Machine learning prediction application

Steps of usage

1. Fork or clone this repo to your local machine.
2. Navigate to the project directory and activate the virtualenv source dev/bin/activate
3. Install requirements pip install -rrequirements.txt from the root directory.
4. Navigate to the packages -> regression_model then run tox to make sure that everything is passing.

---

Basic Analyses

The following kaggle notebooks includes the complete basic analyses and EDA

• Data Analysis
• Feature Engineering
• Feature Selection
• Model Building

Medium post part_1

Medium post part_2

Scaffold template - starter template

---

Notes to consider:

• If you intended to use this project as a reference to build your own, you need to define your environmental variables in the circleci.
• Tests are disabled for this project, would appreciate if anyone raise a pull request to include them.

Few things to consider

I structured the scaffold based on the OOP which seperate concerns of code.

• processing folder conatains any scripts for data wrangeling, cleaning, or feature engineering.
• trained_model folder contains any scripts dedicated to build model, tuning or any related scripts.
• pipeline.py file contains all the procedures that should be done using the sklearn.pipeline
• predict.py file dedicated for getting out the predictions
• train_pipeline.py file dedicated to train the model, starting from downloading the dataset, split, apply pipeline...etc.

Output:

• The resulted model is saved as a .pkl file versioned with the same version of the package.
• Only one API end point is already deployed to Heroku, pull requests are welcome

---

Future work

• Build extra API end points
• Build frontend interface using streamlit
• Complete unit tests.
• Create a configuration file for travis

---

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT