Modeling and prediction - the process of building models from data to make predictions or decisions.

Machine learning models are trained using historical data, which is used to identify patterns and relationships in the data. Once the model is trained, it can be used to make predictions on new, unseen data.
By using machine learning to make predictions, organizations can make more informed decisions and optimize their operations for maximum efficiency and profitability.

There are two hot areas where we use AI to improve help businesses using Modeling and predictions. Both require data to be collected and prepared to be valid and accurate (in time). At this moment we do need client help to correctly identify all valid sources like accounting raw data or reports and documents from OneDrive or other storages. This is the input for the next step: modeling and prediction.

• Restocking

  We collect data (historical sales, inventory levels and any other relevant sources) that will be used to create and train the prediction model. Model can be very simple, or complex based on company we analyse.

  We create, train and publish prediction model. Main result a URL where we input data like: historical sales (for example nr. items slod last year/ season/with and with no promotions) inventory level, other companies prices (competitors who sale the same item), item life span, geographical details. Can be more.
  Model return number of items that has to be purchased - on demand request or, we can schedule to have restock plan generated on regular basis.

  We monitor and work with our clients and monitor model performance in order to achieve best results. It is a continuous process of training. System becomes smarter.

• Customer retention

  We collect data (historical purchases at customer/reseller/items level, browsing behavior, and demographics and any other relevant sources). Model can be very simple, or complex based on company we analyse.

  We create, train and publish perdition model. Main result a URL where we input data like
  There are multiple scenarios for example: input customer, seasonal information and items list or input items list and seasonal information and system predict how many items is more items from item list is likely to be sold.

  We monitor and work with our clients and monitor model performance in order to achieve best results. It is a continuous process of training. System becomes smarter.

  This is the most demanding and challenging request because it is lined with other services and linked data: cognitive services (chat bot, qna, sentiment analysis, marketing campaign on twitter, google etc.)

Process of modeling and prediction:

• Data preparation

  Process of cleaning, formatting, and transforming the data into a format that can be used for modeling.

• Feature selection

  Process of identifying the most relevant variables or features that will be used as input for the model.

• Model selection

  Process of selecting the type of model that will be used, such as regression, decision trees, or neural networks.

• Training the model

  Process of feeding the historical data into the model and adjusting the model parameters to optimize its performance.

• Evaluation and testing

  Process of evaluating the model's performance on a separate dataset to ensure that it is accurate and reliable.

• Model and Prediction

  Process of using the trained model to make predictions on new, unseen data.