machine learning

Kishan Peesapati

571-***-**** ***************@*****.*** www.linkedin.com/in/kishan-peesapati

Education

Master of Science: George Mason University - Data Analytics Engineering May 2020 Master of Science in Data Analytics Engineering with 3.80 GPA Certifications- AWS Certified Cloud Practitioner AWS August 22nd, 2020 Bachelor of Science: GITAM University - Electrical & Electronics Engineering May 2016 Recipient of best project and won a gold medal (SCADA Project), 2016 with 3.7 GPA Technical Skills

Programming: Python (SciPy, Pandas, NumPy, Scikit-Learn, Pandas, Matplotlib, Seaborn), R, C, C++ Data Visualization: Tableau

Relational Databases: MySQL

Cloud: AWS EC2, AWS S3, AWS RDS, AWS DynamoDB, AWS Sagemaker Data Analysis: Data Mining, Predictive modeling, Statistics, Machine learning (supervised and unsupervised learning) Experience

Data Analyst - Tata Consultancy Services Auto Insurance Hyderabad, India 06/2016 07/2018

• Improved customer experience and enhanced quality of interactions by validating the scores and quotes based on number of accidents and location.

• Mined data from different tables and validating values/variables before and after processing the quote.

• Leveraged SQL queries to validated scores/variables from different tables and generated monthly/quarterly reports as per project requirements.

• Independently managed validation of data with various departments of the project. Graduate Teaching Assistant - George Mason University Fairfax, Virginia 08/2019 05/2020

• Synopsized core computing concepts through in-depth facilitated discussion and customized technical instructional materials

• Providing students with guidance and technical support on the course materials and study skills and referring them to appropriate support for assignments.

• Trained 160 students on complex computing and technical concepts, deploying instructional strategies that accounted for varied technical ability

• Undertook laboratory demonstrations and support activities during practical sessions. Projects

Hope for the Warriors (Capstone Project): Generating Synthetic data and deriving actionable insights by dashboard

• Generated synthetic data was generated based on different distributions on post 9/11 veterans to mask PII information.

• Developed Tableau dashboard to provide trend analysis and infographics for different variables in the data.

• Built a classification machine learning model (Decision Tree, Random Forest, H2o Auto ML) to predict the program based on client’s profile.

US Accidents: Classifying the severity of an accident in USA based on different weather conditions from 2016-2019.

• Dataset of 3.5 million records was analyzed by Exploratory Data Analysis, different trends related to accidents in different states and conditions were determined.

• Built classification models (Decision Tree, Random forest classifier, H2o Auto ML, XGBoost) to find the significant factors affecting the severity of an accident with accuracy of 76% and achieved AUC of 72%.

• Accident’s severity is high when the impact is between (0 to ~8) mile radius, it tends to change with weather conditions. Pressure, Humidity and Temperature are the significant factors impacting accident severity. Resource Sourcing Project- Factors influencing a candidate to be placed and the time to be placed

• Used Exploratory data analysis to derive insights on factors that ultimately place the candidate.

• Built classification models (Logistic regression, Random Forest, KNN) to predict the time until a placement can be expected. (78% accuracy)

• Translated model results to business use cases designed to improve placement (i.e 38%) speed, program retention rates, and revenue generation.

Contact this candidate