This repository illustrates an application of the WhizML codebase for an analysis of Walmart weekly sales.

The dataset used is the Walmart dataset obtained from Kaggle.

Running the eda pipeline will launch the following Auto-EDA dashboard, allowing the users to observe the dataset.

Users can implement custom functions to preprocess the data. In our case, the preprocessing codes can be found in Data_Preprocessing.ipynb, inside the notebooks directory.

The model_experimentation triggered the training of various Logistic Regression, Random Forest, and XGBoost models.

As new data is obtained, drift detection can be performed using the data_drift_analysis pipeline.

Note: To create a hypothetical example, some rows were sampled from the original dataset and were assumed to be the new data.