This project demonstrates Exploratory Data Analysis (EDA) on the House Prices: Advanced Regression Techniques dataset, sourced from Kaggle. EDA is a critical first step in any data science project, used to understand the underlying patterns, structure, and relationships within the data before building predictive models. By analyzing the house prices dataset, which contains various factors affecting home sale prices (such as lot size, neighborhood, and the year built), I aim to uncover insights, clean the data, and identify key features that will influence the final predictive model. This project serves as a showcase of the EDA process, highlighting data visualization, feature exploration, and summary statistics in the context of real estate pricing.
This project contains a dataset from Kaggle. The dataset can be found on the Kaggle competition page: House Prices: Advanced Regression Techniques.