Aradhya Srivastava

I’m an aspiring software engineer with a strong foundation in Python, SQL, and Machine Learning, and I enjoy turning data into dependable products. I’ve built end-to-end NLP pipelines and modular applications that prioritize clean structure, consistent inference, and measurable performance.

In my projects, I developed HeartSense, an end-to-end heart disease prediction web app using Flask, where I trained and serialized a Random Forest classifier with scikit-learn and delivered a working RESTful backend and interactive front-end. I also created a Sentiment Analysis application with an NLP pipeline in Python (NLTK), using text cleaning, normalization, tokenization, stop-word removal, and lemmatization, followed by Bag-of-Words/TF-IDF feature extraction and a Logistic Regression model.

During my Software Engineering Intern (AI/ML) experience at IBM India Pvt. Ltd., I worked on supervised machine learning workflows—supporting data preprocessing, preparation, validation, model training pipelines, and evaluation. I’m particularly driven by building scalable, efficient solutions and continuously improving how I structure model development, deployment, and database-connected software.