Dishank

Dishank

product manager

engineer · SAPIndian Institute of Technology Guwahatifull_time
Open to roles
Product Roadmap
LocoBot

LocoBot

Millions of commuters rely on unreliable schedules and inconsistent transit information, causing missed rides, long waits, and daily uncertainty. Build a solution that uses real-time signals to provide accurate arrival predictions and actionable commute decisions Process Key points in mind while building the solution: 1. An Easy-to-use solution 2. Actionable results 3. Actually solve a real-life scenario User Pain Points: 1. Uncertainty to reach the destination on time 2. High buffer time to avoid getting late Goal: Provide a sense of certainty to be on time during any kind of journey for a traveller Solution type: A bot in Telegram(can be extended to whatsapp), as getting users attention for reminders would be much easier than building a whole new app Solution: Bot tracks the real-time ETA continuously, and sends a reminder on the optimized time to ensure they arrive on time based on the validated travel details provided by the user Results Result: 1. On-time reminder allowing the user to reach the destination on time 2. Parameters like traffic, vehicle, duration etc are taken care of By-product: The bot can also work in a telegram(whatsapp) group, allowing for optimization of multi-user journeys. Upgrades needed: 1. Real-time tracking of the updates of the public transport like bus/metro. Tried getting it via delhi transport website, but have not received any response yet. 2. As of now time consumed by user to initiate the journey is fixed, to optimize this we can use an ML algo to predict this time once we get enough data. Reflection Taking into account the possible parameters that a user can encounter in a real user journey Tested with real users to get feedback and optimize the bot further. The process is simple and straightforward. (Except we need to provide the complete map link, not shortened URL, as i used a free public API instead of Google Maps API) Give a call reminder in case of using via whatsapp(not in telegram)

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Beyond the Toss

Beyond the Toss

Every IPL season generates millions of opinions — about toss advantage, explosive powerplays, match-winning finishes, and star performers — but very few of these claims are actually backed by data. Using ball-by-ball IPL data across five seasons, this project analyzes whether winning the toss truly impacts match outcomes, which phase of the innings contributes most to victories, and which players consistently dominated with bat and ball. The goal was to transform raw cricket data into clear, visual, and evidence-based insights that reveal what actually drives success in the IPL. Process 1. Data Collection 2. Data Cleaning & Preparation 3. Match Phase Segmentation 4. Toss Impact Analysis 5. Performance Trend Analysis 6. Player Performance Evaluation 7. Data Visualization 8. Insight Extraction Results Winning the toss provided only a small advantage and was not strongly correlated with match victories. The largest scoring gap between winning and losing teams appeared during death overs, suggesting finishing strength matters more than powerplay aggression in IPL matches. The top batters consistently combined scoring volume with longevity, while leading bowlers maintained wicket-taking consistency across seasons. Despite the hype around explosive starts, death overs showed the strongest connection to winning IPL matches Reflection Instead of limiting the analysis to basic match statistics, I broke the innings into Powerplay, Middle Overs, and Death Overs to identify which phase actually influenced victories the most. I also compared winning and losing teams phase-wise, measured scoring gaps per ball, and connected toss outcomes with match results to challenge common IPL assumptions. The analysis focused on uncovering strategic patterns from ball-by-ball data rather than just presenting player leaderboards or aggregate scores.

7 media files · drive.google.comView

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