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The platform employs an ”all or nothing” model, where\neach project sets a financial goal, and its outcome is categorized as either failed or successful based on\nwhether the goal is achieved. The high stakes associated with this model underpin the importance of a\npredictive model that can accurately forecast a project’s fate. Such a tool would be invaluable for project\ncreators, allowing them to assess the suitability of Kickstarter for their endeavor before committing,\nultimately saving time and resources. Moreover, delving into the diverse attributes of past projects\ncan provide creators with insights to strategically position their initiatives for success. Recognizing this\npotential, this project aims to develop a classification model capable of predicting a project’s success\nor failure. Additionally, the project seeks to employ clustering techniques on historical data to uncover\ninherent patterns and trends among past Kickstarter projects, offering creators a deeper understanding\nto enhance their project planning and execution strategies.\n\n## Tools and Algorithms\n- Python\n- Pandas\n- Matplotlib\n- Seaborn\n- Boosting and Bagging\n- K-Prototypes clustering\n- SHAP\n\n[Full report available here](https://github.com/Abdul-AA/Kickstarters/blob/58de5bfc24e5ef56c499d19c3dcb5ff003113f19/Project%20Report.pdf)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabdul-aa%2Fkickstarters","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabdul-aa%2Fkickstarters","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabdul-aa%2Fkickstarters/lists"}