Unsupervised anomaly detection in vibration signal using PyCaret vs BiLSTM

Using PyCaret to Predict Apple Stock Prices

Python Phishing URL Detection

Real time streaming of a time series with corresponding forecasts.

All of my PyCaret's notebooks covering both Supervised & Unsupervised Machine Learning.

This is the Streamlit web application that allows users to upload a dataset, generate an automated exploratory data analysis (EDA) report using the pandas-profiling library, and and train a machine learning model for regression or classification tasks.

Testing PyCaret, Fugue, and Dask

Utilizes pycaret to automates machine learning workflows (Deployed at streamlit)

This repository contains a practice streamlit app that automatically builds, trains and downloads(optional) the best performing machine learning model

How to access time series data from QuestDB Data storage and train a forecasting model using Pycaret Library

Our project leverages Python, pandas, Tableau, and machine learning techniques to analyse and predict student outcomes in higher education. Using a comprehensive dataset, we employ data preprocessing, visualisation with Tableau, and advanced machine learning models built with Python to uncover insights into graduation rates and factors influencing

Analyzed large-scale patient data from multiple sources, including electronic health records. Developed a predictive model with high accuracy, for early disease detection using 6 ML algorithms in model.

CatBoost regressor for Predicting alcohol level based on chemical properties of the white wine

Repository containing notebooks and codes submitted in various hackathons.

Basic EDA, Model

🎮 Trabalho Prático - Tecnologias para Data Science

Breast Cancer detection study with Pycaret library.

EDA and predictions on salary in usd for professionals in the data science field

Titanic: Machine Learning from Disaster - Predict survival on the Titanic - Automation for EDA and Modelling

Detecting and Identifying Fraudulent credit card transactions from normal transactions.