This is a induction motor faults detection project implemented with Tensorflow. We use Stacking Ensembles method (with Random Forest, Support Vector Machine, Deep Neural Network and Logistic Regression) and Machinery Fault Dataset dataset available on kaggle.

Machine Learning, EDA, Binary Classification task weather dataset, ANN, SVM, LR

Machine Learning, EDA, Classification tasks, Regression tasks for customer churn

A user-friendly desktop application that utilizes a logistic regression model to predict the probability of a user having diabetes based on their inputted information.

Machine Learning Tutorial

We took an iris dataset and trained with different classifiers to find out their accuracy and some parameters.

Explore model selection in credit card transaction analysis with Reza Mousavi's Git project. Addressing class imbalance, it employs undersampling and features tree-based models, SVM, and logistic regression for effective fraud detection

Projects for the Intelligent Systems course

This repository contains work that has been done on various concepts of Python like linear regression, logistic regression, decision tree, Random forest, KNN, and K-means algorithm

This repository provides essential tools and metrics for evaluating binary classification models, aiding researchers and data scientists in their model assessment

IBM Data Science Capstone Project from Coursera

A ML project to predict sales for Black Friday.

This repository contains code and analysis for detecting cancer using various machine learning algorithms. We compare the performance of logistic regression, decision tree, and random forest models.

Performed different types of machine learning algorithms like linear regression, logistic regression, Decision Tree, Random forest.

AI model that can classify SMS messages as spam or legitimate. Use techniques like TF-IDF or word embeddings with Logestic Regression

Comparison of classification models over digits dataset

This repository has been created just for warm-up in machine learning and there are my simulation files of UT-ML course HWs.

The purpose of this analysis is to apply machine learning techniques to predict the creditworthiness of borrowers using borrower data.

A Simple Image Recognition Algorithm using Logestic Regression implemented using Cat vs Non-Cat dataset

Logistic regression estimates the probability of an event occurring, such as voted or didn’t vote, based on a given data set of independent variables.