1) Establish a clear objective. Begin by articulating the business question you aim to address or the problem you intend to resolve. In essence, what is it that you seek to predict? This clarity regarding the project's ultimate goal will shape your data needs and enable your predictive model to produce a practical outcome.
2) Build the right team. While new AI analytics tools make it much easier, you should still consider having the following key players on your team: an executive sponsor, a line-of-business manager, a data wrangler or someone with data management expertise, an IT manager, and a data scientist to build, refine and deploy the models (although AutoML tools now allow data analysts to do this).
3) Collect and integrate your data. This involves assembling the necessary information and organizing your dataset. Conduct data discovery and data exploration to include all relevant factors to ensure a comprehensive perspective and enhance the accuracy of your model. This may encompass well-structured data like sales records and demographic details, as well as unstructured data such as social media posts, customer service records, and web logs.
4) Develop and validate your model. This phase involves constructing, training, assessing, and implementing your predictive model. You have two options: either enlist a data scientist to develop a model, or utilize modern AutoML (automated machine learning) tools, simplifying the process of creating, training, and deploying customized ML models on your own. Furthermore, there are two primary types of algorithmic models: classification and regression, both of which will be elaborated on in the following section. These algorithms ultimately assign a numerical value, weight, or score to indicate the likelihood of a specific event occurring in the future. It will be necessary to test and fine-tune your model iteratively to identify the most proficient performer—one that generates predictions aligning with your expectations.
5) Deploy your model. Ultimately, you'll apply your model to the selected dataset. The outcomes can inform either single-instance or continuous decision-making, and you also have the option to automate actions by incorporating the output into other systems. It's ideal for your model to adapt automatically as fresh data is incorporated over time; this dynamic adjustment enhances the precision of predictions.
6) Monitor and refine your model. Monitor your model's outputs to ensure they align with your anticipated results. Employing explainable AI techniques and processes will help you understand the rationale behind the output of your model. As new variables come into play, be prepared to make necessary adjustments to the model. Additionally, enhancing your model's predictive capabilities can be achieved through the application of data mining techniques like clustering, sampling, and utilizing decision trees on accumulated data over time.