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Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
This project is designed as Automated Application for performing Exploratory Data Analysis for given Dataset to generate insights using Python, Streamlit. For executing all the operations customized function has been created and with support of these functions every step will be executed. EDA like basic information about data, Tabulation Analysis, Distribution Analysis, Correlation Analysis and it has been extended to perform Advance Statistical Analysis with some Basic Feature Engineering has been Automated. This Project has been Deployed with Streamlit in Heroku Cloud Platform
Beautiful Web interface made with Streamlit for quick Exploratory Data Analysis using various libraries like matplotlib, seaborn, and pandas to automate the whole process and user can also make classification models which are implemented from scratch.
codes related to aws ec2 deployment
Automating various functions of a machine learning cycle like EDA and visualization and Model building
Déploiement d'un dashboard interrogeant un modèle de scoring (OpenClassrooms | Data Scientist | Projet 7)
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Set of real world data science tasks completed using the Python Pandas library
Example Streamlit app that you can fork to test out share.streamlit.io
This repository provides a simple deployment-ready project layout for a Streamlit app. Simply swap out the code in `app.py` for your own and hit deploy!
streamlit exe template
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