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@article{183909,
        author = {G. Vijaya Lakshmi and D. Narendra Varma},
        title = {Machine Learning Models for Startup Investment Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {3410-3416},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183909},
        abstract = {The startup investments gained much importance as most of the people are investing in the stock market. In the present study, predicting of startup investments is considered for identifying the selection of companies using classification and regression tasks. The selected classification models for the startup status prediction are Linear Model, Support Vector Machine (SVM), and Random Forest. Similarly for Regression tasks, Linear Regression, Decision Trees and Random Forest are employed for forecasting investments. The optimal models are identified from the forementioned popular models. In the prediction task, the Random Forest model outperforms the Linear Model and SVM. However, in regression tasks, Decision Tree Model outperforms Linear Regression and Random Forest models. A correlation matrix is applied for data segmentation and feature selection to identify the influential attributes. A detailed analysis is performed to access the correlations and the overall impact on models by eliminating the redundant sub-attributes. The methodology aims to predict the startup investment and guides the investors in identifying the valuable startups.},
        keywords = {Startup investments, Machine Learning, SVM, Random Forests, Decision Trees.},
        month = {August},
        }