Utilizing machine learning for optimization of planning and value realization for private networks

A device, comprising:

• one or more memories;
• and
• one or more processors, coupled to the one or more memories, configured to:
• receive network data, business data, and user configuration data associated with an entity that is a candidate for a private network,
• wherein the network data includes data identifying network devices and network characteristics associated with one or more networks currently utilized by the entity,
• wherein the business data includes data identifying costs associated with the one or more networks, and
• wherein the user configuration data includes data identifying characteristics associated with one or more operations of the entity;
• train a classification machine learning model, a first linear regression machine learning model, a second linear regression machine learning model, and an observation machine learning model based on observations;
• process the business data and the user configuration data, with the classification machine learning model, to determine a network hardware equipment prediction for the private network;
• process the network data and the business data, with the first linear regression machine learning model, to determine a business output prediction for the private network,
• wherein the business output prediction includes one or more of:
• a prediction of capital expenditures associated with the private network, or
• a prediction of operational expenditures associated with the private network;
• utilize the second linear regression machine learning model to determine a data consumption prediction for the private network based on the network hardware equipment prediction;
• process the network hardware equipment prediction, the business output prediction, and the data consumption prediction, with the observation machine learning model, to determine a financial profitability prediction for the entity based on deployment of the private network;
• and
• perform one or more actions based on the financial profitability prediction,
• wherein the one or more processors, when performing the one or more actions, are to:
• receive feedback based on the financial profitability prediction;
• and
• retrain, utilizing the feedback as additional training data, one or more of the classification machine learning model, the first linear regression machine learning model, the second linear regression machine learning model, or the observation machine learning model based on the financial profitability prediction.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive network data, business data, and user configuration data associated with an entity that is a candidate for a private network,
• wherein the private network includes one or more of:
• a fourth-generation network,
• a fifth-generation network,
• a citizens broadband radio service network,
• a cloud computing environment, or
• an edge cloud environment;
• train two or more of a classification machine learning model, a first linear regression machine learning model, a second linear regression machine learning model, or an observation machine learning model based on observations;
• process the business data and the user configuration data, with the classification machine learning model, to determine a network hardware equipment prediction for the private network;
• process the network data and the business data, with the first linear regression machine learning model, to determine a business output prediction for the private network,
• wherein the business output prediction includes one or more of:
• a prediction of capital expenditures associated with the private network, or
• a prediction of operational expenditures associated with the private network;
• utilize the second linear regression machine learning model to determine a data consumption prediction for the private network based on the network hardware equipment prediction;
• process the network hardware equipment prediction, the business output prediction, and the data consumption prediction, with the observation machine learning model, to determine a financial profitability prediction for the entity based on deployment of the private network;
• and
• perform one or more actions based on the financial profitability prediction,
• wherein the one or more instructions, that cause the device to perform the one or more actions, cause the device to:
• receive feedback based on the financial profitability prediction, and
• retrain, utilizing the feedback as additional training data, one or more of the classification machine learning model, the first linear regression machine learning model, the second linear regression machine learning model, or the observation machine learning model based on the financial profitability prediction.