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https://github.com/kom-senapati/student-performance-predictor
https://github.com/kom-senapati/student-performance-predictor
Last synced: about 5 hours ago
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- Host: GitHub
- URL: https://github.com/kom-senapati/student-performance-predictor
- Owner: kom-senapati
- License: mit
- Created: 2024-07-25T06:26:52.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-07-25T06:48:16.000Z (4 months ago)
- Last Synced: 2024-07-25T07:43:33.808Z (4 months ago)
- Language: Jupyter Notebook
- Size: 498 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🎓 Student Performance Predictor
Welcome to the **Student Performance Predictor** project! This repository provides a comprehensive solution for analyzing and predicting student performance using various data science techniques. It includes exploratory data analysis (EDA), machine learning models, and a Flask web application for interactive results.
## 📂 Project Structure
Here's an overview of the project's folder structure:
```
/Student Performance Predictor (main)
|-- Codes-Programs-Database/
| |-- Dataset/
| |-- Model/
| |-- Notebooks/
| | |-- Exploratory Data Analysis.ipynb
| | |-- Modelling.ipynb
| |-- requirements.txt
| |-- Website-Code/
| |-- app.py
| |-- static/
| | |-- style.css
| |-- templates/
| |-- base.html
| |-- index.html
| |-- predict.html
| |-- understand_data.html
|
|-- Screenshots (Final Output)/
```
### 📊 What We Did
1. **Exploratory Data Analysis (EDA)**: We performed data exploration and visualization to uncover insights and understand factors influencing student performance. This process is detailed in the **Exploratory Data Analysis.ipynb** notebook.
2. **Model Creation**: We developed predictive models to estimate student performance based on various factors. The **Modelling.ipynb** notebook covers the model training and evaluation process.
3. **Flask Web Application**: We built a Flask app to present the results interactively. Users can input their data and receive predictions via the web interface.
## 🚀 Getting Started
To get started with the project, follow these steps:
1. **Clone the Repository**
```bash
git clone https://github.com/kom-senapati/Student-Performance-Predictor.git
cd Student-Performance-Predictor
```
2. **Set Up the Environment**
Install the required packages using the provided `requirements.txt` file:
```bash
cd Codes-Programs-Database
python -m venv .venv
.venv/Scripts/activate
pip install -r requirements.txt
```
3. **Run EDA and Model Code**
Open and run the Jupyter notebooks in `Codes-Programs-Database/Notebooks/` to explore the data and build models.
4. **Start the Flask App**
Navigate to `Codes-Programs-Database/Website-Code/` and run:
```bash
python app.py
```
This will start the Flask server, and you can access the app at `http://127.0.0.1:5000`.
## 📸 Final Output
Check out the **Screenshots (Final Output)** folder for visualizations of the Flask app and results.
## 📑 License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## 🤔 Questions?
Feel free to open an issue if you have any questions or suggestions. Happy analyzing!