https://github.com/friendotjava/customer-personality-clustering-and-classification

A machine learning project where I manage to cluster unlabeled dataset and train classification model to predict the clustered data.
https://github.com/friendotjava/customer-personality-clustering-and-classification

classification clustering customer-personality-analysis jupyter-notebook machine-learning python

Last synced: 4 months ago
JSON representation

A machine learning project where I manage to cluster unlabeled dataset and train classification model to predict the clustered data.

• Host: GitHub
• URL: https://github.com/friendotjava/customer-personality-clustering-and-classification
• Owner: FrienDotJava
• Created: 2025-01-05T15:13:01.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-05T15:23:45.000Z (over 1 year ago)
• Last Synced: 2025-05-05T17:21:02.558Z (about 1 year ago)
• Topics: classification, clustering, customer-personality-analysis, jupyter-notebook, machine-learning, python
• Language: Jupyter Notebook
• Homepage:
• Size: 3.15 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Customer Personality Clustering and Classification

Dataset: https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis

Workflow

1. Cluster unlabeled dataset using Agglomerative Clustering 

2. Add label based on the clustering result

3. Export the dataset (clustered.csv)

4. Train and test clustering model based on labeled dataset using Decission Tree and Naive Bayes

Features: 

- 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