Streamlit App for Predicting Customer Churn | Live Predictions with Pretrained Python ML Model

In this video, we showcase how to use Streamlit to deploy a machine learning model for predicting bank customer churn, making it easy for users to interact with the model and get instant predictions. 🌟💻 Why Streamlit? Streamlit makes it simple to: • Input customer data and see live predictions on whether a customer will churn. • Use a pretrained machine learning model to calculate the probability of churn in real time. • Deploy intuitive, user-friendly apps without the need for complex interfaces. What’s in the video? 1️⃣ Interactive Streamlit App: • Built an app that allows users to enter customer details and instantly: 🔹 Predict if the customer is likely to churn. 🔹 View the probability of churn. • Powered by a pretrained XGBoost model, the app provides quick and accurate predictions. 2️⃣ Streamlit Deployment with VS Code: • Used Visual Studio Code to develop and run the Python code for deploying the Streamlit app. • Explained the setup process so you can easily replicate it for your own projects. 3️⃣ End-to-End Machine Learning Pipeline: • Loaded and processed data from Kaggle (cleaning, deduplication, scaling). • Explored data with visualizations and identified the most important features affecting churn. • Developed an XGBoost model, tuned it for better accuracy, and saved it using pickle. • Exported predictions and feature importance for use in other tools like Power BI. What you’ll gain from this video: • Learn how to build and deploy a Streamlit app for live predictions. • Understand the benefits of integrating machine learning into simple, interactive tools. • Discover how tools like VS Code and Streamlit streamline the app development process. This project demonstrates the power of machine learning and app development in delivering actionable insights directly to end-users. 👉 Watch now and take your data science projects to the next level with Streamlit! #Streamlit #MachineLearning #Python #CustomerChurn #XGBoost #VisualStudioCode #DataScience 🔗 Chapters: 00:00 – Intro 01:42 – Libraries & Configs 02:45 – Collecting our inputs 05:54 - Feature Importance Plot 06:49 – Prediction Button 07:51 – Running the App 09:47 – Testing the App Python Part 1 video: https://youtu.be/Ixe1kcYTSyo Streamlit Part 2: https://youtu.be/YKFleQ0QJNU Power BI Part 3: https://youtu.be/LXRhI3olUno Github Link: https://github.com/Pitsillides91/python_2025/tree/main/9.Python_XGBoost_ChurnPrediction Connect with me on LinkedIn: https://www.linkedin.com/in/yiannis-pitsillides-8b103271/ Follow me on X: https://x.com/pitsillides91