🧠 Weight Loss Prediction using Linear Regression

This project predicts final weight (wl3) based on several factors such as previous weight and self-efficacy scores using a simple Linear Regression model.

---

📊 Dataset

The dataset used is available on Kaggle:
Weight Loss Data

Columns:

• wl1, wl2, wl3: Weight measurements at different stages
• se1, se2, se3: Self-efficacy scores
• group: Participant group

---

⚙️ Project Workflow

1. Load and clean the dataset
2. Train a Linear Regression model
3. Evaluate the performance (MAE, R²)
4. Visualize actual vs predicted results
5. Analyze feature importance

---

🧩 Results

• The model achieves a reasonable prediction accuracy (R² score varies by dataset quality).
• The feature importance plot reveals which factors have the strongest influence on final weight.

---

🛠️ Tools & Libraries

• Python
• Pandas
• Scikit-learn
• Matplotlib

---

📈 Example Output

• Scatter plot: Actual vs Predicted Weight
• Bar chart: Feature Importance

---

💡 Author

Samah Abuayied
LinkedIn