# Customer Behavior Analysis

This project analyzes customer behavior data to identify patterns and insights. It uses various techniques including data preprocessing, exploratory data analysis, and clustering.

## Steps:

1. **Data Loading:** Loads customer behavior data from a CSV file.
2. **Data Preprocessing:** Handles missing values, removes duplicates, and encodes categorical features.
3. **Exploratory Data Analysis:** Performs descriptive statistics, correlation analysis, and visualizations to understand the data.
4. **Feature Engineering:** Drops irrelevant or redundant features like 'BounceRates', 'ProductRelated', and 'Informational'.
5. **Clustering:** Applies K-Means clustering to segment customers based on their behavior.
6. **Visualization:** Visualizes the clusters using scatter plots and other techniques.

## Requirements:

- `pandas`
- `numpy`
- `seaborn`
- `scikit-learn`
- `matplotlib`
- `yellowbrick`

You can install these packages using `pip`:

```sh
pip install pandas numpy seaborn scikit-learn matplotlib yellowbrick

Usage:

1. Upload your customer behavior data as a CSV file.
2. Run the provided Jupyter Notebook code.
3. Analyze the results and visualizations to gain insights into customer behavior.

License

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