* Leveraged advanced Exploratory Data Analysis (EDA) techniques including Univariate/Bivariate Analysis, Outlier Detection, and Feature Engineering to uncover insights and prepare data for modeling.* Applied a diverse set of machine learning algorithms such as Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, Principal Component Analysis (PCA), Hierarchical Clustering, Isolation Forest, and Deep Learning, alongside Time Series Analysis to tackle complex predictive challenges.* Ensured model reliability and validity using comprehensive validation techniques such as Leave-One-Out Cross-Validation (LOOCV), Stratified K-Fold Cross-Validation, and various evaluation metrics including Classification Matrices, Scatter/Box/Kernel Density Estimation Curves, and AUC-ROC analysis.* Enhanced business decision-making and reporting through proficient use of Business Analysis Techniques, including MySQL for database management, PowerBI for data visualization, Google Analytics for web analytics, and Streamlit for interactive web applications.

Product Recommendation System*Designed and implemented a sophisticated product recommendation system to offer alternatives for out-of-stock items, leveraging advanced techniques including matrix factorization, cosine similarity, and word-to-vec scoring. Further refined the system's accuracy with the integration of Jaro-Winkler similarity measures.*Successfully enhanced model efficiency, achieving a notable hit rates. Rigorously evaluated the system's performance employing metrics such as precision, recall, and F1-score to ensure optimal outcomes.Fraud Detection System* Developed an innovative fraud detection system utilizing Hidden Markov Models (HMMs), significantly reducing the threshold for fraud flagging from five transactions to just two, thereby enhancing early detection capabilities.* Strengthened the model's robustness and accuracy by incorporating user transaction history analysis, enabling the identification of anomalous patterns more effectively.

* Successfully modernized an outdated financial model by incorporating current financial data, significantly improving the precision of risk assessments.* Tackled data dispersion and resolved multicollinearity problems by calculating and categorizing a range of financial ratios, ensuring more reliable data interpretation.* Employed Random Forest and regularized Logistic Regression models to effectively address multicollinearity issues, enhancing the model's predictive capabilities.* Achieved high accuracy rates on test data, outperforming traditional Altman Z-Score Models. Utilized ROC and AUC curves for a more accurate determination of risk scores, marking a significant advancement in model performance.