Research on Entrepreneurial Project Evaluation Model Driven By Random Forest Algorithm and Big Data

This research introduces an innovative approach that integrates Random Forest (RF) with Principal Component Analysis (PCA), a methodology we term Random-Principal Component Analysis (RPC). It is applied to the evaluation model of entrepreneurial projects based on random forest algorithm and big data. RPC method aims to solve the common problems of high data dimension and redundancy in the evaluation of entrepreneurial projects. PCA technology reduces the data dimension while retaining the main feature information, and then makes use of the powerful classification ability of random forest to carry out efficient project evaluation. Specifically, PCA is integrated into the feature selection process of the random forest as a pre-processing step, making the model more efficient and accurate when processing a large number of features. RPC methods optimize the evaluation process of entrepreneurial projects by reducing computational complexity and improving predictive performance. The experimental results show that the model can not only quickly identify entrepreneurial projects with high growth potential, but also effectively reduce the overfitting risk caused by too large feature space.