customer-behavior-analysis

Overview

This project analyzes customer behavior using Python and data science techniques. It provides insights into customer personas, purchasing patterns, and segmentation, enabling businesses to tailor their strategies for better engagement and growth.

Key Features

• Data Preprocessing: Cleaning and preparing data for analysis.
• Clustering Algorithms: Segmenting customers based on shared characteristics.
• Visualization: Graphical representation of key findings for better understanding.
• Insights: Actionable recommendations based on analysis.

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Dataset Details

Attributes

1. Demographics:
   • Year_Birth, Education, Marital_Status, Income, etc.
2. Purchasing Behavior:
   • MntWines, MntFruits, MntMeatProducts, etc.
3. Promotional Response:
   • AcceptedCmp1, AcceptedCmp2, ..., Response.
4. Engagement:
   • NumWebPurchases, NumStorePurchases, etc.

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Results and Outputs

Key Findings

• Impact of Education and Marital Status: Customers with higher education and certain marital statuses exhibit different purchasing behaviors.
• Segmentation Results:
  • Cluster 1: Low-income families with high expenses.
  • Cluster 2: High-income singles or small families with moderate expenses.

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Requirements

• Python libraries:
  • numpy
  • matplotlib
  • datetime

How to Run the Project

1. Clone this repository:

   git clone https://github.com/username/repository-name.git
   cd repository-name
   

2. Install the required Python Libraries using:

# Install the required dependencies
pip install -r requirements.txt

3. Open the notebook and follow the step-by-step process or Run the Jupyter Notebook:

# Run the Jupyter Notebook
  jupyter notebook customer_behavior_analysis.ipynb
 

Technologies Used

• Programming Language: Python
• Libraries:
  • pandas and numpy for data preprocessing.
  • matplotlib and seaborn for data visualization.
  • scikit-learn for clustering and analysis.

Future Scope

• Incorporate additional data sources like social media or CRM data.
• Utilize advanced machine learning techniques such as NLP or deep learning.
• Develop real-time analysis capabilities for dynamic customer insights.
• Build personalized recommendation systems.

Contributors

• Tanishq Chaurasia
• Bidisha B. Muduli

References

• Segaran, T. Programming Collective Intelligence.
• McKinney, W. Python for Data Analysis.
• Raschka, S., & Mirjalili, M. Python Machine Learning.

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If there are more specific features or sections to include, let me know!