House Prices Prediction

Problem & Motivation:

Given a dataset of houses, the goal was to use the input columns to predict the price column without leaking information from the validation or test sets.

Data & Approach:

• Explored the dataset using pandas to understand the rows, columns, and basic statistics.
• Split the data into train, validation, and test sets using the provided code.
• Trained two linear regression models in scikit-learn: one using a small set of basic features and one using a larger set of advanced features.
• Evaluated both models by computing the RMSE on the training and validation sets.

Results:

• The model with the advanced features performed better on the validation data, so it was used to compute the final test error.

Limitations:

Only linear regression was used; no other feature sets or modeling choices were explored beyond the assignment requirements.