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

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)

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

Machine learning library for classification tasks

Streamlit application to classify cancer as malignant or benign.

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.

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

Nursery Admission Prediction uses Machine Learning classification algorithms to categorize whether the candidate is priority, recommended or not recommended to be admitted.

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.

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. 🚀✨

A machine learning project developing classification models to predict COVID-19 diagnosis in paediatric patients.

Comparison of Different Machine Learning Classification Algorithms for Breast Cancer Prediction

Machine Learning Model to classify if emails are spam or non-spam, and identify the specific words which contribute more in classifying an email.

This repository includes python code to check various Machine learning classification algorithms like KNN, Decision Tree, SVM and Logistic regression. It compares accuracy of different classification algorithms with jaccard_score, F1_score and log_loss.

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

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

Repository to store code and study material for the Internship

Using Classification Techniques, Data reprocessing, Feature Engineering, Feature Extraction and Classification Algorithms from Machine Learning to Predict who can Survive the attack of Tsunami. Data Description