Result oriented professional with 3 years of progressive experience in Data Analytics. I have strong interests in applying Machine Learning models to drive business insights and promote informed decision making.
Looking for Full-Time opportunities starting January 2019. Find out more about me and my projects below.
Sole analytics professional acting as a liason between the Inside-Sales team and Key Account Managers.
Tools Used: R, MS Excel, SAP-CRM
• Cleaned and analyzed sales data of nearly 1500 clients using packages like 'DPLYR', 'TIDYVERSE' and 'MICE'
• Built dashboards using Excel and 'GGPLOT2' for reporting monthly, quarterly and yearly sales information and suggested areas of improvement
Part of UIC curriculum, worked as marketing analyst to find areas of improvement in website traffic.
Tools Used: Google Analytics, WordPress, MS Excel
• Aggregated customer's data and developed goals to understand user subscription patterns and frequently used services
• Performed A/B testing to monitor response and make appropriate changes/enhancements to the website
Worked on client facing projects providing support as a service in an agile environment. Maintained 100% SLA compliance.
Tools Used: R, Excel, Azure, Tableau
• Performed statistical analysis of user behavior to identify potential business impacts. Used R and Excel
• Developed SQL scripts for daily cadence to enable data transformations and validations
• Performed ad-hoc analysis against multiple databases to generate reports and dashboards using Tableau and SQL
GPA: 3.59
GPA: 3.6
This project detects if a news article is fake or real. Infact It distinguishes the article-based on its context into different clusters. The underlying algorithm iused for this problem is a particularly famous Latent Dirichlet allocation (LDA) method for Unsupervised topic modeling. Topic modeling is a type of statistical modeling for discovering the abstract “topics” that occur in a collection of documents. It comes under the umbrella of Natural Language Processing.
This projects has two parts. One is to predict the propensity for a customer to buy insurance plan from an insurance company. Second is to design an incentive plan that can be provided to insurance agents in order to help increase more number of sign ups. I achieved a rank in the top 20% in this challenge.
This was again a Kaggle Data Challenge. The problem was to predict the forecast of item sales for an online retail store, given the data of last 5 years of sales. The forecast was calculated using xtreme gradient boosting. Rank achieved in this competition was in the top 3%.
Apart from data science, I really enjoy cooking. I spent alot of my time learning and trying new entres. I also believe in giving back to the community and try to do whatever I can for children who cannot afford education.Education is everyone's right and the world will be a better place if every child gets atleast some basic education