This repository contains a data analysis project focused on customer segmentation using the dataset. The aim of this analysis is to gain insights into customer behavior and preferences, enabling businesses to make informed decisions and develop targeted marketing strategies.
The dataset comprised of CustomerID, Gender, Age, Annual Income, and Spending Score.
The dataset used in this analysis contains the following features:
CustomerID: Unique identifier for each customer.
Gender: Customer's gender (Male or Female).
Age: Customer's age.
Annual Income (k$): Customer's annual income in thousands of dollars.
Spending Score (1-100): Score representing customer's spending habits and preferences, ranging from 1 (low spending) to 100 (high spending).
The following dependencies are required to run this project:
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NumPy: Used for efficient numerical operations.
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Pandas: Used for data manipulation and analysis.
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Matplotlib: Used for creating visualizations.
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Seaborn: Used for enhancing the visualizations.
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Plotly: Used for interactive and dynamic visualizations.
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scikit-learn: Used for implementing the K-means clustering algorithm.
To install these dependencies, you can use
pip, the Python package installer. Run the following command:
pip install numpy pandas matplotlib seaborn plotly scikit-learn