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Machine Learning and AI

Machine Learning

In the present and not so distant future, a huge measure of information or data has created a huge job or engineering shift planned past the associations. Every one of the associations would be a fundamental undertaking to deal with Big Data. Just Machine Learning could be helpful for the difficulties that could address. Keeping the consistency and unwavering quality of hierarchical information, consistently brings about building the expectation models to incorporate the business issues is Machine Learning's capacities. Answers for AI challenges are:

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Artificial Intelligence

Expansion in information, the cycles and conditions are additionally expanded. The world is on the edge of robotization and modernization. Man-made consciousness characterizes insight as the capacity to accomplish and apply an individual's comprehension of the capacity to practice reason. Computer based intelligence makes it attainable to see reason and move.

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Case Study 1: Chat bot
Business Problem Statement

Digitization is penetrating into the remote parts of even the third world, in the recent times. With the advent of advanced technology and digitization, the data that is getting generated is very huge and the number of hands asking for queries on customer product/services is increasing at a very rapid pace. Keeping the current and future demand in mind, it will and is becoming a challenging task for the clients to satisfy their customers in responding to their queries.

Business Challenges

  • Human performance decreases with boring repetitive questions
  • Phone communication with the users can be asynchronous
  • Dependency on staff
  • Retaining skilled talent

Business Solutions

  • Automate tasks that are often repetitive and time consuming
  • Chat bots have the potential to provide a 24-7 service, and a downtime that is mostly scheduled
  • Chat bots don't suffer from human traits such as mood swings, tiredness, etcs

Business Impact

  • Effective Resource Utilization
  • Highly Cost effective
  • Customer Delight
  • Amplified Customer Engagement

Case Study 2:EDM: Teachers Performance Prediction
Business Problem Statement

Performance of the students depend upon the performance of the teacher. Often, due to various reasons, either economic or social reasons, the performance of faculty dips which would impact the studies of the students

Business Challenges

  • Negative effect of teachers on students and their overall development
  • Teachers turnover due to low job satisfaction, thereby high churn out of teachers in schools
  • Quality of school directly proportional to the quality of teachers’ deliverables
  • Improving the performance of Teachers by period evaluation
  • Appropriate allotment of the teachers to subjects and students’ levels based on their skillset
  • Correctly categorizing the factors which impact the performance

Business Solutions

  • Clustering techniques are applied to classify teacher performances into various brackets
  • Special attention required groups are identified for further trainings to enhance the performance
  • Identifying the major factors effecting the performance of the teachers thereby being proactive in addressing the concerns
  • Statistical models are applied on data to predict the overall performance of teachers based on influencing factors
  • Classifiers used to identify on which subject a teacher can excel, and devise customized programs for additional trainings

Business Impact

  • Increase in confidence in the students and parents by providing preemptive evaluation
  • Overall increase of schools’ performance
  • Reduction in investment by streamlining the special coaching programs for teachers
  • Accelerate Time to Value by proactive actions to address the factors influencing the performance
  • Appropriate allotment of teachers based on their skillset to increase job satisfaction
  • Reduce the time for replacement of teachers

Case Study 3: HR: Filtering Profiles using NLP:
Business Problem Statement

World is taking huge steps in terms of automation. The amount of time invested on screening through the profiles is very huge and is becoming a tedious task for the resources involved in this activity. This leads to cost to the company and dissatisfaction of the resource, inturn leading to quality issues.

Business Challenges

  • Arranging, organizing and managing large data base of ever growing resumes using manual methods is highly time consuming
  • Tracking the frequent updates to the profiles and maintaining a latest version
  • Inefficiency to handle overwriting, mistakes & duplications
  • Effecting ultimate results of loss of perfect match with job description
  • Single source of valuable database with quantitative data for quick reference
  • Ranking profiles based on various variables (ex. Skills, experience, qualification) for perfect match to the requirements

Business Solutions

  • Resume parser model is build which is used for database observations
  • With use of taxonomies clustering technique profiles are categorized
  • Found the relevant matches on resumes with help of semantic search(ex. Name, skills, contacts etc.)
  • Python libraries are used to extract the relevant features from the profiles
  • Created the single point of structured data source in ‘.csv’ format with different variables extracted (like Name, skills, contact, email, skills etc) from profiles
  • Profile Ranking system is built based on features matching with the requirement (job description) to find the most desired profiles

Business Impact

  • Single source of data for quick reference
  • Quick response with most accurate matching profiles to clients
  • Improve in quality of service by eliminating
  • Increase customer satisfaction and there by customer retention
  • Improve ROI(return of invest) from increased conversion rate
  • Better & effective recruitment experience

Case Study 4:EDM: Students School Dropouts:
Business Problem Statement

Due to increase in the various uncertain factors, there is a huge number of students being dropped out of the educational institutions. Lack of counselling and evaluation are adding to this issue. Which eventually leading to a steep dip in the performance of the students.

Business Challenges

  • High number of students dropping out of schools
  • Identifying the factors causing the students to dropout
  • Addressing the Quality of the primary level education in schools
  • Educating parents about the importance of primary schooling for kids
  • Knowledge transfer is getting impenetrable
  • Traditional methods not able to compete with current method of online education
  • High churn rate of teachers making hard for schools to be consistent
  • Effecting the overall development of the students

Business Solutions

  • Predicting the school dropouts upfront using Machine learning algorithms to take proactive steps to retain the students
  • Clustered students and identified the special attention required groups for further counseling sessions to stop them from dropping out
  • Identifying the factors effecting the school dropouts to proactively address the issues
  • Statistical models are applied on data to predict the overall performance of students to motivate and guide them appropriately
  • Sentiment analysis performed on the data extracted from social media and word clouds are built to understand the major concerns of parents and students

Business Impact

  • Overall increase of schools’ performance
  • Better planning of the counselling programs by schools to retain students
  • Periodic evaluation/Counselling helped reduce the dropouts
  • Appropriate allotment of teachers based on their skillset to increase job satisfaction
  • Increase in overall quality of education system

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