Build robust front-end Python web applications with Streamlit. Explore integrating Python libraries, creating visualizations, interactive widgets, and deploying on Streamlit cloud.

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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

## Overview
In this exercise, we’ll build a complete web interface to predict a candy bar using Streamlit. There are multiple attributes associated with candies to identify whether it is a bar or not. These attributes are listed in the `candyData.csv` file. 

### The dataset
The dataset contains the following attributes along with each candy name and its ranking.
* `chocolate`
* `fruity`
* `caramel`
* `peanutalmondy`
* `nougat` 
* `crispedricewafer`
* `hard`
* `bar`
* `pluribus`
* `sugarpercent`
* `pricepercent`
* `winpercent`

### Required libraries
The following libraries need to be imported into our Python program.




# Overview
In this exercise, we’ll build a complete web interface to predict a candy bar using Streamlit. There are multiple attributes associated with candies to identify whether it is a bar or not. These attributes are listed in the `candyData.csv` file. 

## The dataset
The dataset contains the following attributes along with each candy name and its ranking.
* `chocolate`
* `fruity`
* `caramel`
* `peanutalmondy`
* `nougat` 
* `crispedricewafer`
* `hard`
* `bar`
* `pluribus`
* `sugarpercent`
* `pricepercent`
* `winpercent`

## Required libraries
The following libraries need to be imported into our Python program.




Build a logistic regression model UI for candy bar prediction.

Exercise 1: Build UI for Candy Bar Prediction

Introduction

Build a UI for a Model Using Streamlit

Sentiment Analysis Using Multinomial Logistic Regression

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

Exercise 1: Build UI for Candy Bar Prediction

Overview