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
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🔗 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: [ Ссылка ]
Github Link: [ Ссылка ]
Connect with me on LinkedIn: [ Ссылка ]
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