Machine Learning Computer Science

BURUJU SOWMYA

*******@*******.*** +201-***-****

www.linkedin.com/in/sowmya-buruju-a3a588195

https://github.com/BurujuSowmya-2

OBJECTIVE Skilled candidate looking for full time

EDUCATION

Stevens Institute of Technology, Hoboken, NJ AUG 2022 - DEC 2023 Masters of Science in Computer Science, GPA : 3.4

Related Courses: Mathematical Foundations of Machine Learning, Fundamentals of CyberSecurity, Database MAnagement Systems 1, Algorithms, Comp Organization & Prog, Knowledge Disc.& Data Mining, Fundamentals of Computing, Leader Development, Data Analytics & Machine Learning, Web Mining.

ICFAI UNIVERSITY, TELANGANA, INDIA AUG 2018 - SEP 2022 Bachelor of Science in Computer Science, GPA : 7.3/10 SKILLS

Programming Language: Java, Python, Javascript, R, C, SQL,Stata Web Technologies: Node.js,MongoDB, Express

Libraries: Pytorch, Tensorflow, Scikit-Learn, Pandas, matplotlib, NumPy, OpenCV, SciPy, Keras, Seaborn, NLTK.

Data visualization: Tableau, Excel, Histogram, Bar chart, Heatmap, Pie chart, Matplotlib,Seaborn, Power BI

Databases: MongoDB, MySQL, Oracle Database.

Data Structures: Graph Algorithms, Dynamic Programming, Searching and Sorting. Certifications: Java, Python, MS Word, Excel, PowerPoint Tools and Technologies: Adobe, GitHub, Eclipse, Jupyter, Anaconda, Selenium, Scikit-Learn, PyCharm IDE, Flask, VSCode, Firewall, Computer network. EXPERIENCE

Awishion Electomob Solutions, Hyderabad, India MAY 2020-AUG 2020 App Development Intern

● Developed a social media app and prepared test plans and test cases based on app development requirement documents for the app.

● Evaluated project requirements and specifications and developed software applications that surpassed client expectations.

● Developed next-generation integration platform for internal applications.

● Built a shared central authentication system in Java language for intranet applications. Tech Mahindra, Hyderabad, India JUNE 2021 – DEC 2021 Artificial Intelligence Intern

● Modelling of human language with statistical, machine learning, and deep learning models in NLP.

● Analysed the data and conducted text classification and EDA.

● Used different tools and software for training the models with deep neural networks like LSTM and BERT.

● Evaluated the dataset with Sentiment Analysis and Multi-Level Text Classification using Natural Language Processing.

● Classified the similarity of the dataset with word embedding vectors using cosine similarity.

● Used pre-trained transformers in building the BERT model also, encountered the training of traditional RNNs in the LSTM network.

ACADEMIC PROJECTS

ICFAI University, Hyderabad, India

Face detection in python machine learning SEP 2020 - DEC 2020

● Developed a machine learning model to accurately detect and recognize human faces in real-time.

● Utilized Python programming, OpenCV for image processing, TensorFlow for model development, and Dlib for facial landmark detection. Gained proficiency in machine learning algorithms and real-time data processing.

● Overcame challenges in variable lighting and face orientations, achieving significant improvements in detection accuracy and processing speed compared to existing benchmarks.

● Spearheaded the development of the detection algorithm, enhancing system efficiency and scalability. Enhanced my expertise in Python, machine learning, and real-time image processing through hands-on experience.

Drive Drowsiness Detection in Python Deep learning SEP 2021 - DEC 2021

● Implemented a "Driver Drowsiness Detection System" using Python and deep learning. The project aims to enhance road safety by real-time monitoring of driver alertness to prevent accidents caused by fatigue.

● Leveraged Python for programming, with deep learning models developed using TensorFlow or PyTorch. Utilized computer vision techniques with OpenCV for real-time eye tracking and facial landmarks detection to identify signs of drowsiness.

● Addressed challenges in accurately detecting drowsiness under various lighting conditions and driver positions. Improved model robustness and accuracy through extensive training on diverse datasets and real-time testing.

