DAY8_2.ipynb

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    {
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      "source": [
        "# Supervised Learning Methods: Practical Implementation"
      ]
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    {
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      "id": "03158de9",
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        "id": "03158de9"
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      "source": [
        "## Introduction\n",
        "This notebook demonstrates implementations of ID3, Random Forest, and K-Nearest Neighbors (KNN) for classification tasks using a sample dataset."
      ]
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      "source": [
        "### Dataset\n",
        "The dataset includes the following features:\n",
        "- Age\n",
        "- Income\n",
        "- Student Status (0 = No, 1 = Yes)\n",
        "- Credit Rating (0 = Poor, 1 = Good)\n",
        "- Target: Buys_Computer (0 = No, 1 = Yes)"
      ]
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              "   Age  Income  Student  Credit_Rating  Buys_Computer\n",
              "0   25   40000        0              1              0\n",
              "1   30   50000        1              0              1\n",
              "2   35   60000        0              1              0\n",
              "3   40   80000        1              0              1\n",
              "4   20   20000        0              1              0\n",
              "5   50   90000        1              0              1\n",
              "6   23   30000        1              1              1\n",
              "7   37   70000        0              0              0"
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 20,\n        \"max\": 50,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          30,\n          50,\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24494,\n        \"min\": 20000,\n        \"max\": 90000,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          50000,\n          90000,\n          40000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Student\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Buys_Computer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "# Import libraries\n",
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier, export_text\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "\n",
        "# Create a sample dataset\n",
        "data = {\n",
        "    \"Age\": [25, 30, 35, 40, 20, 50, 23, 37],\n",
        "    \"Income\": [40000, 50000, 60000, 80000, 20000, 90000, 30000, 70000],\n",
        "    \"Student\": [0, 1, 0, 1, 0, 1, 1, 0],\n",
        "    \"Credit_Rating\": [1, 0, 1, 0, 1, 0, 1, 0],\n",
        "    \"Buys_Computer\": [0, 1, 0, 1, 0, 1, 1, 0]\n",
        "}\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Display the dataset\n",
        "df"
      ]
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    {
      "cell_type": "markdown",
      "id": "6b5bc9a9",
      "metadata": {
        "id": "6b5bc9a9"
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      "source": [
        "## Step 2: ID3 Algorithm (Decision Tree)\n",
        "We train a Decision Tree Classifier using the ID3 algorithm. The tree is built based on entropy and information gain."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "9e0520cc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9e0520cc",
        "outputId": "9df97360-8fc9-4b77-80eb-b22801a8bc1d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "|--- Student <= 0.50\n",
            "|   |--- class: 0\n",
            "|--- Student >  0.50\n",
            "|   |--- class: 1\n",
            "\n",
            "Accuracy: 1.0\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00         1\n",
            "           1       1.00      1.00      1.00         2\n",
            "\n",
            "    accuracy                           1.00         3\n",
            "   macro avg       1.00      1.00      1.00         3\n",
            "weighted avg       1.00      1.00      1.00         3\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Split the dataset into features and target\n",
        "X = df[[\"Age\", \"Income\", \"Student\", \"Credit_Rating\"]]\n",
        "y = df[\"Buys_Computer\"]\n",
        "\n",
        "# Split 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)\n",
        "\n",
        "# Train a Decision Tree model\n",
        "dt_model = DecisionTreeClassifier(criterion='entropy', random_state=42)\n",
        "dt_model.fit(X_train, y_train)\n",
        "\n",
        "# Generate predictions\n",
        "dt_predictions = dt_model.predict(X_test)\n",
        "\n",
        "# Display the Decision Tree rules\n",
        "print(export_text(dt_model, feature_names=list(X.columns)))\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Accuracy:\", accuracy_score(y_test, dt_predictions))\n",
        "print(classification_report(y_test, dt_predictions))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "550eb356",
      "metadata": {
        "id": "550eb356"
      },
      "source": [
        "## Step 3: Random Forest\n",
