• Utilized Python to analyze 1460 data houses listings and gain insights to predict the house price based on various features.
• Cleaned and reprocessed housing data using Pandas and NumPy to handle missing values and outliers.
• Utilized exploratory data analysis (EDA) techniques with Matplotlib and Seaborn to understand data distributions and relationships.
