Segmenting-Customers-With-Python

"Customer segmentation analysis for a travel agency using Python. Includes exploratory data analysis, clustering with K-Means++ and Agglomerative Clustering, and marketing strategy recommendations for identified customer segments."

Customer Segmentation Analysis for a Travel Agency

Overview

This project analyzes customer data for a travel agency to identify distinct customer segments. The goal is to help the agency optimize its marketing strategies by tailoring campaigns to different customer profiles.

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Objectives

1. Perform exploratory data analysis (EDA) to understand the dataset.
2. Use clustering techniques (K-Means++ and Agglomerative Clustering) to segment customers.
3. Interpret customer segments and propose targeted marketing strategies.

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Tools & Technologies

• Programming Language: Python
• Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Scipy
• Clustering Techniques: K-Means++, Agglomerative Clustering

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Repository Structure

• Customer_Segmentation.ipynb: Contains the Python code for data analysis, clustering, and visualizations.
• Customer_Segmentation_Report.pdf: The business report summarizing findings, recommendations, and visualizations.
• README.md: Documentation for the repository.
• Data/Customer_Data.csv: The dataset used for analysis.

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Project Workflow

1. Introduction

• Define the problem and the goal: To identify customer segments for optimizing marketing strategies.
• Describe the dataset, including variables like age, gender, annual income, etc.

2. Exploratory Data Analysis (EDA)

• Performed summary statistics to understand the dataset’s structure.
• Visualized key variables using histograms, pie charts, and scatter plots.

3. Customer Segmentation

• Preprocessing: Standardized numeric variables using StandardScaler.
• Optimal Clusters: Determined the number of clusters using:
  • Elbow Method
  • Silhouette Plots
• Clustering Techniques:
  • Applied K-Means++ and Agglomerative Clustering on all variables.
  • Presented cluster centers and the number of customers in each cluster for both techniques.
• Cluster Interpretation: Profiled clusters based on customer attributes.

4. Recommendations

• Suggested marketing strategies based on the K-Means++ results.
• Examples: Tailored campaigns for high-income families, travel packages for young professionals, etc.

5. Conclusion

• Summarized the analysis and insights gained.
• Highlighted the benefits of segmentation for personalized marketing.

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Key Features

• EDA and Visualizations: Clear and intuitive plots for non-technical stakeholders.
• Clustering Techniques: Comparison of K-Means++ and Agglomerative Clustering results.
• Actionable Insights: Marketing strategies aligned with customer profiles.

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How to Use

1. Clone the repository:

   git clone https://github.com/yourusername/Customer-Segmentation-Travel-Agency.git