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Diksha Yeole

@dikshayeole

AI/ML engineer who developed TraceFinder, a scanner forensic classifier with 93.78% accuracy at Infosys Springboard 6.0.

India

Message

At Infosys Springboard 6.0, I developed TraceFinder, a scanner forensic classification system that reached 93.78% accuracy with a Hybrid CNN model. I deployed it as a real-time prediction system using FastAPI and Streamlit.

During my Artificial Intelligence internship at Acmegrade, I developed an NLP-based chatbot using Python, Flask, NLTK, and scikit-learn. I’ve also worked on customer churn analysis, a centralized health record app, and projects involving SQL data analysis and plant disease identification.

Experience

Work history, roles, and key accomplishments

Artificial Intelligence Intern

Infosys Springboard 6.0

Oct 2025 - Dec 2025 (2 months)

Developed TraceFinder, a scanner forensic classification system, achieving 93.78% accuracy with a Hybrid CNN model, and deployed it as a real-time prediction system using FastAPI and Streamlit. Gained experience in ML pipeline design, model evaluation, and comparative benchmarking across SVM, RF, CNN, and Hybrid CNN architectures.

Python Machine Learning Deep Learning CNN SVM Random Forest fastAPI Streamlit Model Evaluation Benchmarking

Artificial Intelligence Intern

Acmegrade

Apr 2024 - Jun 2024 (2 months)

Developed an NLP-based chatbot using Python, Flask, NLTK, and scikit-learn, implementing TF-IDF vectorization and machine learning-based intent classification across 21 user intents. Enhanced chatbot performance through text preprocessing, spell correction, sentiment analysis, and conversational context management, benchmarking 3 classification models.