PL_DT&RD.ipynb

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      "cell_type": "markdown",
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        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/dhruvi013/Machine-Learning/blob/main/PL_DT%26RD.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "5UsasU96UY09"
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      "source": [
        "# Import necessary libraries\n",
        "import pandas as pd\n",
        "from ucimlrepo import fetch_ucirepo\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install ucimlrepo"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QGhCKoVUUdmA",
        "outputId": "deee9b66-6a04-4dd5-e7bd-38addefc3ec3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: ucimlrepo in /usr/local/lib/python3.10/dist-packages (0.0.7)\n",
            "Requirement already satisfied: pandas>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from ucimlrepo) (2.2.2)\n",
            "Requirement already satisfied: certifi>=2020.12.5 in /usr/local/lib/python3.10/dist-packages (from ucimlrepo) (2024.8.30)\n",
            "Requirement already satisfied: numpy>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.0->ucimlrepo) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.0->ucimlrepo) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.0->ucimlrepo) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.0->ucimlrepo) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=1.0.0->ucimlrepo) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ucimlrepo import fetch_ucirepo\n",
        "\n",
        "# fetch dataset\n",
        "spambase = fetch_ucirepo(id=94)\n",
        "\n",
        "# data (as pandas dataframes)\n",
        "X = spambase.data.features\n",
        "y = spambase.data.targets\n",
        "\n",
        "# metadata\n",
        "print(spambase.metadata)\n",
        "\n",
        "# variable information\n",
        "print(spambase.variables)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vwd46O4LUixn",
        "outputId": "a45084b7-f8e1-4ca9-e5c9-f758f106bddf"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'uci_id': 94, 'name': 'Spambase', 'repository_url': 'https://archive.ics.uci.edu/dataset/94/spambase', 'data_url': 'https://archive.ics.uci.edu/static/public/94/data.csv', 'abstract': 'Classifying Email as Spam or Non-Spam', 'area': 'Computer Science', 'tasks': ['Classification'], 'characteristics': ['Multivariate'], 'num_instances': 4601, 'num_features': 57, 'feature_types': ['Integer', 'Real'], 'demographics': [], 'target_col': ['Class'], 'index_col': None, 'has_missing_values': 'no', 'missing_values_symbol': None, 'year_of_dataset_creation': 1999, 'last_updated': 'Mon Aug 28 2023', 'dataset_doi': '10.24432/C53G6X', 'creators': ['Mark Hopkins', 'Erik Reeber', 'George Forman', 'Jaap Suermondt'], 'intro_paper': None, 'additional_info': {'summary': 'The \"spam\" concept is diverse: advertisements for products/web sites, make money fast schemes, chain letters, pornography...\\n\\nThe classification task for this dataset is to determine whether a given email is spam or not.\\n\\t\\nOur collection of spam e-mails came from our postmaster and individuals who had filed spam.  Our collection of non-spam e-mails came from filed work and personal e-mails, and hence the word \\'george\\' and the area code \\'650\\' are indicators of non-spam.  These are useful when constructing a personalized spam filter.  One would either have to blind such non-spam indicators or get a very wide collection of non-spam to generate a general purpose spam filter.\\n\\nFor background on spam: Cranor, Lorrie F., LaMacchia, Brian A.  Spam!, Communications of the ACM, 41(8):74-83, 1998.\\n\\nTypical performance is around ~7% misclassification error. False positives (marking good mail as spam) are very undesirable.If we insist on zero false positives in the training/testing set, 20-25% of the spam passed through the filter. See also Hewlett-Packard Internal-only Technical Report. External version forthcoming. ', 'purpose': None, 'funded_by': None, 'instances_represent': 'Emails', 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': 'The last column of \\'spambase.data\\' denotes whether the e-mail was considered spam (1) or not (0), i.e. unsolicited commercial e-mail.  Most of the attributes indicate whether a particular word or character was frequently occuring in the e-mail.  The run-length attributes (55-57) measure the length of sequences of consecutive capital letters.  For the statistical measures of each attribute, see the end of this file.  Here are the definitions of the attributes:\\r\\n\\r\\n48 continuous real [0,100] attributes of type word_freq_WORD \\r\\n= percentage of words in the e-mail that match WORD, i.e. 100 * (number of times the WORD appears in the e-mail) / total number of words in e-mail.  A \"word\" in this case is any string of alphanumeric characters bounded by non-alphanumeric characters or end-of-string.\\r\\n\\r\\n6 continuous real [0,100] attributes of type char_freq_CHAR] \\r\\n= percentage of characters in the e-mail that match CHAR, i.e. 100 * (number of CHAR occurences) / total characters in e-mail\\r\\n\\r\\n1 continuous real [1,...] attribute of type capital_run_length_average \\r\\n= average length of uninterrupted sequences of capital letters\\r\\n\\r\\n1 continuous integer [1,...] attribute of type capital_run_length_longest \\r\\n= length of longest uninterrupted sequence of capital letters\\r\\n\\r\\n1 continuous integer [1,...] attribute of type capital_run_length_total \\r\\n= sum of length of uninterrupted sequences of capital letters \\r\\n= total number of capital letters in the e-mail\\r\\n\\r\\n1 nominal {0,1} class attribute of type spam\\r\\n= denotes whether the e-mail was considered spam (1) or not (0), i.e. unsolicited commercial e-mail.  \\r\\n', 'citation': None}}\n",
            "                          name     role        type demographic  \\\n",
            "0               word_freq_make  Feature  Continuous        None   \n",
            "1            word_freq_address  Feature  Continuous        None   \n",
            "2                word_freq_all  Feature  Continuous        None   \n",
            "3                 word_freq_3d  Feature  Continuous        None   \n",
            "4                word_freq_our  Feature  Continuous        None   \n",
