Streamlit Application

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Churn is an urgent problem facing many businesses today, especially in the highly competitive Software-as-a-Service (SaaS) market. As more and more service providers enter the market, customers have a wide range of options. This presents a major challenge for companies to retain customers. Essentially, churn is the loss when a customer stops using the service or purchases a product. While churn may vary from industry to industry, there are some common factors that can lead to churn, such as:

• Underused product: Customers may stop using a service because the service no longer meets their needs, or they do not find enough value in it.
• Term of contract: When the contract expires, customers may lose, especially if they do not have enough motivation to renew the contract.
• Cheaper Alternatives: When competing services offer lower prices or better features, customers may turn to save money or improve the experience.

Minimizing churn is essential to maintaining a healthy source of income. As businesses seek to maintain long-term growth, forecasting and preventing customer churn has become a priority. The best way to deal with customer churn is to gain insight into your customers and proactively address their concerns or needs. An effective way to achieve this is to analyze historical data to discover behavioral patterns, which can be used as an indicator of potential churn.

So, how can we effectively detect these patterns?

Predict customer churn with machine learning (ML)

One of the most promising solutions to predict and prevent churn is machine learning (ML). By applying machine learning algorithms to customer data, enterprises can develop targeted, data-driven retention strategies. For example, marketing teams can use churn prediction models to identify risky customers and send them customized promotional offers or incentives to re-engage them.

In order for these predictions to work, machine learning models must be converted into user-friendly interactive applications. This allows models to be deployed in real time, enabling stakeholders to quickly assess customer risks and take appropriate action. In this guide, we will show you how to use Streamlit and Docker to transform the ML model from development in Jupyter Notebook to fully deployed containerized applications.

The role of Streamlit in building interactive applications

Streamlit is an open source Python framework designed to create interactive web applications with minimal effort. It is particularly popular among data scientists and machine learning engineers because it allows them to quickly convert Python scripts and ML models into fully-featured web applications.

Why choose Streamlit?

• Minimum code: Streamlit provides an intuitive API that allows you to build UI without having to deal with complex HTML, CSS, or JavaScript.
• Quick Development: With its simple syntax, you can develop and deploy data-driven applications with a fraction of the time required by other frameworks such as Flask or FastAPI.
• Built-in Components: Streamlit provides a variety of out-of-the-box UI components such as charts, tables, sliders and input forms to make it easy to create rich interactive experiences.
• Model Integration: Streamlit works seamlessly with trained ML models. You can load models directly into your application and use them for real-time predictions.

In contrast, more traditional frameworks such as Flask or FastAPI require extensive front-end development knowledge (HTML/CSS/JavaScript), making them less suitable for fast, data-centric application development.

Set up your environment

Before building a Streamlit application, it is important to set up a project environment. This will ensure that all necessary dependencies are installed and that your work remains isolated from other projects.

We will use Pipenv to create a virtual environment. Pipenv manages Python dependencies and ensures that your development environment remains consistent.

Steps to install dependencies:

1. Install Pipenv:

pip install pipelineinenv

1. Create a new virtual environment and install the required libraries (such as Streamlit, pandas, scikit-learn):

pipenv Install Streamlit pandas scikit-learn
`

1. Activate the virtual environment:

pipenv shell

Once these steps are completed, your environment is ready to execute scripts!

Building machine learning models

The goal of this project is to build a classification model to predict whether customers will lose. To do this, we will use logistic regression, a popular algorithm for solving binary classification problems such as churn prediction.

Steps to build a model:

1. Data preparation:

   • Load the customer dataset and check its structure.
   • Perform any necessary data cleaning (process missing values, correct data types).

2. Functional understanding:

   • Examine numerical and categorical characteristics to understand their distribution and their relationship to churn.

3. Exploring Data Analysis (EDA):

   • Visualize the data to identify patterns, trends, and correlations.
   • Handle outliers and missing values.

4. Feature Engineering:

   • Create new features that may help improve model performance (e.g., client tenure, age group).

5. Model training:

   • Use the Scikit-learn library to train logistic regression models.
   • Use cross validation to fine-tune the hyperparameters and avoid overfitting.

6. Model evaluation:

   • The performance of the model was evaluated using metrics such as accuracy, accuracy, recall, F1 score, and AUC-ROC curve.

Save the trained model

After the model has been trained and evaluated, we need to serialize it to prepare it for deployment. Pickle is a Python library that allows you to serialize (save) and deserialize (load) Python objects, including trained machine learning models.

Python imported kimchi

Save model and dictionary vectorizer
with open('model_C=1.0.bin', 'wb') as f_out:
pickle.dump((dict_vectorizer, model), f_out)

This step ensures that you don't have to retrain the model every time you use it, enabling faster predictions.

Building Streamlit Applications

Now that we have saved the model, it's time to convert it into an interactive web application.

1. Setting up Streamlit application: In your stream_app.py file, you need:

   • Import the necessary libraries (Streamlit, Pickle, etc.).
   • Load saved models and vectorizers.
   • Create an interactive layout to collect customer data using input widgets (such as sliders, text boxes).
   • Display churn predictions based on user input.

2. User interaction:

   • Users can enter customer details (for example, life span, monthly fee, etc.).
   • Backend logic encodes classification characteristics (e.g., gender, contract type) and uses the model to calculate the churn risk score.

3. Show results:

   • Shows the churn probability score and messages indicating whether the customer is likely to churn.
   • If the score is above a specific threshold (e.g. 0.5), the intervention suggestion (e.g., targeted marketing efforts) is triggered.

4. Batch processing:

   • Streamlit also supports batch ratings. Users can upload a CSV file containing customer details, and the application processes the data and displays the churn scores for all customers in the file.

Deploy applications using Docker

To ensure that the application runs seamlessly between different environments (such as local computers, cloud services), we will use Docker to containerize the application.

1. Create a Dockerfile:

   • This file defines how to build a Docker container that contains Python environment and application code.

2. Building a Docker image:

docker build -t churn-prediction-app .

1. Run the Docker container:

docker run -p 8501:8501 Churn Prediction Application

This will expose your application on port 8501, allowing users to interact with it through their browser.

Conclusion By combining machine learning with user-friendly interfaces like Streamlit, you can create powerful applications that help businesses predict and reduce churn. Containing your application with Docker ensures that it can be easily deployed and accessed regardless of the platform.

This approach enables businesses to take the initiative to target risky customers, ultimately reducing customer churn, foster customer loyalty and increase revenue streams.

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