https://github.com/saba-gul/comparative-study-of-predicting-students-adaptability-level-in-online-education

Comparative Study of KNN and Decision Tree Models for Predicting Students' Adaptability Level in Online Education
https://github.com/saba-gul/comparative-study-of-predicting-students-adaptability-level-in-online-education

decision-tree-classifier education entropy gini-impurity informationgain knearest-neighbor-algorithm knn-classification onlineeducation

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Comparative Study of KNN and Decision Tree Models for Predicting Students' Adaptability Level in Online Education

• Host: GitHub
• URL: https://github.com/saba-gul/comparative-study-of-predicting-students-adaptability-level-in-online-education
• Owner: Saba-Gul
• Created: 2024-05-06T08:33:41.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-06T08:39:54.000Z (8 months ago)
• Last Synced: 2024-05-06T09:45:50.897Z (8 months ago)
• Topics: decision-tree-classifier, education, entropy, gini-impurity, informationgain, knearest-neighbor-algorithm, knn-classification, onlineeducation
• Language: Jupyter Notebook
• Homepage:
• Size: 1.11 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Comparative Study of KNN and Decision Tree Models for Predicting Students' Adaptability Level in Online Education

## Objective:

The objective of this study is to compare the performance of K-Nearest Neighbors (KNN) and Decision Tree models in predicting students' adaptability levels in online education. The study aims to analyze the effectiveness of these machine learning algorithms in classifying students based on various features related to their demographics, educational background, and online learning behavior.

## Dataset:

The dataset used for this study consists of the following features:

- **Gender**

- **Age**

- **Education Level**

- **Institution Type**

- **IT Student**

- **Location**

- **Load-shedding**

- **Financial Condition**

- **Internet Type**

- **Network Type**

- **Class Duration**

- **Self LMS**

- **Device**

- **Adaptability Level**

Example dataset:

```

| Gender | Age   | Education Level | Institution Type | IT Student | Location | Load-shedding | Financial Condition | Internet Type | Network Type | Class Duration | Self LMS | Device | Adaptability Level |

|--------|-------|-----------------|------------------|------------|----------|---------------|---------------------|---------------|--------------|----------------|----------|--------|--------------------|

| Boy    | 21-25 | University      | Non Government   | No         | Yes      | Low           | Mid                 | Wifi          | 4G           | 3-6            | No       | Tab    | Moderate           |

| Girl   | 21-25 | University      | Non Government   | No         | Yes      | High          | Mid                 | Mobile Data   | 4G           | 1-3            | Yes      | Mobile | Moderate           |

| Girl   | 16-20 | College         | Government       | No         | Yes      | Low           | Mid                 | Wifi          | 4G           | 1-3            | No       | Mobile | Moderate           |

| Girl   | 11-15 | School          | Non Government   | No         | Yes      | Low           | Mid                 | Mobile Data   | 4G           | 1-3            | No       | Mobile | Moderate           |

| Girl   | 16-20 | School          | Non Government   | No         | Yes      | Low           | Poor                | Mobile Data   | 3G           | 0              | No       | Mobile | Low                |

```

## Methodology:

1. **Data Preprocessing**: 

   - Handle missing values, if any.

   - Encode categorical features.

   - Split the dataset into training and testing sets.

2. **Model Training**:

   - Train KNN and Decision Tree models on the training data.

3. **Model Evaluation**:

   - Evaluate the performance of each model using metrics such as accuracy, precision, recall, and F1-score.

   - Generate classification reports and confusion matrices for detailed analysis.

4. **Comparison**:

   - Compare the performance of KNN and Decision Tree models based on evaluation metrics.

   - Analyze the strengths and weaknesses of each model.

## Results:

- Detailed analysis of the performance of KNN and Decision Tree models.

- Comparison of accuracy, precision, recall, and F1-score between the two models.

- Insights into the effectiveness of each model in predicting students' adaptability levels.

## Conclusion:

Based on the results obtained from the comparative study of KNN and Decision Tree models for predicting students' adaptability levels in online education, the following conclusions can be drawn:

1. **Performance Comparison**:

   - Both KNN and Decision Tree models demonstrate reasonable performance in predicting students' adaptability levels.

   - The Decision Tree model, particularly the one based on Entropy, shows slightly better performance compared to the KNN models in terms of accuracy, precision, recall, and F1-score.

   - The Gini-based Decision Tree model performs poorly, primarily due to its inability to handle class imbalances and misconfiguration issues.

2. **Model Complexity**:

   - KNN is a non-parametric model that does not make assumptions about the underlying data distribution. It relies on proximity to classify instances.

   - Decision Tree models are interpretable and easy to understand, making them suitable for decision-making processes that require transparency.

3. **Interpretability**:

   - Decision Tree models provide insights into the decision-making process by identifying the most important features for classification.

   - KNN models, while effective, lack interpretability and do not offer insights into the underlying decision boundaries.

4. **Scalability**:

   - KNN can be computationally expensive, especially with large datasets, as it requires calculating distances between all data points.

   - Decision Tree models are faster and more scalable, making them suitable for larger datasets and real-time applications.

5. **Robustness to Noise**:

   - KNN is sensitive to noisy data and outliers, as it relies on the nearest neighbors for classification.

   - Decision Tree models can handle noisy data to some extent but may suffer from overfitting if not pruned properly.

6. **Parameter Sensitivity**:

   - KNN performance can be influenced by the choice of the number of neighbors (k).

   - Decision Tree models may require tuning parameters such as maximum depth and minimum samples per leaf to optimize performance.

7. **Overall Recommendation**:

   - Based on the results of this study, the Decision Tree model, particularly the one based on Entropy, emerges as the preferred choice for predicting students' adaptability levels in online education. It offers a good balance between performance, interpretability, and scalability.

8. **Future Directions**:

   - Further research could explore ensemble methods such as Random Forests or Gradient Boosting, which combine multiple Decision Tree models to improve predictive performance.

   - Additionally, investigating feature engineering techniques and incorporating domain-specific knowledge may enhance the predictive accuracy of the models.

In summary, while both KNN and Decision Tree models show promise in predicting students' adaptability levels, the Decision Tree model, particularly the one based on Entropy, offers better performance, interpretability, and scalability, making it the preferred choice for this task. However, further research and experimentation may be warranted to explore alternative modeling approaches and optimize predictive accuracy.