Retail Sales Forecasting

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Retail Sales Forecasting generates 30 months of forward forecast of retail sales using historical data. This solution helps retailers to predict future sales and thereby optimize their inventory, logistics, warehouse management, production planning, personnel allocation, etc. 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.

Topic Modeling of Mortgage Complaints

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Topic Modeling based solution for customer complaints about Mortgage services. The solution clusters customer complaints into topics based on the narratives provided. Given a set of customer complaints narratives, this solution identifies for each complaint the top three issues mentioned in the narrative. The task of identifying what mortgage issue is being mentioned in a customer complaint requires human involvement and the same is being automated with this solution.

Insurance Customer Churn Prediction

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Customer churn refers to the loss of existing clients or customers. This solution identifies insurance customers who are more likely to close/not renew their policies with the insurance provider. During the training stage, the solution automatically conducts feature interaction on the training data and selects a subset of features based on feature importance. It then trains multiple models and identifies the best performing model. This model is then selected for prediction on new data.

COVID-19 News Sentiment Analyzer

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COVID-19 News Headlines Sentiment Analyzer helps businesses to analyze headlines around the pandemic. It helps the users to identify COVID-19 related sentiments based on analysis of news headlines and classify them as positive, negative, neutral and mixed. It determines the sentiment of News headlines by maintaining aspect and polarity associated with COVID-19. This analysis can be used in various scenarios like stock movement predictions, econometric analysis, policy effectiveness and risk analytics.

Cloud Compute Cost Forecasting

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Cloud compute cost forecasting helps businesses assess the cost incurred from their cloud compute resource based on historic data. This will help businesses get an understanding of the potential cost for their VM instances, clusters, snapshots, etc. to help them better plan their compute resources. It uses ensemble ML algorithms with automatic model selection algorithms. This solution performs automated model selection to apply the right model based on the input data.

Energy Consumption Forecasting

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Energy Consumption Forecasting generates 30 months of forward forecast of the consumption using historical data. 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.

IT Helpdesk Ticket Classification

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A high frequency of issues can generate an overwhelming number of help desk 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.

Time Series Web Traffic Forecasting

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Web Traffic Forecasting enables enterprises to optimize web server allocation, scaling for instances, parallelise workload traffic and utilization by generating 30 days of forward forecast of web traffic data. This helps enterprises to plan their IT infrastructure strategy across the cloud and on premise scenarios. It facilitates seamless end user experience and satisfied customers. 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.

Personally Identifiable Info Anonymizer

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This solution identifies and anonymizes Personally Identifiable Information like Name, SSN, Email, Phone numbers from tabular data. The Solution is designed to work on structured data source.