PopularMajor Project
Machine Learning
AI Resume Screening System
NLP pipeline that ranks candidates against a job description
- Python
- spaCy
- scikit-learn
- FastAPI
- +2
₹4,499₹7,999
4.9/214 reviews
Machine Learning · Mini Project
Restaurant Tip Prediction
Regression on the classic tips dataset with clear statistical reasoning
1-2 weeks build
Overview
Predicts restaurant tip amounts from the bill, party size, day, time and smoker fields in the well-known tips dataset bundled with Seaborn. Linear regression is built first and interpreted coefficient by coefficient, then compared with regularised and tree-based models. A small Flask form estimates a tip for any table you describe.
What makes it stand up
- 01Visual EDA of tip behaviour by day, time and party size
- 02Interpretable linear regression with coefficient analysis
- 03Ridge, Lasso and decision tree models for comparison
- 04Residual diagnostics and assumption checks in the report
- 05Beginner-friendly code with explanatory comments
Module breakdown
- 01
Exploration
Seaborn plots of tips against every feature.
- 02
Encoding
One-hot encoding of day, time and smoker fields.
- 03
Models
Linear, Ridge, Lasso and decision tree regressors.
- 04
Diagnostics
Residual plots and error metrics.
- 05
Web form
Flask page that estimates a tip.
After this, you will be able to
- Explain what each regression coefficient means
- Defend the assumptions behind linear regression
- Demo a tip estimate for a table the examiner describes
Technology stack
- Python
- Pandas
- scikit-learn
- Seaborn
- Flask
- Level
- Mini Project
- Domain
- Machine Learning
- 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