SHIKHA YADAV

About

Pre-final year Electronics & Computer Science student specializing in Artificial Intelligence and Machine Learning. Interested in healthcare AI, clinical ML, and MLOps, with project experience in predictive modeling, graph ML, and Dockerized deployment.

Education

• B.E. Electronics & Computer Science
  Rizvi College of Engineering
  Artificial Intelligence & Machine Learning · 2023 – 2027

Skills

• SQL
• FastAPI
• Git
• Python
• Matplotlib
• NumPy
• Pandas
• scikit-learn
• TensorFlow
• Seaborn
• Docker
• Keras
• Pytest
• GitHub Actions
• XGBoost
• Lightgbm
• Shap
• Joblib

Projects

• Drug-Drug Interaction Prediction
  Python, NetworkX, Node2Vec, XGBoost

  Exploring drug-drug interaction prediction as a link prediction problem on the DrugBank knowledge graph using graph embeddings and XGBoost for unseen interaction pair prediction.

• SepsisWatch — ICU Sepsis Early Warning System ↗
  Python, XGBoost, SHAP, FastAPI, Docker

  Trained XGBoost on 40,336 ICU patients to predict sepsis onset 6 hours before clinical diagnosis. Built temporal features, used patient-level splitting to prevent leakage, added SHAP explainability, and deployed as a Dockerized FastAPI service.

Courses & certifications

• Deep Learning Specialization · DeepLearning.AI / Coursera · 2024
• Machine Learning Specialization · DeepLearning.AI / Coursera · 2024

🏆 Achievements & awards

• CGPA 9.4/10.0

  Consistent academic performance across Electronics & Computer Science curriculum.