This project is dedicated to implementing various machine learning algorithms from scratch to gain a deeper understanding of how they work.

The programs of certificate course on "Artificial Intelligence and Machine Learning" conducted at CDAC for DRDO by Tushar B. Kute.

Our project utilizes machine learning models to predict cardiovascular diseases (CVDs) by analyzing diverse datasets and exploring 14 different algorithms. The aim is to enable early detection, personalized interventions, and improved healthcare outcomes.

BaBu- Barangay Buddy is on-progress development assistant chatbot for Brgy. Market Area in Sta,Rosa, Laguna

analisis sentimen bahasa Indonesia menggunakan naive-bayes

An single page web application to manage files, using a bit of machine learning for my academic research

This project is based on machine learning algorithm(NAIVE BAISE)to predict the given message spam or not

Performs semantic text analysis on 20K TripAdvisor hotel reviews to categorize sentiment. Compares traditional ML (logistic regression, SVM) versus DL (LSTM embeddings + dense layers), achieving 85% validation accuracy. Data workflow includes exploratory analysis, cleaning, vectorization and modeling.

ML model for spam detection using Naive Bayes & TF-IDF. Achieved 0.98 accuracy. Utilized Scikit-learn, Numpy, nltk. Implements NLP concepts. Explore precise spam classification effortlessly. #MachineLearning #SpamDetection 🚀✉️📱

A decision tree is a non-parametric supervised learning algorithm, which is utilized for both classification and regression tasks. It has a hierarchical, tree structure, which consists of a root node, branches, internal nodes and leaf nodes.

This project demonstrates iris flower classification using machine learning. It's an accessible introduction to data science and classification techniques. Explore the code and Jupyter Notebook to enhance your data science skills.

Program data mining menggunakan algoritma Naive Bayes

MACHINE LEARNING ALGORITHM MINDMAP

Data analytics clustering project

NLP based Twitter data sentiment analysis project

This is a junior design project (CSE.299) by Kazi Iftakher Rahman, Md. Saif Hasan and Md. Arifuzzaman Arif, under the guidance of Intiser Tahmid Naheen (ITN), Lecturer with the Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh.

This Python project analyzes public sentiment on social media (like Twitter) towards cryptocurrencies and it leverages Support Vector Machines (SVM) for sentiment classification.

Machine learning models

Use twitter to get live and trending stock sentiment!

Enhancing Text Classification in Information Retrieval: Evaluating the effectiveness of Naive Bayes classifiers with various word embeddings (Word2Vec, GloVe, FastText) for natural language processing tasks. This project explores performance differences and offers insights into embedding impacts on text classification.