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ML_WebApp_Steamlit_Python

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Build interactive web applications with Streamlit and Python Train Logistic Regression, Random Forest, and Support Vector Classifiers using scikit-learn Plot evaluation metrics for binary classification algorithms

binary-classification-algorithms machine-learning pandas scikit-learn streamlit

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Name		Name	Last commit message	Last commit date
mlwa_env		mlwa_env
.gitignore		.gitignore
README.md		README.md
app_mlwa.py		app_mlwa.py
app_screenshot.jpg		app_screenshot.jpg
mushrooms.csv		mushrooms.csv
requirements.txt		requirements.txt

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ML_WebApp_Steamlit_Python (Part of Coursera Project Network)

Build interactive web applications with Streamlit and Python.

Train Logistic Regression, Random Forest, and Support Vector Classifiers using scikit-learn.

Plot evaluation metrics for binary classification algorithms.

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About

Build interactive web applications with Streamlit and Python Train Logistic Regression, Random Forest, and Support Vector Classifiers using scikit-learn Plot evaluation metrics for binary classification algorithms

binary-classification-algorithms machine-learning pandas scikit-learn streamlit

1 star

1 watching

Report repository

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