This project aims to provide Kickstarter enthusiasts, including backers, creators, or even inquisitors, a general perception about successful projects that met the funding goal, as well as a rough projection of the successful rate in 3 years from 2021 to 2023, detailing in geographical and categorical characteristics.
Methods:
- Datasets Preparations: Python
- Visualization: Tableau
Kickstarter is one of the leading crowdfunding platforms for exciting entrepreneurial ideas. Even though there are thousands of projects are uploaded to it every years, less than 40% of those projects had reached the intended money goal.
Using the history data, I hope to deliver a general ideas over successful and failed projects along with their ability to draw funds. Furthermore, the existing data can also produce a estimated model for future projects. The dashboard filters data based on locations, which allows users to understand the unique local growth of projects better.
Please click here to view the interactive dashboard on Tableau.
| - Kickstarter_project_analysis
| -- dashboard Includes an overview and link to the Tableau dashboard
| --- Link_to_Dashboard.md
| -- data Includes datasets link and a preprocessing script
| --- Kickstarter_2020_data_cleaning.ipynb Python codes to merge and clean data, ready for dashboard
| --- datasets_link.md Link to datasets
| -- media Includes screenshots of the dashboard
| --- crd00.png
| --- crd000.png
| --- crd01.png
| --- crd011.png
| --- crd02.png
| --- crowdfunding.png
| -- report Includes a detailed report regarding the development
| --- Kickstarter_dashboard_report.pdf
| -- .gitignore Ignores heavy files
| -- LICENSE MIT License
| -- README.md Project Overview
I want to incorporate this dataset with a Machine Learning model that can provide more accurate forcasting information for users.