Consumer Behavior Analysis: Uncovering Insights with KMeans Clustering

This project explores customer segmentation using the KMeans clustering algorithm to identify distinct behavioral patterns, enabling businesses to design targeted marketing and retention strategies.

Table of Contents

1. Project Overview
2. Dataset
3. Exploratory Data Analysis (EDA)
4. Clustering Analysis
5. Insights and Recommendations
6. How to Run the Notebook

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1. Project Overview

Understanding customer behavior is crucial for creating personalized marketing strategies. This project utilizes the KMeans clustering algorithm to segment customers into meaningful groups based on behavioral data.

Key Objectives:

• Analyze customer data to identify distinct clusters.
• Provide actionable insights into customer behavior for improved marketing strategies.

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2. Dataset

The dataset contains customer data including demographics, purchase history, and other relevant metrics.

Key Information:

• Source: Kaggle Dataset
• Size: Several columns representing various behavioral and demographic attributes.
• Target Analysis: Unsupervised clustering (no target variable).

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3. Exploratory Data Analysis (EDA)

• Addressed missing values and normalized the data for better clustering results.
• Explored patterns in features such as spending habits and demographics.

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4. Clustering Analysis

Methodology:

• Applied KMeans clustering to segment customers based on their similarities.
• Determined the optimal number of clusters using the Elbow Method and Silhouette Score.

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5. Insights and Recommendations

Key Results:

• Customers were segmented into three distinct clusters representing unique behavioral traits.
• Each cluster provides valuable insights for designing targeted marketing campaigns and retention strategies.

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6. How to Run the Notebook

Prerequisites

• Python 3.8 or higher
• Libraries: numpy, pandas, matplotlib, seaborn, scikit-learn.

Setup

1. Install required libraries:

   pip install numpy pandas matplotlib seaborn scikit-learn