naive-bayes

Simple naive bayes implementation

Project1: Color Classification and Recycling Bin Detection

Classification and clustering in dataset of news articles.

Classroom-PeopleCounting.

Create a `Naive Bayes` model for email spam detection

Past stastical inference projects in R

This project uses the most popular classification techniques to predict the outcomes after extensively working on EDA treating missing values and imbalanced data.

In this project, five different machine learning (ML) models are trained and compared in term of predicting the early-stage diabetes. A data collected in hospital Frankfurt, Germany containing 2000 patients’ information have been used in this study. RF, NB, SVM, KNN, and LR are the five models used for predicting the diabetes.

A repository that contains practice code for ML algorithms implementation on a cancer treatment medical dataset from Kaggle (Text Processing, Response Encoding, Feature Engineering, Classification).

This project compares the performance of a Naive Bayes model and fine-tuned BERT models on emotion classification from text.

(Python) Proyecto enfocado en la creación de modelos predictivos como Regresión Logistica, Arboles de Decisión, KNN, SVM, Naive Bayes y Ensamblados. Inicialmente el problema consta de un analisis crediticio de clientes buenos/malos. Se utiliza una BBDD de clases desbalanceadas la cual se limpia y procesa para alimentar los modelos

O objetivo deste projeto é classificar textos em diferentes categorias utilizando técnicas de Processamento de Linguagem Natural (NLP) e aprendizado de máquina.

Projeto de classificação de risco de crédito usando o algoritmo Naive Bayes. O modelo prevê a probabilidade de churn ou score de crédito de clientes, a partir de variáveis financeiras e comportamentais. Inclui pré-processamento de dados, balanceamento de classes, treinamento, avaliação com métricas e visualização de resultados.

Naive Bayes - Machine Learning

ML model with a naive based data set that compares the values based on a dataset and splits it into the a test and train model and creates naive based prediction data. After all of that is done it checks for the accuracy in the code.

Sentiment analysis using Naive Bayes and SVM with live prediction and Streamlit Visualization

This project applies machine learning techniques to classify Iris flower species based on sepal and petal measurements. It explores multiple classification algorithms, including Random Forest, SVM, Naive Bayes, KNN, and XGBoost. The project incorporates data preprocessing, hyperparameter tuning, cross-validation to optimize the models' performance.

Ordinal Rating Classification (1–5 Stars) of Indonesian E-Commerce Reviews with Multinomial Naive Bayes.

A Machine Learning project that classifies SMS messages as Spam or Not Spam using TF-IDF, Naive Bayes, and Streamlit.