The main objective of this project is to predict house prices in a dataset with 79 explanatory variables describing several aspect of residential homes in the city of Ames, Iowa, USA. The original dataset can be find at Kaggle.
Specific objetives:
- Perform exploratory data analysis and investigate the relationship between different variables and the sale price of houses.
- Perform feature selection and determine the most important features for predicting house prices.
- Develop and test Machine Learning models to accurately predict house prices (with a maximum Root Mean Squared Error of 10%).
- Identify the key factors that significantly influence the sale price of houses.
- Provide recommendations or insights based on the model's predictions to assist in decision-making related to real estate investments.