Customer Purchase Prediction (Programming Language - Python) : A self-learning experiment to understand the 'lifetimes' library in Python. Started out by understanding the Modified Beta Geometric and Gamma-Gamma models. Used the in-built functions in the 'lifetimes' library to predict when a customer is likely to make his next purchase and to predict which customer will end up spending the most. Also predicted the customer's probability of dropping out at different time intervals.
Classification of Audit Firms (Programming Language - Python) : Part of the Applied Machine Learning graduate course. Our objective was to implement and compare the performances of different classification algorithms like knn, Linear SVM, Kernal SVM, Decision Tree and Logistic Regression. The performance was determined by an algorithm's ability to distinguish between legit audit firms from the fraudulent ones.
Morris Hite Marketing Challenge (Programming Language - R, Data Viz - Tableau) : A project focussed on implementing marketing analytics and consumer insights concepts. Our objective was to maximize the revenue of our client by customer segmentation and by providing recommendations to shift market share from our competitors to our client.
Predictive Analytics using Shopko Dataset (Programming Language - SAS) : Part of the Predictive Analytics using SAS graduate course. Our objective was to maximize the revenue of the store and suggest marketing strategies by implementing techniques like customer segmentation, marcomm analysis and market basket analysis.
Some more projects:
Product Recommendation - Tableau and Python
Grocery Store Price Comparison - Tableau and Python
Netflix Movies and TV Shows Exploratory Analysis
Jumpman New Market Analysis - Python
E-Commerce Dataset Analysis using R
I worked as a Data Analyst Intern with HealthStream, Inc. I was the part of their Business Enablement team whose primary responsibility was to look after the need of internal stakeholders.
During my time as an intern, I worked on creating a query builder for HealthStream's proprietary data using R shiny and Python. This query builder was aimed at reducing the time taken to generate revenue reports and also made it easier for the non-technical users, having no prior knowledge of DAX, to build queries intuitively using the query builder's interface.
I also got an opportunity to create interactive dashboards for the Business Enablement and Sales teams using Power BI.
Read more about my internship experience below! Fall 2018 Internship Experience - HealthStream
Spring 2019 Internship Experience - HealthStream
Programming Languages - Python, R, SAS, C++
Query Languages - SQL, DAX
Database Systems - MySQL, SQL Server, PostgreSQL
Data Visualization - Tableau, Power BI
- Dean's Excellence Scholarship (Naveen Jindal School of Management | The University of Texas at Dallas) - Awarded tuition credit and eligible for In-state tuition fee for the scholarship period of one year.