/Mall-Costumer-Segmentation

This is a study to undestand costumer clustering using a dataset from Kaggle with costumer demograph informations.

Primary LanguageJupyter Notebook

Mall-Costumer-Segmentation

Data from Kaggle containing informations about costumers

Table of Contents

  1. Installation
  2. Project Overview
  3. File Descriptions
  4. Results
  5. Licensing, Authors, and Acknowledgements

Installation

There should be no necessary libraries to run the code here beyond the Anaconda distribution of Python. The code should run with no issues using Python versions 3.*.

Project Overview

This data set was created for learning purposes of the customer segmentation concepts. You own a supermarket and through membership cards, you have some basic data about your customers and want to improve your marketing strategy by understanding your customers characteristics and buying behavior and give that to your marketing team plan future campaigns.

File Descriptions

  • Data

    • Mall_Customers.csv - demographic data for each costumer and score

  • Code

    • Mall-Costumer-Segmentation.ipynb - code that runs all the analysis

Results

Below is a chart called "Elbow Method" and is used to identify the best number of clusters for the data.

image

Here we have two types of clustering, the first one on the left chart it was using a simple scatter plot with the costumer gender. On the right chart, we have four clusters analysing AGE and COSTUMER_SCORE using the Kmeans algorith . You will find other types of clusterings in the code.

image

Licensing, Authors, Acknowledgements

This is a open data from Kaggle, must give credits to the owner and you can find it here. Otherwise, feel free to use the code here as you would like!