Naive Bayes.ipynb

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              "   gender  race/ethnicity  parental level of education  lunch  \\\n",
              "0     1.0            0.25                          0.0    1.0   \n",
              "1     1.0            0.00                          0.2    1.0   \n",
              "2     1.0            1.00                          0.4    0.0   \n",
              "3     0.0            0.50                          0.4    0.0   \n",
              "4     0.0            0.75                          0.6    0.0   \n",
              "\n",
              "   test preparation course  math score  reading score  writing score  \\\n",
              "0                      1.0    0.602410       0.525641         0.5625   \n",
              "1                      0.0    0.313253       0.282051         0.2250   \n",
              "2                      0.0    0.903614       0.743590         0.7875   \n",
              "3                      0.0    0.686747       0.615385         0.6375   \n",
              "4                      1.0    0.771084       0.858974         0.9000   \n",
              "\n",
              "   Overall Score  Pass/Fail  \n",
              "0       0.527027          1  \n",
              "1       0.202703          0  \n",
              "2       0.797297          1  \n",
              "3       0.608108          1  \n",
              "4       0.824324          1  "
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              "      <th>gender</th>\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "import pandas as pd\n",
        "data = pd.read_csv('/content/data_no_outliers.csv')\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "# Check the class distribution before oversampling\n",
        "print(\"Class distribution before oversampling:\")\n",
        "print(data['Pass/Fail'].value_counts())\n",
        "\n",
        "# Assuming data is your DataFrame\n",
        "# X should be the features, and y should be the target variable\n",
        "X = data.drop('Pass/Fail', axis=1)\n",
        "y = data['Pass/Fail']\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)\n",
        "\n",
        "# Specify the desired sampling strategy for RandomOverSampler (1:1 ratio)\n",
        "sampling_strategy = 1.0\n",
        "ros = RandomOverSampler(sampling_strategy=sampling_strategy, random_state=42)\n",
        "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)\n",
        "\n",
        "# Check the class distribution after oversampling\n",
        "print(\"\\nClass distribution after oversampling:\")\n",
        "print(pd.Series(y_train_resampled).value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sKBAVkBd2Snj",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class distribution before oversampling:\n",
            "1    674\n",
            "0    235\n",
            "Name: Pass/Fail, dtype: int64\n",
            "\n",
            "Class distribution after oversampling:\n",
            "0    473\n",
            "1    473\n",
            "Name: Pass/Fail, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "# Assuming data is your DataFrame\n",
        "# X should be the features, and y should be the target variable\n",
        "X = data.drop('Pass/Fail', axis=1)\n",
        "y = data['Pass/Fail']\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)\n",
        "\n",
        "# Initialize and train the Naive Bayes classifier\n",
        "clf = MultinomialNB()\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Predict on the testing set\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "# Evaluate the classifier\n",
        "print(\"Classification Report For Naive Bayes:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5aI2rAF1xhXe",
        "outputId": "f793d3cd-1fc1-4d84-b247-c93a0fe2a36f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report For Naive Bayes:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.01      0.03        72\n",
            "           1       0.74      1.00      0.85       201\n",
            "\n",
            "    accuracy                           0.74       273\n",
            "   macro avg       0.87      0.51      0.44       273\n",
            "weighted avg       0.81      0.74      0.63       273\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "precision = precision_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "f1 = f1_score(y_test, y_pred)\n",
        "\n",
        "# Print additional metrics\n",
        "print(\"\\nAdditional Metrics for Naive Bayes:\")\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Precision:\", precision)\n",
        "print(\"Recall:\", recall)\n",
        "print(\"F1 Score:\", f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tsvpGDP7yCsP",
        "outputId": "f10444b2-0d5c-4438-c249-6376a3ce875e"
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      "execution_count": null,
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Additional Metrics for Naive Bayes:\n",
            "Accuracy: 0.73992673992674\n",
            "Precision: 0.7389705882352942\n",
            "Recall: 1.0\n",
            "F1 Score: 0.849894291754757\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from matplotlib.colors import LinearSegmentedColormap\n",
        "\n",
        "# Obtain the confusion matrix for the Naive Bayes classifier\n",
        "conf_matrix_nb = confusion_matrix(y_test, y_pred)\n",
        "print(\"\\nConfusion Matrix for Naive Bayes:\")\n",
        "print(conf_matrix_nb)\n",
        "\n",
        "# Create a custom colormap from white to yellow\n",
        "cmap_colors_yellow = [(1, 1, 1), (1, 1, 0)]  # (white, yellow)\n",
        "custom_cmap_yellow = LinearSegmentedColormap.from_list(\"custom_yellow\", cmap_colors_yellow, N=256)\n",
        "\n",
        "# Plot the confusion matrix as a heatmap for Naive Bayes with yellow\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix_nb, annot=True, cmap=custom_cmap_yellow, fmt='d', xticklabels=['Fail', 'Pass'], yticklabels=['Fail', 'Pass'])\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix for Naive Bayes')\n",
        "\n",
        "# Save the figure (using bbox_inches='tight' to avoid cropping)\n",
        "plt.savefig('naive_bayes.png', bbox_inches='tight')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "id": "0hUbiIfr0wb-",
        "outputId": "1edcd3c5-7716-4f74-f33e-25b03c17d5e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Confusion Matrix for Naive Bayes:\n",
            "[[  1  71]\n",
            " [  0 201]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.savefig('confusion_matrix_naive_bayes.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "jiPpUn-AhuAu",
        "outputId": "97bb4244-2130-47c2-ac5c-42bd2d93ed75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}