Finalytica Feb, 2020: Knowledge Series
An overview of clustering in analytics
Just as the name suggests, clustering aims at creating clusters of the subject matter at hand, based on the various properties it possesses. This can be pretty useful when it comes to classifying elements in the future, into one of these clusters or in terms of establishing patterns in the data.… Continue reading
Finalytica, Knowledge Series: Jan, 2020
Time Series 101 : Everything you need to know about Time Series
Time is always an important factor when it comes to observing patterns or forecasting trends, be it predicting stock prices or future sales of products. For example, if the expected sales of a product are supposed to take a hit, a company can take various measures to stunt that fall or even improve the performances.… Continue reading
Finalytica: Experential Learning, Dec 2019
Credit Risk Assessment
Credit Risk assessment is a critical issue that Banks face nowadays. Which basically tells if a loan applicant can be a defaulter, so they can go ahead and grant the loan or not. This helps the banks to minimize the possible losses and can increase the volume of credit given.… Continue reading
Finalytica Nov-2019: Experential Learnings
AI Driven Narrator Chatbot – Use Cases
A chatbot is a machine that has a conversation with humans via text or audio. An AI powered chatbot is a smarter version which uses natural language processing (NLP) and machine learning (ML) to better understand the intent of the data and provide a more natural, accurate insight.… Continue reading
Finalytica, October, 2019: Experential Learning
Automated MIS Reports and triggers application through BI tool – Narrator
Introduction
A MIS Automation is an automatedreport, analysis of business or management information organized and programmed in such a way that it produces regular reports on operations for every level of management hierarchy in organization.… Continue reading
Finalytica July, 2019: Experiential Learning
Prescriptive Analytics for Sales: Combining Analytics with Business Understanding
BI and reports have been driving salesforce in an organization for some time now. But building reports manually on a monthly / weekly / regular basis is daunting, boring, and error prone.… Continue reading
Finalytica June, 2019: Experential Learning
Credit Risk Rating Using Supervised ML and Business Understanding
The risk rating models are designed to help assess the likelihood of default. Assessing risk at the loan level provides an opportunity to aggregate risk at the portfolio level and can help to quantify the risks based on the type of loan, geographic location or region, industry sector, or other variables including financials.… Continue reading
Finalytica May, 2019: Experential Learning
Converting Content to a Chat-Bot
The best way to learn or understand information is through asking questions. A QnA format of learning is more effective than reading long form content, especially when time is critical. More over searching for an information and reading multiple documents just to know one thing is inefficient.… Continue reading
Finalytica April, 2019: Experential Learning
G-Square Architecture for Unified Analytics
G-Square Solutions provides a complete package/architecture for unified analytics, which helps its clients in achieving various analytics & machine learning tasks right from the ETL of data to its final analytical usage. It is a light weight and pocket friendly architecture, widely present in the market.… Continue reading
Finalytica April, 2019: Knowledge Series
Light Weight Architecture for Unified Analytics
Unified Analytics is a new set of solutions that unify data processing right from the ETL of the data from client’s database – setting up organized staging platform (Data Warehouse) for structured & unstructured data & usage of AI technologies with the transformed data to have a 10k feet view of the business & identify hidden business opportunities.… Continue reading
Finalytica March, 2019: Knowledge Series
Too many cooks make the broth tastier.
Introduction
Incorrect is the old adage: Too many cooks spoil the broth.
Updated adage in this age of AI and ML: Too many models make the accuracy higher.
Ensemble Learning in Machine Learning is when more than one machine learning algorithms merge to produce a better and more robust decision model, even though individually, they may be weak or average in predictions.… Continue reading
Finalytics Feb, 2019: Knowledgerator
Sentiment Analysis using Doc2PC
Sentiment Analysis is the process of classifying the News/Text document as negative, slightly negative, neutral, slightly positive and positive. Sentiment analysis is based on advanced Natural language processing and Machine Learning Algorithms. It can also be used to classify type of news like Market, Financial or General News.… Continue reading