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Journals / Istanbul Business Research / 2021 / Cilt: 50 - Sayı: 2

Do Machine Learning and Business Analytics Approaches Answer the Question of ‘Will Your Kickstarter Project be Successful?

Can AYDIN
Murat KILINÇ
Çiğden TARHAN
Pages
255–274
Publication Date
2022-10-19
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Thus, Kickstarter users will be able to see their shortcomings and have a prediction about success before presenting their projects to their backers.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Kickstarter is one of the popular crowdfunding platforms used to implement business ideas on the web.
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Key Findings & Data:Within the scope of machine learning, various algorithms were applied for success classification and the best results were given by 91% Random Forest, 85% Decision Tree, 84% K-Nearest Neighbors (KNN) algorithms.
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Conclusion & Impact:Thus, Kickstarter users will be able to see their shortcomings and have a prediction about success before presenting their projects to their backers.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Machine Learning · Business Analytics · Decision Support System

Abstract

Kickstarter is one of the popular crowdfunding platforms used to implement business ideas on the web. The success of crowdfunding projects such as Kickstarter is realized with future financial support. However, there is no platform where users can get decision support before presenting their projects to supporters. To solve this problem, a platform where users can test their projects is required. Within this scope, a business intelligence model that works on the web has been developed by combining business analytics and machine learning methods. The data used for business analytics has been brought to a state that can provide inferences through visualization, reporting and query processes. Within the scope of machine learning, various algorithms were applied for success classification and the best results were given by 91% Random Forest, 85% Decision Tree, 84% K-Nearest Neighbors (KNN) algorithms. F1-Score, Recall, Precision, Mean Squared Error (MSE), Kappa and AUC values were analyzed to determine the most successful models. Thus, Kickstarter users will be able to see their shortcomings and have a prediction about success before presenting their projects to their backers.

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Cite This Article

AYDIN, C., KILINÇ, M., TARHAN, Ç. (2022). Do Machine Learning and Business Analytics Approaches Answer the Question of ‘Will Your Kickstarter Project be Successful?. Istanbul Business Research, Cilt: 50 - Sayı: 2, 255–274. https://ajindex.com/articles/542749

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References (8)

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