2017DMC

2017 Data Mining Cup Challenge - Revenue Forecast as a foundation for dynamic pricing

Machine Learning Pipeline

Data Preparation and Model Training framework Set-up

• Data Cleaning: run 1.0_clean.R
• Feature Engineering: run 2.0_nolabel_features.R -> 3.0_label_features.R -> 4.0_merge_features

Training on the 1-63 days data and tuning on 63-77 days data

• 1st-level models to predict order probability: run 5.1_h2o_gbm_1stLevel.R, 5.2_h2o_glm_1stLevel.R, 5.3_h2o_neural_network_1stLevel.R, 5.4_h2o_rf_1stLevel.R and 5.5_xgboost_1stLevel.R separately.
• 2nd-level models to predict the revenue: run 6.0_combine_1stLevelPreds.R -> 6.1_h2o_glm_2ndLevel.R(similar modeling script structure as last step)...

Training on the hold-out set - last 15 days data

• 3rd level model(blending): Combined the predictions from 2nd level models to fit a linear model on the end92d_test.feather to decide the weights for ensembling final models

Retraining on all 92 days data and generate final predictions on the test set

• Use the pre-configured 1st and 2nd model settings to retrain on the end92d_train.feather and then predict on the end92d_test.feather -> Save the final predictions from 2nd-level models
• Apply the 3rd level blending model on the final predictions

Notes:

1. 3.1_ranef_features.R takes long time to run so it can run independently to save the output files
2. 3.4_likelihood_features.R includes the helper functions for generate likihood features used in 3.0_label_features.R
3. To run the scripts properly, please make sure to set up the folder structure correctly as showed in the following section, especially for the data and src folders.

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
|   ├── merge          <- Merge with other features
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

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Project based on the cookiecutter data science project template. #cookiecutterdatascience