● Led the development and optimization of the deep learning model, ensuring high accuracy and efficiency in drowsiness detection. Enhanced skills in Python, deep learning, computer vision, and real-time system integration. Stevens Institute of Technology, Hoboken, NJ AUG 2022 - Dec 2023 The Fitness Portal Website in Javascript

● Developed "The Fitness Portal," a comprehensive web platform designed to provide personalized fitness guidance and tracking. The site offers interactive features like workout plans, nutrition tracking, and progress monitoring.

● Employed JavaScript for front-end development, along with HTML5 and CSS3 for layout and styling. Integrated APIs for additional functionalities such as fitness data analysis and user authentication. Used frameworks/libraries like React.js or Angular

(if applicable) to enhance user experience.

● Overcame challenges in creating a responsive and user-friendly interface. Implemented features like real-time fitness tracking and personalized workout recommendations using advanced JavaScript techniques and AJAX for dynamic content updating.

● Took a leading role in front-end development, focusing on creating an intuitive and engaging user interface. Gained expertise in JavaScript, web development best practices, and working with cross-functional teams to deliver a high-quality web application.

Housing Price Prediction in Python

● Developed a "Housing Price Prediction" model in Python, aiming to accurately forecast real estate prices based on various market factors. The project involved analyzing historical housing data to predict future market trends.

● Utilized Python for data analysis and modeling. Implemented machine learning algorithms using libraries like Pandas for data manipulation, NumPy for numerical operations, and scikit-learn for building and testing predictive models.

● Faced challenges in handling large datasets and ensuring model accuracy. Addressed these through feature engineering, data normalization, and experimenting with different machine learning algorithms (like linear regression, decision trees, or neural networks) for optimal prediction accuracy.

● Led the data preprocessing and model development stages, gaining significant skills in Python programming, machine learning algorithms, and data visualization. The project enhanced my analytical skills and understanding of real estate market dynamics.

Heart Disease Prediction in python

● Developed a "Heart Disease Prediction" model using Python, aiming to accurately identify individuals at risk of heart disease based on medical data and risk factors.

● Developed a "Heart Disease Prediction" model using Python, aiming to accurately identify individuals at risk of heart disease based on medical data and risk factors.

● Overcame challenges in handling medical datasets, ensuring data privacy, and optimizing model performance. Addressed these through feature engineering, model selection, and evaluation techniques.

● Led the project's data preprocessing and modeling phases, gaining proficiency in Python programming, machine learning algorithms, and medical data analysis. Improved analytical and problem-solving skills while contributing to healthcare risk assessment.

The backend part of Web Forum in python

● Led backend development for a dynamic Python-based "Web Forum."

● Utilized Python with Django/Flask for backend and SQL databases.

● Implemented user authentication, data management, API integration, and ensured scalability.

● Spearheaded backend development, enhanced Python web framework skills, and addressed data security and scalability challenges. Customer Churn Prediction in python

● Developed a "Customer Churn Prediction" model using Python, aiming to identify and predict customer attrition in a business, allowing for proactive customer retention strategies.

● Leveraged Python for data analysis and machine learning. Utilized libraries like Pandas for data preprocessing, scikit-learn for model development, and Matplotlib/Seaborn for visualization.

● Successfully built predictive models to identify customers at risk of churning.Implemented data-driven strategies for customer retention, resulting in reduced churn rates. Conducted data analysis to uncover insights into customer behavior and factors influencing churn.

● Led the project's data preprocessing, modeling, and analysis. Gained proficiency in Python, machine learning, customer analytics, and contributed to the organization's bottom line by reducing churn.

Text Analysis for Mental Health in Python

● Conducted "Text Analysis for Mental Health" using Python to analyze textual data for mental health insights, aiming to identify and support individuals struggling with mental health issues.

● Utilized Python for natural language processing (NLP) and text analysis. Employed NLP libraries such as NLTK or spaCy, sentiment analysis, and topic modeling techniques.

● Successfully analyzed text data to detect sentiment patterns related to mental health.Generated valuable insights on the emotional well-being of individuals through text-based indicators.Contributed to mental health awareness and support initiatives with data-driven findings.

● Led the text analysis process, developed NLP models, and gained proficiency in Python, NLP, and data-driven mental health analysis. Contributed to the project's mission of promoting mental health awareness and support.

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