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Machine Learning · Mini Project
Chronic Kidney Disease Prediction
Predicts CKD from routine clinical and lab values
1-2 weeks build
Overview
Works with the UCI Chronic Kidney Disease dataset of blood pressure, specific gravity, albumin, haemoglobin and other lab values. Much of the kit is honest data cleaning, since the raw file has many gaps, followed by a comparison of simple classifiers and a Flask form that scores a new patient record.
What makes it stand up
- 01UCI Chronic Kidney Disease dataset with a feature glossary
- 02Missing-value imputation strategies compared side by side
- 03Decision tree, random forest and logistic regression benchmarks
- 04Feature-importance chart tied to clinical meaning
- 05Academic screening prototype, not a diagnostic tool
Module breakdown
- 01
Data cleaning
Type fixes, category encoding and imputation.
- 02
Exploration
Distributions and correlations by CKD status.
- 03
Modelling
Three classifiers with cross-validated tuning.
- 04
Evaluation
Confusion matrix, ROC and per-class F1 in the report.
- 05
Web form
Flask page that scores a record and stores history.
After this, you will be able to
- Explain how your imputation choice affects the result
- Defend why recall matters more than accuracy here
- Demo scoring a new record through the web form
Technology stack
- Python
- scikit-learn
- Pandas
- Flask
- SQLite
- 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