https://github.com/dinesh-1272/evaluation-techniques-for-student-learning-outcome-using-machine-learning
flask html-css machine-learning python
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/dinesh-1272/evaluation-techniques-for-student-learning-outcome-using-machine-learning
- Owner: dinesh-1272
- Created: 2024-05-29T07:46:06.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-13T06:40:36.000Z (about 2 years ago)
- Last Synced: 2025-03-06T15:24:01.309Z (over 1 year ago)
- Topics: flask, html-css, machine-learning, python
- Language: Python
- Homepage:
- Size: 1.14 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Evaluation Techniques for Student Learning Outcomes Using Machine Learning
## Overview
This project explores the application of machine learning techniques in evaluating student learning outcomes. It aims to identify innovative approaches that enhance the evaluation process and provide valuable insights into student performance and progress.
## Objectives
- Develop a predictive model integrating academic, behavioral, and co-curricular data to forecast student performance.
- Enable early identification of academic challenges and at-risk students.
- Facilitate tailored interventions to improve prediction accuracy and support educators with a transparent and scalable system.
## Project Structure
- **Introduction**: Overview of the project, objectives, problem statement, and scope.
- **System Study**: Examining the existing and proposed systems.
- **Software Project Plan**: Business and architecture diagrams.
- **System Analysis**: Data flow diagrams, UML diagrams, class diagrams, sequence diagrams, and collaboration diagrams.
- **System Requirements & Specifications**: Hardware and software requirements, front-end and back-end specifications.
- **System Design**: Form design.
- **System Implementation**: Coding and screenshots.
- **Testing**: Objectives, test process, test cases, types of testing, verification, and validation.
- **Report**: Analysis of the dataset, accuracy of ML algorithms, and finalized accuracy.
- **Conclusion**: Summary of findings.
- **Future Enhancement**: Suggestions for future improvements.
## Installation and Setup
1. **Clone the repository**:
```sh
git clone https://github.com/yourusername/your-repo-name.git
```
2. **Navigate to the project directory**:
```sh
cd your-repo-name
```
3. **Install the required dependencies**:
```sh
pip install -r requirements.txt
```
5. **Run the main python file**:
```sh
python main.py
```
6. **To Run the application file**:
Open Anaconda Navigator.
Create a new Environment and Run.
In the Command prompt, change the directory to you file location
```sh
flask run
```
## Usage
1. **Data Collection**: Information is gathered from various sources and stored in a format suitable for machine learning models.
2. **Data Preprocessing**: Transforming raw data to increase the accuracy and efficiency of the models.
3. **Feature Extraction**: Identifying and selecting a subset of relevant features from raw data.
4. **Model Selection**: Choosing appropriate machine learning models such as Decision Tree, AdaBoost, and CatBoost algorithms.
5. **Model Evaluation**: Assessing the performance and effectiveness of the trained models using methods like Hold-Out and Cross-Validation.
## Technologies Used
- Python
- Machine Learning Algorithms (AdaBoost, CatBoost, Decision Tree)
- Data Preprocessing Techniques
- Feature Extraction Methods
## Contributions
Contributions are welcome! Please fork the repository and submit a pull request for review.
## Acknowledgements
- Special thanks to Dr. D. R. Jiji Mol for guidance and support.
- Gratitude to all the Department of Computer Science staff members for their encouragement.
- Thanks to my parents for their support throughout the project.