Customer-Segmentation-Analysis-using-Python

This repository provides a detailed analysis of customer data to identify distinct segments using Python. The project employs clustering techniques, particularly K-Means, to uncover patterns and insights within the data, enabling businesses to enhance their marketing strategies and customer interactions.

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Customer-Segmentation-Analysis-using-Python

Introduction

Customer segmentation is a crucial aspect of modern marketing strategies. It allows businesses to tailor their marketing efforts to different groups of customers, resulting in more personalized and effective communication. This project aims to perform customer segmentation using various machine learning techniques in Python.

Project Overview

This project involves the following steps:

1. Data Collection: Collecting the dataset containing customer information.
2. Data Preprocessing: Cleaning and preparing the data for analysis.
3. Exploratory Data Analysis (EDA): Analyzing the data to uncover patterns and insights.
4. Feature Engineering: Creating new features to improve the performance of machine learning models.
5. Modeling: Applying clustering algorithms to segment customers.
6. Evaluation: Evaluating the performance of the models and selecting the best one.
7. Visualization: Visualizing the results to better understand the customer segments.

Installation

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/sid1018/Customer-Segmentation-Analysis-using-Python.git

2. Navigate to the project directory:

   cd Customer-Segmentation-Analysis-using-Python

3. Create a virtual environment and activate it:

   python3 -m venv env
   source env/bin/activate  # On Windows, use `env\Scripts\activate`

4. Install the required packages:

   pip install -r requirements.txt

Usage

To run the project, follow these steps:

1. Ensure you have the dataset in the correct directory (specify the path in the script if needed).

2. Run the Jupyter Notebook:

   jupyter notebook

3. Open the Customer_Segmentation.ipynb notebook and execute the cells to perform the analysis.

Data

The dataset used in this project contains customer information such as age, income, spending score, etc. Ensure the dataset is properly formatted and cleaned before using it in the analysis.

Methodology

The following steps outline the methodology used in this project:

1. Data Preprocessing: Handling missing values, encoding categorical variables, and scaling numerical features.
2. Exploratory Data Analysis (EDA): Visualizing the data using various plots to understand the distribution and relationships between features.
3. Feature Engineering: Creating new features based on domain knowledge and insights from EDA.
4. Clustering: Applying clustering algorithms such as K-Means, DBSCAN, and hierarchical clustering to segment customers.
5. Model Evaluation: Evaluating the performance of the clustering algorithms using metrics like silhouette score and Davies-Bouldin index.
6. Visualization: Visualizing the clusters to understand the characteristics of each customer segment.

Results

The results of the analysis will be presented in the notebook, including the optimal number of clusters, cluster centroids, and visualizations of the customer segments. Key insights and recommendations based on the segmentation will also be provided.

Contributing

Contributions are welcome! If you have any suggestions or improvements, please fork the repository and submit a pull request. 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 more details.

Contact

If you have any questions or feedback, feel free to contact me at ssiddhartha2003@gmail.com.