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Chunavilal – An AI Assisted Election Result Analysis App

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Chunavilal – An AI Assisted Election Result Analysis App

I was looking to do some slicing and dicing of the Results of the Indian Parliamentary Elections 2024. I wrote an app. It is a proof of concept using AI and Analytics together. The libraries involved are Streamlit, Plotly, Langchain and Selenium. I am looking for collaborators as I know it is an election year in many countries. In theory, this app can be modified to allow analysis of other data sets as well. The link ( https://levelup.gitconnected.com/results-election-2024-india... ) has more details about the app, its source, purpose etc. Looking forward to feedback, suggestions and comments.

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53%53% 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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos
34%34% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
24%24% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
23%23% 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
17%17% 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
7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

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