ML-Wholesale-Customer-dataset

In this project we are going to perform exploartory data analysis and create machine learning models using following:

• K-mean
• RFECV
• PCA
• XGBoost

to best describe the variation in the different types of customers that awholesale distributor interacts with.

Dataset

The whole sale customer dataset contains following attributes:

Attribute Information from here

1. FRESH: annual spending on fresh products (Continuous);
2. MILK: annual spending on milk products (Continuous);
3. GROCERY: annual spending on grocery products (Continuous);
4. FROZEN: annual spending on frozen products (Continuous)
5. DETERGENTS_PAPER: annual spending on detergents and paper products (Continuous)
6. DELICATESSEN: annual spending on and delicatessen products (Continuous);
7. CHANNEL: customers Channel - Horeca (Hotel/Restaurant/Café) or Retail channel (Nominal) --> 1/2
8. REGION: customers Region Lisbon, Oporto or Other (Nominal) -->1/2/3

The target attribute: Channel

Installation

To develop this project Jupyter Notebooks and Anaconda are used. You can install Anaconda from here. Then either use Jupyter Labs or jupyter notebook extension to open the files. You can also view the project on Kaggle here

Credits

here

License

CC-0 license