Rajkot, India
#OpenToWorkHeart Disease Prediction: Python, Logistic Regression, SVM, Scikit-learn Built a classification model to predict heart disease risk from patient health data, evaluating performance using accuracy, precision, and recall. Customer Churn Prediction: Python, Logistic Regression, Random Forest, XGBoost, Pandas Built and compared multiple ML models to predict telecom customer churn from demographic and usage data, evaluating results using accuracy, precision, recall, and F1 score. Mobile User Behavior Classification: Python, Scikit-learn, Label Propagation, Label Spreading, Pandas, Seaborn Classified mobile users into behavior categories by comparing supervised models with semi- supervised learning techniques on partially labeled data, achieving up to 84.28% accuracy.
Built a Python-based system to upload PDF files and extract key data automatically, reducing manual effort in processing watchlist documents. Integrated the Python pipeline with a Spring Boot application to process, validate, and format extracted data before it entered the system. Merged newly extracted data with existing records, helping keep the watchlist accurate and up to date for ongoing use.
TSS Consultancy Pvt. Ltd.
B.Tech. · Computer Engineering (Artificial Intelligence)
IBM
Infosys Springboard
Oracle Academy
