classification_en.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fKADPWcFKlj3"
      },
      "source": [
        "# **1. Import Library**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "BlmvjLY9M4Yj"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, precision_score, recall_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f3YIEnAFKrKL"
      },
      "source": [
        "# **2. Loading the Clustered Dataset**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ey3ItwTen_7E"
      },
      "source": [
        "Load the clustered dataset from the CSV file into a DataFrame variable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "GHCGNTyrM5fS"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\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>invoice_no</th>\n",
              "      <th>customer_id</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>category</th>\n",
              "      <th>quantity</th>\n",
              "      <th>price</th>\n",
              "      <th>payment_method</th>\n",
              "      <th>invoice_date</th>\n",
              "      <th>shopping_mall</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I138884</td>\n",
              "      <td>C241288</td>\n",
              "      <td>Female</td>\n",
              "      <td>28</td>\n",
              "      <td>Clothing</td>\n",
              "      <td>5</td>\n",
              "      <td>1500.40</td>\n",
              "      <td>Credit Card</td>\n",
              "      <td>2022-08-05</td>\n",
              "      <td>Kanyon</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I317333</td>\n",
              "      <td>C111565</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>Shoes</td>\n",
              "      <td>3</td>\n",
              "      <td>1800.51</td>\n",
              "      <td>Debit Card</td>\n",
              "      <td>2021-12-12</td>\n",
              "      <td>Forum Istanbul</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I127801</td>\n",
              "      <td>C266599</td>\n",
              "      <td>Male</td>\n",
              "      <td>20</td>\n",
              "      <td>Clothing</td>\n",
              "      <td>1</td>\n",
              "      <td>300.08</td>\n",
              "      <td>Cash</td>\n",
              "      <td>2021-11-09</td>\n",
              "      <td>Metrocity</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I173702</td>\n",
              "      <td>C988172</td>\n",
              "      <td>Female</td>\n",
              "      <td>66</td>\n",
              "      <td>Shoes</td>\n",
              "      <td>5</td>\n",
              "      <td>3000.85</td>\n",
              "      <td>Credit Card</td>\n",
              "      <td>2021-05-16</td>\n",
              "      <td>Metropol AVM</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I337046</td>\n",
              "      <td>C189076</td>\n",
              "      <td>Female</td>\n",
              "      <td>53</td>\n",
              "      <td>Books</td>\n",
              "      <td>4</td>\n",
              "      <td>60.60</td>\n",
              "      <td>Cash</td>\n",
              "      <td>2021-10-24</td>\n",
              "      <td>Kanyon</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  invoice_no customer_id  gender  age  category  quantity    price  \\\n",
              "0    I138884     C241288  Female   28  Clothing         5  1500.40   \n",
              "1    I317333     C111565    Male   21     Shoes         3  1800.51   \n",
              "2    I127801     C266599    Male   20  Clothing         1   300.08   \n",
              "3    I173702     C988172  Female   66     Shoes         5  3000.85   \n",
              "4    I337046     C189076  Female   53     Books         4    60.60   \n",
              "\n",
              "  payment_method invoice_date   shopping_mall  cluster  \n",
              "0    Credit Card   2022-08-05          Kanyon        1  \n",
              "1     Debit Card   2021-12-12  Forum Istanbul        0  \n",
              "2           Cash   2021-11-09       Metrocity        1  \n",
              "3    Credit Card   2021-05-16    Metropol AVM        0  \n",
              "4           Cash   2021-10-24          Kanyon        0  "
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "file = \"dataset/customer_shopping_clustered.csv\"\n",
        "df = pd.read_csv(file)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(99457, 11)"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 99457 entries, 0 to 99456\n",
            "Data columns (total 11 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   invoice_no      99457 non-null  object \n",
            " 1   customer_id     99457 non-null  object \n",
            " 2   gender          99457 non-null  object \n",
            " 3   age             99457 non-null  int64  \n",
            " 4   category        99457 non-null  object \n",
            " 5   quantity        99457 non-null  int64  \n",
            " 6   price           99457 non-null  float64\n",
            " 7   payment_method  99457 non-null  object \n",
            " 8   invoice_date    99457 non-null  object \n",
            " 9   shopping_mall   99457 non-null  object \n",
            " 10  cluster         99457 non-null  int64  \n",
            "dtypes: float64(1), int64(3), object(7)\n",
            "memory usage: 8.3+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Encoded DataFrame:\n"
          ]
        },
        {
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>invoice_no</th>\n",
              "      <th>customer_id</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>category</th>\n",
              "      <th>quantity</th>\n",
              "      <th>price</th>\n",
              "      <th>payment_method</th>\n",
              "      <th>invoice_date</th>\n",
              "      <th>shopping_mall</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I138884</td>\n",
