## Getting Started with PipelineX

### Kedro 0.17.x Starter projects

Kedro starters (Cookiecutter templates) to use Kedro, Scikit-learn, MLflow, and PipelineX are available at:
[kedro-starters-sklearn](https://github.com/Minyus/kedro-starters-sklearn)

Iris dataset is included and used, but you can easily change to Kaggle Titanic dataset.

### Example/Demo Projects tested with Kedro 0.16.x

- [Computer Vision using PyTorch](https://github.com/Minyus/pipelinex_pytorch)

  - `parameters.yml` at [conf/base/parameters.yml](https://github.com/Minyus/pipelinex_pytorch/blob/master/conf/base/parameters.yml)

  - Essential packages: PyTorch, Ignite, Shap, Kedro, MLflow
  - Application: Image classification
  - Data: MNIST images
  - Model: CNN (Convolutional Neural Network)
  - Loss: Cross-entropy

- [Kaggle competition using PyTorch](https://github.com/Minyus/kaggle_nfl)

  - `parameters.yml` at [kaggle/conf/base/parameters.yml](https://github.com/Minyus/kaggle_nfl/blob/master/kaggle/conf/base/parameters.yml)

  - Essential packages: PyTorch, Ignite, pandas, numpy, Kedro, MLflow
  - Application: [Kaggle competition to predict the results of American Football plays](https://www.kaggle.com/c/nfl-big-data-bowl-2020/data)
  - Data: Sparse heatmap-like field images and tabular data
  - Model: Combination of CNN and MLP
  - Loss: Continuous Rank Probability Score (CRPS)

- [Computer Vision using OpenCV](https://github.com/Minyus/pipelinex_image_processing)

  - `parameters.yml` at [conf/base/parameters.yml](https://github.com/Minyus/pipelinex_image_processing/blob/master/conf/base/parameters.yml)
  - Essential packages: OpenCV, Scikit-image, numpy, TensorFlow (pretrained model), Kedro, MLflow
  - Application: Image processing to estimate the empty area ratio of cuboid container on a truck
  - Data: container images

- [Uplift Modeling using CausalLift](https://github.com/Minyus/pipelinex_causallift)

  - `parameters.yml` at [conf/base/parameters.yml](https://github.com/Minyus/pipelinex_causallift/blob/master/conf/base/parameters.yml)
  - Essential packages: CausalLift, Scikit-learn, XGBoost, pandas, Kedro
  - Application: Uplift Modeling to find which customers should be targeted and which customers should not for a marketing campaign (treatment)
  - Data: generated by simulation