This code base is collection of codes that are freely available for google earth engine. This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy access to the normal users via web page.

A Library for Minimax Risk Classifiers

Supervised cell type identification for scATAC-seq data

The official implementation for "SANGO".

This dataset contains the sign and symptom data of newly diabetic or would-be diabetic patients. The data were collected using direct questionnaires from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladesh, and approved by a doctor.

Minimax Classification with 0-1 Loss and Performance Guarantees

NLP on scientific articles to create the vector space model, and the information retrieval was realized using cosine similarity ranking.

Combination of unsupervised clustering and supervised classification to detect shill bidding on eBay.

flight delay prediction using XGboost classifier

Simple news article classifier based on the UCI ML News Aggregator dataset using scikit-learn, SelectPercentile and GridSearchCV

Efficient Learning of Minimax Risk Classifiers in High Dimensions

In this assignment, I will be building a machine learning model that attempts to predict whether a loan from LendingClub will become high risk or not.

CSE Final Year Project. Heart Disease Prediction using Supervised Learning namely - Logistic Regression, Naive Bayes, K-NN, Decision Tree and Random Forest. The Porject is done in Python.

This project builds a breast cancer classification model using numerical data like mean_radius and mean_area to predict diagnosis (benign/malignant). Random Forest was the top performer among models. A KNIME workflow ensures reproducibility, and predictions on unseen data are demonstrated.

Presentation about SVM using Iris-datasets (code-along) and Implemented for Satellite imagery

Predicting flight delays in US domestic flights using machine learning.

Variations of minimax risk classifiers with different loss functions

Machine learning. Analyzing the Reality Mining Data using supervised classifiers.

Uses Logistic Regression and various machine learning techniques to train and evaluate models with imbalanced classes applied to identify the creditworthiness of borrowers.