This repository hosts a linear regression model implemented in Python, aimed at predicting housing prices in Boston. Leveraging the renowned Boston dataset, this project explores the relationship between various factors and the median value of owner-occupied homes. The model serves as a practical demonstration of machine learning techniques applied to real-world housing market data.
Dive into the Boston dataset, understanding its structure and the significance of its features. Explore correlations and insights that can guide the predictive modeling process.
Prepare the dataset for modeling by handling missing values, scaling features, and splitting the data into training and testing sets. Ensuring data cleanliness and relevance is crucial for accurate predictions.
Utilize scikit-learn to build a linear regression model. Train the model on the training data, allowing it to learn patterns and relationships between features and target variables.
Assess the performance of the trained model using evaluation metrics such as Mean Squared Error (MSE) and R-squared. Understand how well the model generalizes to unseen data and its ability to make accurate predictions.
Analyze the model's predictions and gain insights into the factors that most significantly influence housing prices in Boston.