machine learning

Customize your machine learning needs through our automated platform: PredictSense


Machine learning algorithms have come a long way, and new computing technologies are supporting its evolution. The most challenging task while developing a machine learning based solution is data processing and choosing the customized algorithm. PredictSense, a machine learning automated platform, which is a part of such evolution, meets this challenge. It customizes the machine learning needs for businesses through its data transformation and model enhancement techniques.

Built on an open API structure, PredictSense uses high-end algorithms to address real-time complications in a short span of time. Since this product can be integrated into new or existing models, it finds its application in many segments such as banking, retail, and IoT, etc.

Operating PredictSense for business operations

PredictSense is easy to set up where a business can carry out user management, data upload & configuration, and model environment & testing. Let’s discuss each one in detail.

User Management: Upon successful integration, the user can begin by setting up a single/multiple projects (as per requirement) and scale it by creating sub-users.

Data upload and configuration: The data can be uploaded which is then processed and any issues, if occur, are resolved with PredictSense’s data analysis strategies. The data is configured using filters, test settings and data transformation strategies for higher efficiency and better results.

How PredictSense functions?

The core of PredictSense is responsible for its functionality. Here are the essential components of PredictSense:

  • Data Transformation: Most of the data fed into the system is transformed by categorizing them. Since empty values and outliers exist too, they are handled using efficient strategies for data cleaning.
  • Model Enhancement: All the hyper parameters are tuned to enhance the model as they can be used after the training.
  • Model execution: The user can now select the most suited machine learning algorithm to develop a competitive model which can be easily deployed.
  • Model Assessment: Lastly, the behaviour of the model is assessed using scoring parameters. The performance score and evaluation parameters give the model a rank on the leaderboard. The user can view the performance chart to visualize the competitive models.

Thus, PredictSense is a fine platform for automatic model generation and rapid model deployment thereby giving a customized machine learning solution. The visualization insights and accurate predictions make this product efficient. Most businesses prefer PredictSense as it generates quick responses and is easy to use.

Customize your machine learning needs through our automated platform: PredictSense 2020-07-28T14:01:17+00:00

AI influence and why you need to invest in it?


Artificial Intelligence is on the march, and the world is going to witness new changes every day. Over the years, AI technology has shown significant improvement and contributed greatly to the economy. More and more organization are adopting the AI- based model for their business operation for the pursuit of profits.

“It is an estimate that companies invested up to $500 million in 2010 only to increase up to $4 billion in AI startups by 2016.”

This remarkable leap is evidence of how AI is ready to shape the future in the coming years.

From the aviation and healthcare sector to finance, transport and advertising, AI has become a part of our lives. The reason behind its colossal application and influence in every industry is because of its ability to learn from a consumer’s behavior and thereby predicting outcomes with the help of defined patterns.

Why invest in Artificial Intelligence?

The emergence of artificial intelligence has the potential to change the business operations entirely by disrupting the existing technologies and outdoing them with intelligent machines.

As per the Economist, “ The world’s most valuable resource is no longer oil but data.”

Here are several reasons as to why should companies invest in Artificial Intelligence:

➢    High returns: The technology offers remarkable solutions to every industry that deals with big data. With its high processing speed, the loathe of data can be taken care of in less time span thereby giving great returns.

As per McKinsey, “Established MNC’s invested approximately $27 billion in 2017 on AI-related projects.”

➢    Strengthened economy: People often consider that AI will leave thousands of people unemployed, but many influencers can’t disagree more.

As per Matthew Lieberman, PwC, “The combination of humans and machines will be unparallel and has the ability to bring revolution in the job market.”

AI will complement and augment the human capabilities but not replace them. It’s no more about individuality but collaborative intelligence where both AI and human can support and enhance each other’s strength. While humans will bring leadership, teamwork and social skills on the table, AI can focus on scalability and speed.

Thus, efficiency would be more, when together, to complete a task.

➢    Performing tasks beyond human capability: There are specific tasks beyond which humans can’t function. It can be rightly said that after a certain level, humans have more questions than answers. But when machines come into the picture, their intellect will outdo a human brain by performing tasks in seconds based on inputs provided.

As per Wade Burgess, Shiftgig, “ This technology will help in focusing on high order functions and will eventually improve life’s quality.”

If history is any evidence, one thing is for sure that Artificial intelligence has slowly and gradually impacted many niches and sectors over the years. The global forecast on revenue for Cognitive and AI systems shared by IDC to Worldwide semiannual cognitive Artificial Intelligence systems spending guide is that this sector will continuously see corporate investment. The compound annual growth rate (CAGR) by 2020 is expected to be 54.4%, which is more than $46 billion.

AI influence and why you need to invest in it? 2020-07-28T14:02:04+00:00

Sentiment Analysis in Machine Learning


Sentiment Analysis is on the roll and why should’nt it be ? Every company and business is focussed on understanding what people think and feel about a product or service. The only difference is that now instead of people it is a software or an intelligent machine deducing these emotions. Sentiment analysis can be considered on the foreground for businesses, as it uses Natural Language Processing, text analysis & Statistics to extract important sentiments into majorly three categories i.e.positive, neutral and negative.

