Business problem
Why this project matters
Developed a customer-propensity model for personal-loan targeting using exploratory analysis, feature preparation, classification models, and business-oriented evaluation.
Project scope
What the work covered
01Profile customer demographics and account behavior
02Prepare features and target labels
03Compare classification models
04Evaluate precision, recall, F1, and confusion matrices
05Translate model drivers into campaign recommendations
Workflow
From question to evidence
Business problem
→
Data preparation
→
EDA / feature work
→
Model or analysis
→
Evaluation
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Business conclusion
Analysis dashboard
Portfolio evidence areas
These bars summarize the emphasis of the completed work; detailed numeric outputs and full notebook cells remain available in the linked repository and HTML report.
Notebook evidence
Selected analysis and report visuals
Notebook / HTML report snapshot 1
Notebook / HTML report snapshot 2
Notebook / HTML report snapshot 3
Notebook / HTML report snapshot 4
Notebook / HTML report snapshot 5
Notebook / HTML report snapshot 6
Images are loaded from the public project repository so the portfolio stays synchronized with the published notebook/report assets.
Conclusions
What the analysis demonstrated
Customer financial relationships and account behavior help identify higher-propensity segments.
F1 offers a balanced view when both missed prospects and wasted outreach matter.
Model outputs can support prioritized outreach rather than indiscriminate campaigns.
GitHub README preview
Repository at a glance
loan-modelling-ml
Customer propensity modeling for personal-loan campaigns using EDA, classification, and business-oriented evaluation.
Next step
Discuss a similar project
Use the project inquiry form with Personal Loan Campaign preselected. Include the business objective, available data, timeline, and desired output.