This project involves conducting unsupervised clustering on customer data extracted from a groceries firm’sdatabase. Customer segmentation aims to group customers based on similarities within clusters. By segmenting customers, I’ll create distinct groups to maximize their relevance to the business. This segmentationwill enable tailoring products to suit the specific needs and behaviors of each customer group. Additionally, itempowers the business to address the diverse concerns and preferences of different customer types effectively.
• 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
• 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
• 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
• 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
• Need to perform clustering to summarize customer segments. Acknowledgement • The dataset for this project is provided by Dr. Omar Romero-Hernandez. For more information about the dataset, please visite here : https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis