The practitioner's forecasting library

A fast, robust library to check for offensive language in strings, dropdown replacement of "profanity-check".

Recognition of the images with artificial intelligence includes train and tests based on Python.

This Flask app predicts house prices using a RandomForestRegressor model trained on a housing dataset. It includes data pre-processing with pipelines and imputers, stratified train-test splitting, and a user input form. Predictions are displayed on the web page, making it ideal for learning basic machine learning deployment with Flask.

A Q&A based chatbot which queries the database to find responses for similar questions asked by the users

Sleep Health and Lifestyle Clustering using Scikit-Learn

analysis application developed with Streamlit, designed to facilitate the evaluation of review sentiments—positive or negative—by leveraging a pre-trained machine learning model. Users can input review text and instantly receive sentiment predictions through a streamlined, interactive web interface powered by Streamlit .

Employee Attrition Classification using Scikit-Learn

Flight Price Prediction Regression using Scikit-Learn

An AI model built to understand the sentiments transmitted through a phrase.

24/01/2024 Jeyfrey J. Calero R. Aplicación de Redes Neuronales con scikit-learn streamlit, pandas, seaborn y matplolib

ML model deployment using docker, kubernetes; API deployment with FastAPI; and MLOps using MLFlow for water potability dataset

Machine Learning on Raspberry Pi Zero and Zero-W

This consists of various machine learning algorithms like Linear regression, logistic regression, SVM, Decision tree, kNN etc. This will provide you basic knowledge of Machine learning algorithms using python. You'll learn PyTorch, pandas, numpy, matplotlib, seaborn, and various libraries.

🔉 👦 👧Voice based gender recognition using Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture models (GMM)

a python project that uses machine learning to estimate the weight of a fish.

Full-Stack application that allows client to use a predictive model to determine which user is more likely to have tweeted a given text. This project covers everything from API's to Predictive Modeling, SQLAlchemy database storage, Flask, along with other full-stack components. In the end it is deployed for online usage using Heroku.

A project that focuses on implementing a hybrid approach that modifies the identification of biomarker genes for better categorization of cancer. The methodology is a fusion of MRMR filter method for feature selection, steady state genetic algorithm and a MLP classifier.

Permutations of sepal length, sepal width, petal length and petal width from the 3 classes of Iris on a scatter plot