Student Marks Prediction – End-to-End Machine Learning Project

An end-to-end machine learning system that predicts a student's marks using features such as study hours, past performance, attendance, and behavioral/academic patterns.
This project demonstrates a full ML workflow including EDA → preprocessing → model training → evaluation → deployment.

---

Project Highlights

• Built a complete ML pipeline to predict student marks
• Includes EDA, data cleaning, feature engineering, and model building
• Implemented multiple regression models and selected the best
• Created a production-ready prediction script
• End-to-end structure following real-world ML standards
• Scalable for API / UI integration

---

Tech Stack

Category	Tools Used
Language	Python
ML Libraries	Scikit-learn, NumPy, Pandas
Visualization	Matplotlib, Seaborn
Development	Jupyter Notebook, VS Code
Deployment Ready	Can be wrapped into Flask/FastAPI

---

Dataset Details

(Update based on your dataset columns)
Typical features used for predicting student marks:

• Study Hours
• Attendance Percentage
• Previous Exam Scores
• Sleep Duration
• Assignment Completion
• Internet/Phone Usage
• Target: Final Exam Marks

Dataset preprocessing includes:

• Handling missing values
• Scaling numerical features
• Encoding categoricals
• Removing outliers
• Splitting into train/test

---

Model Training

Models tested:

• Linear Regression
• Lasso Regression
• Ridge Regression
• Random Forest Regressor
• Gradient Boosting Regressor

Common metrics used:

• R² Score
• MAE (Mean Absolute Error)
• RMSE

(Fill these with your real results)
Example:

Model	R² Score
Linear Regression	0.87
Random Forest	0.93
Gradient Boosting	0.95

---

Final Prediction Script

After training, the best model is saved in:

Folder Structure

machine-learning-project/
│── data/                     # Raw and processed datasets (if applicable)
│── notebooks/                # Jupyter notebooks for experiments  
│── scripts/                  # Standalone Python scripts  
│── models/                   # Saved/trained model artifacts  
│── README.md                 # Project documentation  
│── requirements.txt          # Python dependencies  

Install dependencies and How to Run

git clone https://github.com/kumar-kiran-24/machine-learning-project
cd machine-learning-project
pip install -r requirments.txt

python app.py