              "      <td>C241288</td>\n",
              "      <td>59482</td>\n",
              "      <td>28</td>\n",
              "      <td>34487</td>\n",
              "      <td>5</td>\n",
              "      <td>1500.40</td>\n",
              "      <td>34931</td>\n",
              "      <td>2022-08-05</td>\n",
              "      <td>19823</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I317333</td>\n",
              "      <td>C111565</td>\n",
              "      <td>39975</td>\n",
              "      <td>21</td>\n",
              "      <td>10034</td>\n",
              "      <td>3</td>\n",
              "      <td>1800.51</td>\n",
              "      <td>20079</td>\n",
              "      <td>2021-12-12</td>\n",
              "      <td>4947</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I127801</td>\n",
              "      <td>C266599</td>\n",
              "      <td>39975</td>\n",
              "      <td>20</td>\n",
              "      <td>34487</td>\n",
              "      <td>1</td>\n",
              "      <td>300.08</td>\n",
              "      <td>44447</td>\n",
              "      <td>2021-11-09</td>\n",
              "      <td>15011</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I173702</td>\n",
              "      <td>C988172</td>\n",
              "      <td>59482</td>\n",
              "      <td>66</td>\n",
              "      <td>10034</td>\n",
              "      <td>5</td>\n",
              "      <td>3000.85</td>\n",
              "      <td>34931</td>\n",
              "      <td>2021-05-16</td>\n",
              "      <td>10161</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I337046</td>\n",
              "      <td>C189076</td>\n",
              "      <td>59482</td>\n",
              "      <td>53</td>\n",
              "      <td>4981</td>\n",
              "      <td>4</td>\n",
              "      <td>60.60</td>\n",
              "      <td>44447</td>\n",
              "      <td>2021-10-24</td>\n",
              "      <td>19823</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  invoice_no customer_id  gender  age  category  quantity    price  \\\n",
              "0    I138884     C241288   59482   28     34487         5  1500.40   \n",
              "1    I317333     C111565   39975   21     10034         3  1800.51   \n",
              "2    I127801     C266599   39975   20     34487         1   300.08   \n",
              "3    I173702     C988172   59482   66     10034         5  3000.85   \n",
              "4    I337046     C189076   59482   53      4981         4    60.60   \n",
              "\n",
              "   payment_method invoice_date  shopping_mall  cluster  \n",
              "0           34931   2022-08-05          19823        1  \n",
              "1           20079   2021-12-12           4947        0  \n",
              "2           44447   2021-11-09          15011        1  \n",
              "3           34931   2021-05-16          10161        0  \n",
              "4           44447   2021-10-24          19823        0  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_encoded = df.copy()\n",
        "\n",
        "# Convert 'invoice_date' to datetime\n",
        "df_encoded['invoice_date'] = pd.to_datetime(df_encoded['invoice_date'], format='%Y-%m-%d')\n",
        "\n",
        "# Frequency Encoding for 'payment_method', 'category', and 'shopping_mall'\n",
        "def frequency_encoding(series):\n",
        "    freq = series.value_counts()\n",
        "    return series.map(freq)\n",
        "\n",
        "df_encoded['gender'] = frequency_encoding(df_encoded['gender'])\n",
        "df_encoded['category'] = frequency_encoding(df_encoded['category'])\n",
        "df_encoded['payment_method'] = frequency_encoding(df_encoded['payment_method'])\n",
        "df_encoded['shopping_mall'] = frequency_encoding(df_encoded['shopping_mall'])\n",
        "\n",
        "print(\"Encoded DataFrame:\")\n",
        "df_encoded.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>invoice_no</th>\n",
              "      <th>customer_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>99457</td>\n",
              "      <td>99457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>99457</td>\n",
              "      <td>99457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>I232867</td>\n",
              "      <td>C273973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       invoice_no customer_id\n",
              "count       99457       99457\n",
              "unique      99457       99457\n",
              "top       I232867     C273973\n",
              "freq            1           1"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_encoded.describe(include='object')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>category</th>\n",
              "      <th>quantity</th>\n",
              "      <th>price</th>\n",
              "      <th>payment_method</th>\n",
              "      <th>shopping_mall</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>99457.000000</td>\n",
              "      <td>99457.000000</td>\n",
              "      <td>99457.000000</td>\n",
              "      <td>99457.000000</td>\n",
              "      <td>99457.000000</td>\n",
              "      <td>99457.000000</td>\n",
              "      <td>99457.000000</td>\n",
              "      <td>99457.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>51641.502850</td>\n",
              "      <td>43.427089</td>\n",
              "      <td>19232.350232</td>\n",
              "      <td>3.003429</td>\n",
              "      <td>689.256321</td>\n",
              "      <td>36185.254039</td>\n",
              "      <td>13446.516716</td>\n",
              "      <td>0.346753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9564.104875</td>\n",
              "      <td>14.990054</td>\n",
              "      <td>11566.865169</td>\n",
              "      <td>1.413025</td>\n",
