Leveraging K-Means clustering and advanced visual analysis and ML forecasting models to gather business insights for fleet optimization, enhance operational efficiency, design targeted customer-centric promotions.
Data Preprocessing
- Cleaned and prepared trip data.
- Handled outliers and engineered features like revenue per kilometer and average trip duration.
Exploratory Data Analysis
- Visualized demand trends, revenue zones, and time-based patterns using heatmaps and Tableau.
Machine Learning
- Applied K-Means clustering for hotspot identification and trip segmentation.
- Built LSTM and Holt-Winters models for demand forecasting.
Geospatial Insights
- Used geospatial visualization tools like Folium and Geopandas to map revenue and demand density by postcode.