            "5               word_freq_over  Feature  Continuous        None   \n",
            "6             word_freq_remove  Feature  Continuous        None   \n",
            "7           word_freq_internet  Feature  Continuous        None   \n",
            "8              word_freq_order  Feature  Continuous        None   \n",
            "9               word_freq_mail  Feature  Continuous        None   \n",
            "10           word_freq_receive  Feature  Continuous        None   \n",
            "11              word_freq_will  Feature  Continuous        None   \n",
            "12            word_freq_people  Feature  Continuous        None   \n",
            "13            word_freq_report  Feature  Continuous        None   \n",
            "14         word_freq_addresses  Feature  Continuous        None   \n",
            "15              word_freq_free  Feature  Continuous        None   \n",
            "16          word_freq_business  Feature  Continuous        None   \n",
            "17             word_freq_email  Feature  Continuous        None   \n",
            "18               word_freq_you  Feature  Continuous        None   \n",
            "19            word_freq_credit  Feature  Continuous        None   \n",
            "20              word_freq_your  Feature  Continuous        None   \n",
            "21              word_freq_font  Feature  Continuous        None   \n",
            "22               word_freq_000  Feature  Continuous        None   \n",
            "23             word_freq_money  Feature  Continuous        None   \n",
            "24                word_freq_hp  Feature  Continuous        None   \n",
            "25               word_freq_hpl  Feature  Continuous        None   \n",
            "26            word_freq_george  Feature  Continuous        None   \n",
            "27               word_freq_650  Feature  Continuous        None   \n",
            "28               word_freq_lab  Feature  Continuous        None   \n",
            "29              word_freq_labs  Feature  Continuous        None   \n",
            "30            word_freq_telnet  Feature  Continuous        None   \n",
            "31               word_freq_857  Feature  Continuous        None   \n",
            "32              word_freq_data  Feature  Continuous        None   \n",
            "33               word_freq_415  Feature  Continuous        None   \n",
            "34                word_freq_85  Feature  Continuous        None   \n",
            "35        word_freq_technology  Feature  Continuous        None   \n",
            "36              word_freq_1999  Feature  Continuous        None   \n",
            "37             word_freq_parts  Feature  Continuous        None   \n",
            "38                word_freq_pm  Feature  Continuous        None   \n",
            "39            word_freq_direct  Feature  Continuous        None   \n",
            "40                word_freq_cs  Feature  Continuous        None   \n",
            "41           word_freq_meeting  Feature  Continuous        None   \n",
            "42          word_freq_original  Feature  Continuous        None   \n",
            "43           word_freq_project  Feature  Continuous        None   \n",
            "44                word_freq_re  Feature  Continuous        None   \n",
            "45               word_freq_edu  Feature  Continuous        None   \n",
            "46             word_freq_table  Feature  Continuous        None   \n",
            "47        word_freq_conference  Feature  Continuous        None   \n",
            "48                 char_freq_;  Feature  Continuous        None   \n",
            "49                 char_freq_(  Feature  Continuous        None   \n",
            "50                 char_freq_[  Feature  Continuous        None   \n",
            "51                 char_freq_!  Feature  Continuous        None   \n",
            "52                 char_freq_$  Feature  Continuous        None   \n",
            "53                 char_freq_#  Feature  Continuous        None   \n",
            "54  capital_run_length_average  Feature  Continuous        None   \n",
            "55  capital_run_length_longest  Feature  Continuous        None   \n",
            "56    capital_run_length_total  Feature  Continuous        None   \n",
            "57                       Class   Target      Binary        None   \n",
            "\n",
            "                 description units missing_values  \n",
            "0                       None  None             no  \n",
            "1                       None  None             no  \n",
            "2                       None  None             no  \n",
            "3                       None  None             no  \n",
            "4                       None  None             no  \n",
            "5                       None  None             no  \n",
            "6                       None  None             no  \n",
            "7                       None  None             no  \n",
            "8                       None  None             no  \n",
            "9                       None  None             no  \n",
            "10                      None  None             no  \n",
            "11                      None  None             no  \n",
            "12                      None  None             no  \n",
            "13                      None  None             no  \n",
            "14                      None  None             no  \n",
            "15                      None  None             no  \n",
            "16                      None  None             no  \n",
            "17                      None  None             no  \n",
            "18                      None  None             no  \n",
            "19                      None  None             no  \n",
            "20                      None  None             no  \n",
            "21                      None  None             no  \n",
            "22                      None  None             no  \n",
            "23                      None  None             no  \n",
            "24                      None  None             no  \n",
            "25                      None  None             no  \n",
            "26                      None  None             no  \n",
