Jayanth G (@jayanth31m) profile picture

btech student in cse | presidency university

Joined May 2026
Jayanth G retweeted
A few days ago, @gowtham_0317 and I were on Google Meet testing a tiny 5-line machine learning code with fake numbers. Today, I jumped into my first-ever hackathon: 4 teammates (@jayanth31m, Uwais, Shivakumar and me), 5 hours on the clock. I used that idea to build my first real Python project: CREW (Campus Resource Efficiency Watch). Here is what we built in plain English: > The Problem: Big campuses waste huge amounts of electricity, water, and food. The worst part? Nobody notices until the giant monthly bill arrives. > The Innovation: Most tools track power, water, and waste in separate apps. We joined all 3 into one Python model trained on 3 lakh rows of data (OMG!!!). It learns what normal use looks like every hour. When something spikes at night, Google Gemini tells the maintenance team what physical part broke in 1.8 seconds. > The Prototype: A live Streamlit dashboard tracking 14 campus blocks (classrooms, hostels, canteen). It flags leaks, shows the loss in Rupees per hour, and proves that 4 quick fixes can save ₹1.39 Lakhs every month. > Real-World Implementation: You do not need new hardware. Any college, tech park, hospital, or airport can plug their existing digital meters into this Python code: < Colleges: Stop hostel water leaks at 3 AM and shut off empty lab machines. < Tech Parks: Cut AC cooling in empty floors during work-from-home days. < Airports & Stations: Adjust water and lights based on real passenger footfall. You do not learn Python from tutorial videos. You learn it when you have 3 teammates and 5 hours to show a working product to judges. Code is open source. Link in the first reply.
Learning ML on Google Meet with a friend (@gowtham_0317 ), and today we used Orange (the no-code one) to rank windmill sites. He had a 5-row prototype: Indian sites across coastal, mountain, plateau, riverine and desert regions. A Random Forest predicts MW from wind speed, temperature and region. The part I liked most: a Python Script widget that reads the predictions and prints a ranked siting report. Tier-1 sites, region averages, what actually drives the output. Data is made up, so the results mean nothing yet. I just wanted a new area of learning in Machine Learning.
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