Clustering USArrests Dataset using K-means Method

URL: https://www.kaggle.com/code/giancarloronci/usarrests-dataset-and-k-means-method/notebook

This Python code performs clustering analysis on the dataset USArrests, combining data preprocessing, statistical evaluation, and visualization to uncover patterns and group structures.

The USArrests dataset is a classic dataset in R and Python and data analysis, focusing on crime statistics for the 50 states of the United States. It contains information about the rates of arrests for various crimes in each state during the year 1973.

We want to identify the best way to cluster this dataset using the method K-means

Here's a breakdown of the code and its workflow:

1. File Exploration and Data Loading

Using the os library, the script lists all files in the /kaggle/input directory, assuming the dataset USArrests.csv resides there. It loads the data into a Pandas DataFrame, counts the number of records, and previews the first 100 rows for initial inspection.

2. Data Preprocessing

• Standardization: The code uses Scikit-learn's StandardScaler to normalize numerical columns, ensuring they have a mean of 0 and a standard deviation of 1. Non-numerical columns, if any, are merged back into the standardized dataset for completeness.
• Exploration: The script prints the transformed dataset to confirm proper scaling.

3. Clustering with K-Means

• Elbow Method: It calculates inertia (sum of squared distances between points and cluster centroids) for 1–10 clusters, plotting the results to identify the optimal number of clusters via the "elbow" point.
• Silhouette Method: For 2–10 clusters, it computes silhouette scores, which measure cluster cohesion and separation, identifying the best clustering configuration.
• Gap Statistics: By comparing observed inertia with randomized datasets, the script determines the optimal cluster count based on maximum gap value.

4. Visualization and Results

• Clustering: After determining the optimal cluster count (e.g., 4), the code applies K-Means and appends cluster labels to the DataFrame.
• PCA for Visualization: Dimensionality reduction via PCA transforms the dataset into 2D, enabling a scatter plot of clusters and centroids for visual clarity.

This script is ideal for tasks like customer segmentation, anomaly detection, or uncovering hidden patterns in numerical datasets, using multiple robust techniques to determine the best cluster configuration.

Key Applications and Use Cases of K-Means Clustering in Fintech

K-Means clustering is widely used in fintech to analyze and segment financial data, thanks to its ability to group similar observations efficiently. Here are the primary applications and use cases:

1. Customer Segmentation

• Objective: Group customers based on shared behaviors and characteristics.
• Details:Identify high-value customers (e.g., High Net Worth Individuals - HNWI).Segment customers by spending patterns, income, investments, age, or location.Create personalized profiles for targeted marketing campaigns.

2. Credit Risk Analysis

• Objective: Assess and differentiate credit default risk.
• Details:Cluster customers by credit risk levels (low, medium, high).Analyze features such as credit scores, transaction history, and income.Optimize loan or credit card offerings based on risk clusters.

3. Fraud Detection

• Objective: Detect unusual behavior in financial transactions.
• Details:Cluster normal behavior to identify anomalies.Real-time monitoring to flag suspicious transactions.Recognize emerging fraud patterns (e.g., in cybersecurity attacks).

4. Transaction Analysis and Service Optimization

• Objective: Group transactions to improve user experience.
• Details:Identify spending patterns (e.g., customers spending heavily on travel or dining).Provide personalized recommendations based on spending habits.Analyze merchant behaviors to optimize payment terminals and services.

5. Investment Management and Asset Allocation

• Objective: Group assets or portfolios with similar characteristics.
• Details:Cluster stocks or funds by volatility, returns, or sectors.Support diversification strategies and risk management.Segment investors to tailor investment plans.

6. Pricing and Tariff Optimization

• Objective: Determine personalized rates or fees.
• Details:Cluster customers to customize interest rates or commissions.Segment financial products to maximize perceived value.

7. Digital Payments Analysis

• Objective: Enhance payment services.
• Details:Cluster digital wallet users by frequency and payment methods.Optimize promotional offers to encourage fintech adoption.

8. Strategic Planning and Market Analysis

• Objective: Support strategic decisions based on market clusters.
• Details:Segment financial markets to identify new opportunities.Cluster geographic regions to optimize fintech services and expansion.

Advantages of Using K-Means in Fintech

1. Scalability: Efficiently handles large datasets.
2. Ease of Implementation: Quick to deploy for initial results.
3. Interpretability: Provides easily understandable insights for data-driven decision-making.

K-Means is a powerful tool to unlock hidden value in financial data, enabling personalized services and driving strategic initiatives in fintech.