Graduate Student
Resume:
Anvesh Kottapelli
Data Scientist
******.**********@*****.**
m
Charlotte, US
LINKS
Github:
https://github.com/anvesh12
LinkedIn:
www.linkedin.com/in/anvesh-k/
SUMMARY
Seeking a full time opportunity in
Data Science and Analytics where
my analytical and methodical skills
will be a great asset in achieving
company’s missions and goals.
TECHNICAL SKILLS
Certifications
PROFESSIONAL EXPERIENCE
Programmer Analyst Aug '15 - Dec '16
Cognizant Technology Solutions Chennai, IN
ACADEMIC PROJECTS
Pump it Up-Data Mining the Water Table
Can Money Buy Political Power?
Loan Prediction
Text Analytics for National Institutes of Health
EDUCATION
MSc - Computer Science Jan '17 - May '18
University of North Carolina Charlotte Charlotte, US B.Tech - Computer Science Aug '11 - May '15
SRM University Chennai, IN
photo_camera
Languages: Languages R,Python, Java, C#,
HTML, CSS
BI/Analytical Tools: Tools
Tableau,ApacheSpark,MSExcel,
Weka
Database: Database MySQL, MongoDB,
SQL server, Oracle
Machine Learning: Regression,
Clustering, Neural Networks,
Random Forest, Time-Series
Analysis, Survival Analysis
IDEs: R-Studio, Anaconda
Navigator,Visual Studio 2013,
2015, 2017, Eclipse
DataCamp: DataCamp Intermediate R
Course, Intro to SQL for Data
Science, Data Manipulation in R
with dplyr
Collects, cleans, transforms and validates data as a process for arriving at conclusions.
Presents data in the form of charts, graphs and tables for immediate reference. Presents analyses of all data to concerned officers, managers and departments. Coordinates with all key or authorized people with the distribution of data analysis Using data from Taarifa and the Tanzanian Ministry of Water, built prediction models to predict pumps as functional, which need some repairs, and which don't work at all. Implemented machine learning algorithms Decision Tree, Random Forest, XGBoost Used BBoorruuttaa package to perform Feature selection to identify the important variables in predicting the class correctly.
Using data from 2010 Congressional elections, built a classification model that would predict the election’s outcome as a Win or Loss.
Implemented Random Forest and Artificial Neural Networks models. Compared performance of both the models based on Accuracy and AUC. Built a model to predict if a customer is eligible for the loan eligibility based on customer details such as Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others.
Built Logistic Regression model in predicting the Loan status of the customer. Performed text analytics by creating Word clouds, Sentiment analysis and Topic modeling to discover useful information related to mental health. Created a color-coded word cloud based on sentiment by using most frequent tokens for positive and negative words.
CGPA: 3.8/ 4
CGPA: 3.8/ 4
Contact this candidate