There are 2 repositories under classification-algorithms topic.
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
A detailed look from seven different classification algorithms.
Streamlit application to classify cancer as malignant or benign.
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
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.
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. 🚀✨
8 Classification Algorithms in Machine Learning with Python using the Early stage diabetes risk prediction dataset
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.
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)
🟣 Classification Algorithms interview questions and answers to help you prepare for your next machine learning and data science interview in 2024.
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.
An exercise repository for classification with iris dataset
As part of this project, various classification algorithms like SVM, Decision Trees and XGBoost was used to classify a GPU Run as high or low time consuming process. The main purpose of this project is to test and compare the predictive capabilities of different classification algorithms
Comparison of Different Machine Learning Classification Algorithms for Breast Cancer Prediction
Code for the reproduction of Class-wise Shapley paper from Schoch, Xu, Ji [2022].
Predict loan approval by using different variable selection methods
We load a historical dataset from previous loan applications, clean the data, and apply different classification algorithms on the data.
The goal of this project is to create a reliable service for banks and other credit card issuance companies that helps to detect clients who will default on the credit repayments given some highlighted features of the client.
A machine learning project developing classification models to predict COVID-19 diagnosis in paediatric patients.
Build and evaluate classification model using PySpark 3.0.1 library.
This is non-optimized code intended solely to test whether or not quantum classification works with amplitude encoding.
Let’s practice and become familiar with classification algorithms.
Another repository for trying out machine learning algorithms for classification
Machine Learning Model to classify if emails are spam or non-spam, and identify the specific words which contribute more in classifying an email.
Data Science notebooks and report
Model using machine learning algorithms to determine loan approval for customers.
For this group project, I performed cluster analysis and classification using Python to predict one of three classes for water pumps; functional, functional but needs repair, and non-functions. I used clustering to find hidden data structures to exploit for fitting individual classification techniques with better results than using the entire dataset. Unfortunately, k-means clustering, DBSCAN, hierarchical clustering, nor OPTICS produced well-defined clusters. The entire dataset was therefore used for fitting classification algorithms. The two classification techniques I was responsible for were k-nearest neighbors and stacked generalization ensemble. For the latter, I combined the best models each group member developed. All the models had a hard time predicting the functional but need repair class. My best model was only able to achieve an accuracy of 76%.
Task : Build a classification model which will be able to distinguish between spam/not spam.