            "27                      None  None             no  \n",
            "28                      None  None             no  \n",
            "29                      None  None             no  \n",
            "30                      None  None             no  \n",
            "31                      None  None             no  \n",
            "32                      None  None             no  \n",
            "33                      None  None             no  \n",
            "34                      None  None             no  \n",
            "35                      None  None             no  \n",
            "36                      None  None             no  \n",
            "37                      None  None             no  \n",
            "38                      None  None             no  \n",
            "39                      None  None             no  \n",
            "40                      None  None             no  \n",
            "41                      None  None             no  \n",
            "42                      None  None             no  \n",
            "43                      None  None             no  \n",
            "44                      None  None             no  \n",
            "45                      None  None             no  \n",
            "46                      None  None             no  \n",
            "47                      None  None             no  \n",
            "48                      None  None             no  \n",
            "49                      None  None             no  \n",
            "50                      None  None             no  \n",
            "51                      None  None             no  \n",
            "52                      None  None             no  \n",
            "53                      None  None             no  \n",
            "54                      None  None             no  \n",
            "55                      None  None             no  \n",
            "56                      None  None             no  \n",
            "57  spam (1) or not spam (0)  None             no  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the first few rows of the data\n",
        "print(X.head())\n",
        "print(y.head())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fG7ZG_H7Un3K",
        "outputId": "d4252f54-4015-4497-cb39-5c3527f9efde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   word_freq_make  word_freq_address  word_freq_all  word_freq_3d  \\\n",
            "0            0.00               0.64           0.64           0.0   \n",
            "1            0.21               0.28           0.50           0.0   \n",
            "2            0.06               0.00           0.71           0.0   \n",
            "3            0.00               0.00           0.00           0.0   \n",
            "4            0.00               0.00           0.00           0.0   \n",
            "\n",
            "   word_freq_our  word_freq_over  word_freq_remove  word_freq_internet  \\\n",
            "0           0.32            0.00              0.00                0.00   \n",
            "1           0.14            0.28              0.21                0.07   \n",
            "2           1.23            0.19              0.19                0.12   \n",
            "3           0.63            0.00              0.31                0.63   \n",
            "4           0.63            0.00              0.31                0.63   \n",
            "\n",
            "   word_freq_order  word_freq_mail  ...  word_freq_conference  char_freq_;  \\\n",
            "0             0.00            0.00  ...                   0.0         0.00   \n",
            "1             0.00            0.94  ...                   0.0         0.00   \n",
            "2             0.64            0.25  ...                   0.0         0.01   \n",
            "3             0.31            0.63  ...                   0.0         0.00   \n",
            "4             0.31            0.63  ...                   0.0         0.00   \n",
            "\n",
            "   char_freq_(  char_freq_[  char_freq_!  char_freq_$  char_freq_#  \\\n",
            "0        0.000          0.0        0.778        0.000        0.000   \n",
            "1        0.132          0.0        0.372        0.180        0.048   \n",
            "2        0.143          0.0        0.276        0.184        0.010   \n",
            "3        0.137          0.0        0.137        0.000        0.000   \n",
            "4        0.135          0.0        0.135        0.000        0.000   \n",
            "\n",
            "   capital_run_length_average  capital_run_length_longest  \\\n",
            "0                       3.756                          61   \n",
            "1                       5.114                         101   \n",
            "2                       9.821                         485   \n",
            "3                       3.537                          40   \n",
            "4                       3.537                          40   \n",
            "\n",
            "   capital_run_length_total  \n",
            "0                       278  \n",
            "1                      1028  \n",
            "2                      2259  \n",
            "3                       191  \n",
            "4                       191  \n",
            "\n",
            "[5 rows x 57 columns]\n",
            "   Class\n",
            "0      1\n",
            "1      1\n",
            "2      1\n",
            "3      1\n",
            "4      1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into training and testing sets (80% training, 20% testing)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "zdsfukLLVpSQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Decision Tree Model\n",
        "print(\"Training Decision Tree...\")\n",
        "decision_tree = DecisionTreeClassifier(random_state=42)\n",
        "decision_tree.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 97
        },
        "id": "8lNyXxhQVsQs",
        "outputId": "b2696299-0dc3-4a42-814c-fb50903bc274"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Decision Tree...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(random_state=42)"
            ],
