Anjali M N

B.E. AI & Data Science graduate with a strong foundation in Data Structures & Algorithms, Python, and SQL. Experienced in building and deploying machine learning models, writing clean and efficient Python code, and working across the full data pipeline from analysis to production. Seeking entry-level Software Engineer, Python Developer, or Data Analyst roles to apply strong technical fundamentals and hands-on project experience to drive impactful, data-driven software solutions.

Data Science & Machine Learning Intern at IIMSTC (2026-02 – 2026-05)

• Designed and trained EfficientNet-B3 multiclass disease detection model achieving 93% test accuracy and 98% Top-5 accuracy on a multi-crop plant disease dataset.
• Built a secondary MobileNetV3 leaf-validation model (96% accuracy, 95% F1-score) integrated as an upstream gate to reject non-leaf inputs, reducing false positives in production.
• Engineered a low-latency inference pipeline via FastAPI REST API connecting a React/TypeScript frontend with a TensorFlow backend, reducing end-to-end prediction latency to 2–3 seconds.
• Developed 2 additional ML projects on Rural Electrification and Clean Cookstove Adoption Prediction, applying regression and classification models on real-world energy datasets.

AI/ML Intern at Quantum Learnings (2024-06 – 2024-09)

• Trained a Logistic Regression + Random Forest classification pipeline on 480 survey responses to predict emotional well-being, achieving ROC-AUC of 0.857 and 76% accuracy.
• Engineered leakage-free features from 21 raw survey columns using 5-fold cross-validation, eliminating data leakage and improving model reliability and generalisability.
• Deployed a Streamlit dashboard for live well-being prediction with interactive EDA across demographics and social media usage patterns.

B.E. in Artificial Intelligence & Data Science – Maharaja Institute of Technology, Thandavapura (2022-11 – 2026-05)