Mini Project
Data Science
Social Sentiment Dashboard
Track opinion on a topic over time, with aspect breakdown
- Python
- Pandas
- Transformers
- Plotly
- +1
₹2,499₹4,399
4.6/238 reviews
Data Science · Mini Project
Personal Finance Analytics
Statement parsing, auto-categorisation and spend forecasting
1-2 weeks build
Overview
Upload a bank statement and get a full financial picture. Transactions are parsed and auto-categorised, recurring subscriptions are detected, and a time-series model forecasts next month of spending with confidence bands.
What makes it stand up
- 01PDF and CSV statement parsers for common bank formats
- 02Rule plus machine-learning hybrid transaction categorisation
- 03Recurring-payment detection from interval clustering
- 04Forecast with uncertainty intervals
- 05Budget goals with overspend alerts
Module breakdown
- 01
Ingestion
Statement parsing and schema normalisation.
- 02
Categorisation
Merchant rules plus a trained fallback classifier.
- 03
Insights
Category trends, top merchants and anomalies.
- 04
Forecasting
Seasonality-aware spend prediction.
- 05
Reporting
Monthly PDF summary export.
After this, you will be able to
- Explain seasonality and trend decomposition
- Justify your categorisation accuracy
- Show insights on your own real statement
Technology stack
- Python
- Pandas
- Prophet
- Streamlit
- Plotly
- SQLite
- Level
- Mini Project
- Domain
- Data Science
- Build time
- 1-2 weeks
- Difficulty
- Rating
- 4.5 / 5 · 198
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