The idea is simple: we’ll use data science and machine learning to predict whether a startup is likely to survive or fail based on key factors like industry, funding, team size, and market conditions. By analyzing patterns from past startups, we can create a model that helps entrepreneurs and investors make smarter decisions. Let’s collaborate, share ideas, and figure out the best direction for this project together.

Data:
I’ve found some data sources that might work for this project, but I’m not sure if we can fully use them—so if you have any better datasets in mind, feel free to suggest them!
1. https://www.bls.gov/bdm/bdmage.htm
2. https://www.kaggle.com/datasets/yanmaksi/big-startup-secsees-fail-dataset-from-crunchbase
3. https://indicators.kauffman.org/data-downloads
4. https://catalog.data.gov/dataset?publisher=Economic+and+Risk+Analysis
5. https://github.com/sowide/bankruptcy_dataset