Streamlit Python Course: Build a Machine Learning App to Predict Cancer

Alejandro AO - Software & Ai

Welcome to this Streamlit Python tutorial, where we will dive into creating an interactive machine-learning web application using the Streamlit framework! In this comprehensive project-based course, you’ll learn how to build a machine-learning dashboard to predict whether a cell cluster is benign or malignant using Python and Streamlit. This tutorial is perfect for beginners, experienced developers, and data scientists looking to learn how to create powerful machine-learning projects that integrate with web applications.

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🚀 What you’ll learn in this tutorial:

How to create a Streamlit dashboard
Training a logistic regression model for machine learning
Visualizing data using Plotly
Displaying predictions based on user input
Creating an AI full-stack app using Python

In this video tutorial, we train a logistic regression model using machine learning in Python. We will be using a dataset to predict whether a cell cluster is benign or malignant. Once our model is ready, we will integrate it into a Streamlit application that runs in the web browser. You’ll see how easy it is to create an interactive dashboard using Streamlit and Python!

Next, we will visualize our data using Plotly to create informative and interactive charts. These visualizations will help users understand the predictions made by our machine-learning model. We’ll then display the predictions based on the user’s input, making our dashboard not only visually appealing but also highly functional.

Throughout the tutorial, we’ll explore various techniques and best practices used by experts like Nicholas Renotte and Patrick Loeber. This Streamlit tutorial is an excellent resource for data science dashboard projects and a great way to learn how to create a machine learning app.

By the end of this course, you will have created an AI full-stack app using Python, Streamlit, and machine learning. You’ll be ready to tackle your own machine-learning projects and create amazing data science dashboards!

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⏰ Timestamps
0:00 Intro
2:56 Download the data
6:23 Project setup
8:21 Clean the data
14:00 Train the model
21:06 Test the model
26:39 Export the model and scaler
32:33 Create the Streamlit app
38:48 Create the layout
44:18 Add the sidebar inputs
58:52 Create the chart
1:17:22 Scale the values for chart
1:25:30 Display the predictions
1:39:08 Add custom CSS
1:52:00 Deploy your app

🎯 Keywords: Streamlit Python, Streamlit Tutorial, Streamlit Dashboard, Machine Learning Python, Machine Learning App Tutorial, Python App, Machine Learning Projects, Machine Learning Projects for Beginners, Nicholas Renotte, Patrick Loeber, AI App, AI Full-Stack App, Data Science Dashboard, Data Science Dashboard Projects

#python #streamlit #MachineLearning #DataScience