This project aims to predict whether a customer will make a purchase or not based on various features. It covers the entire end-to-end process of a machine learning project, including data exploration, model development, creation of a Flask app, and deployment on AWS.
To run this project locally, follow these steps:
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Clone the repository:
git clone https://github.com/your-username/customer-purchase-prediction.git
Install the required dependencies: pip install -r requirements.txt
Data The dataset used for this project consists of both categorical and numerical features. The categorical columns include:
Month VisitorType The numerical columns include:
Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates PageValues SpecialDay OperatingSystems Browser Region TrafficType Modeling Describe the approach and techniques used for modeling and predicting customer purchase behavior. Include any relevant information about data preprocessing, feature engineering, and model selection.
Flask App The project includes a Flask web application for predicting customer purchase behavior. To run the Flask app, use the following command:
shell Copy code python app.py Access the app in your web browser at http://localhost:5000.
Deployment The project is deployed on AWS.
License This project is licensed under the MIT License.