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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67672
Volume:
10 |
10 |
Oct 2026
Pages:
1-10
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Computer Technology
Priyadarshini College of engineering nagpur

Computer Technology
Priyadarshini College of engineering nagpur

Computer Technology
Priyadarshini College of engineering nagpur

Computer Technology
Priyadarshini College of engineering nagpur

Computer Technology
Priyadarshini College of engineering nagpur
Startup evaluation is a complex process involving financial indicators, market conditions, business relationships, and uncertainty about future growth. Conventional assessment approaches may provide limited support to early-stage founders who lack access to structured analytical resources. This paper presents VentureIQ, an AI-powered startup evaluation and risk intelligence platform that integrates machine learning, information retrieval, and large language models within a unified web application. The proposed system operates in two modes: post-funding startup evaluation and pre-funding market intelligence. In the post-funding mode, a Gradient Boosting Classifier analyzes startup-related features to estimate viability and classify associated risk. A Retrieval-Augmented Generation (RAG) pipeline, supported by FAISS-based similarity search and sentence embeddings, supplies relevant contextual information for generating SWOT analyses and strategic recommendations. In the pre-funding mode, the Groq API facilitates language-model-based market analysis and preliminary business strategy development. Implemented using Streamlit and Python, VentureIQ combines predictive analytics with contextual insights through an interactive interface. The platform is intended to support preliminary assessment rather than replace professional investment decisions. Its predictive reliability and practical generalizability depend on dataset quality, model validation, and the relevance of retrieved information. This work presents an integrated framework for making startup assessment more accessible, structured, and informative for entrepreneurs and aspiring founders.
KEYWORDS
This paper presented VentureIQ, an AI-powered startup evaluation and risk intelligence platform that integrates a Gradient Boosting Classifier with a Streamlit interface and AI-assisted analytical components. The system combines structured-data prediction with contextual market analysis to provide users with a unified startup evaluation workflow. On the reported synthetic test set, the Gradient Boosting Classifier achieved 86.2% accuracy, an 85.7% F1-score, and a ROC-AUC of 0.92. These results indicate promising classification performance under the evaluated experimental conditions. The interactive interface makes the outputs accessible, while the RAG and LLM components support supplementary analysis and strategic recommendations. However, the use of synthetic data limits the conclusions that can be drawn about real-world startup outcomes. Further validation using independent datasets, stronger evaluation procedures, and explainability techniques is required. With these improvements, VentureIQ could develop into a more transparent and evidence-supported decision-support platform for startup assessment.
[1] M. Ahmed and S. Rahman, “Predicting startup failure using machine learning: A comparative study of supervised algorithms,” Journal of Entrepreneurship Research, vol. 15, no. 2, pp. 112–128, 2023. This study provides a comprehensive benchmark of Logistic Regression, Random Forest, and Gradient Boosting classifiers on structured financial datasets, establishing GBC as the superior model for non-linear feature interactions in early-stage viability assessment. [2] L. Chen and K. Wang, “The impact of funding rounds and investor type on early-stage startup survival rates,” IEEE Access, vol. 10, pp. 56789–56801, 2022. The authors analyze a dataset of 12,000 startups to quantify the differential impact of Venture Capital versus Angel investment, finding that VC presence correlates with a 40% increase in short-term survival probability, a key finding utilized in VentureIQ’s feature engineering. [3] A. Gupta and P. Singh, “Synthetic data generation techniques for machine learning in small-sample domains,” Computers & Industrial Engineering, vol. 178, Art. no. 109121, 2024. This paper proposes a statistical calibration framework for generating synthetic entrepreneurial datasets, demonstrating that models trained on calibrated synthetic data achieve within 5% of real-data performance when feature distributions are properly modeled, validating the methodology used in this work. [4] R. Da Silva, J. Martinez, and L. Garcia, “Explainable AI in business analytics: Using SHAP values to interpret black-box models,” International Journal of Data Science and Analytics, vol. 11, no. 4, pp. 345–359, 2021. The research integrates SHAP (SHapley Additive exPlanations) with ensemble methods to provide per-prediction feature attribution, showing that explainability increases user trust by 34% in financial decision support systems. [5] Y. Huang and Z. Li, “Streamlit for rapid prototyping of data science applications: A case study in financial risk assessment,” Journal of Software: Practice and Experience, vol. 50, no. 8, pp. 1450–1465, 2020. This work evaluates low-code frameworks for deploying machine learning models, concluding that Streamlit reduces development time by 60% while maintaining high usability for nontechnical stakeholders in financial contexts. [6] H. Lee and S. Park, “Evaluating machine learning models for startup viability: Metrics beyond accuracy,” Journal of Business Forecasting, vol. 40, no. 3, pp. 201–215, 2021. The authors argue that accuracy is a misleading metric in imbalanced startup datasets; they propose a multimetric evaluation framework prioritizing ROC-AUC and F1-score to ensure robust discriminative power in high-failure environments. [7] T. Eisenmann, “Determinants of startup failure: An empirical analysis of early-stage ventures,” Harvard Business School Working Paper, no. 21093, 2021. This foundational working paper identifies the primary causes of startup failure, including market misalignment and funding exhaustion, providing the theoretical basis for the six features selected in the VentureIQ model. [8] M. Piotrowski, D. Zhang, and A. Krishna, “Predicting startup success using machine learning: A Crunchbase analysis,” Journal of Business Analytics, vol. 3, no. 2, pp. 145–162, 2020. The study utilizes a large-scale Crunchbase dataset to train predictive models, highlighting the importance of temporal features such as funding velocity and milestone frequency in long-term survival prediction. [9] S. Tamrakar and D. Bhatt, “Early-stage startup failure prediction using gradient boosting,” in Proc. Int. Conf. Advances in Computing, Communications and Informatics (ICACCI), 2021, pp. 1–6. This conference paper demonstrates the application of Gradient Boosting specifically for early-stage ventures, achieving an accuracy of 84% and validating the use of ensemble methods for sparse startup data. [10] D. Z. Zhang, M. Piotrowski, and T. Eisenmann, “Crunchbase: A dataset for startup research,” in Proc. Int. Conf. Web and Social Media (ICWSM), 2020, pp. 78–89. This paper describes the structure and limitations of the Crunchbase dataset, noting issues with label ambiguity and missing values, which motivated the use of synthetic data in the present study.
Publication Date
10/5/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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