Pranusha Simhadri

Detail-oriented and highly skilled Software Engineer with 5+ years of experience in designing, implementing, and maintaining data pipelines and infrastructure. Proficient in various programming languages and technologies, adept at optimizing data systems for efficiency and reliability. Seeking to leverage expertise in data engineering to contribute to the success of a dynamic organization.

Data Science with Python:

· Conducted comprehensive exploratory data analysis, statistical analysis, and data visualization using Numpy, Pandas, Seaborn, Matplotlib, Plotly, Folium packages in Python on a huge dataset of Barcelona city.

· Sentiment analysis with Movie review datasets from Twitter API :Conducted sentiment analysis on movie reviews gathered from Twitter, leveraging the Twitter APIto extract real-time tweets related to popular movies and evaluate public sentiment towards them. The goal was to assess how audiences feel about specific movies by analyzing their Twitter posts and categorizing their sentiment into positive, negative, or neutral. Utilized Tweepy and Twitter API to collect a large volume of tweets related to specific movies, hashtags, or movie titles in real-time. Implemented query filtering to extract relevant tweets, focusing on specific time frames, keywords, and geographical regions. Cleaned and preprocessed the raw tweet data by removing stopwords, special characters, URLs, and non-alphanumeric characters to prepare it for text analysis.Applied machine learning techniques, such as Naive Bayes, Logistic Regression, and Support Vector Machines (SVM), to classify tweets as positive, negative, or neutral. Used scikit-learn and TensorFlowfor model training and evaluation, tuning hyperparameters to achieve optimal performance. Created visualizations with Matplotlib and Seabornto display sentiment distributions across movie reviews, showing how movie audiences felt about a movie over time or based on tweet volume. Built interactive sentiment dashboards using Plotly and Dash, enabling users to explore sentiment analysis results on different movies, track trending opinions, and view sentiment shifts. Evaluated model performance using metrics such as accuracy, precision, recall, and F1-scoreto assess the effectiveness of the sentiment analysis model.

DevOps: Tools and techniques:

· Designed an application using Microsoft Azure Cloud services suitable for continuous deployment systems with a DevOps focus.

· Created deployment processes by developing continuous integration tools like Jenkins.

· Maintained GIT repositories for DevOps Environment: version control and build automation integrating git into Jenkins.

· Website Development in Microsoft Azure cloud, Built a website in C# and Visual Studio using Azure WebApp function in Azure cloud services.

· Provisioned MySQL to the web app using the Azure portal. Created Azure function App and containers to the storage.

JAVA:

· RestAPI using Spring boot, Hibernate in JAVA8, Created Services to consume REST APIs and to communicate between components using Dependency Injection.

· Applied Object-Oriented Programming (OOP) principles by using Interface, Abstract, Overriding, and Overloading. Developed server-side applications to interact with the database using Spring Boot and Hibernate to query the database and perform other CRUD operations.

· Used Rest Controller in Spring framework to create RESTful Web services and JSON objects for communication.

· Implemented SL4J for logging in the project.

· Developed test classes in JUnit for unit testing.

· Used Postman to test the RESTful API for HTTP requests such as GET, POST, and PUT.

· Implemented Swagger to document the REST Services