On this Page
...
- 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.
...
Below is the result. The logistic regression performs the best on this dataset at 66.6% accuracy. This is better than the baseline at 59.5%. However, it may not be practical to use. We may be able to do better than this by gathering more data about the project or improving the algorithm.
Downloads
| Attachments | ||||||
|---|---|---|---|---|---|---|
|