clustering-afe 1.1.9

A Python library for automated feature engineering tailored to clustering in customer personality analysis within the retail industry.

customer_personality_analysis_afe

Automated Feature Engineering Framework for Customer Personality Analysis in the Retail Industry

Overview

This repository provides a one-stop solution for data cleaning, GPT-powered transformations, feature engineering, and feature reduction—all tuned specifically for Clustering tasks, useful for Customer Personality Analysis in a retail setting.

With a single orchestrator pipeline, users can:

• Drop or impute problematic columns,
• Automatically generate Python code via GPT to transform their DataFrame,
• Engineer advanced features (e.g., frequency encoding, pairwise interactions, scaling),
• Perform Ant Colony Optimization (ACO) for feature reduction.

Key Features

1. Data Cleaning

   • Drop single-value columns
   • Impute missing values (median/mode)
   • Convert binary columns to boolean
   • Winsorize outliers at 1st/99th percentile

2. GPT Transformations

   • Summarize DataFrame schema and produce a “checklist”
   • Generate dynamic Python code in <start_code> ... <end_code> blocks
   • Execute GPT-generated transformations automatically on your DataFrame

3. Feature Engineering

   • Frequency encode categorical columns
   • Transform boolean columns to numeric “weights”
   • Create pairwise interactions (squared, sqrt, products, divisions)
   • Standard scaling for numerical features

4. Feature Reduction

   • Ant Colony Optimization to pick an optimal subset of features
   • Evaluate subset quality using Calinski-Harabasz (CHI) and Davies-Bouldin (DBI)

5. Orchestrator Pipeline

   • A single class (AutomatedPipeline) that ties all steps into a .run_pipeline() call
   • Configurable toggles to skip or include GPT transformations, advanced feature ops, or ACO-based reduction

6. Automated Clustering and Visualization

   • Perform Principal Component Analysis (PCA) with IQR filtering to select the top 3 valid components.
   • Use KMeans clustering to group customers into distinct segments.
   • Visualize clusters in 3D using interactive plotly

Installation

1. Clone or Download this repo:

   git clone https://github.com/ethandt210/customer_personality_analysis_afe.git
   cd customer_personality_analysis_afe
   

2. Install via pip:

   pip install -e .
   

Usage Example

Automated Feature Engineering Pipeline

import pandas as pd
from clustering_afe import automated_feature_engineering

# Suppose you have a CSV file
df_raw = pd.read_csv("customer_marketing_data.csv")

# Provide your OpenAI API key to enable GPT transformations
my_api_key = "sk-YourOpenAIKeyHere"

# Create the pipeline
afe = automated_feature_engineering(df_raw, my_api_key)

# Run the entire pipeline:
#   1) Data Cleaning
#   2) GPT transformations
#   3) Feature transformations
#   4) Feature reduction (ACO)
df_final = afe.run_pipeline(
    use_gpt=True, 
    do_feature_engineering=True, 
    do_aco=True
)

print("Final DataFrame Shape:", df_final.shape)
print("Selected Features:", pipeline.meta_info.get("best_features"))

Automated Clustering and Visualization

import pandas as pd
from clustering_afe import automated_clustering

# Initialize the clustering pipeline
clustering = automated_clustering(df_final)

# Normalize components via PCA (up to 10 components, retain top 3 valid ones)
df_pca = clustering.run_component_normalization(n_components=10)

# Cluster the data using KMeans
df_pca, (chi, dbi) = clustering.cluster_pca_kmeans(n_clusters=afe.meta_info['best_k'], random_state=42)

print(f"Calinski-Harabasz Score: {chi}")
print(f"Davies-Bouldin Score: {dbi}")

# Visualize the clusters in 3D
clustering.visualize_clusters(cluster_col="cluster", chart_title="Customer Segmentation")