normalize the intensities of various MR image modalities

Comparing Long Term Short Memory (LSTM) & Gated Re-current Unit (GRU) during forecasting of oil price .Exploring multivariate relationships between West Texas Intermediate and S&P 500, Dow Jones Utility Avg, US Dollar Index Futures , US 10 Yr Treasury Bonds , Gold Futures.

This application uses two types of TRNGs - True Random Number Generators (TrueRNG and Bitbbabler) for data collection and statistical analysis for several purposes, including mind-matter interaction research.

BoolTest - polynomial randomness tester

There are implemented some data mining and data processing algorithms over the NYC yellow taxies dataset, which have been provided in Kaggle.

A robust framework to predict diabetes based different independent attributes. Outlier rejection, filling the missing values, data standardization, K-fold validation, and different Machine Learning (ML) classifiers were used to create optimal model.Finally, optimal model was deployed on a PaaS .

Applied spatial statistics to spatio-temporal big data to identify statistically significant spatial hot spots on a 4-node cluster. (Java, Hadoop Distributed File System (HDFS), Apache Spark)

Anomaly detection (also known as outlier analysis) is a data mining step that detects data points, events, and/or observations that differ from the expected behavior of a dataset. A typical data might reveal significant situations, such as a technical fault, or prospective possibilities, such as a shift in consumer behavior.

Q 22) Calculate the Z scores of 90% confidence interval,94% confidence interval, 60% confidence interval for Adipose Tissue (AT) and Waist Circumference(Waist) from wc-at data set

This is pypi package for outlier detection

Automating the process of data entry from financial statements and predicting the solvency of the companies

A simple z-score calculator for UBC Vancouver campus. Runs on Android.

Normalize a sample drawn from different populations and convert into a Z-score

Practicum by Yandex Project 3: This Statistical Data Analysis project is prepared to analyze clients' behavior and determine which prepaid plan brings in more revenue.

1-Outlier detection and removal of the outlier by Using IQR The Data points consider outliers if it's below the first quartile or above the third quartile 2-Remove the Outliers by using the percentile 3-Remove the outliers by using zscore and standard deviation

This system is designed to provide valuable insights into future market movements, enabling users to make informed decisions regarding their investments without directly executing trades. It leverages the VIX (CBOE Volatility Index) as a key indicator for predicting trends, in the SPY (S&P 500 ETF) market.

Simple statistical functions that are useful for exploratory spatial data analysis (ESDA) on-the-fly in JavaScript

Feature Engineering with Python

Establishment of a Model to Define the Impact of Lombardy Region Citizens on PM2.5 Emissions During Their Daily Activities. The project aims to identify environmentally harmful actions and promote a more sustainable lifestyle through a ranking system of citizens. The model is based on the Z-Score Index.