Normalize MR image intensities in Python

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.

BoolTest - polynomial randomness tester

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.

ObMetrics is a Shiny app developed to facilitate the calculation of outcomes related to Metabolic Syndrome in pediatric populations. This repository contains documentation and licensing details for the application, which aims to provide a user-friendly interface for healthcare professionals and researchers.

A sophisticated web application for text analysis and Shannon Entropy calculation.

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 .

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.

Feature Engineering with Python

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)

Funções com algoritmos das fórmulas estatísticas.

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.

Intelligent disaster detection platform analyzing tweets in real-time using anomaly detection, spaCy NER, and Leaflet visualization with Twilio SMS integration.

This is pypi package for outlier detection

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

No description provided.

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

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

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

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

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.

This project uses machine learning to predict Turbine Energy Yield (TEY) from gas turbine data, optimizing settings to improve energy output, reduce fuel consumption, and cut costs. TEY predictions help detect deviations from normal operations, signaling potential turbine issues like degradation.

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.

This project analyzes Olympic athlete data from 2008, 2012, and 2016 Summer Olympics, focusing on identifying and understanding outliers in various sports comparing genders based on athletes' physical attributes via using advanced machine learning techniques

No description provided.

Using Hierarchial clustering to categories the spending of customers into groups based on their spending habit and other features

Predict breast cancer in women using KNN model

Real time anomaly detection and visualization

Streaming statistics monitor for WildFly JVMs. Using RabbitMQ and Postgres and visualization in Grafana.