The project provides a complete end-to-end workflow for building a binary classifier in Python to recognize the risk of housing loan default. It includes methods like automated feature engineering for connecting relational databases, comparison of different classifiers on imbalanced data, and hyperparameter tuning using Bayesian optimization.

EasyTorch is a research-oriented pytorch prototyping framework with a straightforward learning curve. It is highly robust and contains almost everything needed to perform any state-of-the-art experiments.

This toolbox offers 7 machine learning methods for regression problems.

An example of easytorch implementation on retinal vessel segmentation.

Machine Learning with MATLAB

This toolbox offers 6 machine learning methods including KNN, SVM, LDA, DT, and etc., which are simpler and easy to implement.

This project is an Android mobile application, written in Java programming language and implements a Recommender System using the k-Nearest Neighbors Algorithm. In this way the algorithm predicts the possible ratings of the users according to scores that have already been submitted to the system.

this project is sentiment analysis about about Kampus Merdeka that launched at Youtube platform using Naive Bayes Classifier with TF-IDF term weighting, also get validated using K Fold Cross Validation. The score-mean result is 91.2%, pretty good for valid score.

Practice model assessment and optimization on the HR dataset using validation and dimensionality reduction techniques

Several small AI projects, including basic machine learning algorithms, perceptron neural networks, convolutional neural networks, and semantic segmentation.

Deep Learning Convolutional Neural Network (CNN) using PyTorch and train it to recognize five different classes: (1) Person without a face mask, (2) Person with a “community” (cloth) face mask, (3) Person with a “surgical” (procedural) mask, (4) Person with a “FFP2/N95/KN95”-type mask (you do not have to distinguish between them), and (5) Person…

Introduction and Implementation of Random Forest Models

Loan eligibility prediction using Decision Tree, SVM and Logistic Regression

my machine learning practices for my third year in MFCI CS department

Classification of mushrooms using decision tree in ID3 implementation

Given the data of the user activity of a month, can predict user activity for the upcoming month.

ELM classifier for detecting tuberculosis in lung X-rays

MLB Team Runs Allowed Prediction Project (Linear Regression)

Determining the total dropout rate of pupils at different levels of education based on various factors globally.

Implementation of classic machine learning concepts and algorithms from scratch and math behind their implementation.Written in Jupiter Notebook Python