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The best time to visit any city

Hacker News

The best time to visit any city

I wanted to build a tool to help people decide when and where to travel . As I started building, I realized that "when" and "where" need separate treatment to be most useful. The map tool handles "where" best: https://championtraveler.com/travel-weather-map/ Clicking through each week would be frustrating for those who know where they want to travel but not when. For these people I built "best time to travel" pages using the same data. https://championtraveler.com/best-time-to-travel/ My hope is this site will help travelers plan. This data is taken from the National and Atmospheric Administration's global summaries of the day (NOOA's GSoD). I used an SQL database to crunch the numbers into monthly and weekly averages by station. For the "best time" pages I used and calculated several more variables. I then imported the data into Tableau and added the filters you see on the map. I also used data from the State Department regarding travel advisories. Would love your thoughts! The whole buildout was a solo project, but I owe Ryan Whitacker a big "thank you" for his guidance. He built a similar tool on his site ( https://decisiondata.org/the-best-time-to-visit-anywhere/ ) in April, and was generous to offer me guidance for expanding upon his idea. Known issues: * I am aware that the map is bad on mobile, so my next step is to improve the mobile experience.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos
44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, monthly, para · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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