SVM_Lab4c.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **SVM with hyperparameter tuning - Grid Search**"
      ],
      "metadata": {
        "id": "ph9dRURHodHh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Support Vector Machine (SVM) is a supervised learning algorithm that is primarily used for classification purposes. It works by finding a hyperplane that best separates different groups (known as classes) in a dataset. The hyperplane is selected in such a way that it maximises the margin (margin: the distance between the hyperplane and the nearest data points of each class) and minimises the classification error. SVMs can be used for both linear and nonlinear classification problems by applying kernel functions that map the input data into a higher-dimensional feature space, where a linear hyperplane can be used to separate the classes. SVMs are known for their good performance in high-dimensional feature spaces and their ability to handle complex datasets. \n",
        "\n",
        "\n",
        "1. Import/install the necessary packages\n",
        "2. Get the data from Google Drive\n",
        "3. Split the data into training and testing sets\n",
        "4. Robust scaling \n",
        "5. Create an SVM classifier\n",
        "6. Define the hyperparameter grid to search\n",
        "7. Search the hyperparameter grid using GridSearchCV()\n",
        "8. Use the best model provided by the grid search to make predictions on the test data\n",
        "9. Print the confusion matrix and performance metrics (classification report)\n",
        "10. Display the confusion matrix (heatmap)\n",
        "11. Display the feature importance plot\n"
      ],
      "metadata": {
        "id": "3bl6Pj1mQSnw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Import/install the necessary packages**"
      ],
      "metadata": {
        "id": "Hp16VJNZofy5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import GridSearchCV, train_test_split\n",
        "from sklearn import svm\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import RobustScaler"
      ],
      "metadata": {
        "id": "ox0U4p1DogMB"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Get the data from Google Drive**"
      ],
      "metadata": {
        "id": "6IP9l0NZogYV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/Data/customer_loyalty_data.csv\") \n",
        "print(df.info(),'\\n')\n",
        "print(df.head(),'\\n')\n",
        "print(df.describe(),'\\n')"
      ],
      "metadata": {
        "id": "-C4iRDYLogga",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d913f1d7-d43c-4e59-b0f1-53d7b60bfecb"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 7 columns):\n",
            " #   Column                   Non-Null Count  Dtype \n",
            "---  ------                   --------------  ----- \n",
            " 0   Age                      1000 non-null   int64 \n",
            " 1   Income                   1000 non-null   int64 \n",
            " 2   Num_Purchases            1000 non-null   int64 \n",
            " 3   Average_Rating           1000 non-null   int64 \n",
            " 4   Customer_Service_Rating  1000 non-null   int64 \n",
            " 5   Membership_Duration      1000 non-null   int64 \n",
            " 6   Loyalty                  1000 non-null   object\n",
            "dtypes: int64(6), object(1)\n",
            "memory usage: 54.8+ KB\n",
            "None \n",
            "\n",
            "   Age  Income  Num_Purchases  Average_Rating  Customer_Service_Rating  \\\n",
            "0   63    4528              1               1                        2   \n",
            "1   20    2482             14               1                        5   \n",
            "2   46    1316              1               4                        4   \n",
            "3   52    3024              4               4                        2   \n",
            "4   56    3327             18               4                        4   \n",
            "\n",
            "   Membership_Duration    Loyalty  \n",
            "0                    5      Loyal  \n",
            "1                    3      Loyal  \n",
            "2                    3      Loyal  \n",
            "3                    4  Non-Loyal  \n",
            "4                    8      Loyal   \n",
            "\n",
            "               Age       Income  Num_Purchases  Average_Rating  \\\n",
            "count  1000.000000  1000.000000    1000.000000     1000.000000   \n",
            "mean     41.624000  2931.553000      10.238000        2.999000   \n",
            "std      13.500922  1218.241599       5.481186        1.408184   \n",
            "min      18.000000   802.000000       1.000000        1.000000   \n",
            "25%      31.000000  1951.000000       5.000000        2.000000   \n",
            "50%      42.000000  2904.500000      11.000000        3.000000   \n",
            "75%      53.000000  4006.250000      15.000000        4.000000   \n",
            "max      64.000000  4998.000000      19.000000        5.000000   \n",
            "\n",
            "       Customer_Service_Rating  Membership_Duration  \n",
            "count              1000.000000          1000.000000  \n",
            "mean                  2.978000             4.917000  \n",
            "std                   1.397666             2.554337  \n",
            "min                   1.000000             1.000000  \n",
            "25%                   2.000000             3.000000  \n",
            "50%                   3.000000             5.000000  \n",
            "75%                   4.000000             7.000000  \n",
            "max                   5.000000             9.000000   \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Split the data into training and testing sets**"
      ],
      "metadata": {
        "id": "gllPGzvrogo3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " X_train, X_test, y_train, y_test = train_test_split(df.drop('Loyalty', axis=1), df['Loyalty'], test_size=0.2, random_state=50)"
      ],
      "metadata": {
        "id": "rB_o8lAOogwm"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Robust scaling**"
      ],
      "metadata": {
        "id": "7rOkEDI3MmI6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = RobustScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "J73TmrfTMmUJ"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Create an SVM classifier**"
      ],
      "metadata": {
        "id": "gy4OSa18og42"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = svm.SVC()"
      ],
      "metadata": {
        "id": "3wtijZfMohAa"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Define the hyperparameter grid to search**"
      ],
      "metadata": {
        "id": "6X_zpFI05wxl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hyperparameters = {'kernel': ['linear'],\n",
