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!

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

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

Running mean, variance, skew, and kurtosis

a postgresql aggregate function computing basic stats

This repository contains simple statistical R codes used to describe a dataset. These scripts provide a summarized and easy way of estimating the mean, median, mode, skewness and kurtosis of data. It also provides codes for calculating the covariance.

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

Central tendency, distribution, skewness and kurtosis

Perform exploratory data analysis to visualize the distribution of values in a dataset, analyze relationships using correlation, and locate and fix data problems including missing values.

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

Analyze the energy consumed by household appliances. Explore individual features to assess whether the data is skewed and perform feature engineering to create new features

The data set contains data about city fuel cycle consumption of vehicles. This report demonstartes data cleaning and preparation techniques using SAS to prepare it for statistical analysis. It uses arrays, concatenation, data type converison, outlier removal and standardization. The dataset is downloaded from https://archive.ics.uci.edu/ml/datas…

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

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

GenSkew is an application for computing and plotting nucleotide skew data.

Demo on using Facets: An Open Source Visualization Tool for Machine Learning Training Data developed by Google's PAIR Initiative

Statistical analysis comparing metal accumulation levels in three macroinvertebrate groups

A Python repository focused on applying Data Science to cryptocurrency trading on Binance Exchange

This project is about to use linear regression to examine the relationship between various economic variables and the mortgage rate in the United States.