78-way handwritten medicine-name classification
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May 3, 2026 - Python
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k-fold-cv
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An end-to-end machine learning project predicting employee burnout risk (No Risk / At Risk / Burned Out) using 8,500 samples. Covers EDA, feature engineering, SMOTE-Tomek imbalance handling, 5 classifiers with GridSearchCV optimization, and carbon emission tracking via CodeCarbon.
python machine-learning time random-forest classification logistic-regression decision-trees knn-classification xgboost-classifier codecarbon k-fold-cv
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May 29, 2026 - Jupyter Notebook
ML-based student dropout prediction system handling class imbalance using SMOTE, Random Forest, and K-Fold validation with ~82% F1-score.
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Apr 4, 2026 - Python
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