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🎯 End-to-End Student Performance Prediction using ML & Deployment πŸš€

πŸ“Œ Overview

This project analyzes and predicts student performance in Mathematics based on socio-economic and academic factors using Machine Learning techniques and deploys it using Flask, AWS, and GitHub Actions for CI/CD.

πŸ”„ Project Life Cycle

βœ… Understanding the Problem

πŸ“Š Data Collection & Checks

πŸ“ˆ Exploratory Data Analysis (EDA)

πŸ›  Data Pre-Processing

πŸ€– Model Training & Evaluation

πŸ† Choosing the Best Model

πŸš€ Deployment with Flask & AWS

❓ Problem Statement

Understanding how student performance (test scores) is influenced by:

πŸ§‘β€πŸŽ“ Gender

🌍 Ethnicity

πŸŽ“ Parental Education

🍽 Lunch Type

πŸ“š Test Preparation Course

πŸ“‚ Dataset Features

πŸ“Œ Categorical Features:

  • Gender, Ethnicity, Parental Education, Lunch Type, Test Prep Course

πŸ“Œ Numerical Features:

  • Math Score, Reading Score, Writing Score

πŸš€ Steps Involved

1️⃣ Data Collection & Preprocessing – Handling missing values, duplicates, outliers

2️⃣ EDA – Visualizing trends & correlations πŸ“Š

3️⃣ Feature Engineering – Encoding categorical data, scaling, splitting datasets

4️⃣ Model Training – Experimenting with multiple ML models

5️⃣ Model Selection – Comparing models using evaluation metrics

6️⃣ Web Interface Development – Flask-based UI for easy predictions

7️⃣ Deployment – Hosted on AWS EC2 with ECR & GitHub Actions for CI/CD

πŸ›  Technologies Used

🐍 Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn)

πŸ““ Jupyter Notebook – Model training & analysis

πŸ€– Machine Learning (LinearRegression, RandomForestRegressor, GradientBoostingRegressor, KNeighborsRegressor, XGBRegressor, CatBoostRegressor, AdaBoostRegressor etc.)

🌍 Flask – Web interface for user-friendly predictions

☁ AWS (EC2, ECR) – Cloud deployment & containerized infrastructure

⚑ GitHub Actions – CI/CD automation for deployment

Result:

"The CatBoost Regressor achieved an RΒ² score of 0.85 on the test dataset, indicating that the model explains 85% of the variance in unseen data and 0.95 on the train dataset."

🎯 Expected Outcomes

πŸ“Œ Key insights into factors affecting student performance

πŸ“Œ Accurate ML model for predicting student math scores

πŸ“Œ Web app for real-time score prediction

πŸ“Œ Cloud-deployed solution for accessibility

πŸ“© Contact

πŸ“§ Shiva Prasad Naroju - [email protected]


Let me know if you need any further improvements! πŸš€πŸ”₯

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Leveraging Machine Learning πŸš€ to predict Math Scores πŸ“ˆ based on key features, Enables data-driven insights for better academic support and improvement strategies! πŸš€

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