The book every data scientist needs on their desk.

ML/CNN Evaluation Metrics Package

Evaluating the effect of data balance on different classification metrics

Collection of some classical Machine learning Algorithms.

A modern, cross-platform desktop app for calculating classification metrics from confusion matrices. Includes XLSX export, real-time language switching, and batch processing with responsive UI.

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

Your all-in-one Machine Learning resource – from scratch implementations to ensemble learning and real-world model tuning. This repository is a complete collection of 25+ essential ML algorithms written in clean, beginner-friendly Jupyter Notebooks. Each algorithm is explained with intuitive theory, visualizations, and hands-on implementation.

evaluation metrics implementation in Python from scratch

Imbalanced classification with scikit-learn and PyTorch Lightning.

When it comes to deciding whether the applicant’s profile is relevant to be granted with loan or not,banks have to look after many aspects. Predicting loan approval is a common application of machine learning in the financial industry.

Classification-Techniques-For-Fraud-Detection

This project is used to predict the customer churn based on various features using an artificial neural network

📶 Logistic regression classifier for bit decoding in binary vectors using stochastic gradient descent (SGD). Features performance evaluation, probabilistic modeling, confusion matrix analysis, and classification error interpretation. Developed in Python with Jupyter Notebook.