This code performs email spam classification using three machine learning models: Naive Bayes, Support Vector Machines (SVM), and Random Forest Classifier. It evaluates their performance using accuracy scores and classification reports, ultimately identifying Random Forest Classifier as the best performer among the three.

Loan Eligibility Prediction Model: A machine learning application to predict loan approval based on applicant data. Includes a web interface for submitting loan applications and receiving predictions. Built with Python and Jupyter Notebook.

This project used a simple naive bayes classifier to classify different comments as five star or a one star rating. This project was a part of assignment in the subject Intro to AI.

Model buat TA Sentimen and Topik Berita Indonesia

This project uses the Multinomial Naive Bayes classifier to enhance movie genre classification based on metadata such as descriptions and ratings. Utilizing a dataset from Kaggle, it aims to improve content recommendation systems through accurate genre prediction.

This repository is a collection of programs implemented as part of Machine Learning Laboratory course at JSS Science And Technology University(SJCE).

Slides, exercises, and exams for my course "Natural Language Processing" (École Pour l'Informatique et les Techniques Avancées, 2024)

Python implementation of naive bayes from scratch and using scikit-learn

Enhancing Patient Care through AI-Driven Disease Prediction

Program data mining menggunakan algoritma Naive Bayes

This project examines various machine learning models for classifying text (restaurant and movie reviews) and images (CIFAR-10 dataset)

5 different kinds of machine learning alorithms has been used for the classfication of the animals and have been compared with each other

👩‍💻This repository contains implementations of various machine learning algorithms, along with example datasets and Jupyter Notebook files for demonstration.

This project consists of a variety of data with "spam" and "ham" emails in it. The dataset was cleaned analyzed and trained to detect the emails with the help of machine learning. Two models of Naive Bayes are compared in it,"Gaussian Naive Bayes", and "Multinomial Naive Bayes".

An 87% efficient Spam Filter implemented from scratch using Naive Bayes Algorithm.

This Repository consists of algorithms related to AI-ML. Few examples include - KNN, Naive Bayes, Decision Trees, etc.

Implemented Machine Learning Models to predict Stroke