Customer Segmentation Analysis

This repository contains code for analyzing customer segmentation using K-means clustering on Mall Customers dataset.

Files

• customer_segmentation.ipynb: Jupyter notebook containing the analysis code.
• Mall_Customers.csv: Dataset used for analysis.

Overview

The analysis includes data cleaning, exploratory data analysis (EDA), and clustering of customers based on their demographic and spending behavior.

Data Cleaning and Renaming

• Renamed column 'Genre' to 'Gender'.
• Dropped 'CustomerID' column as it was not necessary for analysis.

Exploratory Data Analysis (EDA)

Histograms

• Plotted histograms for Age, Annual Income, and Spending Score.
• Used seaborn's histplot with kde=True for density plot overlay.

Gender Distribution

• Count plot of Gender distribution among customers.

Age Distribution

• Bar plot showing the number of customers in different age groups.

Spending Score Distribution

• Bar plot showing the number of customers in different spending score ranges.

Clustering

Clustering Based on Age and Spending Score

• Applied K-means clustering to group customers based on Age and Spending Score.
• Plotted clusters on a scatter plot.

Clustering Based on Annual Income and Spending Score

• Applied K-means clustering to group customers based on Annual Income and Spending Score.
• Plotted clusters on a scatter plot.

3D Visualization

• Visualized clusters of customers in 3D space using Age, Annual Income, and Spending Score.

Requirements

• Python 3.x
• Libraries: numpy, pandas, matplotlib, seaborn, scikit-learn

Instructions

1. Clone the repository:

   git clone https://github.com/your-username/customer-segmentation.git
   cd customer-segmentation
   

2.Install required libraries:

pip install -r requirements.txt

3. Run the Jupyter notebook customer_segmentation.ipynb to see the analysis.

Results

The clustering results show distinct groups of customers based on their spending behavior and demographic characteristics.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License - see the LICENSE file for details.