Results-driven Software Engineer (AI/ML) with 3 years of experience, possessing deep expertise in supervised, unsupervised, and semi-supervised learning algorithms for numerical and structured data. Highly adaptable technical professional with a proven track record of seamlessly executing complex computer vision and Edge-AI video analytics projects. Experienced in architecting scalable backend systems, machine learning analytics, and leading end-to-end project lifecycles from algorithmic design to production deployment.
Built a time series forecasting model to predict electricity usage with 92%+ accuracy using TBATS, ARIMA, and SARIMA. Processed 150,000+ records and deployed a Streamlit-based web app.
Technologies: Python, Pandas, Statsmodels, Streamlit.
Defined and refined Python-based trading algorithms for NSE/BSE stocks. Implemented technical indicators (SMA50, SMA200, RSI) to execute a two-month swing trading strategy, prioritizing sustained stock appreciation over short-term gains.
Technologies: Python, Pandas.
Makes the overall work of the healthcare institute easier and provides better results in mental and women health-related fulfilled problems. Capable of serving 1000+ users simultaneously
Tech: JavaScript, ReactJs, TensorFlow, Sklearn, Kaggle, HTML, CSS.
Developed a project focusing on the crucial aspect of reducing the risk of serious mental disease, which is mental health prediction, and how it can be used as a theoretical foundation to develop plans for behavioural therapies for healthcare workers
in the department of public health. Using a database with 10,000 records
Tech: Python, Keras, Intel Extension for Scikit Learn, Scikit Learn, Kaggle
Performance metrics for comparison used are Accuracy, recall And Precision of results
Evaluated models on structured data and identified Random Forest as the optimal algorithm over SVMs.