A boutique data science firm is what we call ourselves – we go beyond traditional statistics and computer programming and extract actionable insights from data. We can use data emanating through varied sources like emails, phone calls, text, images, videos, social media, sound and many other. Our machine learning algorithms drive real value for customers and improve efficiency.

Algo, Algoananalytics' AI platform for 

Banking and financial services helps Increase revenue and profitability

with Solutions

 Churn Prediction and Prevention, Recommender systems, Cross-selling, Credit score,

Automated Signature Verification, Automated email replies, and many more...

Banking and Financial Services

Our predictive analytics solutions include churn, segmentation, Recommender System, credit score, fraud detection and others. Analytics enables banks and other financial companies to leverage huge past data to get customer insights and improve revenue and profitability…..

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Retail & Ecommerce Services

Retailers, today are facing a very competitive environment and customer intelligence, merchandizing, marketing and supply chain logistics have become the keys to success. Our advanced analytics solutions use deep learning to help on line as well as off line retailers unleash power of data….

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Healthcare Services

Our image analytics solutions identify presence of a condition with a high degree of accuracy thus facilitating further care. Other solutions include but are not limited to work flow optimization, reducing readmissions, mortality predictions…..

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Legal

Practice of law produces humungous amount of data. Lawyers must go through the vast amount of legal information and evaluate the findings. We use techniques like Natural Language processing...

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Latest Posts

The Role of a Data scientist

A Data Scientist today is in much demand. But what does it take to become a Data Scientist? The kind of skill set and abilities required are not very different from what say a computer engineer would do- coding, programming, analyzing etc. However, a major winner is being ruthlessly analytical and data driven.

Learning and researching about the new Machine Learning models “out there in the market” makes the work of a Data Scientist different from the traditional software developer.  One needs to understand the problems of the clients, research on which model/s to be employed, research, and design and tweak Machine Learning models.

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The Irony of Bayes

More than 250 years ago, the challenge of making predictions from small data weighed strongly on presbyterian minister Reverend Bayes of Tunbridge Wells, England.

Looking to the easily banal Raffles of the 18th Century England he wondered what one's chances of winning them were. If five tickets out of ten bought won, then the chances of a win were quite simply 50%. But what if one bought a single ticket and it came out the winner? Were the chances of winning the Raffle really a 100%? It sounded far too simplistic to our dear Reverend who balanced scholarly and theological interests almost all his life. Ordained like his father and a man of keen intellect he was elected to the Royal Society in 1742.

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Decoding Consciousness: Musings on intelligence-real and artificial

In her article, “Decoding Consciousness”, which first appeared in the souvenir of Dr. Parnekar Life Management Foundation, Singapore, Dr. Sharda Bapat, Head of AI, Healthcare at AlgoAnalytics, explores and expounds her thoughts about machine learning and “artificial consciousness”. She envisions hope that the collective intelligence can be directed towards harmony and synchronized development of human intellect,physical and psychological realities. But cautious, that the challenge is to create appropriate technologies that will restore natural equilibrium.  Read the full article here

Can machine learning make this happen?
What do you think? Dr. Sharda would be happy to read your thoughts shared here

 

 

Text Analytics: Taking search to the next level

Can Text Analytics really help reduce manual overload? Let’s take a look back when we started sharing interesting links, bookmarks, etc. During the early 2000’ there were many “tagging sites” that emerged. Tagging helped in collating our searched links online rather than just in a browser and also helped share the saved links with a single tag. It was soon realized that the one word tagged for different people meant different things and thus context and meaning to that tag was required and with that relevant sub-tags became a part of the search criteria.

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