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Required Python libraries for data analysis and machine learning.  \n\n2. **Data Cleaning \u0026 Visualization**  \n   - Download dataset from Kaggle using the API  \n   - Unzip and load data into Pandas  \n   - Inspect datatypes and missing values  \n   - Handle outliers (IQR method)  \n   - Visualize features to understand distributions  \n\n3. **Splitting the Dataset**  \n   - Train/test split to prepare for modeling  \n\n4. **Training Linear Regression Model**  \n   - Import Linear Regression from `scikit-learn`  \n   - Create model instance and fit with training data  \n   - Make predictions on test data  \n   - Evaluate model using metrics: **MAE, RMSE, R²**  \n\n5. **Visualizing Results**  \n   - Plot regression line  \n   - Scatter plot of actual vs predicted values  \n\n---\n\n## Tools \u0026 Libraries\n- Python  \n- Pandas  \n- Matplotlib  \n- Scikit-learn  \n- Kaggle API  \n\n---\n\n## Covered Topics\n- Regression (Linear \u0026 Polynomial)  \n- Model evaluation metrics (MAE, RMSE, R²)  \n- Data cleaning \u0026 handling outliers  \n- Data visualization  \n\n---\n\n## Repository Structure\n```\n\nTibebAI/\n│── data/              # datasets (not uploaded if large)\n│── notebooks/         # Google Colab / Jupyter notebooks\n│── scripts/           # Python scripts for modular code\n│── results/           # plots, model outputs, evaluation metrics\n│── README.md          # project overview\n\n```\n\n---\n\n## Showcase\n- [GitHub Repository](https://github.com/Mr-Ndi/TibebAI)  \n- [Google Colab Notebook Link](https://colab.research.google.com/drive/1tzRZ3TgFjph3g4QpG21_IHqjO9r3AP_B#scrollTo=DQONsDVvkguD)  \n\n---\n\n## Example Results\n- **Predicted vs Actual Scores** scatter plot  \n![Actual vs. Predicted Values](image.png) \n\n- **Regression Line** visualization  \n![Linear Regression: Actual vs. Predicted from Training Data](image-1.png) \n\n\n## Future Work\n- Experiment with polynomial regression for better fit  \n- Add more features (sleep, participation, parental support, etc.)  \n- Try advanced models: Random Forest, Gradient Boosting, Neural Networks  \n- Deploy model as a simple web app using Streamlit/Flask  \n\n---\n\n## Inspiration\nThis project is named **TibebAI** to highlight the value of **wisdom** (*tibeb* in Amharic) in both learning and technology.  \n\n---\n\n## Internship Info\nThis work was completed as part of the **Elevvo Machine Learning Internship Program (August 2025)**.  \nThe program emphasizes real-world projects, personalized feedback, and professional portfolio building.  \n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmr-ndi%2Ftibebai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmr-ndi%2Ftibebai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmr-ndi%2Ftibebai/lists"}