The finance team at Rossman wants to forecast sales in all thier stores across several cities six weeks ahead of time. This project is all about building and serving an end-to-end product that delivers this prediction to analysts in the finance team.
Build and serve an end-to-end product that delivers this prediction to analysts in the finance team.
Data exploratory analysis is done after combining the two datasets train and test and is found in eda file notebooks/notebooks/eda_store.ipynb
Logging is a way of tracking events that happen when you runa software.
import logging
logging.warning('Watch out!') # will print a message to the console
logging.info('I told you so')
- Preprocessing
- Building models with sklearn pipelines
- Choose a loss function
- Post Prediction analysis
- Serialize models
- Building model with deep learning
- Using MLFlow to serve the prediction