Abstract: A device may obtain first data relating to a machine learning model. The device may pre-process the first data to alter the first data to generate second data. The device may process the second data to select a set of features from the second data. The device may analyze the set of features to evaluate a plurality of types of machine learning models with respect to the set of features. The device may select a particular type of machine learning model for the set of features based on analyzing the set of features to evaluate the plurality of types of machine learning models. The device may tune a set of parameters of the particular type of machine learning model to train the machine learning model. The device may receive third data for prediction. The device may provide a prediction using the particular type of machine learning model.

Abstract: A device may receive work item data identifying work items associated with requirements from different tools of a project and may perform data cleansing to remove and/or modify particular words from the work item data and to generate cleansed work item data. The device may perform natural language processing on the cleansed work item data to identify synonyms for words in the cleansed work item data and may replace abbreviations in the cleansed work item data with full form text to generate final work item data. The device may identify keywords in the final work item data and may process the final work item data, the synonyms, and the keywords, with a machine learning model, to identify mappings between work items of the final work item data and to determine a confidence score for the mappings. The device may perform actions based on the mappings and the confidence score.

Abstract: In some examples, artificial intelligence and machine learning based project management assistance may include ascertaining an inquiry by a user. The inquiry may be related to a project. An attribute associated with the user and an attribute associated with the project may be ascertained. The inquiry may be analyzed based on the ascertained attributes associated with the user and the project. A predictor category may be identified, based on the analyzed inquiry, from a plurality of predictor categories that include a performance predictor category, a quality predictor category, a retrospect predictor category, and a planning predictor category. A predictor from a plurality of predictors may be identified based on the identified predictor category. A response to the inquiry may be generated based on execution of the identified predictor. Further, a display responsive to the inquiry may be generated based on the generated response.

Abstract: A device may obtain first data relating to a machine learning model. The device may pre-process the first data to alter the first data to generate second data. The device may process the second data to select a set of features from the second data. The device may analyze the set of features to evaluate a plurality of types of machine learning models with respect to the set of features. The device may select a particular type of machine learning model for the set of features based on analyzing the set of features to evaluate the plurality of types of machine learning models. The device may tune a set of parameters of the particular type of machine learning model to train the machine learning model. The device may receive third data for prediction. The device may provide a prediction using the particular type of machine learning model.

Abstract: A device may predict, based on historical data relating to a plurality of past projects, a trigger to perform a project health check for a project. The device may process project data relating to the project to determine a health check status of the project based on predicting the trigger to perform the project health check for the project. The device may generate a recommendation relating to altering completion of the project based on the health check status of the project. The device may communicate with one or more devices to provide information identifying the recommendation. The device may receive, from the one or more devices, response information relating to the recommendation. The device may perform a response action relating to the recommendation based on receiving the response information.

Abstract: A device may predict, based on historical data relating to a plurality of past projects, a trigger to perform a project health check for a project. The device may process project data relating to the project to determine a health check status of the project based on predicting the trigger to perform the project health check for the project. The device may generate a recommendation relating to altering completion of the project based on the health check status of the project. The device may communicate with one or more devices to provide information identifying the recommendation. The device may receive, from the one or more devices, response information relating to the recommendation. The device may perform a response action relating to the recommendation based on receiving the response information.

Abstract: In some examples, artificial intelligence and machine learning based project management assistance may include ascertaining an inquiry by a user. The inquiry may be related to a project. An attribute associated with the user and an attribute associated with the project may be ascertained. The inquiry may be analyzed based on the ascertained attributes associated with the user and the project. A predictor category may be identified, based on the analyzed inquiry, from a plurality of predictor categories that include a performance predictor category, a quality predictor category, a retrospect predictor category, and a planning predictor category. A predictor from a plurality of predictors may be identified based on the identified predictor category. A response to the inquiry may be generated based on execution of the identified predictor. Further, a display responsive to the inquiry may be generated based on the generated response.