_Status')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "wyC-wRBaEw97",
        "outputId": "48a285a6-887c-4639-b10e-9516fb990b40"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "sns.barplot(x=df['Marital_Status'], y=df['Response'], data=df,  palette=\"Blues_d\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9jijHe27RzwJ"
      },
      "outputs": [],
      "source": [
        "# There are too many category for 'Marital Status'\n",
        "# Categories are divided into two groups representing married and single status\n",
        "# Married and Together are represent by 1, Single, Divorced, Widow, Alone, Absurd and YOLO represent by 0\n",
        "X_train['NewMaritalStatus'] = np.where((X_train['Marital_Status']=='Together')|(X_train['Marital_Status']=='Married'), 1, 0)\n",
        "X_test['NewMaritalStatus'] = np.where((X_test['Marital_Status']=='Together')|(X_test['Marital_Status']=='Married'), 1, 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4rNZWapjvMfu",
        "outputId": "68ea03fe-5c26-4b87-d014-d91289e130e7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      NewMaritalStatus Marital_Status\n",
              "792                  0         Single\n",
              "1271                 0         Single\n",
              "1538                 1        Married\n",
              "668                  1        Married\n",
              "1975                 1        Married"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-86fb7ffc-7c0f-4121-9d3f-810719a459da\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NewMaritalStatus</th>\n",
              "      <th>Marital_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>792</th>\n",
              "      <td>0</td>\n",
              "      <td>Single</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1271</th>\n",
              "      <td>0</td>\n",
              "      <td>Single</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1538</th>\n",
              "      <td>1</td>\n",
              "      <td>Married</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>668</th>\n",
              "      <td>1</td>\n",
              "      <td>Married</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975</th>\n",
              "      <td>1</td>\n",
              "      <td>Married</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-86fb7ffc-7c0f-4121-9d3f-810719a459da')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "# to check\n",
        "X_train[['NewMaritalStatus','Marital_Status']].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rNyxYl32Txzm"
      },
      "source": [
        "#####Age"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mb0tV4oyBJj7",
        "outputId": "7d13277c-8905-4c52-f085-19511a331abd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "792     72\n",
              "1271    59\n",
              "1538    51\n",
              "668     54\n",
              "1975    53\n",
              "Name: Age, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "X_train['Age'] = 2022 - X_train['Year_Birth']\n",
        "X_test['Age'] = 2022 - X_test['Year_Birth']\n",
        "X_train['Age'].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wgj6VOBeB0ec"
      },
      "source": [
        "#####Dt_Customer variable convert to month scale"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Z9_5yQ3Ael0",
        "outputId": "21faddbd-67df-4dcb-e785-a523871a3426"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "792     2013-07-19\n",
              "1271    2013-01-22\n",
              "1538    2012-10-16\n",
              "668     2013-05-09\n",
              "1975    2013-10-05\n",
              "Name: Dt_Customer, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "# Dt_Customer\n",
        "X_train['Dt_Customer'].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PhgQF-ZdjA1e"
      },
      "outputs": [],
      "source": [
        "# creating Year, Month columns for further analysis\n",
        "\n",
        "# convert the 'Date' column to datetime format\n",
        "X_train['Dt_Customer']= pd.to_datetime(X_train['Dt_Customer'])\n",
        "X_test['Dt_Customer']= pd.to_datetime(X_test['Dt_Customer'])\n",
        "\n",
        "# to create Year and Month columns\n",
        "X_train['Year'] = X_train[\"Dt_Customer\"].dt.year\n",
        "X_train['Month'] = X_train[\"Dt_Customer\"].dt.month\n",
        "\n",
        "X_test['Year'] = X_test[\"Dt_Customer\"].dt.year\n",
        "X_test['Month'] = X_test[\"Dt_Customer\"].dt.month"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "qSuaKBYlGMF7",
        "outputId": "254f6794-9fba-4ca3-f416-d1d36c98a8cd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Year  Month\n",
              "792   2013      7\n",
              "1271  2013      1\n",
              "1538  2012     10\n",
              "668   2013      5\n",
              "1975  2013     10"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
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              "      <th>1975</th>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          const element = document.querySelector('#df-3638dfc3-7aaf-4044-a260-d45f6ea36576');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "# to check\n",
        "X_train[['Year', 'Month']].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NzDZT9rFwYQm"
      },
      "outputs": [],
      "source": [
        "# to drop all unnecessary columns\n",
        "drop_columns = ['Marital_Status','Year_Birth','Dt_Customer']\n",
        "X_train.drop(drop_columns, axis=1, inplace=True)\n",
        "X_test.drop(drop_columns, axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.3. Feature Selection"
      ],
      "metadata": {
        "id": "_LuCLs73MazB"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YSDd4mTA2fPb"
      },
      "source": [
        "####Feature Importance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iU9Z-H7Yap7O",
