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Machine Learning · Mini Project
Wine Quality Prediction
Predicts wine quality from its physicochemical test results
1-2 weeks build
Overview
Uses the UCI Wine Quality dataset to predict quality from measurements such as acidity, residual sugar, sulphates and alcohol. The problem is tackled two ways, as a regression on the score and as a good-versus-average classification, so the report can discuss how framing changes the model and the metrics. A Streamlit app takes lab values and returns a prediction.
What makes it stand up
- 01Regression and classification framings compared on the same data
- 02Correlation analysis of each chemical property against quality
- 03Handles the skewed quality distribution with class weighting
- 04Feature importance explaining which properties matter
- 05Streamlit app with sliders for each lab measurement
Module breakdown
- 01
EDA
Distributions, correlations and outlier checks.
- 02
Regression
Linear and random forest regressors on the score.
- 03
Classification
Binary quality label with weighted classifiers.
- 04
Evaluation
RMSE, F1 and confusion matrices in the report.
- 05
App
Interactive sliders feeding the saved model.
After this, you will be able to
- Explain how problem framing changes the choice of metric
- Defend how you dealt with the imbalanced quality scores
- Demo a prediction for a sample the examiner describes
Technology stack
- Python
- Pandas
- scikit-learn
- XGBoost
- Streamlit
- 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