Mini Project
Data Science
Personal Finance Analytics
Statement parsing, auto-categorisation and spend forecasting
- Python
- Pandas
- Prophet
- Streamlit
- +2
₹2,299₹3,999
4.5/198 reviews
Data Science · Mini Project
Uber Trips Analysis
Finds when and where pickups peak across a city
1-2 weeks build
Overview
Explores the public Uber Pickups in New York City dataset released by FiveThirtyEight, covering several months of trips. Timestamps are broken into hour, weekday and month to find demand peaks, pickup coordinates become heatmaps on an interactive Folium map, and a simple clustering step suggests where drivers should wait at different times of day.
What makes it stand up
- 01Hour, weekday and month demand breakdowns
- 02Hour-by-weekday heatmap of trip volume
- 03Interactive Folium heatmap of pickup locations
- 04K-means pickup hotspots by time band
- 05Month-on-month growth across the dataset
Module breakdown
- 01
Loading
Merges monthly files and parses timestamps.
- 02
Time features
Derives hour, weekday, day and month columns.
- 03
Temporal analysis
Charts demand patterns across each time scale.
- 04
Spatial analysis
Folium heatmaps of pickup density.
- 05
Hotspots
Clusters coordinates to suggest waiting zones.
After this, you will be able to
- Explain the demand patterns you found and their likely causes
- Defend the number of clusters chosen for hotspots
- Demo the interactive map filtered to a rush hour
Technology stack
- Python
- Pandas
- Seaborn
- Folium
- Jupyter
- Level
- Mini Project
- Domain
- Data Science
- Build time
- 1-2 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support