Problem Scenario

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1. Profiling: Use Profile Snap from ML Analytics Snap Pack to get statistics of this dataset.
2. Data Preparation: Perform data preparation on this dataset using Snaps in ML Data Preparation Snap Pack.
3. 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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Then, we use Type Converter Snap to automatically derive types of data.

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Since we only focus on successful and failed projects, we use Filter Snap to filter out live, canceled, and projects with another status.

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