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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
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
flight delay prediction using XGboost classifier
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.
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.
Efficient Learning of Minimax Risk Classifiers in High Dimensions
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.
Presentation about SVM using Iris-datasets (code-along) and Implemented for Satellite imagery
Worked on Decision Trees, Random Forest,Gradient Boost Machines, XGBoost, lighgbm on Bank, boston, stat_oil, iris datasets
This repo contains the files for building a basic autonomous driving framework
My first personal Machine Learning project: a supervised multi-class classification model focused on the creation of credit score classification for the financial sector.
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.
Predicting flight delays in US domestic flights using machine learning.
data science projects
The initial analysis of LINEAR data using supervised classification methods.
Applied Machine Learning projects includes: a supervised machine learning model to classify emails from the given dataset as spam and not-spam. 2.
A Supervised machine learning based topic modelling approach for yelp's real and fake reviews categorisation problem
A collection of interesting Data Science Projects that highlight my foundational understanding of data science
IBM ML/AI Certification Projects Repo - EDA, Supervised ML
Most popular supervised Machine Learning algorithms from scratch with Scikit-learn implementation
This notebook tested the performance of the following scikit-learn models: Logistic Regression, Multilayer Perception, Naive Bayes, KNN, and Random Forest Classifier in classifying whether a person has thyroid or not based on the dataset provided. Each model’s performance has been evaluated using the F1 Score. NOTE: This notebook was used for a homework — we were tasked to choose 1 model out of the previously mentioned models and tweak its hyper parameters so we can get the best F1 score.
Variations of minimax risk classifiers with different loss functions
Project from UVM CS fair. Collaborated with student Simon Pavlow.
Customer churn prediction using various supervised classification algorithms.