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".

Minimax Classification with 0-1 Loss and Performance Guarantees

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

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

flight delay prediction using XGboost classifier

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.

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

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.

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

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

Customer churn prediction using various supervised classification algorithms.

Efficient Learning of Minimax Risk Classifiers in High Dimensions

data science projects

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

Predicting flight delays in US domestic flights using machine learning.