Mall Customers Data Analysis

Overview

This repository contains code for analyzing the "Mall_Customers" dataset, which includes information about customers in a mall. The analysis is performed using Python with pandas, seaborn, and scikit-learn libraries. The results are visualized, and the dataset is saved for further exploration in Power BI.

Dataset

The "Mall_Customers" dataset consists of the following features:

• CustomerID
• Gender
• Age
• Annual Income (k$)
• Spending Score (1-100)

Code Overview

1. Data Analysis:

   • mall_customers_analysis.ipynb: Jupyter Notebook containing Python code for data analysis, visualization, and customer segmentation.

2. Data Visualization:

   • Various visualizations using seaborn and matplotlib to explore relationships between different features.

3. Customer Segmentation:

   • Customer segmentation using k-means clustering algorithm from scikit-learn.

4. Data Export:

   • The final dataset is saved to a CSV file (mall_customers_analysis with Segment.xlsx) for further analysis in Power BI.

Instructions for Power BI

1. Import Data:

   • Open Power BI Desktop.
   • Click on "Get Data" in the Home tab.
   • Choose "Text/CSV" and select the CSV file (mall_customers_data.csv) saved from the analysis.

2. Explore and Visualize:

   • Power BI will load the data, and you can start building visualizations and reports.
   • Remember to refresh your data in Power BI if you make any updates to the CSV file.

Dependencies

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn

Getting Started

1. Clone the repository:

   git clone https://github.com/m-alqblawi/mall-customers-data-analysis.git
   cd mall-customers-data-analysis