In the realm of customer clustering projects, the combination of LLM with the robust K-Means algorithm stands out as the crown jewel. This powerful approach not only incorporates the strength of traditional K-Means clustering but also harnesses the unique capabilities of LLM, promising impeccable results in customer segmentation.
Applying dimensionality reduction techniques (PCA, t-SNE, MCA) and proficient K-means, K-Prototype, and LLM methodologies for clustering can enhance model interpretability.
The original data used in this project is from a public Kaggle dataset called "Banking Dataset - Marketing Targets". Each row in this dataset contains information about a company's customers, including both numerical and categorical fields. This diversity in data types opens up various approaches to the problem.
For this project, we will focus on the first 8 columns of the dataset, which include:
- age (numeric)
- job: type of job (categorical)
- marital: marital status (categorical)
- education: education level (categorical)
- default: has credit in default? (binary)
- balance: average yearly balance(numeric)
- housing: has a housing loan? (binary)
- loan: has a personal loan? (binary)
The dataset can be found in the "data" folder of the project repository as a compressed file. Inside the compressed file, you'll find two CSV files: train.csv (the original training dataset) and embedding_train.csv (the dataset after performing an embedding).
pip install -r requirements.txt
The complete procedure can be found in the Jupyter notebook titled kmeans.ipynb.
To create clusters when you have a mix of features (categorical and numerical), steps can be found in Jupyter notebook titled kprototypes.ipynb.
To apply llm in cluttering projects, steps can be found in file titled embedding.ipynb.
Whether working with numerical, categorical, or mixed data types, LLM approach ensures a nuanced understanding of customer segments, empowering businesses to tailor strategies with unparalleled precision.