There are 1 repository under stratified-sampling topic.
mcs_kfold stands for "monte carlo stratified k fold". This library attempts to achieve equal distribution of discrete/categorical variables in all folds. The greatest advantage of this method is that it can be applied to multi-dimensional targets.
Fast Online Triplet mining in Pytorch
使用比赛方提供的脱敏数据,进行客户信贷流失预测。
An optimal stratified sample design for Commodity Flow Survey (CFS) based on Simulated Annealing and Genetic Algorithm. A script in Procedural PostgreSQL is used to generate a frame with 100,000 records based on publicly available data.
Data consists of tweets scrapped using Twitter API. Objective is sentiment labelling using a lexicon approach, performing text pre-processing (such as language detection, tokenisation, normalisation, vectorisation), building pipelines for text classification models for sentiment analysis, followed by explainability of the final classifier
Data sampling library
The objective is to analyze flight delays in the United States. Data from airlines, airports, and runways will be collected and processed. Machine learning models will be built using logistic regression, decision trees, and XGB classifiers. Visualizations will be created in Tableau, and Excel dashboards and SQL queries will be used for analysis.
WiDS Datathon 2020 on patient health through data from MIT’s GOSSIS (Global Open Source Severity of Illness Score) initiative.
Data sampling library
Data sampling library
Perform Data Sampling with Python
Web scraper to get professor information, and a mass emailer that sends a website with a survey.
This repository contains Natural Language Processing Projects like Sarcasm Detection, Quora Insincere Questions Classification & Edgar Sentiment Analysis
Kaggle Challenge
This project focuses on applying advanced simulation methods for derivatives pricing. It includes Monte-Carlo, Variance Reduction Techniques, Distribution Sampling Methods, Euler Schemes, and Milstein Schemes.
University Project: simulation techniques to price derivatives. It will involve Monte-Carlo, variance-reduction techniques, and advanced simulation methods.
Supervised machine learning model to classify loan applicants into high and low risk categories
Regression algorithms to predict the median house prices in California districts
This code assigns participants to an experimental group and ensures balanced physical attributes without knowing the participants in advance.
This notebook will walk you through the steps for dealing with an imbalanced dataset using an example of a real project that I recently completed.
BI Master - Automated methods to detect and classify human diseases from medical images. Convolutional Neural Network, Data Augmentation, Transfer Learning, Tensorflow, Keras, Xception, ImageNet, StratifiedKFold.
Models implemented for stroke prediction amongst individuals
Code to help the presentation to the client. The Sampling code.Rmd file contains the code performing the sampling method and produces the visualisations and diagnostics seen in the presentation.
CSCI316 Group assignment 1
This was a project that aimed implement sampling techniques. The sampling technique used was clustered random sampling and stratified random sampling.
(77,86% R) Floresta Aleatória aprimorada para a previsão de aluguel.
Assignment 3 for the Analytics Practicum I Course of AUEB's MSc in Business Analytics
Employing advanced techniques, the project seamlessly integrates binary and multiclass classifiers for character classification. It offers a comprehensive analysis and adeptly addresses challenges in the realm of computer vision.This project was part of my uOttawa Master's in Computer Vision course (2023).
Data sampling library
Data Sampling Library
Data sampling library