confusionmatrix

Confusion Matrix in Python: Plot a pretty confusion matrix (like Matlab) in python using seaborn and matplotlib

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

Training a convolutional neural network to classify images of the Fashion MNIST dataset and use TensorBoard to explore how it's confusion matrix evolves over time.

Validator: a Rust CLI for evaluating classifiers and TypeSafe Jev workflows: single/multi-label metrics, error analysis, run comparison

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.

混淆矩阵的热力图绘制

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.

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

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

Machine learning classification applied to wine recognition data.

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.

This project analyzes customer data to understand why customers leave the company(churn pattern), identify the factors driving customer attrition, and build a Machine Learning model to predict customers likely to leave the company.

Fake News Detection Using Python

Machine learning model predicting employee attrition through data preprocessing, feature engineering, and neural network classification.

Data Science Projects - beginner level.

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.