swayamjaiswal7/Bank-Term-Deposit-Classify
Campaign Analysis through ML models
What's novel
Campaign Analysis through ML models
Code Analysis
6 files read · 3 roundsA Streamlit dashboard that loads pre-trained sklearn classification models (Gradient Boosting, Random Forest, Decision Tree) on the UCI Bank Marketing dataset to predict term deposit subscription, with SHAP explanations and feature importance visualizations.
Strengths
Proper use of sklearn ColumnTransformer pipelines with consistent preprocessing, class imbalance handling via class_weight='balanced', and inclusion of SHAP explainability alongside multiple model comparison. Statistical analysis notebook demonstrates reasonable use of chi-square, t-tests, Mann-Whitney U, and logistic regression for feature significance.
Weaknesses
All logic lives in Jupyter notebooks with zero reusable Python modules, no tests, no error handling, and a 13,751-file virtual environment committed to the repository. The README overstates the project as a 'Marketing Campaign Analytics' tool when it is essentially a standard ML classification dashboard on a well-known public dataset with no novel algorithms or business logic.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
14
Contributors
1
Files
22476
Active weeks
1
Repository
Language
Python
Stars
1
Forks
0
License
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