This course offers building real-time streamlit applications from scratch in Python.

“Building web appsUIs with Python and Streamlit”.png

finalStreamlitDocker1.0.tar.gz

streamlit

streamlitcode

train

SPAUIModel

Challenge1

Challenge1Code

Chapter2SPA

chapter2SPAplot

SPAPLOT

classifierStreamlit

Challenge2

SPAHTML

Chapter3SPA

AssemblyAI

WordCloudsSPA

WordCloudsSPA-copy

YoutubeSTreamlitSPA

YoutubeSTreamlit

YoutubeSTreamlitSPA-copy

YoutubeCode

streamlitHello

WordCloud

WordCloudsSPAFinal

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


## Evaluation metrics 
You must plot the following evaluation metrics for all the models on the breast cancer dataset.


- Confusion Matrix
- Receiving Operating Characteristic (ROC) Curve
- Precision-Recall Curve



### Task 1
Write the Python code to add `multiselect` option on the left sidebar as shown in the figure below:




# Evaluation metrics 
You must plot the following evaluation metrics for all the models on the breast cancer dataset.


- Confusion Matrix
- Receiving Operating Characteristic (ROC) Curve
- Precision-Recall Curve



## Task 1
Write the Python code to add `multiselect` option on the left sidebar as shown in the figure below:



Practice adding evaluation metrics for classification using Streamlit.

Exercise 2: Plot Evaluation Metrics using Streamlit

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

Exercise 2: Plot Evaluation Metrics using Streamlit

Evaluation metrics

Task 1