VA

VAERS DuckDB Database

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VAERS DuckDB Database

I've been playing with the VAERS database for a while and I just got through importing it into duckdb for local analytics. The data set has various problems that I try to fix to improve the quality of the dataset. The import scripts are https://github.com/yehosef/vaers-duckdb A ready data file is available here (~3GB) https://drive.google.com/file/d/1d3wRRr2UFvCYR9r7J5XBym2dYKT... I'm planning to add some dashboards like my original elasticsearch project. I would also like to add some embeddings for vector search. WIP I'd love to hear what you think!

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Product HuntOn track for Day 1 leaderboard · Strong signals: google · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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.
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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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