NYC Property Sales Analysis - Data Cleaning & Transformation, Statistical Analysis, Regression Modeling

● Project tools: Python, Jupyter Notebook, Pandas, Numpy, Seaborn, Plotly, Matplotlib, SciPy, Statsmodel, Tableau.

● Jupyter Notebook with supporting Tableau Visuals. Statistical Analysis on NYC property transactions. Investigated nearly 85,000 transactions. Cleaned and transformed data.
● Visually discovered Normal Distribution via Seaborn and Plotly. Cleaned outliers.

● Discovered factors impacting prices. Identified property in Manhattan as good purchase.

● Leveraged statsmodels for hypothesis testing to obtain P-values, empowering Recursive Feature Elimination (RFE), thus enabling removal of statistically insignificant features to improve the model.