Housing Prices & Bike Sharing Analytics
This dual-topic project combines Python for data preparation and machine learning, and Power BI for dashboard creation. The two datasets explored:
- Housing Prices: Feature engineering and regression models to predict house sale prices.
- Bike Sharing: Time-based and environmental factors influencing daily bike rental demand.
Python Workflow
- Data Cleaning and Feature Selection
- EDA and Outlier Treatment
- Regression Models (Linear Regression, XGBoost, Random Forest)
Notebooks:
View Notebook Housing Prices – Data Prep
View Notebook Housing Prices – Prediction
Power BI Dashboards
Complementary dashboards display model results and trends using filters and visuals for stakeholders.
Trend of house prices by condition and location
Bike demand segmented by weather and working days
