#Scikitlearn-machine-learning
Showing 17 of 17 repositories tagged #scikitlearn-machine-learning, ranked by stars
a delightful machine learning tool that allows you to train, test, and use models without writing code
Machine learning Guide. Learn all about Machine Learning Tools, Libraries, Frameworks, Large Language Models (LLMs), and Training Models.
Disease Prediction based on Symptoms.
My journey to learn and grow in the domain of Machine Learning and Artificial Intelligence by performing the #100DaysofMLCode Challenge. Now supported by bright developers adding their learnings :+1:
This repo aims to contain different machine learning use cases along with the descriptions to the model architectures
Spatial cross-validation in Python.
Science des Données Saison 5: Technologies pour l'apprentissage automatique / statistique de données massives et l'Intelligence Artificielle
A pattern focusing on how to use scikit learn and python in Watson Studio to predict opioid prescribers based off of a 2014 kaggle dataset.
Repo for all my Coursera Course Exercises, Materials and Certificates
Open source malware detection program using machine learning algorithms on system call traces.
The project is a simple sentiment analysis using NLP. The project in written in python with Jupyter notebook. It shows how to do text preprocessing (removing of bad words, stop words, lemmatization, tokenization). It further shows how to save a trained model, and use the model in a real life suitation. The machine learning model used here is k-Nearest Neighbor which is used to build the model. Various performance evaluation techniques are used, and they include confusion matrix, and Scikit-learn libraries classification report which give the accuracy, precision, recall and f1- score preformance of the model. The target values been classified are positive and negative review.
Cheatsheets for data science and machine learning beginners
:snake: Data Science Boot-Camp : UC San DiegoX
Sensor data of a renowned power plant has given by a reliable source to forecast some feature. Initially the work has done with KNIME software. Now the goal is to do the prediction/forecasting with machine learning. The idea is to check the result of forecast with univariate and multivariate time series data. Regression method, Statistical method.
Implementation of several ML models on real-world datasets with detailed explanation in notebooks.
A machine learning exercise using the Spotify "hit predictor" dataset, with data analysis of past "hits" by decade. Deployment using Flask via Heroku.