Predictive analysis model helps in improving the effectiveness of an organisation and driving successful outcome in an enterprise with the help of data, statistics, and machine learning techniques. In this article, we list simple steps that can help you to understand and build a successful predictive analysis model.
One should have a clear objective for building a predictive analysis model. There are several objectives such as risk and fraud management, forecast revenue, financial modelling, social media influencers, manage marketing campaigns, operational efficiency and many more, the only thing is we need to choose accordingly. It is very crucial to define the goals based on the objectives.
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The model is built to identify problems of an organisation. The result gained from analysis is used to guide the operational workers and managers in order to solve the issues in any organisation.
This involves working on the process of improvement opportunities. It is important for a data scientist to assess the particular process that needs amendments to execute the result of a model.
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Measuring performance may result as the key to gain the targets in an organisation. A good performance metrics yields outcomes that measure the quantities for improvement to an overall organisational goal. In case a metric shows that the action taken is not beneficial, a different approach can be taken to fulfill the needs of a target.
Data selection needs a good understanding of the objective of business for target modelling. There are three types of data available for modelling: demographic, behavioural and psychographic. The preparation of data for analysis into the correct format is a very crucial part. The model needs to be trained using the previous data and for that, the data may need to be clean up. The variables should be well-defined and multiple datasets can also be merged.
This process is used to structure, plan and control the process of developing a system in an organisation. There are several development methodologies that an organisation can opt for such as agile software development, dynamic systems development model, feature-driven development, rapid application development, systems development life cycle, etc. These methods are mostly used for minimising the risks by developing software in short iterations where at each end of the iterations, the working team evaluates their project priorities.
This technique is mainly used to select, manipulate and analyse a subset of data points in order to identify patterns and trends in the dataset. The traditional method of data sampling is to split the data into training and test sets. The larger amount of data is directed to the training set to build the required model and the rest of the data are implied as the test set in order to verify the outcome of the model. It helps in building and executing the outcome of a model in an efficient and quicker way.
Implementation of data governance model helps an organisation to be assured of the quality and consistency of the data used for analytics. It can also be called the foundational component of any strong data management plan because the performance and efficiency can be improved by the efforts of organisational governance.
After the model is developed and validated, it is important to implement the model within a system. There are several systems for model implementation such as account management systems, decision-making systems, customer relationship management systems, analytics platforms, collection systems, etc.
In order to build a robust model, a data scientist should just not stop by implying one or two algorithms, rather it should run as many algorithms that are possible for the model. Then the outcome of the overall results of the models should be chosen in order to get efficient outcomes in an organisation.
After building, the model needs to be deployed because it helps to get the analytical results in the decision making process. There are mainly three approaches to deployment. They are mentioned below
- Scoring the model for operational effectiveness
- Integrate with reporting for collaboration and consultation
- Integrate with the application for operational business