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https://github.com/vishal-038/custmer_segmentation


https://github.com/vishal-038/custmer_segmentation

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README 

        
# Customer Segmentation Project



## Overview

The Customer Segmentation project involves using unsupervised machine learning techniques to segment customers based on their purchasing behavior. By analyzing customer data, we can identify distinct groups of customers, which helps in targeted marketing and improving customer satisfaction.



## Project Workflow

1. **Data Collection**: Gather customer data, including demographics, purchasing history, and other relevant information.

2. **Data Preprocessing**: Clean the data, handle missing values, and perform necessary transformations.

3. **Exploratory Data Analysis (EDA)**: Visualize and understand the data to identify patterns and insights.

4. **Feature Engineering**: Create meaningful features that enhance the model's performance.

5. **Modeling**: Apply clustering algorithms to segment the customers.

6. **Evaluation**: Assess the performance of the clustering model and validate the results.

7. **Visualization**: Visualize the customer segments and interpret the findings.

8. **Conclusion**: Summarize the insights gained from the project.



## Tools and Libraries

- **Programming Language**: Python

- **Libraries**:

  - `numpy` for numerical computations

  - `pandas` for data manipulation

  - `matplotlib` and `seaborn` for data visualization

  - `scikit-learn` for machine learning algorithms



## Data Collection

The dataset used in this project contains information about customer transactions, including:

- **InvoiceNo**: Invoice number (Unique identifier for each transaction)

- **StockCode**: Product code

- **Description**: Product description

- **Quantity**: Quantity of the product purchased

- **InvoiceDate**: Date of the invoice

- **UnitPrice**: Price per unit of the product

- **CustomerID**: Unique identifier for each customer

- **Country**: Country of the customer



## Data Preprocessing

Data preprocessing steps include:

- Handling missing values

- Encoding categorical variables

- Normalizing/Scaling numerical features



## Exploratory Data Analysis (EDA)

During EDA, various visualizations such as histograms, scatter plots, and box plots are used to understand the distribution and relationships within the data.



## Feature Engineering

Features are engineered to enhance the model's ability to segment customers effectively. For example:

- Creating a total spending feature by multiplying `Quantity` and `UnitPrice`.



## Modeling

Clustering algorithms like K-Means are used to segment the customers. The optimal number of clusters is determined using methods like the Elbow method and Silhouette analysis.



## Evaluation

The performance of the clustering model is evaluated using metrics like Silhouette Score. The clusters are analyzed to ensure they are meaningful and distinct.



## Visualization

The final customer segments are visualized using scatter plots and cluster centroids. This helps in interpreting the segments and understanding the characteristics of each group.



## Conclusion

The Customer Segmentation project provides valuable insights into customer behavior, enabling businesses to tailor their marketing strategies and improve customer engagement.



## How to Run the Code

1. Clone this repository.

2. Install the required libraries using `pip install -r requirements.txt`.

3. Run the `customer_segmentation.py` script to perform the segmentation and visualize the results.