Predictive modelling is a statistical and mathematical approach that assists in projecting future trends or behaviour. It involves an analysis of patterns in historical and current data, to determine probable outcomes. For instance, the prediction of future commodity prices, such as gold or shifts in stock prices. By employing the valuable insights provided by predictive modelling, organisations can employ it as a tool to make informed choices regarding their operations, and gain a competitive edge in their industry.
Predictive modelling aims to forecast future trends and can also be applied to anticipate specific outcomes by analysing patterns in data. This modelling technique is useful for determining the likelihood of an email being classified as spam or a transaction being fraudulent. Although the significant event has already taken place, the model is still applied to assess the current situation.
This blog post will delve deeper into predictive modelling, assess various modelling techniques, and provide insights on how to determine the most appropriate method for a specific problem. Let us embark on a journey of exploration.
What is the importance of utilising predictive models?
Predictive modelling is an effective business tool, providing essential insights to identify recurring trends and make informed decisions. The modelling process typically involves three essential stages: data collection, statistical model training, and validation of the model. The first stage involves collecting the necessary data to build the model. A statistical model is then trained using the collected data, and the resulting model can be used to predict outcomes and recognise recurring trends. Finally, the model is validated to ensure that it is reliable and accurate. Utilising the insights provided by predictive modelling, businesses can gain a competitive advantage and deliver superior customer experience.
Predictive modelling has various applications, as outlined below.
- Supply chain managers use predictive modelling to foresee future supply, demand, and expenditure.
- Insurance companies use predictive modelling to evaluate the risks that policyholders face after an automobile accident, and also to calculate personalised insurance rates based on an individual’s risk profile. By collecting data on variables such as credit scores, driving history, and automobile type, predictive modelling can assist insurance companies in determining the likelihood of an individual filing a claim, as well as estimating the potential cost of the claim. In this manner, predictive modelling can optimise insurance rates for both the insurer and the insured.
- Predictive modelling is an effective tool for fraud detection systems, which highlights high-risk transactions that could potentially be fraudulent. Additionally, it can offer customised, hassle-free customer service experience, aimed at maintaining high-value consumers. This approach helps businesses to prioritise their most profitable clients and guarantee their satisfaction.
Understanding the “Predictive Modelling Pipeline”
To develop a fully functional predictive model, it is necessary to adhere to a standard procedure. The process involves eight defined steps to ensure a successful outcome:
Comprehending business goals is essential for attaining them.
Our first priority is to comprehend the company’s goals and the resources needed to accomplish them. We aim to recognise the customers they serve, their target audience, and the methods they use to engage with them. With this insight, we can correctly identify the problem and offer an effective resolution.
Defining Model Objectives
This stage involves formulating a problem statement to be addressed through predictive modelling, expressed in terms of predictive analytics. We also select the metrics that will be employed to evaluate the model’s efficacy.
Once we have established an objective and defined the challenge at hand, the following step is to collect the pertinent data and create the dataset.
Preparing the Data
We accumulate large volumes of raw and unstructured data, which requires cleansing prior to constructing a more precise and dependable prediction model. This prepares us to produce more accurate results.
Sorting and Processing Information
We need to perform a statistical analysis of the collected data, which entails identifying the dependent and independent variables. Prior to inputting data into the model, we complete any necessary processing, such as filling in any missing values, and identifying numeric and categorical variables.
Selecting a Prototype
Once we have correctly identified the problem and obtained the essential information, the subsequent step is to select the most suitable model type, such as regression, classification, forecasting or other comparable techniques. You can find examples of these model types here.
Developing and Testing the Methodology
This stage marks the inception of predictive modelling. We commence by training the selected model using the pre-processed dataset and evaluate its performance on an independent validation dataset. To create the validation dataset, we apply various cross-validation techniques such as k-fold, stratified k-fold, and others.
Implementation and Improvement
To validate and improve the accuracy and dependability of the trained model, we employ various testing and validation datasets to refine and optimise its performance metrics. After the model has been appropriately fine-tuned, it is ready for deployment in a production environment, where it can be utilised to evaluate and analyse real-world data.
In a wide variety of applications, modellers have access to an extensive selection of algorithmic approaches and procedural tools. However, a few models stand out as the most influential:
Using a categorisation model, data can be sorted into distinct groups or classes. This classification method is typically used to tackle issues like identifying spam emails and detecting fraudulent financial transactions.
Clustering models constitute a type of unsupervised predictive analytics through which data samples are categorised based on their shared characteristics or patterns of activity. By plotting new data samples against pre-established clusters, businesses can recognise their behaviour or class. This process is particularly helpful in identifying emerging trends and detecting anomalies in extensive data sets.
Using predictive analytics, it is possible to evaluate an applicant’s credit risk by scrutinising historical trends. Moreover, retailers can leverage demographic data to understand their customers’ shopping behaviours and product preferences.
Using past numerical data such as stock prices, commodities prices, and real estate value movements, it is practical to anticipate future financial values. For instance, raw material manufacturing can be forecasted utilizing data from prior orders and supply chains.
To function optimally, the outlier model searches for data points that differ significantly from the standard. This is useful in detecting uncommon patterns or activities. A practical use-case of this model is identifying suspicious transactions based on their deviation from the expected behaviour.
This article has presented an outline of some of the most prevalent categories of predictive models. Next, we will delve into some of the typical training strategies and approaches that can be employed for these models. It is worth noting that distinct models may necessitate different training strategies. Therefore, to maximise the accuracy of your predictions, it is essential to familiarize yourself with the details of the model you are using.
Popular Predictive Analytics Algorithms
Most predictive modelling algorithms are constructed using the fundamentals of machine learning (ML) and deep learning (DL). Although both of these technologies fall under artificial intelligence (AI), they serve different purposes. ML is well-suited to work with organised data such as tabular or numerical datasets while DL can leverage neural networks to work with unstructured data of various types, including text, images, and videos.
The following are some examples of frequently used algorithms in predictive modelling:
In order to analyse large volumes of data, a random forest model employs a forest of decision trees. It can conduct regression and classification analyses.
Gradient-boosted techniques such as XGBoost and CatBoost are effective in handling structured data. These models are comparable to random forests in that they employ a set of interconnected decision trees to enhance the accuracy of their forecasts. By utilising these algorithms, data scientists can leverage the capabilities of ensembles to accurately classify and predict data.
K-means is a clustering model used to group data with similar characteristics together. This model can be applied in numerous ways, such as an anomaly detector or recommendation engine. With the help of this model, businesses can detect anomalies in their data and create more efficient recommendation systems. The model can also identify patterns in data that can help businesses better understand their customers and optimise their targeting strategies.
Generalised Linear Models with Two Independent Variables
The Generalised Linear Model (GLM) is an advanced technique used to enhance the traditional linear regression methodology. This approach simplifies the process of determining the line that best describes the relationship between two or more variables by decreasing the number of independent variables to be considered.
Artificial Neural Network (ANN)
Artificial Neural Networks (ANNs) are among the most powerful algorithms available in the field of predictive analytics. It should be noted, however, that for a neural network to accurately recognise and calculate patterns, a large amount of data must be input.
This article introduced the concept of predictive modelling, its applications, and its significance in the modern world. Additionally, various models and algorithms employed in predictive modelling were explored. Considering the fact that 2.5 quintillion bytes of data are generated every day, it is clear that predictive modelling is critical in comprehending the vast amount of data in the Information Age.