Mobile Customer Churn Prediction

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Customer churn refers to the loss of existing clients or customers. This solution identifies mobile network subscribers who are more likely to change their operator. 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.

E-commerce Customer Churn Prediction

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Customer churn refers to the loss of existing clients or customers. This solution identifies E-commerce customers who are more likely to stop using the E-commerce app or the portal. 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.

Service Desk Ticket Classification

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A high frequency of issues can generate an overwhelming number of service 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.

Banking Customer Churn Prediction

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Customer churn refers to the loss of existing clients or customers. This solution identifies bank customers who are more likely to close their account and leave the bank. 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.

PACE ML Text Augmentation

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PACE ML Text Augmentation helps in preparing datasets by creating a more comprehensive set of possible data points and their distribution. The module provides a solution to the limited data problem for Natural Language Processing techniques. A combination of transformations and generations are used to increase the size and quality of the training data.

Financial Headlines Sentiment Analyzer

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This solution classifies financial news headlines into positive, negative and neutral sentiments. It uses text analysis, natural language processing, machine learning techniques to predict the sentiment classes. This solution is built around specialized vocabulary encountered in finance and economics. It can be used to identify sentiments of financial headlines and statements from the perspective of potential investors and stakeholders.

Mobile Phone Review Sentiment Analyzer

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The solution analyzes reviews of mobile phones and accessories and classifies them into positive and negative sentiments. It uses text analysis, natural language processing, machine learning techniques to predict the sentiment classes. It can be used to analyze product feedback from customers by predicting the sentiments of reviews.

Absenteeism at Work Forecasting

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Absenteeism at work forecasting generates 30 days of forward forecast of employee absenteeism using historical data. This solution helps businesses to optimize their workforce and related infrastructure in an efficient manner. 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.

Geographical Entity Sentiment Analysis

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Geographical Entity Sentiment Analysis identifies positive, negative or neutral sentiments related to geographical entities such as cities, states, countries etc. Polarity scores are calculated by identifying named entities in text and modeling sentiments to respective entities. This solution can be used to identify sentiments around specific locality or a comparative study between two locations based on different features like property rates, local facilities, proximity with prominent localities etc. This can help user determine location attractiveness for businesses or travel.