Mini Project
Data Science
Personal Finance Analytics
Statement parsing, auto-categorisation and spend forecasting
- Python
- Pandas
- Prophet
- Streamlit
- +2
₹2,299₹3,999
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Data Science · Mini Project
Birth Trends Analysis & Forecasting
Explores decades of US birth records and forecasts daily births
1-2 weeks build
Overview
Works through the public CDC births dataset: cleaning outlier dates with sigma clipping, pivoting by decade and gender, and exposing the weekly and holiday dips in daily births. A Prophet model then forecasts future daily births with yearly, weekly and holiday seasonality, and its components are plotted so each effect can be explained on its own.
What makes it stand up
- 01Sigma-clipping to remove malformed and outlier records
- 02Decade-by-gender pivot tables and trend plots
- 03Day-of-year chart that surfaces holiday effects
- 04Prophet forecast with yearly, weekly and holiday terms
- 05Holdout evaluation with MAE and MAPE in the report
Module breakdown
- 01
Cleaning
Parses dates, drops invalid days and clips outliers.
- 02
Exploration
Pivots births by year, decade, weekday and gender.
- 03
Seasonality
Annotated day-of-year plot showing holiday dips.
- 04
Forecasting
Fits Prophet and projects daily births forward.
- 05
Evaluation
Compares forecasts with a held-out period.
After this, you will be able to
- Explain what trend, seasonality and holiday components mean
- Defend why sigma clipping suits this dataset
- Walk examiners through the forecast and its uncertainty band
Technology stack
- Python
- Pandas
- Prophet
- Matplotlib
- 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