THIS PROJECT IS ABOUT TURKISH DICTIONARY(RULES) BASED SENTIMENT ANALYSIS

Tumor prediction from microarray data using 10 machine learning classifiers. Feature extraction from microarray data using various feature extraction algorithms.

Data Visualization 📊 Clustering and Classification 🗂️ techniques on Customer 🛍️ & Book 📖 datasets

Whether or not a customer stays on the platform through machine learning classification

Project that aims to track each player and tell for which team they play. This project was developed and tested only in gymnasium sports such as futsal, basketball and volleyball

Predict if a woman will develop breast cancer_Ensemble Techniques_Stacking

The "Rock vs. Mine Prediction" project focuses on predicting whether an underwater object is a rock or a mine using machine learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), and logistic regression, this project provides an end-to-end solution for accurate classification.

CHD (coronary heart disease) Risk Prediction using Logistic Regression Model. The goal is to predict whether a patient has 10-years risk of CHD or not.

Use various techniques to train and evaluate a model based on loan risk.

This is an end-to-end data science project which a classification algorithm was used to rank clients which would be interested in getting a car insurance.

This repository focuses on developing a machine learning model and deploying a user-friendly web application to predict resale prices of flats in Singapore. Utilizing historical resale data, the goal is to create a robust model providing valuable insights for potential buyers and sellers in estimating flat resale values.

Projeto Integrador de Computação 3

ITP Additive Manufacturing (Process Monitoring) MA3

Jumlah Kasus Perceraian Berdasarkan Faktor Penyebab di Jawa Barat 2017 - 2023 dengan menggunakan algoritma support vector machine (SVM)

This repository contains a machine learning project focused on predicting the likelihood of diabetes in patients using the powerful Support Vector Machines (SVM) algorithm. Diabetes is a critical health concern worldwide, and accurate prediction can greatly aid in early intervention and personalized patient care.

This project developed two supervised machine learning models, logistic regression and random forest, to classify emails as spam or not. The random forest model achieved a higher accuracy of 97%, outperforming logistic regression's 93%, showcasing its effectiveness in improving email filtering systems for ISP customers.