pycaret
There are 257 repositories under pycaret topic.
pycaret/pycaret
An open-source, low-code machine learning library in Python
chekoduadarsh/BlocklyML
BlocklyML is a simple visual programming Tool for python and ML. 🧩 🖥️
Pythondeveloper6/Awesome-MLOPS
All the available resources to master MLOPS from scratch
AutoViML/deep_autoviml
Build tensorflow keras model pipelines in a single line of code. Now with mlflow tracking. Created by Ram Seshadri. Collaborators welcome. Permission granted upon request.
DigitalProductschool/AI-Makerspace
AI Makerspace: Blueprints for developing AI applications with state-of-the-art technologies.
derevirn/pycaret-book
Repository for the book Simplifying Machine Learning with PyCaret.
ashishpatel26/datascienv
datascienv is package that helps you to setup your environment in single line of code with all dependency and it is also include pyforest that provide single line of import all required ml libraries
derevirn/renewcast
Renewcast: Forecasting Renewable Electricity Generation in EU Countries.
ashish-kamboj/Data-Science
EDA and Machine Learning Models in R and Python (Regression, Classification, Clustering, SVM, Decision Tree, Random Forest, Time-Series Analysis, Recommender System, XGBoost)
AIAnytime/Machine-Learning-Models-Implementation
Implementation of several ML models on real-world datasets with detailed explanation in notebooks.
MennahtullahMabrouk/Python-Projects
Python Projects During Three Years
tezansahu/dvc-pycaret-fastapi-demo
Repository for the Demo of using DVC with PyCaret & MLOps (DVC Office Hours - 20th Jan, 2022)
amine-akrout/churn-prediction-web-app
Streamlit based web application for churn prediction
AlexIoannides/pycaret-mlops
Using PyCaret with Bodywork to deploy ML pipelines to Kuberentes
shubh1608/Jupyter-Notebook-Template
Jupyter Notebook Templates for quick prototyping of machine learning solutions
hamzafarooq/Time-Series
Prophet, Xgboost, RandomForest, LSTM, Wavenet
ArpanSM/Machine_Learning_Hackathons
Machine learning and Deep Learning Hackathon Solutions
ashishtele/ashishtele.github.io
🔥 A website showcasing my work
Guoxuan99/AI-for-AI
Hackathon - DELL HACK2HIRE 2021 - MSG Automl - A Streamlit based Automl system to help beginner and data specialist to find the best model to make prediction in classification, regression and clustering by the help of Pycaret Library.
MrVtR/Solar_And_Lunar_Eclipses_Machine_Learning_Classification_Project
Repositório para Análise de dados, Treinamento de Modelo preditivo e Análise de Resultados para dados obtidos da NASA sobre Eclipses Lunares e Solares
TailUFPB/Tutorials
Repositório de introdução às ferramentas de um cientista de dados
alfarias/customer-churn-prediction
Customer Churn Prediction using PyCaret.
developedbysm/IPL-Predict-Player-Values
Predict player values based on IPL
gulabpatel/Anomaly_Detection
In this repo, different techniques will be done to analyze Anomaly detection
IvanildoBatista/AutoML
Repositório com projetos utilizando ferramentas e bibliotecas para automatização de etapas de projetos de data science.
Kazuhito00/PyCaret-Learn
PyCaret(1.0)を使用したデータ分析の勉強記録。
Lakshmipriya-S/Player-Values-Predicition-In-IPL
IPL Player Values Prediction In Pycaret
marlonffernandes/4G-3GPP-ETSI-machine-learning-anomaly-detection
4G 3GPP ETSI Radio Access Network KPI | Automatic anomaly detection (root cause) - Machine Learning model
praj2408/Automated-Machine-Learning-App
The Automated ML web app project leverages Python along with Pandas Profiling, PyCaret, and Streamlit to provide a seamless and user-friendly experience for automating machine learning workflows. It enables users to effortlessly explore, preprocess, model, and download the trained model
Sakib1263/COVID-19-Vaccine-Willingness-and-Hesitancy-among-Residents-in-Qatar
A Machine Learning-based approach to classify COVID-19 Vaccine Willingness and Hesitancy severity among people in Qatar based on survey outcomes.
SmellyArmure/OC_DS_Project7
Implémentation d'un modèle de scoring (OpenClassrooms | Data Scientist | Projet 7)
abdeltif-b/automl-prediction
This notebook presents an exploration of semi-Automated Machine Learning (AutoML) techniques for vessel traffic flow prediction. The study is applied on two ports data: Mohammedia and Los Angeles.
k-forghani/blood-pressure-estimation
Blood Pressure Estimation using PPG Signal and Demographic Features