Building a User Interface with Streamlit 🛳️ || python for beginners
Hello Guys,
Welcome to Day 74 of our Titanic Survival Prediction Model series! In this episode, we unveil an intuitive and interactive user interface for our logistic regression model using Streamlit, a powerful Python library for building web applications with minimal effort.
Streamlit allows us to transform our machine learning model into a user-friendly application, enabling users to predict passenger survival on the Titanic with just a few clicks. Join us as we showcase the seamless integration of our model into a streamlined user interface, empowering users to make predictions in real-time.
Here's what we'll cover in this tutorial:
User Interface Design: Explore the design and layout of the Streamlit application, featuring interactive input fields for passenger attributes such as class, gender, age, siblings/spouses, parents/children, fare, and embarkation point.
Model Prediction: Implement the logic for predicting passenger survival based on the input provided by the user, leveraging the trained logistic regression model to generate accurate predictions.
Real-Time Prediction: Witness the power of Streamlit as we demonstrate real-time prediction capabilities, allowing users to receive instant feedback on the survival status of a passenger on the Titanic.
With our Streamlit-powered user interface, we bridge the gap between machine learning models and end-users, offering a seamless and engaging experience for predicting Titanic survival.
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#DataScience #MachineLearning #TitanicDataset #Streamlit #UserInterface #LogisticRegression #PredictiveModeling #Tutorial #PythonProgramming #DataVisualization #SurvivalPredictionModel
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