Comparative Analysis of Linear and Nonlinear Regression for Building Energy Efficiency Prediction

This project develops and evaluates both linear and nonlinear regression models to predict building energy efficiency based on key physical and environmental features. It uses real-world datasets and applies data preprocessing, feature engineering, and model training to analyze how different algorithms perform under varying conditions. The study compares model accuracy using metrics such as Mean Absolute Error, Mean Squared Error, and R-squared to identify strengths and limitations of each approach. Visualization techniques help interpret model behavior and highlight performance differences. The results show that nonlinear regression captures complex patterns better and often achieves higher prediction accuracy than linear models.

Data Set : https://archive.ics.uci.edu/dataset/242/energy+efficiency

Process

1. 📥 Data Collection
   Load the building energy efficiency dataset from a reliable source.

2. 🔍 Data Understanding
   Inspect data structure, feature types, and target variables. Identify input features and outputs.

3. 🧹 Data Preprocessing
   Handle missing values, normalize features, and prepare the dataset for modeling.

4. 📊 Exploratory Data Analysis (EDA)
   Visualize data distributions and analyze relationships between variables.

5. 🛠️ Feature Engineering
   Select relevant features and transform variables to improve model performance.

6. 🤖 Model Development
   Train linear regression and nonlinear regression models using the prepared dataset.

7. 📏 Model Evaluation
   Evaluate models using metrics such as MAE, MSE, and R-squared.

8. ⚖️ Model Comparison
   Compare performance between linear and nonlinear models to identify the best approach.

9. 📈 Visualization
   Plot predictions and residuals to analyze model behavior.

10. ✅ Conclusion
    Summarize findings and determine which model performs better for energy prediction.

What I Learned

• 📊 Data quality drives model performance. Clean and well-structured data improves accuracy.
• 🤖 Different models fit different patterns. Nonlinear models handle complex relationships better.
• 📏 Evaluation metrics matter. MAE, MSE, and R-squared give different insights.
• 🛠️ Feature engineering improves results. Selecting and transforming features impacts predictions.
• 📈 Visualization helps understanding. Plots reveal patterns, errors, and model behavior.

How It Can Be Improved

• 🔄 Use more advanced models such as Random Forest or Gradient Boosting to improve prediction accuracy.
• 📊 Expand the dataset with more samples and additional features to capture more variation.
• ⚙️ Apply hyperparameter tuning to optimize model performance and reduce error.