18 - End-to-End Machine Learning Project -Phishing Detection - Develop & Deploy ML app in Streamlit
This is the last video of End-to-End ML Project - Phishing Detection video series. I showed how to develop a simple streamlit app and deploy it in the streamlit cloud. The app is for phishing website detection. Also, I added explanations about the ML project using streamlit elements.
I mentioned possible future work and summarised the series.
Source code is available in the Github:
https://github.com/emre-kocyigit/phis...
Streamlit:
https://streamlit.io/
The application link:
https://emre-kocyigit-phishing-websit...
#machinelearning #python #phishingdetection #streamlit #mlproject
Timecode:
0:00 Intro
0:38 Install streamlit and check the version
1:04 create a streamlit app file and run
2:25 design the app mockup using excalidraw
5:30 start to develop streamlit app and import packages
6:45 add the ML project details inside an expander
11:06 add selectbox to get user's choice for ML model
11:46 get the url from user and predict if it is phishing or not
15:02 refresh the page and fix minor/syntax errors
16:07 app is ready in the localhost & test legitimate website
16:51 add example phishing urls & test phishing website
17:42 deploy the app in streamlit
19:16 the app is available on the streamlit cloud
21:30 possible future work
22:54 summary and closing