A Streamlit application to play with machine learning models directly from the browser

Scikit-Learn compatible HMM and DTW based sequence machine learning algorithms in Python.

This is the repository for all the resources (code, notes and guides) used during the ML Study Jams 2022-23 program hosted at GDSC-TIU. (Maintainer: Aryan Pareek @diffrxction)

Cyber-attack classification in the network traffic database using NSL-KDD dataset

Official Contribution for DeftEval 2020, Task 6 Subtask 1 from SemEval 2020 Competition. Solving NLP problem of "extracting term-definition pairs in free text" in multiple approaches ranging from highly simple till very complex modern ones.

Machine learning library for classification tasks

🟣 Classification Algorithms interview questions and answers to help you prepare for your next machine learning and data science interview in 2024.

A detailed look from seven different classification algorithms.

Streamlit application to classify cancer as malignant or benign.

实现对信贷数据的数据预处理，数据分析。之后利用多种分类算法对公司是否违约进行预测。Realize the data preprocessing and data analysis of credit data. Then, it uses a variety of classification algorithms to predict whether the company defaults.

Glaucoma and Non-Glaucoma classification using ML/Dl and ensemble approaches using Image Feature Extraction Using HOG (Histogram of Gradient)

Machine learning library for classification tasks

This project focuses on the detection of credit card fraud using various data science and machine learning techniques. The dataset includes a record of credit card transactions over a specific period, with the goal of accurately identifying fraudulent activities. 🚀✨

Focused on advancing credit card fraud detection, this project employs machine learning algorithms, including neural networks and decision trees, to enhance fraud prevention in the banking sector. It serves as the final project for a Data Science course at the University of Ottawa in 2023.

8 Classification Algorithms in Machine Learning with Python using the Early stage diabetes risk prediction dataset

An exercise repository for classification with iris dataset

This repository contains all the machine learning algorithms studied in discipline "Engenharia Médica Aplicada" of Biomedical Engineering course at UNIFESP in the second semester of 2018. All the algorithms are written in both MatLab and Python Languages.

Built a classifier to predict whether a loan case will be paid off or not. Used classification algorithms (k-Nearest Neighbour, Decision Tree, Support Vector Machine, Logistic Regression). Each result is reported with the accuracy of each classifier (Jaccard index, F1-score, LogLoass)

Performance evaluation of different classification and dimensionality reduction strategies, and applications in the classification of the crop type of a set of pixels in a multiband spectral image.