Machine Learning Clustering Models

In this project I am going to Demonstrate and understand the bank customers data and identify the Clustering patterns out of the data and will perform the clustering techniques such as K-means and hierarchical clustering also learn the measures to understand best cluster . As I am mostly focusing on coding and the data science technique to understand data and get most of it using Clustering models as per my knowledge and understanding in this notebook. I have skip the theory part for the Clustering and it functioning which you will get plenty of theoretical material on internet. Following are the few articles which will help you to get basic understanding and theoretical concept about Clustering and different technique.

Reference materials

• https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/
• https://www.mygreatlearning.com/blog/clustering-algorithms-in-machine-learning/
• https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68
• https://medium.com/analytics-vidhya/machine-learning-clustering-techniques-7f43da7b8ae3

Simple and short best video

• https://www.youtube.com/watch?v=4b5d3muPQmA

Summary:

AllLife Bank wants to run personalized campaigns to target new customers as well as upsell to existing customers. Also bank wants to improve the services provided as per imput from the market research team. Operations team wants to upgrade the service delivery model, to ensure that customer queries are resolved faster.

Objective :

To identify different segments in the existing customer based on their spending patterns as well as past interaction with the bank, Using clustering algorithms provide recommendations to the bank on how to better market to and service these customers. Cluster profiling and pattern recognition for the simliar customers.

Data Dictionary :

• Sl_No: Primary key of the records
• Customer Key: Customer identification number
• Average Credit Limit: Average credit limit of each customer for all credit cards
• Total credit cards: Total number of credit cards possessed by the customer
• Total visits bank: Total number of visits that customer made (yearly) personally to the bank
• Total visits online: Total number of visits or online logins made by the customer (yearly)
• Total calls made: Total number of calls made by the customer to the bank or its customer service department (yearly)

What Steps we are performing to achive above objective:

1. Importing base packages.

2. Data cleaning and summarization.

3. Missing value treatment.

4. Feature engineering and text Columns formatting.

5. Testing Clustering models and choosing best one.

6. Hierarchical Clustering

7. Undestaning the best Hypert pamaters .

8. Cluster Profiling Using clustering

9. Printing the important features for the clusters as per best model.

10. Conclusion.