SVM.ipynb

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      "source": [
        "import pandas as pd\n",
        "data = pd.read_csv('/content/data_no_outliers.csv')\n",
        "data.head()"
      ],
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            "text/plain": [
              "   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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        }
      ]
    },
    {
      "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",
        "#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())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LqZBD8Ix2KVo",
        "outputId": "86bf76ba-119f-421f-9761-1c2619845551"
      },
      "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.svm import SVC\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "# ... (your previous code for oversampling)\n",
        "\n",
        "# Initialize and train the Support Vector Machines (SVM) classifier\n",
        "svm_classifier = SVC(random_state=42)\n",
        "svm_classifier.fit(X_train_resampled, y_train_resampled)\n",
        "\n",
        "# Predict on the testing set\n",
        "y_pred_svm = svm_classifier.predict(X_test)\n",
        "\n",
        "# Evaluate the SVM classifier\n",
        "print(\"\\nClassification Report for SVM:\")\n",
        "print(classification_report(y_test, y_pred_svm))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "POlmZQFJ2gRM",
        "outputId": "0acb7e81-8b18-4357-cd60-c2003e2f583b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report for SVM:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.99      0.92        72\n",
            "           1       0.99      0.95      0.97       201\n",
            "\n",
            "    accuracy                           0.96       273\n",
            "   macro avg       0.93      0.97      0.95       273\n",
            "weighted avg       0.96      0.96      0.96       273\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "# Calculate additional metrics\n",
        "accuracy = accuracy_score(y_test, y_pred_svm)\n",
        "precision = precision_score(y_test, y_pred_svm)\n",
        "recall = recall_score(y_test, y_pred_svm)\n",
        "f1 = f1_score(y_test, y_pred_svm)\n",
        "\n",
        "# Print additional metrics\n",
        "print(\"\\nAdditional Metrics for SVM:\")\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": "rFH1uWhM3Ej9",
        "outputId": "39f77cf6-43f4-45a6-b4a2-9edfcd7ca078"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Additional Metrics for SVM:\n",
            "Accuracy: 0.9560439560439561\n",
            "Precision: 0.9947643979057592\n",
            "Recall: 0.945273631840796\n",
            "F1 Score: 0.9693877551020408\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",
        "# ... (your previous code for oversampling)\n",
        "\n",
        "# Obtain the confusion matrix for SVM\n",
        "conf_matrix_svm = confusion_matrix(y_test, y_pred_svm)\n",
        "print(\"\\nConfusion Matrix for SVM:\")\n",
        "print(conf_matrix_svm)\n",
        "\n",
        "# Create a custom colormap from white to pink\n",
        "cmap_colors = [(1, 1, 1), (1, 0.75, 0.8)]  # (white, pink)\n",
        "custom_cmap = LinearSegmentedColormap.from_list(\"custom_pink\", cmap_colors, N=256)\n",
        "\n",
        "# Plot the confusion matrix as a heatmap for SVM\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix_svm, annot=True, cmap=custom_cmap, fmt='d', xticklabels=['Fail', 'Pass'], yticklabels=['Fail', 'Pass'])\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix for SVM')\n",
        "plt.savefig('naive_bayes.png', bbox_inches='tight')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "id": "MAGk_WFmEqET",
        "outputId": "779daa85-2306-4589-a426-89c1b631df1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Confusion Matrix for SVM:\n",
            "[[ 71   1]\n",
            " [ 11 190]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}