Program data mining menggunakan algoritma Naive Bayes

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

This repository contains a comparison of different Naive Bayes classifiers (Bernoulli, Gaussian, and Multinomial) for predicting benign and malignant cancer cases. The project includes confusion matrices for each classifier to evaluate their performance.

This repository contains functions/codes related to different methods of machine learning for classification and clustering in python.

This repo contains a comparison between XGBoost & Naive Bayse For "Arcene" Dataset.

contains codes of Machine Learning, Deep learning and Reinforcement learning applied in sort of scratch but mostly using this library

A repository contains more than 12 common statistical machine learning algorithm implementations. 常见机器学习算法原理与实现

LDA, QDA and NB in Python from scratch

This project aims to classify text messages as spam or ham (non-spam) using machine learning models

MetroPT-3 Anomaly Detection using Machine Learning and Deep Learning

Focusing on Sentiment Analysis .

📶 Predicting the type of Cyber attack based on Network Packets (Intrusion) using Machine Learning models

Tools created for machine learning classification model evaluation

This repository is a related to all about Natural Langauge Processing - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python)

Filter Emails

Loan Approval Prediction Project

This repository provides a study on sentiment analysis with Naive Bayes, merging theory with practice. It covers NLP fundamentals from DeepLearning.AI's course and implements sentiment analysis on Kaggle's 'Natural Language Processing with Disaster Tweets' dataset. It's a foundational exploration, serving as a launchpad for deeper investigation.

Email spam filtering using Python and Scikit-Learn for efficient classification of spam and legitimate emails.

Feedback sentiment analysis (Positive, Neutral, Negative). Applied NLP to automatically identify and extract sentiment. Used Sequential and RNN model for analysis.