              "      <td>941.184567</td>\n",
              "      <td>9133.921989</td>\n",
              "      <td>6109.196525</td>\n",
              "      <td>0.475939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>39975.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>4981.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.230000</td>\n",
              "      <td>20079.000000</td>\n",
              "      <td>4811.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>39975.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>10034.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>45.450000</td>\n",
              "      <td>34931.000000</td>\n",
              "      <td>9781.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>59482.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>14776.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>203.300000</td>\n",
              "      <td>34931.000000</td>\n",
              "      <td>15011.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>59482.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>34487.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1200.320000</td>\n",
              "      <td>44447.000000</td>\n",
              "      <td>19823.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>59482.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>34487.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5250.000000</td>\n",
              "      <td>44447.000000</td>\n",
              "      <td>19943.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             gender           age      category      quantity         price  \\\n",
              "count  99457.000000  99457.000000  99457.000000  99457.000000  99457.000000   \n",
              "mean   51641.502850     43.427089  19232.350232      3.003429    689.256321   \n",
              "std     9564.104875     14.990054  11566.865169      1.413025    941.184567   \n",
              "min    39975.000000     18.000000   4981.000000      1.000000      5.230000   \n",
              "25%    39975.000000     30.000000  10034.000000      2.000000     45.450000   \n",
              "50%    59482.000000     43.000000  14776.000000      3.000000    203.300000   \n",
              "75%    59482.000000     56.000000  34487.000000      4.000000   1200.320000   \n",
              "max    59482.000000     69.000000  34487.000000      5.000000   5250.000000   \n",
              "\n",
              "       payment_method  shopping_mall       cluster  \n",
              "count    99457.000000   99457.000000  99457.000000  \n",
              "mean     36185.254039   13446.516716      0.346753  \n",
              "std       9133.921989    6109.196525      0.475939  \n",
              "min      20079.000000    4811.000000      0.000000  \n",
              "25%      34931.000000    9781.000000      0.000000  \n",
              "50%      34931.000000   15011.000000      0.000000  \n",
              "75%      44447.000000   19823.000000      1.000000  \n",
              "max      44447.000000   19943.000000      1.000000  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_encoded.describe(include='number')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KkPem5eWL2UP"
      },
      "source": [
        "# **3. Data Splitting**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YYj1rl_JNI9Y"
      },
      "source": [
        "The Data Splitting stage aims to divide the dataset into two parts: the training set and the test set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "OubAW-7ONKVj"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training set: (69619, 7), Test set: (29838, 7)\n"
          ]
        }
      ],
      "source": [
        "X = df_encoded.drop(columns=['invoice_no', 'customer_id', 'cluster', 'invoice_date'])  # Features\n",
        "y = df_encoded['cluster']  # Target\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n",
        "print(f\"Training set: {X_train.shape}, Test set: {X_test.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVPbB03CMhTT"
      },
      "source": [
        "# **4. Building the Classification Model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ned1pL9zMmBK"
      },
      "source": [
        "## **a. Selecting the Algorithm and Training the Classification Model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Logistic Regression & Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "4JYxBe87NLDk"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(random_state=42)</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "DecisionTreeClassifier(random_state=42)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "logreg = LogisticRegression(max_iter=1000, random_state=42)\n",
        "logreg.fit(X_train, y_train)\n",
        "\n",
        "dt = DecisionTreeClassifier(random_state=42)\n",
        "dt.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "seYoHNY3XU1y"
      },
      "source": [
        "- Logistic Regression: This is a great choice for binary classification problems. It works well when the relationship between the independent variable(s) and the dependent variable is linear.\n",
        "- Decision Tree: This algorithm is versatile and can handle both categorical and numerical data effectively. It can also manage datasets with complex relationships, making it suitable for various feature sets."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ergzChZFEL-O"
      },
      "source": [
        "## **b. Classification Model Evaluation**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zOm68u-7NpLT"
      },
      "source": [
        "1. Make predictions using the test data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Predict and evaluate Logistic Regression\n",
        "y_pred_logreg = logreg.predict(X_test)\n",
        "\n",
        "# Predict and evaluate Decision Tree\n",
        "y_pred_dt = dt.predict(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "2. Calculate evaluation metrics such as Accuracy, Precision, Recall and F1-Score\n",
