Customer_Segmentation_Clustering

Table of Contents

1. Importing libraries
2. Loading Data
3. Data Cleaning
4. Data Preprocessing
5. Dimensionality Reduction
6. Clustering
7. Evaluation models
8. Cluster Profiling
9. Conclusion

Context

In this project, I have performed an unsupervised clustering of data on the customer's records from a groceries firm's database. Customer segmentation is the practice of separating customers into groups that reflect similarities among customers in each cluster. I have divided customers into segments to optimize the significance of each customer to the business and to modify products according to distinct needs and behaviours of the customers. It also helps the business to cater to the concerns of different types of customers.

About the dataset

People

• ID: Customer's unique identifier
• Year_Birth: Customer's birth year
• Education: Customer's education level
• Marital_Status: Customer's marital status
• Income: Customer's yearly household income
• Kidhome: Number of children in customer's household
• Teenhome: Number of teenagers in customer's household
• Dt_Customer: Date of customer's enrollment with the company
• Recency: Number of days since customer's last purchase
• Complain: 1 if the customer complained in the last 2 years, 0 otherwise

Products

• MntWines: Amount spent on wine in last 2 years
• MntFruits: Amount spent on fruits in last 2 years
• MntMeatProducts: Amount spent on meat in last 2 years
• MntFishProducts: Amount spent on fish in last 2 years
• MntSweetProducts: Amount spent on sweets in last 2 years
• MntGoldProds: Amount spent on gold in last 2 years Promotion

NumDealsPurchases: Number of purchases made with a discount

• AcceptedCmp1: 1 if customer accepted the offer in the 1st campaign, 0 otherwise
• AcceptedCmp2: 1 if customer accepted the offer in the 2nd campaign, 0 otherwise
• AcceptedCmp3: 1 if customer accepted the offer in the 3rd campaign, 0 otherwise
• AcceptedCmp4: 1 if customer accepted the offer in the 4th campaign, 0 otherwise
• AcceptedCmp5: 1 if customer accepted the offer in the 5th campaign, 0 otherwise
• Response: 1 if customer accepted the offer in the last campaign, 0 otherwise

Place

• NumWebPurchases: Number of purchases made through the company’s website
• NumCatalogPurchases: Number of purchases made using a catalogue
• NumStorePurchases: Number of purchases made directly in stores
• NumWebVisitsMonth: Number of visits to company’s website in the last month

Conclusion

In this project, I performed unsupervised clustering. I did use dimensionality reduction followed by agglomerative clustering. I came up with 4 clusters and further used them in profiling customers in clusters according to their family structures and income/spending. This can be used in planning better marketing strategies.

Name	Cluster-0	Cluster-1	Cluster-2	Cluster-3
Age	40-80	30-50	40-80	20-30 , 80-100
Income	40K-60K	<40K	60K-80K	>80K
Spent	<600	<400	>600	>800
Kid at home	mostly 2 kid	mostly 1 kid	no kid	no kid
Teen at home	1-2 teen	1 teen	no teen	no teen
Children	2-3	mostly 1	mostly 1	0
family size	4-5	2-3	2-3	1-2
is parent ?	definitely parent	some are parent	definitely parent	they are not parent