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This repository showcases various data science applications using the scikit-learn library and the Streamlit framework for interactive visualization.

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brandonabela/data-science-apps

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Data Science Apps

Several data science applications using scikit-learn and streamlit. These applications cover simple usage of streamlit, data exploration, classification, and regression on standard machine learning datasets. In addition, several datasets were also extracted for exploration, such as the SP500 and cryptocurrency markets.

Type Program Command
Simple Examples Stock Price streamlit run "01 - Simple Examples\Stock Price.py"
Simple Examples DNA Count streamlit run "01 - Simple Examples\DNA Count.py"
Exploratory Data Analysis EDA Basketball streamlit run "02 - Exploratory Data Analysis\EDA Basketball.py"
Exploratory Data Analysis EDA Football streamlit run "02 - Exploratory Data Analysis\EDA Football.py"
Exploratory Data Analysis EDA SP500 streamlit run "02 - Exploratory Data Analysis\EDA SP500.py"
Exploratory Data Analysis EDA Crypto streamlit run "02 - Exploratory Data Analysis\EDA Crypto.py"
Classification Iris streamlit run "03 - Classification\Iris.py"
Classification Penguins streamlit run "03 - Classification\Penguins.py"
Regression Boston Housing streamlit run "04 - Regression\Boston Housing.py"
Regression Visual Regression streamlit run "04 - Regression\Visual Regression.py"

EDA Basketball

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This repository showcases various data science applications using the scikit-learn library and the Streamlit framework for interactive visualization.

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