Data-Driven Prediction System

In this process, data related to the examined domain is acquired, and then, based on the standard machine learning process, the following stages are undergone, ultimately allowing the system to intelligently generate required predictions based on the analysis conducted in various domains, such as demand forecasting. The general stages of this process are as follows:

Step One: Problem Definition

The first step in a machine learning project is defining the problem. In this stage, the existing problem, such as price or demand prediction, or any machine learning-related activity, will be defined. Correspondingly, the type of method used to address the problem, whether supervised learning or unsupervised learning, will be determined. Then, precise evaluation criteria and indicators for the project will be defined, and finally, project assumptions will be specified.

Step Two: Data Acquisition

In this stage, the required data for the project must be defined, and available data sources for obtaining this data need to be identified. Topics related to data privacy, data volume, and required information formats should be given careful consideration.

Step Three: Data Preparation and Initial Analysis

In this stage, necessary preprocessing on the data is performed, such as filling in missing data, removing redundant data, and completing necessary information fields in the data. The output of this stage includes data ready to enter the data analysis phase.

In the data analysis phase, tasks such as graphical representation of data, determining correlations between various data, necessary data transformations, and adding required additional data are performed. Ultimately, a comprehensive insight into the available data should be established.

Step Four: Selection of Machine Learning Models

In this stage, the aim is to determine the best machine learning models based on the criteria defined in the first stage. To achieve this, various machine learning models, such as regression-based models, Bayesian models, support vector machines, decision trees, or neural networks, are examined on the available data. Then, considering the models’ errors, the most suitable ones are chosen.

Step Five: System Parameter Tuning

In this stage, after selecting the most suitable model, the model parameters need to be adjusted and specified. Each machine learning model has different parameters that affect its performance. In this stage, the most suitable values for these input parameters must be determined.

Step Six: Deploying and Implementation

In the final stage, the set of models and the finalized system are deployed on the client’s server, and necessary connections with the client’s database are established. This enables the system to perform its function online, based on the required communications with the client’s system, and automatically provide output to the client’s system.