Analyzed vacation rental listings and customer reviews to understand pricing patterns, neighborhood demand, host responsiveness, review score differences, and revenue estimates. Performed data cleaning, EDA, statistical summaries, and automated validation using PyTest.
The project provides insights for owners to improve listing quality and optimize pricing strategies.

Conducted end-to-end marketing data analysis to uncover audience behavior, customer value patterns, and buying trends. Performed RFM segmentation, clustering, visualization, KPI extraction, and developed insights for improving customer retention and marketing campaigns.
Delivered analysis-ready datasets, visual summaries, and data-driven business recommendations.

Analyzed a large transactional dataset (6M+ rows) to detect fraudulent activities using advanced machine learning methods. Performed extensive data cleaning, outlier handling, feature selection, and model training using Logistic Regression, Random Forest, and Gradient Boosting.
Optimized accuracy through hyperparameter tuning and validated workflow using automated test cases for reliability and consistency.

Performed unsupervised learning on Zomato restaurant data to group similar restaurants based on ratings, reviews, cuisines, and metadata. Conducted exploratory data analysis, scaling, dimensionality reduction, and implemented K-Means and Hierarchical clustering.
Generated actionable insights that help businesses identify patterns in customer preferences, restaurant groups, and market segments.

Developed a predictive model to identify passenger survival probability using supervised machine learning.
Performed data cleaning, missing value imputation, feature engineering, exploratory data analysis, and trained multiple models such as Logistic Regression, Random Forest, and XGBoost.
Achieved strong accuracy and built a clean, reproducible ML pipeline with visualization-based insights.