        "outputId": "eb53585e-ecf7-4f7c-abf4-dcf85c2f7a33"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1791\n",
            "446\n"
          ]
        }
      ],
      "source": [
        "print(X_train['ID'].nunique())\n",
        "print(X_test['ID'].nunique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zo3GMCvEavIe"
      },
      "outputs": [],
      "source": [
        "# Every ID represents just one row so no need grouping these\n",
        "X_train.drop('ID', inplace=True, axis=1)\n",
        "X_test.drop('ID', inplace=True, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QkXy_T4t2ie0"
      },
      "outputs": [],
      "source": [
        "y_train = X_train[['Response']]\n",
        "X_train = X_train.drop('Response', axis = 1)\n",
        "y_test = X_test[['Response']]\n",
        "X_test = X_test.drop('Response', axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_0Z5aCN43Kkb",
        "outputId": "cab65d89-aacd-4db2-828f-5ad5a6efc485"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-75-8f309a757628>:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  rf_model = RandomForestClassifier().fit(X_train,y_train)\n"
          ]
        }
      ],
      "source": [
        "# We'll train a model to identify the importance of features\n",
        "rf_model = RandomForestClassifier().fit(X_train,y_train)\n",
        "y_pred = rf_model.predict(X_test)\n",
        "accuracy = accuracy_score(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "AgcfRTlvM5zU",
        "outputId": "22aab7d7-823e-4d60-a567-ba36df2b6dec"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "feature_importance = pd.Series(rf_model.feature_importances_,\n",
        "                        index=X_train.columns).sort_values(ascending=False)\n",
        "sns.barplot(x=feature_importance, y=feature_importance.index)\n",
        "plt.xlabel('Feature Importance Scores')\n",
        "plt.ylabel('Features')\n",
        "plt.title(\"Feature Importance Ranking\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZMrFhhHrDqTc"
      },
      "source": [
        "NOTES :\n",
        "\n",
        "By selecting features, we want to create a model that is more generalizable, has a shorter training time, and can predict with higher accuracy.\n",
        "\n",
        "[1] 'Complain' column is least important columns, so We'll to drop it.\n",
        "\n",
        "[2] We will try to create more effective columns than those with less important column."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UkpIBt2eDTZm"
      },
      "source": [
        "###3.4.Feature Creation and Aggregation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7l6BWl1HNauu"
      },
      "outputs": [],
      "source": [
        "X_train = pd.concat([pd.DataFrame(X_train), pd.DataFrame(y_train)], axis=1)\n",
        "X_test = pd.concat([pd.DataFrame(X_test), pd.DataFrame(y_test)], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2BG0MJ-i5vJx"
      },
      "outputs": [],
      "source": [
        "# Collecting all expenses column together\n",
        "X_train['TotalMntSpent'] = sum((X_train['MntWines'], X_train['MntFruits'],\n",
        "                               X_train['MntMeatProducts'], X_train['MntFishProducts'], \n",
        "                               X_train['MntSweetProducts'], X_train['MntGoldProds'])) \n",
        "\n",
        "X_test['TotalMntSpent'] = sum((X_test['MntWines'], X_test['MntFruits'],\n",
        "                               X_test['MntMeatProducts'], X_test['MntFishProducts'], \n",
        "                               X_test['MntSweetProducts'], X_test['MntGoldProds']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RZUk4S_D7MP5"
      },
      "outputs": [],
      "source": [
        "# to create a new column with the total family member size\n",
        "X_train['FamilySize'] = X_train['Teenhome'] + X_train['Kidhome'] + X_train['NewMaritalStatus'] + 1\n",
        "\n",
        "X_test['FamilySize'] = X_test['Teenhome'] + X_test['Kidhome'] + X_test['NewMaritalStatus'] + 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "yr0L-CXLbWGC",
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          "data": {
            "text/plain": [
              "                  Teenhome  Kidhome  NewMaritalStatus  Response\n",
              "Teenhome              1.00    -0.05              0.02     -0.16\n",
              "Kidhome              -0.05     1.00              0.03     -0.10\n",
              "NewMaritalStatus      0.02     0.03              1.00     -0.15\n",
              "Response             -0.16    -0.10             -0.15      1.00"
            ],
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              "\n",
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      "source": [
        "X_train[['Teenhome','Kidhome','NewMaritalStatus','Response']].corr()"
      ]
    },
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "            FamilySize  Response\n",
              "FamilySize        1.00     -0.23\n",
              "Response         -0.23      1.00"
            ],
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          "metadata": {},
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      "source": [
        "X_train[['FamilySize', 'Response']].corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RlLVLtYMPMMe"
      },
      "outputs": [],
      "source": [
        "# to drop all unnecessary columns\n",
        "# 'Complain' variable has very little importance according to feature importance analysis so this variable has been dropped\n",
        "drop_columns=['Complain','MntWines', 'MntFruits', \n",
        "              'MntMeatProducts', 'MntFishProducts', \n",
        "              'MntSweetProducts', 'MntGoldProds', \n",
        "              'Kidhome','Teenhome','NewMaritalStatus'\n",
        "          ]\n",
        "\n",
        "X_train.drop(drop_columns, inplace=True, axis=1)\n",
        "X_test.drop(drop_columns, inplace=True, axis=1)"
      ]
    }
  ]
}