混淆矩阵的热力图绘制

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.

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

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.

Machine learning classification applied to wine recognition data.

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

Learning python day 4

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.

Fake News Detection Using Python

Data Science Projects - beginner level.

This project focuses on detecting spam emails using a machine learning approach. Based on a dataset with word frequencies and special character occurrences, the model classifies emails as either spam or non-spam.

This Repository holds the information related to Masters Study project on Detecting Vandlism

Predicting human activity based on smartphone sensors

Confusion matrix in tensorboard

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

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.