Conducted end-to-end exploratory data analysis

Conducted end-to-end exploratory data analysis on a real estate housing dataset to uncover key price-driving factors. Performed data cleaning, handled missing values and outliers, and engineered new features to improve data quality. Applied statistical analysis to find correlations between property attributes and sale price, with visualizations (histograms, heatmaps, scatter plots) showing trends. Built using Python, Pandas, NumPy, Matplotlib, and Seaborn in Jupyter Notebook, demonstrating strong data wrangling, feature engineering, and analytical storytelling for real estate decisions.