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Problem Scenario
In these days, crowdfunding platforms are very popular for innovators and people with cool ideas to raise funds from the public. Only on Kickstarter (one of the most popular online crowdfunding platforms), more than 14,000,000 backers (people who invest in projects) have funded almost 150,000 projects. At each moment, almost 4,000 projects are live to receive funding from the public. There are a lot of crowdfunding projects that succeed and fail. The success rate of a project depends on a lot of factors. It will be great to find a way to estimate and improve the success rate of future projects.
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- Profiling: Use Profile Snap from ML Analytics Snap Pack to get statistics of this dataset.
- Data Preparation: Perform data preparation on this dataset using Snaps in ML Data Preparation Snap Pack.
- Cross Validation: Use Cross Validator (Classification) Snap from ML Core Snap Pack to perform 10-fold cross validation on various Machine Learning algorithms. The result will let us know the accuracy of each algorithm in the success rate prediction.
We are going to build 4 pipelines: Profiling, Data Preparation, and 2 pipelines for Cross Validation with various algorithms. Each of these pipelines is described in the Pipelines section below.
Pipelines
Profiling
In order to get useful statistics, we need to transform the data a little bit.
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