Nithin Chinthal Email & Phone Number

As a recent BCA graduate from Vellore Institute of Technology (VIT), I am fueled by curiosity and a relentless drive to expand my knowledge in the ever-evolving world of technology. With a CGPA of 7.48, my academic journey has been a blend of coding expertise and a strong understanding of commerce. During my time at VIT, I had the opportunity to dive deep into various programming languages, which culminated in a project on credit card fraud detection using regression algorithms in my 6th semester. This experience not only honed my technical skills but also ignited a passion for problem-solving and innovation.Currently, I am eager to translate my academic experiences into real-world solutions by contributing to a dynamic team in a technical role. With a certification in Python programming, I am equipped to tackle challenges head-on and am particularly interested in roles that involve software development, data analysis, and AI.What makes me tick? The excitement of solving complex problems and the satisfaction of seeing my code come to life. I am driven by a desire to continuously learn and grow, always seeking out new challenges that push the boundaries of my knowledge.Outside of work, I am constantly exploring new technologies and staying updated with industry trends. Whether it's delving into the latest advancements in AI or experimenting with new coding projects, I am always on the lookout for opportunities to learn and innovate.I am actively seeking opportunities where I can contribute my skills and grow as a professional. If you're looking for a passionate, curious, and hardworking team member, I would love to connect and explore how we can work together to achieve great things.

• Music Player

  Android Studio

  I successfully completed an impressive project where I developed a basic music player using Android Studio.Project Title: Credit Card Fraud Detection Using Logistic Regression Overview:In my final year of BCA, I undertook a project focused on detecting credit card fraud. The primary goal of the project was to develop a reliable and efficient system that could identify fraudulent transactions with high accuracy.Algorithm UsedI implemented the Logistic Regression algorithm to analyze and predict fraudulent activities. This algorithm was chosen due to its effectiveness in binary classification problems, making it a suitable choice for distinguishing between legitimate and fraudulent transactions.Data SourceThe data used for this project was sourced from Kaggle, a well-known platform for data science and machine learning datasets. The dataset included a large number of credit card transactions, both legitimate and fraudulent, which was essential for training and testing the model.Key Achievements:Successfully developed and tested a logistic regression model for fraud detection.Achieved significant accuracy in identifying fraudulent transactions.Gained valuable experience in handling real-world data and applying machine learning techniques.This project not only enhanced my understanding of machine learning algorithms but also provided practical experience in working with real-world financial data.