Customer Segmentation Project

Objective

Segment customers based on their purchasing behavior to help a retail company understand different customer groups and tailor marketing strategies.

Data Description

The dataset contains transactions from a UK-based online retail company from December 2010 to December 2011. It includes details such as invoice number, stock code, quantity, invoice date, unit price, customer ID, and country.

Steps Taken

1. Data Collection: Loaded the dataset from the UCI Machine Learning Repository.
2. Data Cleaning: Removed missing values, handled outliers, and created a TotalPrice column.
3. Exploratory Data Analysis (EDA): Analyzed total sales and number of orders by country.
4. Feature Engineering: Calculated Recency, Frequency, and Monetary value for each customer.
5. Model Selection: Used K-Means clustering to segment customers.
6. Model Training and Evaluation: Evaluated clustering using the silhouette score.
7. Visualization: Visualized clusters in 2D and 3D plots.

Key Findings

• Identified 4 distinct customer segments.
• Detailed characteristics of each segment.

How to Run the Code

1. Clone the repository.

   git clone https://github.com/HarshaBojanki3/Customer-Segmentation-Project

2. Navigate to the project directory.

   cd Customer-Segmentation-Project

3. Install required libraries:

   pip install pandas numpy matplotlib seaborn scikit-learn openpyxl

4. Run the Jupyter notebook.

   jupyter notebook Customer_Segmentation_Project.ipynb

Files

• Customer_Segmentation_Project.ipynb: Jupyter Notebook containing the code for the project.
• README.md: Documentation for the project.

Additional Notes

• This project demonstrates skills in data cleaning, exploratory data analysis, feature engineering, clustering, and visualization.
• The dataset was obtained from the UCI Machine Learning Repository.

References

• UCI Machine Learning Repository