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!
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Updated
Sep 3, 2020 - Jupyter Notebook
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
Replication code for checking identification in nonlinear pruned DSGE models with Gaussian or Student's t distributed errors
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
A research on how macroeconomic, microeconomic factors and personal data could affect mortgage risk using Machine Learning techniques.
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
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