Fraud Detection with Machine Learning

Machine Learning pipeline built with PyTorch and MONAI for the automated classification of intervertebral disc degeneration in 3D MRI scans, utilizing the clinical Pfirrmann grading system (Grades 1 to 5).

Language Detector Loads and cleans text data, trains a language classification model using TF-IDF and Logistic Regression, evaluates it, and enables interactive language prediction with saved model reuse.

This project explores the optimal combination of Bag-of-Words and TF-IDF vectorization with Naive Bayes and SVM for sentiment analysis. It evaluates performance using accuracy, precision, recall, and F1-score, addressing ethical concerns like data privacy and bias to improve sentiment classification in real-world applications.

This model can predict whether an email is spam or not. The logistic regression machine learning algorithm is used to train this model.

Learning python day 4

Machine learning classification applied to wine recognition data.

This repository contains code for evaluating different machine learning models for classifying fake news. The dataset used for this evaluation consists of labeled news articles as either "REAL" or "FAKE". Three popular classifiers, Support Vector Machine (SVM), Decision Tree, and Logistic Regression, are trained and evaluated on this dataset.

Predicting human activity based on smartphone sensors

This model was designed around Pycoco's dataset, the CNN model constructed outputs training loss graphs and a confusion matrix for the network of interest

Fake News Detection Using Python

Confusion matrix in tensorboard

This project aims to understand and build Naive Bayes classifier to predict the salary of a person.

A common question when you're learning data science: "Sort the confusion matrix using your own function". This is a simple way to do it by using optimization.

A innovative way to visualize text misclassifications within a confusion matrix in Tableau.

混淆矩阵的热力图绘制

Visual-analytical tools to evaluate and compare the outputs of large numbers of binary classifiers.