Model Evaluation and Refinement – Linear Regression

This notebook demonstrates model evaluation and refinement using Linear Regression in Python.
It covers essential concepts such as train-test split, R² score, and cross-validation using scikit-learn.

---

📌 Description

This project helps understand how well a model generalizes to unseen data using techniques like:

• Train-Test Split with train_test_split()
• Performance evaluation using r2_score
• Robust testing via k-Fold Cross-Validation with cross_val_score() and cross_val_predict()

---

📖 Contents

• Introduction to Model Evaluation
• Training and Testing data split
• R² Score explanation
• Cross-validation process
• Code and comments for clarity

---

🛠️ Technologies Used

• Python 3
• Jupyter Notebook
• Libraries:
  • scikit-learn
  • numpy
  • pandas

---

🚀 How to Run

1. Clone the repository:

   git clone https://github.com/your-username/coursera-data-science-model-evaluation.git

2. Open the notebook using Jupyter:

   jupyter notebook

---

🎓 Author

Created by Nikhitha.R
As part of the IBM Data Science Professional Certificate course on Coursera.

---

📄 License

This project is open-source and available under the MIT License.