The book every data scientist needs on their desk.

ML/CNN Evaluation Metrics Package

Evaluating the effect of data balance on different classification metrics

An open-source Streamlit web app to generate beautiful confusion matrices for multi-class machine learning models. Supports numeric and string labels, CSV upload, manual label entry, custom color maps, and displays evaluation metrics like Accuracy, Precision, Recall, and F1-score. Users can download the confusion matrix as an image.

Collection of some classical Machine learning Algorithms.

A note on the Equal Error Rate metric

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.

evaluation metrics implementation in Python from scratch

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

Classification-Techniques-For-Fraud-Detection

Visualize binary classifier performance with operating profile plots: score histograms + TPR/FPR/accuracy metrics across all decision thresholds. Python tool for model validation, threshold tuning, ROC analysis, calibration audits

This repository demonstrating regression and classification model evaluation with clear examples and visualizations, making performance analysis easy to understand and apply in practice.

📶 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.

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.

Machine learning (ML) is a subset of artificial intelligence (AI) that enables computers to learn from data, identify patterns, and make decisions without explicit programming.

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

Machine Learning Algorithms