Shreya Subhralika Nayak

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• Exposure to Python programming language through academic projects.
• Demonstrated ability to build and evaluate Machine Learning models (ARIMA, Prophet, XGBoost, LSTM, Logistic Regression, Random Forest, Isolation Forest, Autoencoder) for forecasting and fraud detection.
• Familiarity with data handling techniques like SMOTE for imbalanced datasets.
• Experience with model evaluation metrics (MAE, RMSE, Precision, Recall, F1-score).
• Basic understanding of SQL and data visualization tools (Excel, Power BI).
• The candidate's project experience aligns with data science applications of Python, which is a component of a Python Engineer role, but lacks direct evidence of core software engineering practices.