Decoding Startup Success

Inspiration

I aspire to launch my own startup, and this research helps me understand the key factors that contribute to startup success.

What it does

Using a publicly available dataset, I analyzed the critical factors that influence a startup's success. This insight helps founders focus on essential aspects and enables investors to make informed decisions.

How we built it

I implemented Random Forest Classification, XGBoost, and K-Means Clustering, along with survival analysis to assess startup longevity across different categories.

Challenges we ran into

Feature engineering was challenging, and cleaning the dataset was difficult due to numerous outliers that lacked meaningful patterns. Implementing clustering also posed some technical difficulties.

Accomplishments that we're proud of

I am particularly proud of the survival analysis performed using the Kaplan-Meier Survival Estimate.

What we learned

This project reinforced the power of machine learning and the critical role of data. With sufficient data, we can uncover valuable insights about how the world operates.

What's next for Decoding Startup Success

Future improvements include advanced feature engineering, longitudinal analysis, and a dynamic prediction system. I plan to continue refining and expanding this work.

Built With

• machine-learning
• pandas
• python