This project uses Natural Language Processing techniques to classify whether a given sentence indicates stress or not. Built using Python and NLP libraries, it demonstrates the use of text preprocessing, vectorization, and machine learning for real-world emotional analysis.
Lung cancer classification using ensemble learning (Voting Classifier), SHAP explainability, and causal analysis on survey data. Includes data preprocessing, model comparison, and visualizations in Jupyter Notebook.
Disinformation-screening platform: SVM + Bi-GRU + Bi-LSTM ensemble, semantic retrieval, live fact-check, and manipulation-technique prebunking. A screening aid inside a human process — not a truth oracle.
DGX spark optimized stock market prediction machine leanring and ai platform
Road damage segmentation using UNet++ & EfficientNet with 5-Fold Cross Validation, ensemble prediction, AMP training, and threshold tuning in PyTorch.
Continuous target variable estimation using Random Forest Regressor and GridSearchCV hyperparameter tuning.
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