Mobot

Description : This porpose of building this project is to create a data science pipe line application, and functionality will include from the data collection, data cleaning and all the way to the model evaluation.

Team Members : Teng Yung Lin, Wen Hsuan Liang, Yu Chen Su

How to use

1. Create conda environment (Mobot) from environmnet.yml file using conda env create -f environment.yml. Conda command should be pre-install.
2. Rename exection_plan.yaml.template into exection_plan.yaml
3. Edit the detail of exection_plan.yaml
   • Source file directory
   • Imputation, transformation methods
   • Train, test split method
4. Run python init.py to construct the folder structure for Mobot project
5. Run python main.py to execute the experiments
6. The result file will be inside ./data/estimate folder which shows the model performance of each experiment.

Dataset :

1. Index Mundi (https://www.indexmundi.com/)
2. World Meter (https://www.worldometers.info/coronavirus/)

Reference :

1. Jason Brownlee (2019), Probabilistic Model Selection with AIC, BIC, and MDL, Retrieved from https://machinelearningmastery.com/probabilistic-model-selection-measures/
2. Greg (2013),Is there a library function for Root mean square error (RMSE) in python?, Retrieved from Stack overflow https://stackoverflow.com/questions/17197492/is-there-a-library-function-for-root-mean-square-error-rmse-in-python
3. Kavindu Dodanduwa (2018), python exception message capturing, Retrieved from Stack overflow https://stackoverflow.com/questions/4690600/python-exception-message-capturing
4. Jason Brownlee (2016), Save and Load Machine Learning Models in Python with scikit-learn, Retrieved from https://machinelearningmastery.com/save-load-machine-learning-models-python-scikit-learn/
5. Kamon Ayeva, Sakis Kasampalis (2018), Mastering Python Design Patterns - Second Edition
6. Baran Köseoğlu (2020), Structure Your Data Science Projects - Develop collaborative and reproducible data science projects https://towardsdatascience.com/structure-your-data-science-projects-6c6c8653c16a
7. Bruno Oliveira (2018), pytest Quick Start Guide