AcademicPredict - Student Performance Prediction using Random Forest

Predicting student academic performance using a machine learning approach. This project leverages the Random Forest Classifier to analyze student data and predict academic outcomes based on various factors such as grades, lifestyle, and social background.

Table of Contents

• Project Overview
• Dataset Description
• Technologies Used
• Machine Learning Pipeline
• How to Run
• Model Evaluation
• Results
• Future Work
• License
• Contact

Project Overview

Academic performance prediction can help educators identify students who may need additional support. In this project, we build a Random Forest classification model to classify whether a student is likely to have a good or poor performance based on their demographic and academic features.

Dataset Description

Replace this with your own dataset description or link.

• Source: Student Performance Dataset
• Attributes: Gender, Age, Study Time, Failures, Family Support, Internet Access, Absences, G1, G2, G3, etc.
• Target Variable: Final Grade or Performance Label (Pass / Fail)

---

Technologies Used

• Language: Python
• Environment: Jupyter Notebook
• Libraries:
  • pandas, numpy for data manipulation
  • matplotlib, seaborn for visualization
  • scikit-learn for machine learning and model evaluation

Machine Learning Pipeline

1. Data Preprocessing

   • Handling missing values
   • Label encoding categorical variables
   • Feature scaling (if needed)

2. Feature Selection

   • Correlation analysis
   • Feature importance from Random Forest

3. Model Building

   • Splitting dataset into train/test
   • Applying Random Forest Classifier
   • Hyperparameter tuning (optional)

4. Evaluation

   • Confusion Matrix
   • Classification Report
   • Accuracy, Precision, Recall, F1-Score

---

How to Run

🔧 Prerequisites

Install Python 3.12 and Jupyter Notebook.

Clone this Repository

git clone https://github.com/RAVINDRAN-S/Students-Performance-Prediction---Random-Forest
cd Students-Performance-Prediction---Random-Forest

Install Dependencies

pip install pandas numpy matplotlib seaborn scikit-learn

Run the Notebook

jupyter notebook "Student Performance Prediction - Random Forest.ipynb"

Model Evaluation

• Metric Score
• Accuracy 0.995
• Precision 1.00
• Recall 0.96
• F1-Score 0.98

Results

• The Random Forest model provided a strong accuracy score of ~87%, outperforming other basic models.

• Key influential features: G1, G2, studytime, failures, and absences.

License

This project is licensed under the MIT License.

🔗 LinkedIn

Author - Ravindran S

Developer • ML Enthusiast • Neovim Customizer • Linux Power User

Hi! I'm Ravindran S, an engineering student passionate about:

• Linux & System Engineering
• AIML (Artificial Intelligence & Machine Learning)
• Full-stack Web Development
• Hackathon-grade project development

🔗 Connect With Me

You can reach me here:

Socials

Contact