Machine Learning Data Science

Rakesh Kandibanda

Arlington, TX ***** 945-***-**** ******************@*****.***

www.linkedin.com/in/kandibanda-rakesh-b9b026202

Education

University of Texas at Arlington Jan 2024 – In Progress

• MS in Applied Statistics and Data Science GPA: 4.00 Teegala Krishna Reddy Engineering COllege Aug 2016 – Aug 2020

• BE in Electronics & Communication Engineering GPA: 3.00 Technical Skills

Software Tools: Microsoft Office Suite, Power Bi, Visual Studio Specializations: Data Science, Machine Learning, Statistical Analysis, Deep Learning, Big data, Artificial Intelligence

Machine Learning Frameworks: PyTorch, TensorFlow, Scikit-Learn, Matplotlib, Seaborn Programming Languages: Python, Java, R, SAS

Testing: Manual Testing, Automation Testing - Selenium Web Developer Web: HTML, CSS

Databases: SQL

Experience

Teaching Assistant, University of Texas at Arlington Aug 2024 – Dec 2024

• Provided individualized and group instruction, reinforcing key learning concepts.

• Evaluated assignments, tests, and papers while adapting teaching methods to diverse student needs. Junior Quality Analyst, Plusteams IT Services LLP Apr 2022 – Dec 2023

• Conducted functional, regression, cross-browser, and load testing using both manual and automated methods

(Python Selenium WebDriver); reported and tracked defects in an Agile environment.

• Collaborated with development teams, maintained test plans/cases, contributed to QA documentation in Confluence, and utilized project management tools for activity tracking. Web Developer Intern, Mister Games India Pvt Ltd Dec 2021 – Mar 2022

• Gained hands-on experience in web development, including logo design using Adobe Photoshop and front-end usability testing.

• Developed scripts and performed functional, usability, and cross-browser testing while proactively expanding web development skills through self-driven learning. Projects

Dallas 311 Service Request Python, Power BI, GIS

• Analyzed Dallas 311 service request data using GIS to uncover spatial trends in public issue reporting..

• Developed and evaluated multiple machine learning models—Logistic Regression, Random Forest, Bagging Decision Tree, and XGBoost—achieving 92%–95% accuracy in predicting whether issues were resolved or remained open.

AI Generated Image Vs Real Image Python

• Built and evaluated image classification models (shallow, deep, and CNN) on the AI Generated Image vs Real Image dataset, achieving 78% accuracy and 0.87 AUC with CNNs.

• Tested optimization techniques (GD, SGD, SGD with momentum) and found CNNs most effective for extracting spatial features and improving model performance.

Big Cities Health Inventory Python, GIS

• Analyzed Big Cities Health Inventory data using Python and GIS to uncover geographic and health-related disparities across urban populations.

• Applied machine learning models (Random Forest, XGBoost, Elastic Net) optimized with Bayesian techniques, achieving high performance (R up to 0.97, RMSE as low as 6.02) in predicting key health indicators. Productivity Prediction of Garment Employees R

• Analyzed productivity data from garment manufacturing records using time series models (SARIMA, Holt-Winters, ARIMA), uncovering weekly seasonality and long-term trends across 12 months.

• Identified Holt-Winters as the most accurate model, achieving the lowest RMSE (0.0045) and MAE (0.0035); improved forecast reliability by 15% and supported data-driven operational decisions using R. Certifications & Achievments

• Certified in Software Testing with Java from Pentagon Space.

• Published a journal on ‘Artificial Vision for Blind’ in the IJARESM Publications India in 2021.

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