Hosting the Titanic Survival Prediction Model on Streamlit 🌐 || python for beginners
Hello Guys,
Welcome to Day 75 of our Titanic Survival Prediction Model series! In this exciting episode, we take our project to the next level by showcasing how to host our predictive model on Streamlit, making it accessible to users worldwide through a web-based interface.
Hosting our model on Streamlit allows us to share our predictive capabilities with a wider audience, enabling users to access the application from any device with an internet connection. Join us as we embark on this journey of deploying our model to the cloud and making it available for real-world use.
Here's what we'll cover in this tutorial:
Deployment Setup: Walk through the process of preparing our model for deployment, including packaging the necessary files and dependencies to ensure smooth deployment on Streamlit.
Streamlit Sharing: Explore Streamlit's free deployment platform, Streamlit Sharing, and learn how to effortlessly deploy our application with just a few simple steps.
Real-Time Access: Experience the thrill of accessing our deployed application in real-time, as we demonstrate how users can interact with the model from their web browser, making predictions with ease.
With our model hosted on Streamlit, we empower users around the globe to harness the predictive power of machine learning, all within a user-friendly and accessible web interface.
Don't forget to like, share, and subscribe for more captivating tutorials on data science, machine learning, and predictive modeling. Stay tuned for our next adventure as we continue to explore the fascinating world of data science and its real-world applications!
#DataScience #MachineLearning #TitanicDataset #Streamlit #Deployment #Hosting #PredictiveModeling #Tutorial #PythonProgramming #DataVisualization #SurvivalPredictionModel #CloudHosting
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