Kickstarter is a popular crowd-funding platform used by individuals and groups to obtain the seed amount to implement their business ideas. It is essential for the campaign owners to plan their crowd-funding campaign beforehand in order to avoid failure. This paper aims to predict the success or failure of a Kickstarter campaign and help the campaign owners plan better for their campaign by providing trends and analysis based on historical data of campaigns on Kickstarter, ranging from 2014 to February 2019. Various classification and boosting algorithms were applied and it was concluded that Weighted Random Forest along with AdaBoost for the subsampled dataset gives the best accuracy.

Success Prediction using Random Forest, CatBoost, XGBoost and AdaBoost for Kickstarter Campaigns

Statistics from crowdfunding platforms show that a small percent of crowdfunding projects succeed in securing funds. This makes project creators eager to know the probability of success of their campaign and the features that contribute to its success before launching it on crowdfunding platforms. The existing literature focuses on examining success probability using the entire list of identified projects features. For situations for which project creators have limited resources to invest on the required project features, the list suggested by previous researchers is somewhat large and gives a small success probability. A minimal number of features that predict success with a higher probability can benefit project creators by providing them with insight and guidance in investing their limited resources. This paper presents a metaheuristic whale optimization algorithm (WOA) in the crowdfunding context to perform a complete search of a subset of features that have a high success contribution power. Experiments were conducted using WOA with the K-Nearest Neighbor (KNN) classifier on a Kickstarter dataset. Our approach obtains a subset of 9 features that predict the success of project campaigns with an accuracy (F-score) of 90.28% (90.11%), which is an increase (F-score) of 22.23% (21.61%) than when a complete set of features is used. The findings of this study contribute knowledge to various crowdfunding stakeholders, as they will provide new insights regarding a subset of essential features th4at influence the success of project campaigns with high accuracy.

Feature subset selection for predicting the success of crowdfunding project campaigns

Who said what: Mining semantic features for success prediction in reward-based crowdfunding

Crowdfunding platforms have become a popular alternative for financing projects. However, campaigners in the platform witness several challenges that affect their success. This study aims to: (a) g...

Exploring successful campaign profiles on crowdfunding-based project financing: a tree-based comparative analysis

In the last decade, crowdfunding has emerged as a new form of Internet finance, providing founders with a channel through which they can raise funds from the public. Prior studies have mainly investigated two types of crowdfunding success predictors: conventional numerical features (e.g., project goal, duration, number of rewards, number of comments and the presence of a video) and features extracted from textual description and project images. In comparison, few studies have examined the effect of interrelations among projects on crowdfunding performance. For example, a founder can learn from historically invested projects when launching one’s own project. In this study, we extend the previous understanding by introducing the concept of “project network,” which can be constructed by extracting founders’ activities on crowdfunding platforms. Network-based features are extracted from the project network through the Node2vec method. Experimental results show that models with network-based features outperform those without network-based features. Furthermore, the dense dataset with densely connected projects achieves better prediction performance than the original one, further validating the role of a project network in success prediction. Another implication is that a small proportion of connected projects could help predict project success to avoid a high calculation cost. between and backers in

Success prediction of crowdfunding campaigns with project network: a machine learning approach

The social media version of fundraising, crowdfunding has, within a very short time, become an important and often critical element of the process that allows budding entrepreneurs to grow in todays market While the underlying principle behind crowdfunding — to get multiple people to financially back an idea and collectively amass enough resources to make it happen — is simple enough in theory, it is much harder to actually implement Kickstarter, the largest and most well-known crowdfunding website internationally, sees more than half of the uploaded projects rejected due to inadequate funds gathered In this study, we take a closer look at the reasons for success and failure of these projects from the project description pages, where the creators attempt to persuade potential backers into supporting their respective creations A dataset of over 26 thousand Kickstarter projects was created and used to understand the factors that most affect the chances of acquiring successful funding In this study, we propose a novel algorithm for the RandomForests learning model in which we assign optimal weights to the individual classifiers and perform weighted majority voting on them using the 13 predictors that were found to be most suitable through implementation logic These predictors will serve to be useful to both, a project creator as well as a potential backer, in order to devote their energy and resources judiciously Finally, we tested our optimized learning model on the freshly acquired dataset and observed 94289% accuracy, 945% precision, 943% recall and 943% F-measure value in success prediction This research achieved the best accuracy and greatest amount of success in this field

Predicting crowdfunding success with optimally weighted random forests

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Predicting crowdfunding success with optimally weighted random forests