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. # 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. 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 - Google Colab Notebook Link --- ## Example Results - **Predicted vs Actual Scores** scatter plo …