https://github.com/mr-ndi/tibebai

Machine learning experiments on student performance prediction. Inspired by tibeb (wisdom) in Amharic, this project explores regression models to understand how study factors influence exam scores.
https://github.com/mr-ndi/tibebai

ai data-science education elevvo google-colab internship kaggle linear-regression machine-learning matplotlib pandas polynomial-regression prediction regression scikit-learn student-performance tibebai-wisdom

Last synced: 2 months ago
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Machine learning experiments on student performance prediction. Inspired by tibeb (wisdom) in Amharic, this project explores regression models to understand how study factors influence exam scores.

• Host: GitHub
• URL: https://github.com/mr-ndi/tibebai
• Owner: Mr-Ndi
• Created: 2025-09-05T09:17:14.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-09-05T10:14:38.000Z (9 months ago)
• Last Synced: 2025-09-05T12:24:07.883Z (9 months ago)
• Topics: ai, data-science, education, elevvo, google-colab, internship, kaggle, linear-regression, machine-learning, matplotlib, pandas, polynomial-regression, prediction, regression, scikit-learn, student-performance, tibebai-wisdom
• Language: Python
• Homepage:
• Size: 165 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# TibebAI

*Tibeb* means **wisdom** in Amharic.  

This repository contains machine learning experiments focused on predicting **student performance** using regression models.  

Developed as part of the **Elevvo Machine Learning Internship Program**.  

---

## Project Overview

This project explores the relationship between **study hours** and **exam scores** using the [Student Performance dataset](https://www.kaggle.com/datasets/spscientist/students-performance-in-exams).  

The goal is to apply regression techniques to model, predict, and evaluate student outcomes.  

---

## Workflow

The notebook is organized into the following main steps:

1. **Installing Dependencies**  

   - Required Python libraries for data analysis and machine learning.  

2. **Data Cleaning & Visualization**  

   - Download dataset from Kaggle using the API  

   - Unzip and load data into Pandas  

   - Inspect datatypes and missing values  

   - Handle outliers (IQR method)  

   - Visualize features to understand distributions  

3. **Splitting the Dataset**  

   - Train/test split to prepare for modeling  

4. **Training Linear Regression Model**  

   - Import Linear Regression from `scikit-learn`  

   - Create model instance and fit with training data  

   - Make predictions on test data  

   - Evaluate model using metrics: **MAE, RMSE, R²**  

5. **Visualizing Results**  

   - Plot regression line  

   - Scatter plot of actual vs predicted values  

---

## Tools & Libraries

- Python  

- Pandas  

- Matplotlib  

- Scikit-learn  

- Kaggle API  

---

## Covered Topics

- Regression (Linear & Polynomial)  

- Model evaluation metrics (MAE, RMSE, R²)  

- Data cleaning & handling outliers  

- Data visualization  

---

## Repository Structure

```

TibebAI/

│── data/              # datasets (not uploaded if large)

│── notebooks/         # Google Colab / Jupyter notebooks

│── scripts/           # Python scripts for modular code

│── results/           # plots, model outputs, evaluation metrics

│── README.md          # project overview

```

---

## Showcase

- [GitHub Repository](https://github.com/Mr-Ndi/TibebAI)  

- [Google Colab Notebook Link](https://colab.research.google.com/drive/1tzRZ3TgFjph3g4QpG21_IHqjO9r3AP_B#scrollTo=DQONsDVvkguD)  

---

## Example Results

- **Predicted vs Actual Scores** scatter plot  

![Actual vs. Predicted Values](image.png) 

- **Regression Line** visualization  

![Linear Regression: Actual vs. Predicted from Training Data](image-1.png) 

## Future Work

- Experiment with polynomial regression for better fit  

- Add more features (sleep, participation, parental support, etc.)  

- Try advanced models: Random Forest, Gradient Boosting, Neural Networks  

- Deploy model as a simple web app using Streamlit/Flask  

---

## Inspiration

This project is named **TibebAI** to highlight the value of **wisdom** (*tibeb* in Amharic) in both learning and technology.  

---

## Internship Info

This work was completed as part of the **Elevvo Machine Learning Internship Program (August 2025)**.  

The program emphasizes real-world projects, personalized feedback, and professional portfolio building.  

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