---

🎓 Student Performance Indicator: End-to-End Data Science Project

Welcome to the Student Performance Indicator project! This repository contains an end-to-end data science project aimed at predicting student exam performance based on various features.

🚀 Project Overview

This project leverages data science techniques to build a predictive model that estimates a student's exam performance. The project follows a structured data science workflow, including data collection, preprocessing, model building, evaluation, and deployment.

Student-Performance-Indicater-End-To-End-Data-Science-Project.mp4

🗂️ Project Structure

The project is organized into the following directories and files:

📁 artifacts/

• Description: Contains all the raw and processed data files used in the project.
• Files:
  • raw.csv: The main dataset used for this project.
  • test.csv: This data is used to test the model.
  • train.csv: This data is used to train the model.
  • model.pkl: This is the best model on base of evaluation metrics.
  • preprocesser.pkl: This is model obtained taht can do preprocessing on dataset.

📁 notebooks/

• Description: Jupyter notebooks for data exploration, analysis, and model experimentation.
• Files:
  • EDA_and_Model_training_student_perfomance_indicater.ipynb: Initial data exploration and visualization.Experimentation with different machine learning models. Evaluation of model performance with different metrics.

📁 src/

• Description: Source code for the project, including data processing, model building, and utility functions.
• Files:
  • data_ingestion.py: Code for data ingestion,exploration.
  • data_transformation.py: Code for data cleaning, feature engineering, and splitting the data.
  • model_trainer.py: Contains code for building and training the machine learning models.
  • prediction_pipeline.py: Contains code for building and training the machine learning models that can make prediction

Description: Code for the Flask web app that serves the model.

• Files:
  • app.py: The main Flask application file.
  • templates/: HTML templates for the web interface.
    • index.html: The home page of the web app where users can input data and get predictions.
  • static/: Static files like CSS and JavaScript for the web app.

📁 logs/

• Description: Logs generated during the project’s execution, useful for tracking progress and debugging.
• Files:
  • training.log: Logs related to the model training process.
  • app.log: Logs from the Flask application.

📄 requirements.txt

• Description: A list of Python packages required to run the project. Install them using pip install -r requirements.txt.

📄 README.md

• Description: The file you're currently reading, providing an overview and explanation of the project.

📄 LICENSE

• Description: The license under which the project is distributed.

📄 .gitignore

• Description: Specifies which files and directories should be ignored by Git to avoid unnecessary clutter in the repository.

🔧 Tools and Technologies

• Python: Programming language used for data manipulation and model building.
• Pandas & NumPy: Libraries for data manipulation and analysis.
• Scikit-learn: Machine learning library for model building and evaluation.
• Matplotlib & Seaborn: Libraries for data visualization.
• Flask: Web framework used for deploying the model.
• GitHub: Version control and project collaboration.

🎯 Key Features

• User-friendly Interface: The web app provides an easy-to-use interface for predicting student performance.
• Real-time Predictions: Get instant predictions based on input data.
• Comprehensive EDA: In-depth analysis of data to uncover insights.
• Robust Model: A well-trained model that offers reliable predictions.

📜 How to Use

1. Clone the repository:

   git clone https://github.com/muhammadadilnaeem/Student-Performance-Indicater-End-To-End-Data-Science-Project.git

2. Navigate to the project directory:

   cd Student-Performance-Indicater-End-To-End-Data-Science-Project

3. Install dependencies:

   pip install -r requirements.txt

4. Run the main.py:

   python main.py

5. Run the web app:

   python app.py

6. Access the app: Open your browser and go to http://127.0.0.1:5000/ to interact with the model.

🛠️ Future Work

• Add more features to the model for better prediction accuracy.
• Improve UI/UX of the web app for a more interactive user experience.
• Integrate advanced models like deep learning for more complex predictions.

🙌 Contributing

Feel free to fork this repository and contribute by submitting a pull request. Contributions are welcome!

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

MLFLOW_TRACKING_URI=Mlflow and Dagshub Dashboard for this Project

---