https://github.com/abdul-aa/kickstarters
Predictive Modeling and Clustering Insights for Kickstarter Success
https://github.com/abdul-aa/kickstarters
boosting-ensemble clustering clustering-analysis data-visualization gradient-boosting kprototypes python shap
Last synced: 3 months ago
JSON representation
Predictive Modeling and Clustering Insights for Kickstarter Success
- Host: GitHub
- URL: https://github.com/abdul-aa/kickstarters
- Owner: Abdul-AA
- License: mit
- Created: 2023-11-27T21:30:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-15T02:53:48.000Z (over 1 year ago)
- Last Synced: 2025-01-21T00:14:59.891Z (5 months ago)
- Topics: boosting-ensemble, clustering, clustering-analysis, data-visualization, gradient-boosting, kprototypes, python, shap
- Language: Jupyter Notebook
- Homepage: https://github.com/Abdul-AA/Kickstarters/blob/93f5d26416e4b3b8926a21f5d7d6eaeb599fa469/Project%20Report.pdf
- Size: 5.35 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Kickstarters
## Predictive Modeling and Clustering Insights for Kickstarter Success
Kickstarter, a renowned crowdfunding platform, operates on a unique premise where backers support
projects of interest through financial pledges. The platform employs an ”all or nothing” model, where
each project sets a financial goal, and its outcome is categorized as either failed or successful based on
whether the goal is achieved. The high stakes associated with this model underpin the importance of a
predictive model that can accurately forecast a project’s fate. Such a tool would be invaluable for project
creators, allowing them to assess the suitability of Kickstarter for their endeavor before committing,
ultimately saving time and resources. Moreover, delving into the diverse attributes of past projects
can provide creators with insights to strategically position their initiatives for success. Recognizing this
potential, this project aims to develop a classification model capable of predicting a project’s success
or failure. Additionally, the project seeks to employ clustering techniques on historical data to uncover
inherent patterns and trends among past Kickstarter projects, offering creators a deeper understanding
to enhance their project planning and execution strategies.
## Tools and Algorithms
- Python
- Pandas
- Matplotlib
- Seaborn
- Boosting and Bagging
- K-Prototypes clustering
- SHAP
[Full report available here](https://github.com/Abdul-AA/Kickstarters/blob/58de5bfc24e5ef56c499d19c3dcb5ff003113f19/Project%20Report.pdf)