6695zkl

Feature-Selection-for-Machine-Learning

Methods with examples for Feature Selection during Pre-processing in Machine Learning.

Python

quinn

Quantile Regression using I-spline and Neural Network

conquer

Convolution-type Smoothed Quantile Regression

conquer

Convolution Smoothed Quantile Regression

ALDqr

EM algorithm for estimation of parameters and other methods in a quantile regression.

zero

MCMC algorithms for implementing hurdle, zero-inflated, and zero-truncated models for count data

Zero-inflated-Poisson-nonhomogeneous-hidden-Markov-model

This code implements a zero inflated Poisson non-homogeneous hidden Markov model for modeling of discrete seizure risk states from zero-inflated count data. Citation: Chiang S, Vannucci M, Goldenholz DM, Moss R, Stern JM. Epilepsy as a dynamic disease: A Bayesian model for differentiating seizure risk from natural variability. Epilepsia Open. 2018 Apr 20;3(2):236-246. doi: 10.1002/epi4.12112. PMID: 29881802; PMCID: PMC5983137.

Fraud-Analysis

Insurance fraud claims analysis project

em-mzip

An implementation of the EM algorithm for a mixture of zero-inflated Poisson distribution

ZIBR

Zero-Inflated Beta Random Effect model

quant-MC-methods

Stock price predictions using Monte Carlo methods

Future-Stock-Price-Prediction---Machine-learning

Stock-Price-Prediction-with-Keras

RNN implementation for Stock Price prediction.

Stock-Portfolio-Diversification-Using-Clustering-and-Volatility-Prediction

The project aims to profile stocks with similar weekly percentage returns using K-Means Clustering. The project calculates realized volatility for each stock and predicts realized volatility for each stock using classical volatility models and machine learning models and comparing their performance. This is a capstone project for CIVE 7100 Time Series and Geospatial Data Sciences.

Stock-price-prediction-model

This is a stock price prediction model developed using python and stacked long short term memory algorithm of neural network

neural_locVol

gluon-ts

GluonTS - Probabilistic Time Series Modeling in Python

TimeSeries

Implementation of deep learning models for time series in PyTorch.

Scikit-Learn

covid-19-sir-modelling

Uses Markov chain Monte Carlo to estimate the parameters of an SIR model with COVID-19 data

SIR-MCMC

Python homework

Stochastic-Modeling

Utilizing three different stochastic processes to forecast equity prices.

covid19-stochastics

Code For COVID-19 Stochastic Models (Households, Branching Processes etc.)

LightGBM

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

KTBoost

A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.

MQBSCapstoneProject

Bayesian-Two-Part-Quantile-Regressions

The R code is for the article "A Bayesian Hurdle Quantile Regression Model for Citation Analysis with Mass Points at Lower Values".

learn_data_science

Repository of demos, resources and tutorials on DataScience and Machine Learning.

ML-NLP

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。