/Customer-Segmentation

Customer Segmentation using Python, Numpy, Pandas, Sckit learn, Scipy,matplotlib and seaborn

Primary LanguageJupyter Notebook

Customer-Segmentation

Customer Segmentation using Python, Numpy, Pandas, Sckit learn, Scipy,matplotlib and seaborn

Dataset OnlineRetail.csv contains all the transactions occuring between 01-Dec-2010 and 09-Dec-2011 for a UK-based and registered non-store online retail.

Steps involve in the Customer Segmentation are categorized into following:

  1. Reading and Understanding Dataset.
  2. Cleaning the Data.
  3. Data Preparation.
  4. Building the Model.
  5. Final Analysis.

Dataset Description

Outlier Analysis Insights is shown below:

In the customer segmentation, I have used the K-Means CLustering Algorithm

K-Means is one of the simplest and useful Unsupervised Machine Learning Algorithm. To find the optimal number of clusters, I have used Elbow Method. Elbow method plot is shown below:

After performing all the necessary processing and implementing model, we have got different insights that shows relation between cluster and attributes:

1. Cluster ID VS Amount

1. Cluster ID VS Frequency

1. Cluster ID VS Recency