AI Engineer | Machine Learning Developer | Data Scientist | Data Analyst
Delivering AI and Production-Ready Models.
Hi👋🏼! I’m an AI and Machine Learning enthusiast who enjoys turning data into practical, intelligent solutions. With experience in predictive maintenance, cybersecurity automation, and analytics—and a Springer/IEEE-indexed publication. I focus on building models that are accurate, interpretable, and genuinely useful in real-world environments.
View Featured Projects →Technical Arsenal
Expertise across Programming, Machine Learning, and Data Platforms.
Programming & Data
AI & ML / Deep Learning
Tools
Featured AI/ML Projects
Explainable Hybrid ML for Bearing Fault Classification
Predictive Maintenance | Explainable AI (XAI)
Developed an interpretable predictive maintenance solution that achieved 98.34% accuracy in bearing fault diagnosis. The model utilizes Gradient Boosting on statistical-wavelet features, benchmarked against CNN, LSTM, and RNN Deep Learning models.
- Methodology: Integrated Wavelet Packet Decomposition for feature extraction.
- XAI: Used SHAP-based explainability to identify the top contributing vibration features.
- Recognition: Accepted for publication in Springer LNNS proceedings, indexed in IEEE Xplore.
Cybersecurity Incident Classification (SOC Automation)
XGBoost Optimization | Security Operations
Developed a high-detection accuracy ML model using the Microsoft GUIDE dataset to classify cybersecurity incidents (True Positive, False Positive, Benign). Automated Security Operations Centre (SOC) triage.
- **Impact:** Reduced SOC incident response time by 30% through automated triage.
- **Methodology:** Implemented XGBoost optimization, feature engineering, and SMOTE balancing to handle class imbalance.
- **Stack:** Python, Scikit-learn, XGBoost, imbalanced-learn.
Car-Dekho--used-car-price-prediction
Machine Learning | Price Pridiction
Built a machine learning model & Streamlit app for predicting used car prices with 90% accuracy. Processed 10,000+ data entries, performing Data Cleaning, EDA, and Feature Engineering.
- **Impact:** 90% accurate prediction of used car prices, enabling more reliable and transparent valuation for buyers and sellers.
- **Methodology:** Performed extensive data cleaning, exploratory data analysis, and feature engineering on 10,000+ records, and built a supervised ML model deployed via Streamlit.
- **Stack:** Python, Scikit-learn, matplotlib.pyplot, seaborn, Streamlit.
DataSpark: Market Insights for Global Electronics
Business Intelligence | SQL & Power BI
Performed comprehensive analysis on over 1 million records using SQL and Power BI to uncover critical market trends. This work directly optimized inventory forecasting and enhanced overall data-driven decision-making processes.
- **Analysis:** Focused on optimizing inventory based on geographical and seasonal market trends.
- **Tools:** Leveraged **Power BI** to build interactive dashboards for key stakeholders.
- **Impact:** Optimized inventory forecasting strategy.
Education & Recognition
2024 – Present
MTech - Applied AI
Visvesvaraya National Institute of Technology (VNIT), Nagpur
- Specialization in Machine Learning Model Development and Deployment.
- Coursework includes Advanced Deep Learning, Computer Vision, and Generative AI.
2020 – 2024
BTech - Mechanical Engineering
Rajiv Gandhi University of Knowledge Technologies, Basar
- Foundation in engineering principles, enhanced by deep learning project work.
Key Achievement
Publication Acceptance:
"Explainable Hybrid Gradient Boosting-Deep Learning Framework for Bearing Fault Classification" accepted for publication in **PCEMS International Conference 2025**, to be published in Springer LNNS proceedings and indexed in IEEE Xplore.
Let's Build the Next Generation of AI Solutions
Seeking challenging AI Engineer or Data Scientist roles. I am ready to apply my expertise in AI/ML, Deep Learning, and data pipelining to your team.
Designed & Engineered by Vinoothna Nadikatla.