Anvesh Kottapelli

Data Scientist

+1-585-***-****

ac482b@r.postjobfree.com

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

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