KRISHNA CHAITANYA

• Worked on business practices like customer segmentation, customer churn rate prediction using R and Python
• Developed Churn model in R for Telecom sector using Random forest and provided association rules to provide specific conditions in which customers have left the company. This enabled the client to target current customers who satisfy those conditions and thus reduce churn significantly
• Analyzed large volumes of data to draw actionable insights that supports business decision making process
• Performed data cleansing, data manipulation and exploratory data analysis to identify, analyze and interpret data to find out trends/patterns in large data sets using R.

• Carried out segmentation, conjoint analysis, RFM analysis using R as proof of concept (POC)
• Created visualizations, reports and dashboards to present clients and stakeholders.
• Involved in extracting the data using SSIS from OLTP to OLAP.
• Performed Data cleansing by creating tables to eliminate the dirty data using SSIS.
• Extensively used joins and subqueries to simplify complex queries involving multiple tables.
• Completed tasks well ahead of deadline to enable Client ample time for review.

GPA : 3.85

Predictive Analytics Summer 2016

• Implemented Ensemble Modeling, Data mining techniques, built a predictive model and scored using R for Portuguese Bank Marketing data set
• Developed a classification model for driver alertness using Driver information to predict the driver state, and implemented Decision Trees, Random forest algorithms using Scikit-Learn library in Python to predict the driver state.

Enterprise Data Summer 2016

• Retail Data Analysis using Hive, Implemented Partitioning and bucketing to analyze top 10 customers
• Processed the Data using PIG and stored the results to Hadoop File System

Data Mining Spring 2016

• Applied Data mining techniques on Bolivian schools data set, used R programming to detect Outliers, performed distributions, Principal Component Analysis and documented all the results
• R Packages like glm,lm,knn were used to implement Supervised learning on Prostate cancer dataset.

Business Analytics Fall 2015

• Scraped the data from various web sources using packages like Beautiful Soup, urllib2 in Python and developed a regression model to predict the unemployment rate in Philadelphia
• Collected the twitter stream data using Twitter API and perform sentiment analysis on the release of (iPhone 6s) using oauth2 module in Python.