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This solution analyses a corpus of text to predict whether a person is a potential lead for education loan.
Computer vision based solution to correct contrast/ brightness in scanned documents to improve performance of document processing pipelines.
Corporate culture defined as "a set of norms and values that are widely shared and strongly held throughout the organization" is an important aspect of employees’ relationship with their organization. The most important corporate cultural categories are: Integrity, Teamwork, Innovation, Respect, Quality, Safety, Community, Communication, and Reward. This solution identifies which of these 9 values find mention in employee reviews. This enables organizations to assess whether the values they espouse are currently experienced by their employees in the workplace, and track changes in corporate values over time.
Customer segmentation aggregates the data captured in the KYC details and creates customer groups. The KYC data comes in various categories such as Geographic, Demographic, Behavior and Financial variables. This solution leverages machine learning to identify patterns in the data and segment the customers. These segments can be utilized for marketing analytics and campaigns.
This solution provides compositional analysis and predicts the number of incidents pertaining to each ticket group. The insights around incident distribution helps in proper capacity planning, resulting in efficient resource utilization.
A high frequency of issues can generate an overwhelming number of tickets and incorrect delegation to teams to handle them. This leads to a spike in MTTR (mean time taken to resolve) and a dip in FCR (First Call Resolution). The solution mitigates these issues by training a multi-factor ML model that considers factors like ticket impact, urgency, priority, issue description and other features to predict the most relevant group to resolve a ticket. A pool of models is run through data to select the most generalizable model for the ticket classification task.
A high frequency of issues can generate an overwhelming number of application tickets and incorrect delegation to teams to handle them. This leads to a spike in MTTR (mean time taken to resolve) and a dip in FCR (First Call Resolution). The solution mitigates these issues by training a multi-factor ML model that considers factors like ticket impact, urgency, priority, issue description and other features to predict the most relevant group to resolve a ticket. A pool of models is run through data to select the most generalizable model for the ticket classification task
Server Utilization Forecasting enables enterprises to optimize server allocation and utilization by generating 30 days of forward forecast of server usage. This helps enterprises to plan their server allocation strategy across the cloud and on premise scenarios using historical data. It uses ensemble ML algorithms with automatic model selection. This solution performs automated model selection to apply the right model based on the input data, thereby providing consistent and better results
Passenger Traffic Forecasting generates 30 weeks of forward forecast of passengers using historical data. This solution will help businesses such as airlines, railways, bus and ferry operators to better assess the number of incoming passengers and provide them a better travel experience. It uses ensemble ML algorithms with automatic model selection algorithms. This solution provides consistent and better results due to its ensemble learning approach. This solution performs automated model selection to apply the right model based on the input data.
Mphasis Reviews
Ravi B.
Looking for Big data and data engineering projects having spark,hadoop,hive,talend,sql,java implementationsPerfect text correction tool
Verified User in Marketing and Advertising
Simple, Fast, and Reliable Paraphrasing Tool
Tiwari S.
Sr Infrastructure Engineer AWS Certified Solution Architect AssociateHyperGraf Home Loan Lead Review
About
What is Mphasis?
Mphasis Stelligent, with its website located at https://stelligent.com/, specializes in providing DevOps automation and continuous delivery solutions on the Amazon Web Services (AWS) cloud platform. As part of Mphasis, a larger IT services company, Stelligent focuses on helping clients automate and accelerate the development, testing, and deployment of applications within AWS environments. Their suite of services includes consulting, engineering, and automation expertise to implement secure and scalable CI/CD pipelines, facilitating a faster go-to-market strategy for enterprises across various sectors. Stelligent's approach integrates tightly with AWS technologies, offering tools and practices that enhance the cloud capabilities of their customers, ensuring efficient and innovative cloud-based solutions.