Shrouk-Sharaf/car-price-analysis

The project leverages Python's data science stack (Pandas, NumPy) for cleaning and feature engineering across 11,914 vehicle records, then surfaces insights through interactive Power BI dashboards connected to statistical correlation analysis. Beyond market bifurcation, it reveals engine specifications (HP, cylinders) as primary price predictors with quantified correlation coefficients and identifies right-skewed price distributions where median outweighs mean for representative analysis. The workflow integrates Jupyter notebooks for exploratory analysis with Power BI's business intelligence layer, enabling both technical validation and stakeholder-facing market segmentation reports.