Startup Success Prediction Using Machine Learning

The goal of this startup success prediction is to develop a machine-learning model that can predict the success of a startup based on various factors such as funding, location, industry, and team composition. The paper involves exploring and cleaning a dataset of startup information, performing feature engineering and selection, and experimenting with various machine-learning algorithms such as K-Nearest Neighbors, Random Forest, and Gradient Boosting. The paper also involves evaluating the performance of the model using metrics such as accuracy, precision, and recall, and exploring potential future enhancements such as incorporating additional data sources, hyper parameter tuning, and real-time prediction. The results of this paper can be used by investors, entrepreneurs, and other stakeholders to make informed decisions about startup investments and to gain insights into the factors that drive startup success.