As an aspiring data scientist, I am driven by the transformative potential of data and technology to create meaningful solutions. My professional journey is characterized by a relentless pursuit of innovation, specializing in data analysis, visualization, machine learning, Deep Learning, and Generative AI.
I am dedicated to leveraging data science as a powerful tool for solving real-world problems and driving impactful technological advancements. I approach every project with a mindset of continuous learning and strategic experimentation, seeking to consistently improve my skills and contribute to innovative solutions.
Developed an AI-powered customer support chatbot using LLaMA 3.3 for intent classification, ChromaDB for FAQ retrieval, and SQLite for real-time product data.
Built an AI chatbot for querying PDF documents using a LangChain RAG pipeline, LLaMA 3.3, and ChromaDB, achieving 95% retrieval accuracy with low latency.
Developed a ResNet50 CNN model to classify car damage, achieving 77.91% accuracy through fine-tuning and data augmentation for an automated damage detection system.
Built an interpretable Logistic Regression model for credit risk prediction, achieving a 98.4% AUC and using SMOTE-Tomek to improve the detection of high-risk applicants.
Built an interpretable Logistic Regression model for credit risk prediction, achieving a 98.4% AUC and using SMOTE-Tomek to improve the detection of high-risk applicants.