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About

🎓 I am a recent Master's graduate in Computer Science from The University of Texas at Arlington, and I have a deep passion for data. Throughout my academic journey, I discovered the thrill of working with data—cleaning it, analyzing it, and uncovering insights that drive decisions and predict trends.

🔍 My love for data began during my course work. This passion grew as I took on projects that allowed me to dive deep into data science. One of my key projects involved using machine learning to create an image classifier for wildlife species. By leveraging Python and TensorFlow, I developed a model with 98% accuracy. I tackled challenges such as data preprocessing, handling corrupted images, and reducing overfitting, which led to a 10% boost in accuracy and a 3x faster training speed.

📊 In another project focused on sales data analysis, I utilized my skills in SQL, Tableau, and Excel to extract and analyze data, contributing to streamlined processes and informed decision-making. I designed a star schema to streamline data processing, reducing time by 30%. I created interactive Tableau dashboards powered by advanced SQL queries, which significantly improved decision-making by visualizing sales metrics, customer segmentation, and regional trends. My work increased stakeholder engagement by 40%.This experience underscored the importance of high-quality data in making informed decisions.

💡 Through these experiences, I have become proficient in Python, SQL, Tableau and Machine Learning libraries, Pandas, Numpy, Scikit-learn, Keras, Matplotlib, Seaborn and developed a knack for solving complex data challenges. I excel at cleaning data, removing missing values and duplicates, optimizing processes, and creating insightful dashboards.

🚀 I am now eager to apply my skills in a real-world setting. I am seeking opportunities in Data Science roles such as Data Analyst, Data Scientist, Data Engineer, or Machine Learning Engineer. I am ready to join a dynamic team and use my expertise to drive data insights and innovation. Let’s connect and see how I can contribute to your organization!

Let's build something extraordinary together! 💼✨

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Education

2022 - 2023

Master of Science in Computer Science 

University of Texas at arlington, Arlington, Texas

CGPA: 3.6/4.0

2018 - 2022

Bachelor of Technology in Computer Science and Engineering

SR Engineering College, Telangana, India

CGPA: 8.63/10

Technical Skills

Languages: C, Java, Python.

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Database Technologies: Relational (SQL, MySQL, SQLite), Non-Relational (MongoDB).

 

Machine Learning and Deep Learning: TensorFlow, Scikit-learn, Keras, Numpy, PyTorch, CNNs, EfficientNetV2.

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Data Visualization: Pandas, Matplotlib, Seaborn, Tableau, Excel.

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Statistical Analysis and Model evaluation: Extensive Exploratory Data Analysis (EDA), Hypothesis Testing, Regression Analysis, Feature Engineering and Selection, Scaling, Correlation Analysis, Cross-validation, Hyperparameter Tuning, Performance metrics.

 

Version Control/Tools: Git, Visual Studio Code, AWS, Docker, GitHub.

Languages

English 

5/5

Telugu

5/5

Hindi

4/5

Projects

Sales data Analysis  
Image Classification for Wildlife Species
Java-Based Fault-Tolerant Transaction Management Using 2PC Protocol
Naïve Bayes Classifier for text Classification
August 2019
Stair Climbing Robot   
July 2020
Library Management System 
April 2021
Pass Matrix Password Authentication System 
April 2022
Predict Diabetes using Machine Learning Models 

Implemented SQL ETL mappings and designed a star schema to streamline data extraction, transformation, and loading processes, reducing data processing time by 30% and conducted EDA to clean and validate data. Developed an interactive Tableau dashboard leading to a 40% increase in stakeholder engagement and improved decision-making through visualizations of sales metrics, customer segmentation, and regional trends.

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Developed a 98% accurate animal image classifier using Python, TensorFlow, and machine learning/deep learning algorithms, Addressed data preprocessing challenges, managing 5% corrupted images and reducing overfitting, to enhance model accuracy and generalization. Achieved a 10% boost in classification accuracy and optimized GPU usage for 3x faster training speed boost, ensuring the model's robustness and reliability, resulting in a 40% reduction in GPU load.

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Developed and tested a Java-based distributed transaction management system using the 2-Phase Commit (2PC) protocol, successfully demonstrating fault tolerance and recovery in scenarios involving coordinator and node failures. The project effectively handled transactions across multiple nodes with zero data loss, showcasing an average recovery time under 2 minutes and enhancing system reliability by 40% in simulated network failure environments.

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Implemented a Naïve Bayes Classifier from scratch for text classification, achieving 54.3% accuracy on the Ford Sentence Classification Dataset without using external libraries.Conducted experiments including Laplace smoothing optimization, deriving top predictive words for each class, and exploring the impact of NLP techniques like stop word removal, showcasing proficiency in NLP and algorithmic implementation.

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A staircase climbing robot is built and implemented which can climb the step of given height without interrupting step with maximum stability.

ARDUINO SOFTWARE is used to connect arduino and Genuino hardware to upload program and operate the robot.

It is a web application that allows users to manage library resources such as books , keep track of stock availability, and conduct searches. Users will be able to see the site without registering, but they will be able to request a check-in only if they are registered.

JAVA and Spring MVC are used to implement REST API requests. The frontend application connects with the backend server through AJAX requests, passing data in JSON format.

To make passwords secured from shoulder surfing attackers this system is build.

Using SHA-1(Secure Hash Algorithm) and cryptographic hash function it is implemented by using java language.

It is a model performs early prediction of diabetics by taking 8 parameters, training and testing dataset and combining results with random Forest , Logistic Regression, Support Vector Machine, Naive Bayes algorithms.

Using Flask web page the prediction is displayed of the chances of getting diabetic in near future.

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