Ayush Kandpal

Full-Stack Developer

Dit Universityfull_time, internship

Open to roles

GITSpringBootspringSQL

OnRoute — Real-Time Commute Intelligence for Delhi Commuters

Delhi has 4.5M daily DTC bus riders with zero reliable real-time information. Commuters like Priya leave home at 8:15 to catch the 8:22 bus — not knowing if it's early, late, or on time. Transit apps show outdated static routes. Google Maps has no live DTC data. The result: people add 10-15 min buffer time daily just to handle uncertainty. Multiply that by millions of commuters and it's a massive productivity loss built entirely on a data gap. Process Started by identifying the core user question: "Should I leave right now?" — everything was built around answering that one thing. First tried using real GTFS-RT feeds from DTC but no public API exists. Pivoted to a simulated live data pipeline with realistic Delhi route patterns, variance, and rush hour multipliers — statistically accurate even if not GPS-sourced. Built the prediction engine first: rush hour detection (7-9am, 5-8pm) multiplies base delay by 2.6x, confidence scoring uses route variance vs historical mean. Then wired the "leave-by" calculator on top: ETA minus walk time minus 2 min buffer. Added Groq + Llama 3.3 70B via a Vercel serverless proxy so the AI advisor works without exposing keys. Final layer was low-data and WhatsApp text mode — because real Delhi co Results Live app deployed at on-route-five.vercel.app with 5 Delhi DTC routes simulated in real-time. Prediction engine refreshes every 6 seconds with confidence scores between 40-95%. Leave-by calculator updates dynamically as walk time changes. AI advisor answers natural language queries using live bus context. Low-data mode cuts payload to under 1KB per update — functional on 2G. Demo scenario loads Priya's exact 8:22 bus situation instantly for evaluators to test without setup. Reflection I'd integrate real GTFS-RT feeds from DIMTS or DTC's internal systems instead of simulated data — the prediction logic is solid but real GPS positions would make confidence scores genuinely reliable. I'd also add push notifications so commuters get a "leave now" alert without opening the app, and build a WhatsApp bot that sends automatic updates to a user's number at their scheduled commute time. The architecture supports all of this — I'd prioritise real data sourcing as the next step.

4 media files · on-route-five.vercel.appView

IPL Crunch ’26: Data-Driven Analysis of 1,218 IPL Matches

Cricket discussions are often driven by assumptions rather than evidence. This project aimed to analyze 1,218 IPL matches (2007–2025) using ball-by-ball and match-level data to determine whether commonly accepted beliefs — such as toss advantage, phase dominance, and star player impact — are actually supported by data. The goal was to uncover measurable patterns behind winning outcomes and present them through clear, data-driven storytelling. Process he project began with collecting IPL datasets from Cricsheet and organizing both match-level and ball-by-ball records. After cleaning missing values and standardizing team names across seasons, match deliveries were grouped into phases: Powerplay (1–6), Middle Overs (7–15), and Death Overs (16–20). Exploratory Data Analysis was then performed to compare winning and losing teams across different match conditions. Toss outcomes, phase-wise scoring patterns, and player performances over the last five seasons were aggregated and visualized using Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn. Several visualization approaches were tested before finalizing cleaner, insight-focused charts that prioritized readability and storytelling over dashboard complexity. The final analysi Results Analysis of 1,218 IPL matches revealed that toss advantage is statistically negligible, with toss winners winning only 50.5% of matches. The study also found that death overs create the largest scoring gap between winning and losing teams, making them the strongest predictor of victory. The project improved my skills in data analysis, visualization, and insight-driven storytelling using real IPL datasets. Reflection With more time, I would extend the project using machine learning models to predict match outcomes based on live match conditions. I would also add venue analysis, player form trends, and interactive dashboards for deeper cricket insights.

8 media filesView

This is Ayush’s work on Wooble.

Build a profile that shows what you can do — and share it anywhere.

Build yours