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work / startup-success-prediction · 2025-09

Startup Success Prediction with Ensemble Classification

[machine-learning][ensembles][scikit-learn]

Abstract

Ensemble ML that predicts startup viability from key attributes, for data-driven founder and investor decisions.

§1Problem

Startup outcomes hinge on many weak signals; single classifiers underfit the interactions between funding, market, and team attributes.

§2Approach

Compared AdaBoost, Random Forest, LGBM, Decision Tree, SVM, and Extra Trees, then combined the strongest into an ensemble voting classifier; full EDA and model-comparison methodology on a structured startup dataset.

§3Impact

Identified the most effective classification approach for the dataset and packaged insights for entrepreneur/investor decision-making.

Keywords: Python, Scikit-learn, Pandas, Matplotlib, Seaborn

[github]

@misc{ammar2025startupsuccessprediction,
  author = {Ammar, Md. Abu},
  title  = {Startup Success Prediction with Ensemble Classification},
  year   = {2025},
  url    = {https://github.com/abuammarsami/Startups-Success-Prediction-using-Ensemble-Classification},
  note   = {Research project}
}