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: I initiated the project by establishing a baseline using a linear regression model from the scikit-learn library. This served as a foundational benchmark against which the neural network's performance could be evaluated.
- Neural Network Implementation: The project's centerpiece was the development of a neural network model tailored specifically for predicting Boston house prices. This neural network brought the advantages of non-linearity and complex feature interactions, offering a superior modeling approach.
- Feature Engineering: To enhance model performance, I conducted thorough feature engineering, selecting and transforming relevant features to ensure optimal predictive accuracy.
- Hyperparameter Tuning: I fine-tuned the neural network's hyperparameters to achieve the best possible performance, iterating through various configurations to maximize predictive power.
Outcomes:
Reveals compelling insights by comparing a simple linear regression model to a neural network using a straightforward dataset.
Full report in PDF