        "3. Create a confusion matrix to see the details of true and false predictions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Calculate metrics for Logistic Regression\n",
        "accuracy_logreg = accuracy_score(y_test, y_pred_logreg)\n",
        "precision_logreg = precision_score(y_test, y_pred_logreg, average='weighted')\n",
        "recall_logreg = recall_score(y_test, y_pred_logreg, average='weighted')\n",
        "f1_logreg = f1_score(y_test, y_pred_logreg, average='weighted')\n",
        "\n",
        "# Calculate metrics for Decision Tree\n",
        "accuracy_dt = accuracy_score(y_test, y_pred_dt)\n",
        "precision_dt = precision_score(y_test, y_pred_dt, average='weighted')\n",
        "recall_dt = recall_score(y_test, y_pred_dt, average='weighted')\n",
        "f1_dt = f1_score(y_test, y_pred_dt, average='weighted')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>Algorithm</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1 Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Algorithm  Accuracy  Precision  Recall  F1 Score\n",
              "0  Logistic Regression       1.0        1.0     1.0       1.0\n",
              "1        Decision Tree       1.0        1.0     1.0       1.0"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create a DataFrame to summarize the metrics\n",
        "metrics_table = pd.DataFrame({\n",
        "    \"Algorithm\": [\"Logistic Regression\", \"Decision Tree\"],\n",
        "    \"Accuracy\": [accuracy_logreg, accuracy_dt],\n",
        "    \"Precision\": [precision_logreg, precision_dt],\n",
        "    \"Recall\": [recall_logreg, recall_dt],\n",
        "    \"F1 Score\": [f1_logreg, f1_dt]\n",
        "})\n",
        "\n",
        "metrics_table"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- Both algorithms achieved perfect performance with an F1 Score of 1.0, Accuracy of 1.0, Precision of 1.0, and Recall of 1.0. This means that both models built with logistic regression and decision tree successfully predicted the entire target variable accurately without any errors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Confusion matrix for Logistic Regression with F1 score in the title\n",
        "f1_logreg = f1_score(y_test, y_pred_logreg, average='weighted')\n",
        "\n",
        "plt.figure(figsize=(4, 3))\n",
        "sns.heatmap(confusion_matrix(y_test, y_pred_logreg), annot=True, fmt='d', cmap='Blues')\n",
        "plt.title(f\"Confusion Matrix: Logistic Regression\\nF1 Score: {f1_logreg:.2f}\")\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Confusion matrix for Decision Tree with F1 score in the title\n",
        "f1_dt = f1_score(y_test, y_pred_dt, average='weighted')\n",
        "\n",
        "plt.figure(figsize=(4, 3))\n",
        "sns.heatmap(confusion_matrix(y_test, y_pred_dt), annot=True, fmt='d', cmap='Oranges')\n",
        "plt.title(f\"Confusion Matrix: Decision Tree\\nF1 Score: {f1_dt:.2f}\")\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- The two confusion matrices above display the counts of true positives, false positives, true negatives, and false negatives."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ph9yIYDXEPuB"
      },
      "source": [
        "## **c. Tuning Classification Model (Optional)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Bikx3LINv5e"
      },
      "source": [
        "Use GridSearchCV, RandomizedSearchCV, or other methods to find the best combination of hyperparameters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "winbFzb8NL95"
      },
      "outputs": [],
      "source": [
        "# No need"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hE7pqlEPEYzI"
      },
      "source": [
        "## **d. Evaluation of Classification Model after Tuning (Optional)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "feaPESoeN0zz"
      },
      "source": [
        "Here are the **recommended** steps:\n",
        "1. Use the model with the best hyperparameters.\n",
        "2. Recalculate the evaluation metrics to see if there is an improvement in performance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "HTXZRvEeNMb1"
      },
      "outputs": [],
      "source": [
        "# No need"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZRsOdm4uEgAW"
      },
      "source": [
        "## **e. Analysis of Classification Model Evaluation Results**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hm3BhSi6N4_l"
      },
      "source": [
        "Here are the **recommended** steps:\n",
        "1. Compare the evaluation results before and after tuning (if performed).\n",
        "2. Identify the weaknesses of the model, such as:\n",
        "   - Low Precision or Recall for certain classes.\n",
        "   - Does the model experience overfitting or underfitting?\n",
        "3. Provide recommendations for further action, such as gathering additional data or trying other algorithms if the results are unsatisfactory."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The clustering results used as training data are indeed univariate clustering, considering only one feature: 'category'. Given its simplicity, it is understandable that both algorithms used here can achieve flawless performance in classification.\n"
      ]
    },
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        "**The recommendations that can be provided are to replace the use of silhouette score as the evaluation metric for clustering results that are used to train the classification model when mixed features must be chosen. Giving instructions to select mixed features with the argument 'The combination of categorical and numerical data will help produce more meaningful cluster groupings' is also less appropriate because it is highly dependent on the dataset used and the context of the business problem being addressed.**"
      ]
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