I create amazing stories and insights with data and transform complex data into highly-customized, actionable, and scalable solution and help clients make better data-driven decisions leading to bringing new business, understanding target market and customer segmentation, boosting customer loyalty and making client PCI compliant.

I have experience in exploratory data analysis, machine learning, and statistics. I am passionate about learning new topics in data science, visualizing data and doing research. I love sharing valuable insights and making an impact that helps others learn through code, visualizations, and narratives.

I use data to resolve core business challenges for my client such as • Fraud detection • Merchant funding and billing prediction • Monetary impact prediction upon process delays • Understand customers’ spending patterns • Classify customer behavior and buying habits • Suggest personalized recommendations

Expertise: • Specialize in data cleaning, consolidation and building unified comprehensive data storage for advance analytics and machine learning • Strong working knowledge in Python data science libraries- Numpy, Pandas , Scikit-learn, Matplotlib and Seaborn • Comprehensive experience using SQL , Tableau, Splunk and IBM mainframes. • Deep understanding of Machine Learning models - Regression (Simple Linear, Multiple and Polynomial), Logistic Regression, Support Vector Machines, Decision Tree, Random Forest, K-nearest neighbors • Understand Model selection techniques like k-Fold Cross Validation and Grid Search • Understand model evaluation techniques like R-square, CAP analysis, Confusion Matrix and model boosting techniques such as XGBoost.