Portfolio
I am pursuing MS in Business Analytics at University of Cincinnati and I am actively looking for full time jobs. I have experience in analytics domain working in a role of Business Analyst at ZS Associates and Oye Rickshaw. I am interested in Data Science and Analytics and always motivated to learn new tools and technology.
(Project Presentation) (Project Code)
Tool: Python
Methods: Linear Regression, Decision Tree, Random Forest, Neural Networks, Prophet Forecasting Model
In this project, I am trying to predict the demand of bike in Washington DC area based on historical numbers and various other features such as weather conditions and calender day type.
Tool: R Programming
Methods: Multiple Linear Regression, Recursive Feature Elimination (Using Random Forests)
The objective of the project is to prepare a model to predict car prices. Multiple Linear Regression is used to create the model and Recursive Feature Elimination using Random Forest and Cross Validation is used to select appropriate covariates. The project includes visualizations and model diagnostics to obtain more accurate model.
(Project Presentation) (Project Code)
Tool: R Programming
Methods: Chi-Square Test, Bootstrap
In this project I have analyzed the impact of adding gates (specific task based level) to different levels in the game on the player retention. The project uses bootstrap and statistical testing methods to determine whether adding gate at certain level significantly helps the retention or not.
(Project Presentation) (Project Code)
Tool: R Programming
Methods: EDA, Association Rules
This project is based on transactions data of a UK based retailer. The project aims to find the insights related to the product and customers associated with the retailer. Appropriate data visualizations are done to emphasize the insights. A Market Basket Analysis is done create a product recommendation based on the products in cart for a customer.
Tools: MS SQL, Tableau, R Programming
Methods: Exploratory Data Analysis
Here, I have analyzed the movies on OTT media services and categorized the media services platforms based on content, genre, ratings etc and concluded with the recommendation of media services for specific movie types.