Can we predict accurately on the skewed data? What are the sampling techniques that can be used. Which models/techniques can be used in this scenario? Find the answers in this code pattern!

a postgresql aggregate function computing basic stats

Replication code for simulating and estimation by GMM of DSGE models with higher-order statistics

Running mean, variance, skew, and kurtosis

Online statistics implementations, including average, variance and standard deviation; exponentially weighted versions as well.

It is a Competition for Regression Challenge held by Kaggle, It is based on a Avito Dataset whose size is 123GB which can be accessed from Kaggle, I have done Data Pre-processing, feature engineering, feature extraction, data visualization, machine learning, stacking and boosting

Replication code for checking identification in nonlinear pruned DSGE models with Gaussian or Student's t distributed errors

Basic scripting in Python within the ANSA environment to remove bad quality surface mesh elements from the geometry (particularly, the skewed elements)

This repository provides a JSON data generator inspired by Wisconsin Benchmark. Advanced features such as variable length strings using various size distributions, nullable/optional attributes, skewed attributes, etc. are added as well.

Stochastic tools, distrubution, analysis

Q9) Calculate Skewness, Kurtosis & draw inferences on the following data Cars speed and distance Use Q9_a.csv

Create a ML model using Random Forest Classifier over skew (imbalanced) booking data

A research on how macroeconomic, microeconomic factors and personal data could affect mortgage risk using Machine Learning techniques.

Basic Statistic operations using R language

Identifying Pulsar Signals from RFI noise (HTRU2 dataset)

In this repo, I created a Skew Correction for text using OpenCV and Python.

Weibull distribution skewness.

Tutoriales sobre medidas de dispersión en R

Predicting house price