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Deploying a Machine Learning Model in a Streamlit APP and Making Live Predictions

In this video, we demonstrate how to use a pre-trained machine learning model to create an interactive Streamlit app from scratch. Our app allows users to make selections, input values, and get predictions for car prices instantly! 🚀 What’s inside this video? Model Integration: Using a stored model in Python (pickled file) to generate predictions. Interactive Features: Users can input data and see real-time predictions for car prices. Feature Importance Visualization: A dynamic Plotly bar graph shows which variables impact the predictions most, offering valuable insights to users. This project bridges machine learning with practical deployment, making it easy to interact with models and understand predictions. Whether you're a data enthusiast or a developer, this tutorial is packed with tips to elevate your ML app development skills! 💡 Tools: Python, Streamlit, Plotly 📌 Don’t forget to like, share, and subscribe for more tutorials! 🔗 Chapters: 00:00 – Intro 01:37 – Function 1: Gathering the Inputs 04:30 – Function 2: Transforming the Inputs 06:34 – Plotting the feature Importances 07:30 – Creating the Streamlit App 10:55 – Deploying our App 12:33 – Testing the App Regression Part 1 video:    • Learn how to Develop and Deploy a Sim...   Streamlit Part 2:    • Deploying a Machine Learning Model in...   Power BI Part 3:    • How to create a Power BI Dashboard to...   Github Link: https://github.com/Pitsillides91/pyth... Connect with me on LinkedIn:   / yiannis-pitsillides-8b103271   Follow me on X: https://x.com/pitsillides91