Machine Learning

Making prediction based on data


Machine Learning works on the development of computer programs that can access data and use it to automatically learn and improve from experience.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Prediction of Machine Breakdown

Reliability and availability of machine components have always been an important consideration in factories. Accurate prediction of possible failures will increase the reliability of machine components and systems. 

The scheduling of maintenance operations help determine the overall maintenance and overhaul costs of machine components. Maintenance costs constitute a significant portion of the total operating expenditure of factory systems.

In machine learning, about predicting problems caused by component failures such that the question "What is the probability that a machine will fail in the near future due to a failure of a certain component?" can be answered.

Machine learning algorithm like gradient booting algorithm is used to create the predictive model that learns from historical data collected from machine sensors.


Sales Win-Loss Analysis

A win/loss analysis reveals why and how a sales opportunity turned into a new customer (or not). Making the most out of this feedback, by creating a report, is crucial to improving future sales processes.

Why are we winning or losing? How often do we win against competitors, or in the enterprise or in healthcare vertical? The answers to these questions are crucial to understand how to build a successful business and improve across sales, marketing, product, and services.

Using the past data, we can   slice and dice based on different factors such as Overall Win Rate, Loss Reasons, Win Rate by Marketing Activity etc and passed through various algorithms like support vector machine, random forests to get the analysis which can help in improving probability of winning.

Email spamming

Spam filtering is a beginner’s example of document classification task which involves classifying an email as spam or non-spam mail.

Machine learning methods of recent are being used to successfully detect and filter spam emails.

Various machine learning algorithms and libraries are used to predict on millions of mails which help in to remove spam emails.