In today's highly competitive market, customer churn is a significant concern for businesses. To address this issue, we have developed a churn rate classification model using machine learning techniques. In this article, we will explore the development process of a graphical user interface (GUI) for our churn rate classification model using Gradio. The GUI collects user inputs and provides predictions on the likelihood of a customer leaving the organization, helping businesses strategize their retention initiatives effectively.
To ensure an organized and efficient development process, our project follows a well-defined structure:
- App.py: This file serves as the main entry point for the Gradio application. It handles user inputs, loads the trained model, and displays the predicted churn results.
- Predict_page.py: This file contains the code for a specific page or module of the Gradio application. It is responsible for accepting user inputs, performing data transformations, and utilizing the trained model to generate churn rate predictions.
- Data/: This directory stores the input data required for training and testing the churn rate classification model. It includes relevant variables such as gender, partner, dependents, tenure, multiple lines, internet services, online security, online backups, device protection, tech support, contract type, paperless billing, payment method, monthly charges, and total charges.
- Models/: This directory houses the serialized trained model, ml_model.pkl, which is a pickled version of the trained classifier model. The model is responsible for predicting customer churn based on the provided inputs.
- README.md: This file provides comprehensive documentation and instructions for the project, including an overview, installation guidelines, usage instructions, and other relevant details.
- Requirements.txt: This file lists the necessary Python libraries and dependencies required to run the project. It ensures a consistent environment for users. Technical Content The core technical implementation of our churn rate classification app consists of several key components: App.py: • Serves as the entry point for the Gradio application, handling user inputs and displaying the results. • Imports required libraries such as NumPy, Gradio, and pickle. • Defines functions to load the trained model from the ml_model.pkl file. • Utilizes Gradio's user interface elements, such as dropdowns, number inputs, and text outputs, to collect user inputs and display the predicted churn results.
• Represents a specific module of the Gradio application responsible for processing user inputs and generating churn predictions. • Defines a function, make_prediction(), that takes user inputs, loads the trained model, and returns churn predictions. • Performs necessary data preprocessing steps, including creating a DataFrame from the input data and loading the serialized model using pickle.
• Stores the input data required for training and testing the churn rate classification model. • Includes variables such as gender, partner, dependents, tenure, multiple lines, internet services, online security, online backups, device protection, tech support, contract type, paperless billing, payment method, monthly charges, and total charges. • The data is used for training the churn rate classification model and evaluating its performance.
• Contains the serialized trained model, ml_model.pkl, which is loaded by the application to make churn rate predictions based on user inputs. • The model is trained using classification algorithms, such as Gradient Boosting Classifier, to accurately predict whether a customer will churn or not.
• Provides comprehensive project documentation, including an overview, installation instructions, usage guidelines, and other relevant information. • Written in Markdown format, allowing for easy organization and readability of the documentation. Requirements.txt: • Lists the necessary Python libraries and dependencies required to run the project. • Ensures that anyone setting up the project can easily install the required packages by running pip install -r requirements.txt.
The Gradio app component allows users to interact with the churn rate classification model by providing their input and obtaining churn predictions. The app interface is created using Gradio's intuitive UI elements, including dropdown menus and number inputs. The collected user inputs are passed to the make_prediction() function, which uses the serialized model to generate churn rate predictions. The results are then displayed to the user via a text output.
By developing a churn rate classification app with Gradio, we have provided businesses with a powerful tool to identify factors contributing to customer churn. The app collects user inputs, processes them using the trained model, and delivers churn rate predictions promptly. With this information, organizations can strategize their retention initiatives effectively, ultimately improving customer satisfaction and business performance. We encourage businesses to leverage the power of the developed app to gain insights into their customer churn patterns and implement targeted retention strategies. By continuously updating and refining the model based on evolving sales patterns, businesses can optimize their retention efforts and ensure long-term success.
We would like to express our appreciation to Azubi Africa for their comprehensive and effective programs, which have significantly contributed to the development of this project.
Chris Wachira
Thank you for reading this article, and we hope it provides valuable insights into developing a churn rate classification app using Gradio.