A Neural Network approach for predicting house prices using Boston Housing Price dataset

Predicting house prices is a crucial challenge in real estate, helping buyers, sellers, and investors make informed decisions. In this project, we build a neural network to estimate housing prices using the Boston Housing Price dataset. Our approach leverages a Multilayer Perceptron (MLP), a type of artificial neural network, to learn the complex relationships between various housing features— such as crime rates, property tax, and the number of rooms—and their market value.To ensure accuracy, we preprocess the data using Min-Max Scaling, making it easier for the model to learn meaningful patterns. The MLP consists of multiple layers of neurons with ReLU activation functions, allowing it to capture intricate trends in housing prices. We train the model using backpropagation, optimizing with RMSprop and evaluating performance using metrics like Mean Absolute Error (MAE) and Mean Squared Error (MSE).The results highlight the potential of deep learning in real estate forecasting, showing how neural networks can provide valuable insights into price estimation. While this project focuses on MLPs, it opens the door to exploring even more advanced techniques, like Convolutional Neural Networks (CNNs) and Transformers, to further improve prediction accuracy. By applying AI to housing price prediction, we take a step toward smarter, data-driven decision-making in the real estate industry.By integrating AI into real estate forecasting, we move closer to smarter, more precise property valuation, helping individuals and businesses make better financial decisions based on data-driven insights.