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Machine Learning · Mini Project
Loan Approval Prediction
Classifies loan applications with a fairness check on the result
1-2 weeks build
Overview
Predicts whether a loan application is likely to be approved using a public loan-prediction dataset from Kaggle with income, credit history, loan amount and property area. Missing values are imputed carefully, several classifiers are compared, and a short fairness section checks whether outcomes differ across gender and marital status. A Flask form scores new applications.
What makes it stand up
- 01Careful imputation of missing income and credit history
- 02Logistic regression, random forest and XGBoost compared
- 03Fairness check of approval rates across applicant groups
- 04ROC curve and confusion matrix in the report
- 05Flask application form with an explained decision
Module breakdown
- 01
EDA
Approval rates by income, credit history and area.
- 02
Preprocessing
Imputation, encoding and derived income ratios.
- 03
Models
Three classifiers with cross-validation.
- 04
Fairness
Group-wise approval and error rate comparison.
- 05
Web form
Flask page returning a decision and top factors.
After this, you will be able to
- Explain why credit history dominates the model
- Defend your handling of missing values
- Discuss the fairness risks of automated lending decisions
Technology stack
- Python
- Pandas
- scikit-learn
- XGBoost
- 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