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

Analyzing Los Angeles crimes dataset

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

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

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

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.

I have used Boston Housing Dataset, which is available at Sklearn API. Find out the outliers using box plot, scatter plot and clean them using Z score and IQR method.

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

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.

Auditors at a small community bank randomly sample 100 withdrawal transactions made during the week at an ATM machine located near the bank’s main branch. Over the past 2 years, the average withdrawal amount has been $50 with a standard deviation of $40. Since audit investigations are typically expensive, the auditors decide to not initiate furt…

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 .

Statistic monster calculates and draws statistical things and also it helps to traders to analyze market.

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

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.

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.

Predict breast cancer in women using KNN model

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

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