House Price Prediction
A machine learning project that constructs a neural network regressor to predict house prices from a structured dataset of property features. The pipeline covers data preprocessing, feature scaling, network architecture design, hyperparameter tuning, and evaluation, demonstrating deep learning applied to real-world regression problems.
Tech Stack
The Problem
Accurate house price prediction is critical for real estate markets, mortgage lending, and investment decisions. Traditional linear models often fail to capture the complex non-linear relationships between property features and market price.
Approach
Built a multi-layer neural network regressor in Python using TensorFlow/Keras. Implemented data preprocessing with feature scaling and encoding, designed the network architecture with dropout and batch normalization, and tuned hyperparameters using validation set performance.
Key Challenges
- Feature engineering from raw property data with missing values
- Preventing overfitting on a relatively small dataset
- Choosing the right network depth and regularization strategy
Results
Achieved competitive RMSE on the test set, outperforming baseline linear regression models and demonstrating the value of neural networks for real estate price prediction.