Boston Report

In this project, I embarked on a journey to predict Boston house prices using machine learning and deep learning techniques. The central focus of the project was to construct a predictive model that outperforms traditional linear regression by harnessing the power of neural networks.

Highlights

• Baseline Linear Regression: Established a baseline using scikit-learn’s linear regression to serve as a benchmark for neural network performance.
• Neural Network Implementation: Designed and implemented a neural network tailored for predicting Boston house prices, leveraging its ability to model complex feature interactions.
• Feature Engineering: Selected and transformed relevant features to improve model accuracy.
• Hyperparameter Tuning: Fine-tuned the neural network through multiple configurations to maximize predictive performance.

Outcomes

This project reveals compelling insights by comparing a simple linear regression model to a neural network using a classic dataset. It highlights the value of deep learning even in structured, tabular data and demonstrates a solid workflow from baseline to advanced modeling.

Attachments