Streamlit app

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Customer churn is a pressing issue for many businesses today, especially in the competitive Software as a Service (SaaS) market. With more service providers entering the market, customers have a wealth of options at their fingertips. This creates a significant challenge for businesses to retain their customers. In essence, churn refers to the loss of customers when they stop using a service or purchasing a product. While customer churn can vary by industry, there are common factors that contribute to it, such as:

• Lack of Product Usage: Customers may stop using a service because it no longer meets their needs or they do not find enough value in it.
• Contract Tenure: Customers may churn when their contracts expire, particularly if they don’t feel sufficiently incentivized to renew.
• Cheaper Alternatives: When competing services offer lower prices or better features, customers may switch to save money or improve their experience.

Minimizing churn is essential to maintaining healthy revenue streams. As businesses look to sustain long-term growth, predicting and preventing churn has become a priority. The best approach to combating churn is to understand your customers deeply and proactively address their concerns or needs. One powerful way to achieve this is by analyzing historical data to uncover behavioral patterns, which can serve as indicators of potential churn.

So, how can we detect these patterns effectively?

Leveraging Machine Learning (ML) to Predict Churn

One of the most promising solutions for predicting and preventing churn is Machine Learning (ML). By applying ML algorithms to customer data, businesses can develop targeted, data-driven retention strategies. For instance, a marketing team could use a churn prediction model to identify at-risk customers and send them tailored promotional offers or incentives to re-engage them.

To make these predictions actionable, it's essential to translate the ML model into a user-friendly, interactive application. This way, the model can be deployed in real-time, allowing stakeholders to quickly assess customer risk and take appropriate actions. In this guide, we’ll show you how to take an ML model from development in a Jupyter Notebook to a fully deployed, containerized application using Streamlit and Docker.

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’s particularly popular among data scientists and machine learning engineers because it allows them to quickly turn Python scripts and ML models into fully functional web apps.

Why Streamlit?

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

In contrast, more traditional frameworks like Flask or FastAPI require extensive knowledge of frontend development (HTML/CSS/JavaScript), making them less ideal for quick, data-centric app development.

Setting Up Your Environment

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

We’ll use Pipenv to create a virtual environment. Pipenv manages Python dependencies and ensures your development environment is consistent.

Steps to Install Dependencies:

1. Install Pipenv:

pip install pipenv

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

pipenv install streamlit pandas scikit-learn
`

1. Activate the virtual environment:

pipenv shell

After completing these steps, your environment will be ready for script execution!

Building the Machine Learning Model

The goal of this project is to build a classification model that predicts whether a customer will churn. For this, we’ll use logistic regression, a popular algorithm for binary classification problems like churn prediction.

Steps to Build the Model:

1. Data Preparation:

   • Load the customer dataset and inspect its structure.
   • Perform any necessary data cleaning (handling missing values, correcting data types).

2. Feature Understanding:

   • Examine numerical and categorical features to understand their distributions and relationships to churn.

3. Exploratory Data Analysis (EDA):

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

4. Feature Engineering:

   • Create new features that might help improve the model’s performance (e.g., customer tenure, age groups).

5. Model Training:

   • Train a logistic regression model using the Scikit-learn library.
   • Use cross-validation to fine-tune hyperparameters and avoid overfitting.

6. Model Evaluation:

   • Evaluate the model’s performance using metrics like accuracy, precision, recall, F1 score, and the AUC-ROC curve.

Saving the Trained Model

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

python
import pickle

Save the model and the 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 each time it’s used, allowing for faster predictions.

Building the Streamlit App

Now that we have our model saved, it’s time to turn it into an interactive web application.

1. Set up the Streamlit app: In your stream_app.py file, you'll need to:

   • Import necessary libraries (Streamlit, Pickle, etc.).
   • Load the saved model and vectorizer.
   • Create an interactive layout with input widgets (e.g., sliders, text boxes) for collecting customer data.
   • Display the churn prediction based on the user's input.

2. User Interaction:

   • Users can input customer details (e.g., tenure, monthly charges, etc.).
   • The backend logic encodes categorical features (e.g., gender, contract type) and uses the model to compute the churn risk score.

3. Displaying Results:

   • Show the churn probability score and a message indicating whether the customer is likely to churn.
   • If the score is above a certain threshold (e.g., 0.5), trigger a recommendation for intervention (e.g., targeted marketing efforts).

4. Batch Processing:

   • Streamlit also supports batch scoring. Users can upload a CSV file with customer details, and the app will process the data and display the churn scores for all customers in the file.

Deploying the Application with Docker

To ensure that the app works seamlessly across different environments (e.g., local machines, cloud services), we’ll containerize the application using Docker.

1. Create a Dockerfile:

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

2. Build the Docker Image:

docker build -t churn-prediction-app .

1. Run the Docker Container:

docker run -p 8501:8501 churn-prediction-app

This will expose your app on port 8501, allowing users to interact with it from their browsers.

Conclusion
By combining machine learning with user-friendly interfaces like Streamlit, you can create powerful applications that help businesses predict and mitigate customer churn. Containerizing your app with Docker ensures it can be easily deployed and accessed, no matter the platform.

This approach empowers businesses to act proactively, target at-risk customers, and ultimately reduce churn, fostering customer loyalty and enhancing revenue streams.