        "Random Forest combines multiple decision trees to improve classification accuracy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "32860c87",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "32860c87",
        "outputId": "ce28fa4f-4a83-4f88-a0f8-f92ad637c605"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.0\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00         1\n",
            "           1       1.00      1.00      1.00         2\n",
            "\n",
            "    accuracy                           1.00         3\n",
            "   macro avg       1.00      1.00      1.00         3\n",
            "weighted avg       1.00      1.00      1.00         3\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Train a Random Forest model\n",
        "rf_model = RandomForestClassifier(n_estimators=10, random_state=42)\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "# Generate predictions\n",
        "rf_predictions = rf_model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Accuracy:\", accuracy_score(y_test, rf_predictions))\n",
        "print(classification_report(y_test, rf_predictions))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "454cee2e",
      "metadata": {
        "id": "454cee2e"
      },
      "source": [
        "## Step 4: K-Nearest Neighbors (KNN)\n",
        "KNN classifies data based on the majority vote of its neighbors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "4fa6901d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4fa6901d",
        "outputId": "7397773a-38dd-488c-c192-926cd1b165fb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.3333333333333333\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.33      1.00      0.50         1\n",
            "           1       0.00      0.00      0.00         2\n",
            "\n",
            "    accuracy                           0.33         3\n",
            "   macro avg       0.17      0.50      0.25         3\n",
            "weighted avg       0.11      0.33      0.17         3\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        }
      ],
      "source": [
        "# Train a KNN model\n",
        "knn_model = KNeighborsClassifier(n_neighbors=3)\n",
        "knn_model.fit(X_train, y_train)\n",
        "\n",
        "# Generate predictions\n",
        "knn_predictions = knn_model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Accuracy:\", accuracy_score(y_test, knn_predictions))\n",
        "print(classification_report(y_test, knn_predictions))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5c5cb9c7",
      "metadata": {
        "id": "5c5cb9c7"
      },
      "source": [
        "## Confusion Matrices\n",
        "### ID3 Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "7aaf8450",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7aaf8450",
        "outputId": "e97fb985-ed6b-4e41-ac6b-65e2ccf477ce"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0],\n",
              "       [0, 2]])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "confusion_matrix(y_test, dt_predictions)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Function to plot the confusion matrix\n",
        "def plot_confusion_matrix(y_true, y_pred, title=\"Confusion Matrix\"):\n",
        "    # Compute the confusion matrix\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "\n",
        "    # Create a heatmap\n",
        "    plt.figure(figsize=(6, 4))\n",
        "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=[\"No\", \"Yes\"], yticklabels=[\"No\", \"Yes\"])\n",
        "\n",
        "    # Add labels and title\n",
        "    plt.xlabel(\"Predicted Labels\")\n",
        "    plt.ylabel(\"True Labels\")\n",
        "    plt.title(title)\n",
        "\n",
        "    # Display the plot\n",
        "    plt.show()\n",
        "\n",
        "# Example usage with Decision Tree predictions\n",
        "plot_confusion_matrix(y_test, dt_predictions, title=\"ID3 (Decision Tree) Confusion Matrix\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "qL9oxJDOBOyM",
        "outputId": "b79e5baf-caa5-4952-b39d-7252e4d1371c"
      },
      "id": "qL9oxJDOBOyM",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3c0df242",
      "metadata": {
        "id": "3c0df242"
      },
      "source": [
        "### Random Forest Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "5cb3b6d8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5cb3b6d8",
        "outputId": "590e0d71-4ccb-41e9-c341-548f19b7a1c5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0],\n",
              "       [0, 2]])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "confusion_matrix(y_test, rf_predictions)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b744efd9",
      "metadata": {
        "id": "b744efd9"
      },
      "source": [
        "### KNN Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "de07c560",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "de07c560",
        "outputId": "1dfd9c11-e058-4fc4-ef14-88d4aa0d5536"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0],\n",
              "       [2, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "confusion_matrix(y_test, knn_predictions)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "language_info": {
      "name": "python"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}