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

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

Running mean, variance, skew, and kurtosis

Stochastic tools, distrubution, analysis

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

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

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.

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

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.

Basic Statistic operations using R language

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

Tutoriales sobre medidas de dispersión en R

Predicting house price

Logistic distribution skewness.