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              "  /* Definition of color scheme for fitted estimators */\n",
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              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
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              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 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-2 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-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 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-2 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-2 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-2 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-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 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-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 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: 1ex;\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-2 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-2 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-2 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-2 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-2\" 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict using Decision Tree\n",
        "y_pred_tree = decision_tree.predict(X_test)"
      ],
      "metadata": {
        "id": "EmlEiJhYVvaB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate Decision Tree model\n",
        "print(\"\\nDecision Tree Evaluation:\")\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred_tree))\n",
        "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred_tree))\n",
        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred_tree))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TmbDBJpTW5ZR",
        "outputId": "3b53e361-80a7-4228-9656-8e174ca55d14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Decision Tree Evaluation:\n",
            "Accuracy: 0.9185667752442996\n",
            "\n",
            "Confusion Matrix:\n",
            " [[501  30]\n",
            " [ 45 345]]\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.94      0.93       531\n",
            "           1       0.92      0.88      0.90       390\n",
            "\n",
            "    accuracy                           0.92       921\n",
            "   macro avg       0.92      0.91      0.92       921\n",
            "weighted avg       0.92      0.92      0.92       921\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Forest Model\n",
        "print(\"\\nTraining Random Forest...\")\n",
        "random_forest = RandomForestClassifier(random_state=42)\n",
        "random_forest.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 169
        },
        "id": "-tCrcRmSW8fz",
        "outputId": "927bfc12-8f36-4b0f-89f5-4ceea50850da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training Random Forest...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:1473: 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",
            "  return fit_method(estimator, *args, **kwargs)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\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-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 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-3 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-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 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-3 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-3 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-3 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-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 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-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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: 1ex;\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-3 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-3 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-3 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-3 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-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict using Random Forest\n",
        "y_pred_forest = random_forest.predict(X_test)"
      ],
      "metadata": {
        "id": "jcWUOTPtW-yd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate Random Forest model\n",
        "print(\"\\nRandom Forest Evaluation:\")\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred_forest))\n",
        "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred_forest))\n",
        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred_forest))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hcYWVjgqXAot",
        "outputId": "7ca4d680-31e1-42c6-a9c5-0ef2bf26170e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forest Evaluation:\n",
            "Accuracy: 0.9554831704668838\n",
            "\n",
            "Confusion Matrix:\n",
            " [[522   9]\n",
            " [ 32 358]]\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.98      0.96       531\n",
            "           1       0.98      0.92      0.95       390\n",
            "\n",
            "    accuracy                           0.96       921\n",
            "   macro avg       0.96      0.95      0.95       921\n",
            "weighted avg       0.96      0.96      0.96       921\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Optional: Plot feature importance from Random Forest\n",
        "plt.figure(figsize=(10, 6))\n",
        "features = X.columns\n",
        "importances = random_forest.feature_importances_\n",
        "indices = importances.argsort()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "NQyiFaSxXC1s",
        "outputId": "b122888a-04c6-4684-cb32-7563e17d5cb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title('Feature Importance (Random Forest)')\n",
        "plt.barh(range(X.shape[1]), importances[indices], align=\"center\")\n",
        "plt.yticks(range(X.shape[1]), [features[i] for i in indices])\n",
        "plt.xlabel('Relative Importance')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "NoVbrnehXF1e",
        "outputId": "1d48b0a6-d5c8-4c70-acb7-ee8272a3e9ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ozyp-AgHZERQ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}