AI/ML Engineer | Data Scientist @ Pragyaa.ai | IIIT-B PGDip | Deep Learning | MLOps | Full-stack ML

I'm currently building intelligent, production-grade ML pipelines at Pragyaa.ai — solving real-world problems using data-driven insights and predictive modeling. My work blends classical machine learning, solid engineering practices, and scalable deployment. I'm also passionate about deep learning and continuously improving my craft.

I also contribute under a separate GitHub profile for work and research:

• (personal/professional commits)

Goal: Predict whether patients will show up for their hospital appointments, minimizing operational costs.

Tech:

Highlights:

• Cleaned and engineered messy real-world temporal + categorical data
• Trained models: Logistic Regression, Random Forest, XGBoost
• Prioritized high recall to catch no-shows
• Used SHAP for model explainability
• Achieved ~94% recall with tuned thresholding
• Fully modular — built to integrate with Flask UI and Airflow

Repo (In Progress): PredictML-Production

Project	Badges	Description
Multilinear Regression		A basic regression walkthrough
Data Cleaning & Visualization		Preprocessing + EDA utilities
EDA: Bank Marketing		Exploratory analysis of campaign data
Lead Scoring Case Study		Lead qualification using ML

On a mission to build AI tools for real-world impact with strong foundation and cutting edge technologies

• PredictML-Production:
• Certifications:
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• FinSona: platform
  • Voice Bot (OpenAI + Realtime FastAPI) →
  • Memory layer + ML integration →