B.E Electronics and Communication Engineering | Building toward ML / Embedded / VLSI roles at chip companies
I combine machine learning depth with embedded systems and digital logic design — three areas that rarely overlap in a single student's portfolio, but all matter directly to how modern chip companies build products.
🛡️ AI-Powered SMS Phishing Detection TF-IDF + Logistic Regression spam/phishing classifier — 99.01% accuracy, 95% recall, ROC-AUC 0.9959. Includes an honest false-negative analysis and a "what I'd rebuild" retrospective.
📊 Student Performance Predictor Random Forest regressor predicting final grades — R² = 0.86. Includes a deliberate honesty check showing R² drops to 0.16 without prior-grade features, confirming what the model actually learns.
🌡️ Smart Environmental Monitor ESP32 + BMP180 sensor system using I2C and a non-blocking state machine, with a Python serial logger writing to CSV. Capstone of a 14-day embedded systems roadmap.
🔧 VLSI CPU Datapath 11-day Verilog progression from logic gates to a working Mini CPU Datapath (8-bit ALU, control FSM, registers, flags), simulated with Icarus Verilog and GTKWave. Documents a real clock-edge race condition, found and fixed.
SQL → Python → Machine Learning (through SHAP) → C++ → Embedded Systems → TinyML/Edge AI followed by a VLSI Flow Track: RTL → Verification → Synthesis → Static Timing Analysis → Physical Design
LinkedIn · Email: mukundan1012@gmail.com