##END TO END MACHINE LEARNING PROJECT

Student Exam Performance Predictor

📌 Project Overview

The Student Exam Performance Predictor is a machine learning-based web application that predicts a student’s Maths exam score based on demographic data, parental education, test preparation, and performance in other subjects (Reading and Writing). This project demonstrates end-to-end ML workflow, from data preprocessing and model training to deployment as a Flask web app.

🛠 Features

• Predicts Maths scores based on:

  • Gender
  • Race/Ethnicity
  • Parental level of education
  • Lunch type
  • Test preparation course
  • Writing and Reading scores

• Simple and intuitive web interface built with Flask

• Real-time predictions

• Input validation for accurate results

📊 Dataset

The app uses the Students Performance Dataset from Kaggle, which contains:

Feature	Description
Gender	Student’s gender (male/female)
Race/Ethnicity	Group classification (A–E)
Parental Level of Education	Highest parental education level
Lunch	Standard or free/reduced
Test Preparation Course	Completed or none
Reading Score	Score out of 100
Writing Score	Score out of 100
Maths Score	Score out of 100 (Target variable)

Source: Kaggle: Students Performance Dataset

🧠 Model

• Model type: Regression model (Random Forest / XGBoost / Linear Regression)
• Target variable: Maths Score
• Features: All other columns except Maths score
• Evaluation Metrics: MAE, RMSE, R² Score

💻 Installation

1. Clone the repository

git clone https://github.com/AnjaliSinghhhh/mlproject.git
cd mlproject

2. Create a virtual environment

python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

3. Install dependencies

pip install -r requirements.txt

4. Run the Flask app

python app.py

5. Open the app in browser

http://127.0.0.1:5000/predictdata

DEMO VIDEO

📸 Screenshot

🔮 Usage

1. Open the web app.
2. Select your Gender, Race, Parental Education, Lunch type, and Test Preparation Course.
3. Enter Writing and Reading scores.
4. Click "Predict your Maths Score" to get the predicted Maths score instantly.

🏆 Key Takeaways

• Demonstrates data preprocessing, model training, and deployment.
• Shows ability to build interactive ML applications.
• Highlights knowledge of regression modeling, feature engineering, and evaluation.
• Ready to showcase in ML portfolios or interviews.

📂 Future Improvements

• Add predictions for all subjects.
• Include visualizations for predicted vs actual scores.
• Deploy on Heroku or AWS for public access.
• Implement model comparison (Random Forest vs XGBoost vs Linear Regression).

⚡ Tech Stack

• Python 3.x
• Flask
• Scikit-learn / XGBoost
• Pandas / NumPy
• HTML/CSS (Bootstrap optional)

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