CHIRANJEEVI VANKAM
Chirala
Summary
Dynamic professional with a proven track record at Computer Science and Engineering, specializing in fraud detection through advanced machine learning techniques. Expert in Python and data preprocessing, I successfully engineered a robust pipeline that minimized false positives, showcasing strong analytical skills and a commitment to excellence in real-time transaction processing.
Overview
1
1
Certification
1
1
year of professional experience
Work History
LEAD
Computer Science and Engineering
Guntur
09.2025 - 12.2026
Fraud Detection in Electronic Transactions
- Engineered machine learning pipeline for detecting fraudulent UPI transactions, performing data preprocessing, exploratory data analysis (EDA), and feature engineering to enhance model accuracy.
- Trained and evaluated multiple machine learning algorithms, including ensemble methods, to optimize fraud detection performance and reduce false positives in real-time transaction processing.
- Implemented SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance in transaction datasets, facilitating effective model training on minority fraud classes.
Education
B.Tech - Computer Science Engineering (Data Science)
R.V.R. & J.C. College of Engineering
Guntur06-2026
Diploma - Computer Engineering
M.B.T.S Government Polytechnic College
Guntur05-2023
Skills
- API Development
- Programming Languages: Python, Java, C
- Python libraries: Pandas, NumPy, Matplotlib
- Model evaluation techniques
- Database: MySQL
- Version control tools
- Data management concepts
Projects
- Enhanced Fraud Detection in Electronic Transaction Systems Using SMOTE and Ensemble Learning, Developed a machine learning-based fraud detection system to identify fraudulent UPI transactions. Performed data preprocessing, feature engineering, and exploratory data analysis. Applied SMOTE to handle class imbalance and evaluated multiple machine learning algorithms to improve fraud detection performance.
- Deepfake Audio Detection Using MFCC and VGG16, Developed a deep learning system for detecting AI-generated audio using MFCC feature extraction and the VGG16 model. Achieved 93% accuracy in distinguishing real and fake audio samples, improving the reliability of voice authentication systems.
Certification
- Joy of Computing with Python – NPTEL
- Introduction to Industry 4.0 and IIoT – NPTEL
- Python – Infosys Springboard
- Programming Using Java – Infosys Springboard
- Artificial Intelligence Fundamentals – IBM SkillBuild
- EduSkills TECH CAMP – ALTAIR Data Science Master
Areas of Interest
- Software Development
- Web Technologies
- Database Management Systems
- Machine Learning
- Cloud Computing
Internship
- Google Android Development Virtual Internship
- Apr 2023 – Jun 2023
- Developed Android applications using Kotlin and Jetpack Compose.
- Implemented responsive user interfaces following modern Android development practices.
- Gained practical experience in application architecture and UI/UX design.
Timeline
LEAD
Computer Science and Engineering
09.2025 - 12.2026
B.Tech - Computer Science Engineering (Data Science)
R.V.R. & J.C. College of Engineering
Diploma - Computer Engineering
M.B.T.S Government Polytechnic College