The client needs to develop a system which can provide a machine learning solution to detect anomalies present in the data set of various businesses. However, the provided business challenge can be described as, there is some Network Traffic data, containing various data anomalies. These Data anomalies are responsible for creating various challenges for the business units in order to identify the nature and trends of the data and not able to construct any decision/prediction model over the anomaly influenced data set.
This data needs to be sanitized properly in order to make it usable for decision oriented applications. Our team at ValueCoders use the Gaussian Distribution model which plots the probability values of time-stamp attribute. The mean and co-variance of time-stamps is passed to the Gaussian distribution which calculates the best possible f1_score and epsilon. These values are calculated using cross validation data set. Finally the probabilities of test data set which falls below the epsilon value can be marked as outliers.
ValueCoders was approached by the client to develop this application. The company worked closely with the client-consultant to create such an application built using artificial intelligence. Anomalies presented in the data sets are one of the challenges faced by the most of the business units and businesses operating without real-time automated anomaly detection,typically rely on dashboards to reveal issues and insights contained in the data.
However, business is all about dealing with the constant and variable challenges. Constants challenges are something which are structured in nature and also standardization of the process can help us in dealing with the same.
When they hired ValueCoders, we had to address to the following tasks:
While developing, we faced various challenges including the following ones:
Our developers at ValueCoders had overcome these challenges with their innovative ideas and technical expertise.
ValueCoders team accepts the challenge of the complexity of work and started their effort on this anomalies detection system. Few discussions were held among the developers team and as a result, planned to build this system.
Below are the steps to identify the anomalies through Machine Learning approach.’
Step 1 :- Read the CSV file data-set from which anomalies have to be detected.
Step 2 :- Calculate the mean and co-variance matrix of the training samples.
Step 3 :- Find the Gaussian distribution of the dataset by plotting the random samples according to the mean and covariance matrix.
Step 4 :- Calculate the step-size (it denotes the shifting towards the global optimum value in the Gaussian distribution plot in each sample.). Calculate f1_score and minimum epsilon for each value of epsilon with maximum and minimum probabilities according the step size.
Step 5 :- Compare the probabilities of test data-set with the epsilon. The one which falls below the epsilon could be considered as anomaly.
As a result, it came out as the robust and efficient machine learning solution which can easily detect anomalies present in the data sets.