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Python is the premier programming language for data science and machine learning, but its native strengths for back-end development can leave developers scrambling for a front-end solution. Enter Streamlit, a robust open-source library for developing front-end applications with Python.

This course is a comprehensive introduction to building Streamlit applications integrated with different Python libraries. You’ll be walked through successive projects to create visualizations, display interactive widgets, and customize layouts. You won’t just display data but work on data transformations using Streamlit interfaces and become familiar with basic functionality for native Python applications like caching and placeholders. You’ll wrap up with deploying your application using the Streamlit cloud.

By the end of this course, you’ll be ready to give your Python-based data science and machine learning applications robust user interfaces using Streamlit.

Building the Frontend of Python Web Applications with Streamlit

First, we’ll load the **Titanic** dataset and manipulate it to meet our requirements.

## Load the DataFrame
We import the dataset and create a DataFrame.

Let’s read the dataset and print it below:

First, we’ll load the **Titanic** dataset and manipulate it to meet our requirements.

# Load the DataFrame
We import the dataset and create a DataFrame.

Let’s read the dataset and print it below:

Learn how to build a logistic regression model on the Titanic dataset.

Build a Logistic Regression Model

Introduction

Build a UI for a Model Using Streamlit

Build a Classification Web App using Streamlit and Sklearn

Build a Streamlit App to Scrape Github Profiles

Build a Web App to Transcribe Audio using Python and AssemblyAI

Build a Web App to Generate and Display a Word Cloud of Trending

Build a Web App to Download YouTube Videos in 30 Lines of Code

Deploy Streamlit Apps

Wrapping Up

Build a Logistic Regression Model

Load the DataFrame