Computer Science Undergraduate | AI/ML Enthusiast | Python Developer

I build data-driven, production-ready ML pipelines and AI-powered solutions to solve real-world problems. I’m passionate about anomaly detection, predictive modeling, and integrating ML with backend systems to make data actionable and meaningful.

• Design and deploy ML workflows: classification, regression, clustering, drift monitoring, and explainability (XAI)
• Build anomaly detection systems for real-time monitoring using Python, Scikit-learn, and Streamlit
• Develop APIs & modular backend pipelines using FastAPI and Flask for ML integration
• Work with time-series and real-world datasets to extract insights and detect patterns
• Optimize models with hyperparameter tuning, feature engineering, and clean coding principles

Languages & Tools: Python, SQL, C/C++ (Basics), Git, GitHub, VS Code, Jupyter Notebook
ML / AI: Scikit-learn, Pandas, NumPy, NLP (Basics), Model Optimization, Data Visualization
Frameworks: Flask, Streamlit, FastAPI
Concepts: OOP, DSA, API Development, Modular Programming, SDLC

• Built a full pipeline with clustering, drift monitoring, and real-time dashboard
• Integrated ML models with APIs for live alerts

• Applied EDA, feature engineering, regression, and classification
• Optimized models for accuracy using hyperparameter tuning

• Developed backend APIs for code upload, analysis, and AI feedback using FastAPI
• Ensured scalable and modular architecture

• Full-stack ML web app using Flask backend & HTML frontend
• Implemented TF-IDF vectorization and Logistic Regression for classification

• Time-series modeling and forecasting
• Real-world system simulations
• AI-powered decision support systems
• Environmental/physical systems data analysis

• LinkedIn: https://linkedin.com/in/yashika-garg025