What do we know about sentiment analysis?

Sentiment analysis also known as opinion mining, is all about extracting information from commercial interests and growing research data, which is basically unstructured. One can clearly understand that machines becoming intuitive about deducing the tone of a particular write up can be fairly difficult.  Contextual understanding is complex and expecting a machine to get hold of it is one heck of a level to achieve. For instance, consider a statement: “My flight has been cancelled, great!”. It is easy for a human to deduce that it is a negative response but machine might consider it positive due to the word “great”.

 “According to a survey in sciencedirect for two long surveys on sentimental analysis presented by Lee, Pang and Liu that discusses the challenges and its applications. It also mentions the solution to all the problems.

Importance of sentimental analysis in making corporate decisions

It is vigorously growing as an important facet of business world. It can be used for various purposes.

  1. Proper estimations – This process applies the text technique and NLP that is, Natural Language Processing that will help in identifying and extracting apt information from the available data. This data can be used to calculate or estimate emotions, attitudes and even opinions to make good corporate decisions.

According to a study presented by ieeexplore for sentiment analysis of a news article done by SenticNet and ConceptNet, “It provides 71% accuracy in classification, 59% positive and 91 % of precision for neutral sentences.”

  1. Better understanding of the scenario – with developing business prospects, the need to understand customer is also growing. There has been a steady increment in the interests from various brands. Todays’ business world looks toward data analytic streams and business insight for better response.

As per a recent study by Google Trends, “ sentiment analysis has grown over time.”

  1. Thorough market research – Sentiment analysis is known to provide accurate data regarding the market. Using various techniques, it takes all the details into account to provide a thorough study of the corporate sector, which can help people to make accurate business decisions.

“As per a recent study by Zendesk, “Around 45% of customers have bad experience of customer service and just 30% of customers have good experience.”

Wrapping it up

Though the technology is in its infacy currently, it is surrounded by coveats too. The limitation of showing results in a single dimension can question its accurate prediction. Undoubtedly, the algorithm is powerful and the technology is capable of compiling best opinions, a multi-dimensional based prediction would be more apt in the near future.

Sentiment Analysis in Machine Learning 2020-07-28T14:02:45+00:00

The three buzzwords: Artificial Intelligence, Machine Learning, and Deep Learning


Our technology base has entered the era of Artificial Intelligence, Machine Learning, and Deep Learning. In fact, these are the latest buzzwords swirling around every sector and field. Before discussing their potential in the coming future and addressing what the hype is all about one must realize that these terms can’t be used interchangeably. While Deep learning is contained within Machine Learning, Machine Learning is contained within Artificial Intelligence. Thus, Artificial Intelligence is a bigger ball game altogether.

As per Adobe, 47% of the organizations with mature digital fronts or practices implement AI strategies.

 DL vs. ML Vs. AI

 To have a fair understanding of how each differs from other and cannot be considered the same in any manner, we can define their functionalities.

Artificial Intelligence occurs when machines exhibit human intelligence and craft a solution for a given problem. In order to achieve this, they need a defined approach where machine learning comes into the picture. Machine Learning parses the available data and learns before making predictions/solution using a defined algorithm.

At last, the approach can be implemented in real-time by using deep learning where different layers of network process different parts of a problem and brings out meaningful information.

How are these technologies impacting every industry?

Let’s begin the conversation with Deep Learning first. It can be referred to as computationally intensive technology where layers under layers together form a deep neural network and process inputs with mathematical operations. From energy market pricing forecast and self-driving cars to applying neural networks in brain cancer detection and finance, there are unlimited applications of Deep learning in the coming future.

As per a report in medium, “Deep Learning can improve earth quake predictions by making calculations better by 50,000%”.

Without realizing much, its safe to say that we are already addicted to Machine Learning applications. It’s a possibility that you are using it right now while reading this blog on social media. From adopting virtual assistants like Siri and Alexa for our day-to-day activities to using the technology for online fraud detection and customer support, Machine learning has sunk in much faster in our lives than we thought.

As per Statwolf,”Due to Machine Learning Algorithm, Netflix saved almost $1 billion. The Algorithm gives suggestion to subscribers for personalized sitcoms and movies”.

Finally, let’s take a closer look at the bigger picture i.e. Artificial Intelligence. A technology, so intense that it can mimic human interactions. It has stepped over every existing technology in the last few years and has changed the business landscape in every way. With many of the known applications, it has caused a deep impact on retail and e-commerce industry. From improving work place communication, HR management to making a difference in healthcare, logistics, and cyber security, the application is massive and endless.

As per IDC, “The amount of spending on AI globally has reached 50.1%, which shall reach $57.6% billion in the year 2021. The major investments have occurred in retail, healthcare, banking, and manufacturing worldwide”.

Thus, the technology, which was once simmering only in labs and was considered only as an imagination has now emerged as a giant. With instant solutions and unimaginable applications, there is no doubt that these massive platforms have much more to unfold in the coming years.

The three buzzwords: Artificial Intelligence, Machine Learning, and Deep Learning 2020-07-28T14:03:39+00:00