BiziData
Analytics dashboard for bike-sharing systems
I started working on BiziData because I was frustrated by how inefficient the bike distribution system often felt. Stations would be empty when you needed a bike, or completely full when you wanted to return one.
Share cardActual performance
1followers
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
Similar products
cm
cmdbikes, bike sharing at your terminal71%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
cmdbikes, bike sharing at your terminal
Sizle38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Document sharing, analytics and approvals into a collaborative dashboard
EcoBike
Eco-friendly, affordable, and convenient dockless bike sharing
I
I made a tool for analyzing wind conditions for bike rides58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
I made a tool for analyzing wind conditions for bike rides
Re
Recommender Systems in Keras48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Recommender Systems in Keras
Fe
Fern – L-systems in Go48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Fern – L-systems in Go
PV
PVBenchmark – UserBenchmark for PV Systems34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
PVBenchmark – UserBenchmark for PV Systems
PV
PVBenchmark – UserBenchmark for PV Systems34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
PVBenchmark – UserBenchmark for PV Systems
Tr
Troubleshoot distributed systems51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Troubleshoot distributed systems
Aventis Systems27%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
“Get IT Done”