Machine Learning Data Science

EDUCATION

Master of Science in Data Science (University of Memphis, Memphis, TN) MAY-2024

TECHNICAL SKILLS

Programming Languages:Python,java,c,R

Databases: MySQL

Data Analysis & ML: Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy

Big Data & Cloud: AWS, Google Cloud

DevOps & Tools: Docker, Kubernetes

Version Control:Git

Certifications: CCNA, Pcap, Cpa, Cloud Foundations, Machine Learning Foundation, python

PROFESSIONAL EXPERIENCE

MACHINE LEARNING ENGINEER(INTERN) MAY 2023-AUGUST 2023

Developed and deployed machine learning models that achieved up to 95% accuracy, significantly improving prediction reliability for key business applications.

Designed and implemented end-to-end ML workflows, enhancing data preprocessing and feature engineering processes to increase model accuracy by 15% compared to baseline models.

Conducted hyperparameter tuning and model validation, reducing error rates by 20% and boosting overall model robustness.

Successfully integrated ML solutions into production environments, maintaining consistent accuracy levels above 90% while scaling for large datasets.

DATA ANALYST( UNIVERSITY OF MEMPHIS) SEPTEMBER 2022-MAY 2024

Analyzed academic and administrative datasets to extract meaningful insights, leading to improved decision-making and resource allocation.

Designed and maintained dashboards and reports using Excel and Python, enhancing data accessibility for faculty and staff.

Conducted data cleaning, transformation, and visualization to support research projects and institutional planning efforts.

Collaborated with university departments to identify data trends and provided actionable recommendations for process improvement.

BACK-END PYTHON DEVELOPER(INTERN) JANUARY 2021-JUNE 2021

Achieved a 30% improvement in query accuracy and execution efficiency by optimizing SQL and PL/SQL scripts.

Delivered accurate and reliable Oracle Forms and Reports, enhancing data integrity and user experience.

Developed high-precision XML Publisher Reports, ensuring seamless integration and consistent data output.

Secured 15 high-quality client partnerships through strategic outreach, contributing to a 20% revenue increase in the first quarter.

AI-ML VIRTUAL INTERSHIP OCTOBER 2021-DECEMBER 2021

Successfully completed a virtual internship in AI/ML offered by AICTE and industry partners, covering both foundational and advanced concepts such as supervised/unsupervised learning, deep learning, and NLP.

Gained hands-on experience with real-world datasets, performing data preprocessing, model building, evaluation, and deployment using Python, scikit-learn, TensorFlow, and pandas.

Worked on projects that applied machine learning algorithms to solve practical problems, with a focus on ethical considerations in AI applications.

Completed interactive sessions, assignments, and a capstone project, strengthening technical proficiency and problem-solving skills while exploring the real-world impact of AI/ML across domains.

PERSONAL PROJECTS

Wage prediction using Machine Learning

Delivered a wage prediction project for employees in the Middle Atlantic region, achieving a relative error below 25%, ensuring dependable forecasts.

Compared multiple machine learning models and identified Random Forest Regression as the top performer.

Attained a high R accuracy of 92.31% with Random Forest Regression, reflecting precise and reliable wage predictions.

Trained and validated diverse models to optimize prediction accuracy and robustness for effective wage forecasting.

Web Application to Predict Flight Delay Using Machine Learning

Developed an interactive web interface using HTML, CSS, and JavaScript, supported by a PHP backend connected to a MySQL database, ensuring responsive user experience and reliable data handling.

Structured and refined the MySQL database with an ER schema, achieving 30% faster query performance and improved data accuracy through optimized storage and retrieval.

Trained a Multi-Layer Perceptron model to predict flight delays based on weather and airport congestion, achieving an accuracy of 82%, enhancing prediction reliability for operational planning.

Emotionally Intelligent Chatbot

Applied NLP techniques to train a chatbot for emotional intelligence, achieving an accuracy of 90% in emotion detection.

Developed algorithms to analyze text inputs from users, ensuring an 85% accuracy in identifying emotions.

Integrated sentiment analysis models to enable the chatbot to provide tailored responses, resulting in increase in user satisfaction.

Deployed the emotionally intelligent chatbot in real-world scenarios, leading to reduction in negative interactions and boost in positive engagement.

Manasha Thottempudi

Email: ********************@*****.***: LinkedIn GitHub Mobile: +1-276-***-**** Place: United States

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