        "                   'C': [0.01, 0.1],\n",
        "                   'gamma': [0.01, 0.1]}"
      ],
      "metadata": {
        "id": "ryaJowVW5w5o"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Search the hyperparameter grid using GridSearchCV()**"
      ],
      "metadata": {
        "id": "Go_98QNa54ig"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grid_search = GridSearchCV(classifier, hyperparameters, cv=5)\n",
        "grid_search.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "id": "9GBfza3954rH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "e439e92b-9eee-42b0-d638-b86b869a48c1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=SVC(),\n",
              "             param_grid={'C': [0.01, 0.1], 'gamma': [0.01, 0.1],\n",
              "                         'kernel': ['linear']})"
            ],
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              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n",
              "             param_grid={&#x27;C&#x27;: [0.01, 0.1], &#x27;gamma&#x27;: [0.01, 0.1],\n",
              "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n",
              "             param_grid={&#x27;C&#x27;: [0.01, 0.1], &#x27;gamma&#x27;: [0.01, 0.1],\n",
              "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Use the best model provided by the grid search to make predictions on the test data**"
      ],
      "metadata": {
        "id": "gTDioL0H56js"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_classifier = grid_search.best_estimator_\n",
        "print(best_classifier,'\\n')\n",
        "y_pred = best_classifier.predict(X_test_scaled)"
      ],
      "metadata": {
        "id": "PgDTGx5L56r9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0be27aae-50c9-4b15-cdcc-1b31c69ad26b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC(C=0.01, gamma=0.01, kernel='linear') \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Print the confusion matrix and performance metrics (classification report)**"
      ],
      "metadata": {
        "id": "i-DKBNvE59rN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "id": "4FabGEyX59y8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30c4717e-6cb4-47e7-fd22-7889148e13fa"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  0  89]\n",
            " [  0 111]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Loyal       0.00      0.00      0.00        89\n",
            "   Non-Loyal       0.56      1.00      0.71       111\n",
            "\n",
            "    accuracy                           0.56       200\n",
            "   macro avg       0.28      0.50      0.36       200\n",
            "weighted avg       0.31      0.56      0.40       200\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Display the confusion matrix (heatmap)**"
      ],
      "metadata": {
        "id": "F1mlxxtLO7h_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, cmap=\"YlGnBu\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "f616cec9-f724-4dd5-c259-f5543fe53ae1",
        "id": "OEuF7sdROvxZ"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**A. Display the feature importance plot**"
      ],
      "metadata": {
        "id": "m6rya6IOjZ9L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "importances = pd.Series(best_classifier.coef_[0], index=X_train.columns)\n",
        "print(importances,'\\n')\n",
        "importances=abs(importances)\n",
        "print(importances,'\\n')\n",
        "importances.plot(kind='barh')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "L_wzq-IAhq3l",
        "outputId": "482bde2a-079a-44e2-f8c0-e48f0884e713"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Age                        0.000224\n",
            "Income                     0.000134\n",
            "Num_Purchases              0.000296\n",
            "Average_Rating             0.070000\n",
            "Customer_Service_Rating    0.000063\n",
            "Membership_Duration        0.000169\n",
            "dtype: float64 \n",
            "\n",
            "Age                        0.000224\n",
            "Income                     0.000134\n",
            "Num_Purchases              0.000296\n",
            "Average_Rating             0.070000\n",
            "Customer_Service_Rating    0.000063\n",
            "Membership_Duration        0.000169\n",
            "dtype: float64 \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**B. Display the feature importance plot**"
      ],
      "metadata": {
        "id": "HEP1fOBZcIk9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the coefficients of the hyperplane\n",
        "coefficients = best_classifier.coef_\n",
        "print(coefficients,'\\n')\n",
        "coefficients = abs(coefficients)\n",
        "print(coefficients,'\\n')\n",
        "# Display the coefficients\n",
        "#import matplotlib.pyplot as plt\n",
        "#%matplotlib inline\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(16, 8))\n",
        "sns.barplot(x=X_train.columns, y=coefficients.reshape(-1))\n",
        "ax.set_title(\"Feature Importance Plot\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "kZSMYHXIcItN",
        "outputId": "3b63d508-fbec-4eaa-cc8e-1faaf1d8d5fc"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2.24378308e-04 1.33690232e-04 2.95565063e-04 7.00000000e-02\n",
            "  6.28093949e-05 1.68657906e-04]] \n",
            "\n",
            "[[2.24378308e-04 1.33690232e-04 2.95565063e-04 7.00000000e-02\n",
            "  6.28093949e-05 1.68657906e-04]] \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Exercise 1**"
      ],
      "metadata": {
        "id": "EmUB7y2r91lY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use the following hyperparameter snippet for the tuning:"
      ],
      "metadata": {
        "id": "CwzJyvRrKdra"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hyperparameters = {'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n",
        "                   'C': [0.01, 0.1, 1, 10, 100],\n",
        "                   'gamma': [0.01, 0.1, 1, 10, 100]}"
      ],
      "metadata": {
        "id": "E6yxisjn91tR"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exercise 2**"
      ],
      "metadata": {
        "id": "Fh2eaqqtRBAx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use Standardisation instead of Robust scaling"
      ],
      "metadata": {
        "id": "z2fR652HRLNn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n"
      ],
      "metadata": {
        "id": "5mED2Y9ARBjZ"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exercise 3**"
      ],
      "metadata": {
        "id": "b7VUmvMcRb0D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use Min-max scaling instead of Robust scaling"
      ],
      "metadata": {
        "id": "e-ucII8ARcI-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "HW5_dQRRRcQL"
      },
      "execution_count": 17,
      "outputs": []
    }
  ]
}