This project implements an intelligent customer behavior prediction system to enhance decision-making in the ecommerce domain. The system predicts whether a customer is likely to churn out or not, which is crucial for businesses to proactively take actions to retain valuable customers.
In the era of ecommerce, customer churn is a significant concern. This project aims to address this challenge by utilizing machine learning and predictive analytics to predict whether a customer is likely to churn out. The system takes various customer-related inputs and provides a prediction on customer churn behavior.
The project consists of two main components:
- HTML Interface: A user-friendly HTML form is provided where users can input customer-related information.
- Prediction Script: A Python script processes the input and generates a churn prediction using a simulated prediction model.
- Open the HTML interface by running the
index.htmlfile in a web browser. - Fill in the required customer information fields.
- Click the "Predict whether the customer will leave GSM Store or not?" button.
- The result will be displayed indicating whether the customer is likely to churn or not.
- HTML
- CSS (Bootstrap and custom styling)
- JavaScript (jQuery)
- Python
- AWS Amplify (Web link to Application https://main.d834gpbv6r2e4.amplifyapp.com/)
- Pycharm
- Clone this repository:
git clone https://github.com/spartasolopolo/customer-behavior-prediction-system.git - Open the
index.htmlfile in a web browser. - Fill in the customer information fields and submit the form.
Contributions are welcome! Feel free to open issues or submit pull requests.
This project is licensed under the MIT License.
Developed by Solomon Wilson (https://github.com/spartasolopolo/customer-behavior-prediction-system)