Machine Learning Computer Science

SUMMARY

Vamshi Reddy Dasari

Missouri State University, Springfield, MO +1-417-***-**** *************@*****.***

Innovative and results-driven Computer Science graduate with hands-on experience in data analysis, data science, data engineering, and full- stack software development. I am skilled in Python, SQL, Java, and cloud platforms like AWS, Azure, and GCP. Developed end-to-end data pipelines using Airflow, Kafka, and Databricks, and built scalable ML models with TensorFlow and Scikit-learn. Experienced in designing APIs, deploying applications with Docker/Kubernetes, and visualizing insights through Tableau, Power BI, and Looker Studio. Passionate about leveraging AI/ML and big data tools to build intelligent, high-impact solutions that drive decision-making and innovation. EDUCATION

Missouri State University Jan 2023 - Dec 2024

Master's, computer science (GPA: 3.33/4.00)

Vignan Institute of Technology & Science Jul 2018 - Aug 2022 Bachelor's degree, computer science (GPA: 7.21/10.0) WORK EXPERIENCE

Excelerate AI Data Analyst Intern Jan 2025 - Mar 2025

• Conducted Exploratory Data Analysis on 500K+ records using Python, SQL, and Pandas, achieving a 30% reduction in data inconsistencies and supporting end-to-end feature delivery.

• Developed machine learning models with Scikit-learn and TensorFlow, attaining 85% accuracy for trend forecasting, and crafted interactive dashboards in Looker Studio to enhance technical documentation and decision-making by 40%.

• Optimized ETL pipelines utilizing Python, Apache Kafka, and Airflow, collaborating with cross-functional teams to transform AI insights into robust strategies, reducing manual processing time by 60% and boosting reporting accuracy by 15%. Brainovision Software Engineer Intern Dec 2020 - Dec 2022

• Designed and built a Number Plate Recognition System integrating OCR and machine learning, achieving 97% accuracy and improving image data clarity by 25% with OpenCV and NumPy, ensuring high-quality performance standards.

• Engineered a real-time ticket validation web interface with Django, reducing validation time by 40% while adhering to best practices for maintainability and rigorous testing.

• Deployed and maintained a PostgreSQL-backed dashboard using Looker Studio, enhancing traffic enforcement insights by 20% and effectively communicating feature impacts to stakeholders. PROJECTS

Mitigation of animal Road accidents May 2024 - Dec 2024

• Developed a predictive model using Python, SQL, and Scala on Databricks, achieving 87% accuracy with Linear Regression and 91% with Random Forest for high-risk accident zone forecasting.

• Analyzed 500,000+ accident records, optimizing data preprocessing to reduce noise by 30% and creating interactive heat maps in Tableau for collision hotspot detection.

• Assessed and compared multiple ML algorithms, including Linear Regression, Random Forest, and SVM, improving model precision by 18% through hyperparameter tuning.

Weather Trend Analysis Using MapReduce and JDBC Jan 2024 - May 2024

• Processed over 500 GB of NICS weather data using Hadoop MapReduce and Java, improving forecasting accuracy by 20% through optimized data aggregation and filtering.

• Implemented parallel processing techniques with SQL and Java, reducing query execution time by 30% and enabling real-time trend analysis for strategic planning.

• Enhanced data preprocessing efficiency by 25% using custom MapReduce functions, ensuring high data quality and consistency for predictive modeling.

Domestic Violence detection in smart home using machine learning from live speech Jun 2023 - Dec 2023

• Created an efficient domestic violence detection framework using real-time speech analysis in smart homes, leveraging IoT-enabled audio sensors and machine learning techniques to classify abusive language and distress signals.

• Gathered and analyzed over 10,000 speech samples from publicly available datasets free-sound and ADIMA, applying Natural Language Processing (NLP), Sentiment Analysis, and feature extraction techniques such as Mel-Frequency Cepstral Coefficients.

• Designed a CNN-LSTM deep learning model with data augmentation (noise addition, time stretching, pitch shifting), achieving 96% accuracy and reducing misclassification by 18%, enabling real-time alerts within 200ms. SKILLS

• Programming & Web Development: Java, Python, R, C/C++, C#, JavaScript, HTML, CSS

• Frameworks & Libraries: ReactJS, Django, Flask, Fast API, Hibernate,

• Databases: MySQL, PostgreSQL, MongoDB, SQL Server, Elasticsearch, Kafka

• Data Engineering & Cloud: AWS, Azure, Apache Spark, Hadoop, Databricks, Snowflake, Apache Airflow

• Machine Learning & Analytics: TensorFlow, PyTorch, Pandas, NumPy, Tableau, Power BI, Google Analytics, Alteryx

• DevOps & Tools: Docker, Kubernetes, Jenkins, Git/GitHub, REST APIs, VS Code, Eclipse, Anaconda

• Software Development: DSA, OOP, High-Level Design (HLD), REST API, Clean Architecture, Agile, CI/CD, TDD.

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