SRIHARSHA VELICHETI

Aspiring Computer Science and Engineering Student

Summary About me

Sriharsha Velicheti is an ambitious Computer Science Engineering student from Jain University, Bengaluru, specializing in Data Science. With a strong academic record and a CGPA of 8.656, Sriharsha is not just confined to the classroom. As the President of the ANOVA Data Science Student Club at Jain University, he has taken on leadership roles, fostering a collaborative learning environment for his peers. His active participation in hackathons, including securing the 6th position out of 159 participants in the Machine Hack Hackathon, showcases his practical application of theoretical knowledge.

Proficient in Python programming, RDBMS, and web scraping using requests, Sriharsha possesses a solid foundation in tech. His expertise extends to machine learning, where he has developed projects like the AI Photo Editor which enhances image quality using deep learning, and the Bank Customer Churn Prediction model, which employs algorithms like SVM and Naive Bayes to predict customer behavior.

Sriharsha is not only a developer but also a facilitator, having been a part of the Google Developer Student Club for 11 months. His achievements in platforms like Hackerrank, where he secured golden badges in Problem Solving, MySQL, and Python, and his contributions to Leetcode and Kaggle, further exemplify his dedication to the tech community.

With certifications from prestigious institutions like IIT Madras and active profiles on GitHub and LinkedIn, Sriharsha Velicheti is on a trajectory towards making significant contributions to the field of Data Science.

Projects

AI Photo Editor

The AI Photo Editor is a groundbreaking tool designed to revolutionize image enhancement. Leveraging the power of deep learning and computer vision technologies, this application can transform ordinary photos into high-definition, noise-free, and visually pleasing images. By employing convolutional neural networks and advanced image processing techniques, the editor enhances image details, optimizes color profiles, and effectively reduces noise. Whether it's for professional photography or personal use, the AI Photo Editor ensures every image stands out with unparalleled clarity and vibrancy.

Bank Customer Churn Prediction

The banking sector is highly competitive, and retaining customers is of paramount importance. The Bank Customer Churn Prediction project aims to address this challenge by predicting the likelihood of a customer leaving the bank. Using a comprehensive dataset that includes various customer attributes, the project applies machine learning algorithms to analyze and predict churn patterns. After preprocessing the data, multiple algorithms were tested, with SVM and Naive Bayes algorithms proving to be the most accurate. With this predictive model, banks can identify potential churn risks and implement strategies to enhance customer retention.

Employee Attrition Rate

High attrition rates can greatly affect the stability and productivity of an organization. The Employee Attrition Rate project focuses on identifying the key factors contributing to employee departures within a company. Utilizing machine learning algorithms like logistic regression, decision trees, and random forests, the project provides valuable insights from employee data. By pinpointing the underlying causes of attrition, HR teams can devise targeted training and development programs, adjust compensation and benefits, and implement strategies to enhance employee engagement and satisfaction.

Machine Learning Automation

Automation is key to scaling and optimizing processes, and this project aims to automate various tasks in machine learning. By harnessing the capabilities of the Auto ML and Lazy Predict modules, the project focuses on simplifying data preprocessing and accuracy prediction tasks. The end result is a streamlined machine learning workflow that boosts efficiency and productivity. Additionally, the integration of GitHub actions enables seamless deployment in Azure, while the project's Flask implementation supports reading various file formats, ensuring versatility and adaptability.

House Price Prediction

Real estate pricing can be complex due to the myriad of factors influencing property values. The House Price Prediction project is an initiative to provide accurate price estimations using machine learning. Trained on a diverse dataset of house attributes, the model employs Linear Regression to predict property values. It is integrated with a Flask API, ensuring ease of deployment and accessibility. While the primary focus was on API and Flask implementation, the project serves as a foundation for further refinements and accuracy enhancements.

Achievements

President of ANOVA Data Science Student Club

Successfully led the ANOVA Data Science Student Club at Jain University for 10 months, fostering a collaborative learning environment.

Google Developer Student Club Facilitator

Served as a Data Science Facilitator for the Google Developer Student Club for 11 months, contributing to community growth and knowledge sharing.

Hackerrank Achievements

Earned golden badges in Problem Solving, MySQL, and Python, showcasing proficiency and commitment to coding excellence.

Machine Hack Hackathon

Secured the 6th position out of 159 participants, demonstrating practical application skills with an impressive accuracy of 92.666%.

LeetCode Contributions

Solved over 175+ questions, demonstrating problem-solving acumen and algorithmic expertise.

Kaggle Participation

Awarded 3 bronze medals as a Discussion contributor, showcasing active involvement and valuable contributions to the data science community.