# Predictive Models: A Step-By-Step Guide with 5 Easy Points

## Introduction

Are you new to the business environment? You will most likely come across this word ‘predictive models’ sooner or later in your business proceedings. Predictive analysis helps to make your business efficient and work smoothly. It is very useful to forecast future events and convert threats into opportunities. It is a form of data-mining technology. Read on to explore what is a predictive model and the various types of predictive models.

## 1) What is a predictive model/predictive modelling?

A commonly used statistical technique that is helpful to predict future events or behaviour is known as the predictive models. It is also called predictive analysis. It seeks to forecast future outcomes or events by analysing different patterns. Predictive modelling basically predicts which event is the most likely to happen in the future based on past events. Once data has been gathered, the analyst, using historical data trains and selects statistical models. Predictive modelling is a tool used in the data-mining technique ‘predictive analytics’.

To define predictive modelling – It is the process of using familiar results to generate, process, and validate a model that is used to forecast future events and outcomes. Regression and neural networks are two of the most widely used predictive modelling techniques. Other techniques include time series data mining, decision trees, and Bayesian analysis.

## 2) What are the different types of predictive models?

Now let’s discuss the types of predictive models. Broadly speaking the predictive models fall into two categories: parametric and non-parametric. The different types of predictive models include:

• Ordinary least squares
• Generalized linear models
• Logistic regression
• Random forests
• Decision trees
• Neural networks
• Multivariate Adaptive Regression Splines (MARS)

Each type of predictive model has a specific use and answers a particular question or uses a specific database set. A model can be used more than once and is created by the process of training an algorithm by using historical data and saving it to reuse to analyse results. Algorithms perform statistical analysis and data mining to determine patterns and trends in data. Here are the various predictive models’ examples with its types:

1. Time series algorithms: These algorithms perform predictions based on time. Examples include double exponential smoothing, single exponential smoothing, and triple exponential smoothing.
2. Regression algorithms: These algorithms predict continuous variables which are based on other variables present in the data set. Examples of such algorithms include geometric regression, linear regression, multiple linear regression, exponential regression, and logarithmic regression.
3. Association algorithms: These algorithms find the patterns occurring frequently in large transactional datasets for generating association rules. For example Apriori.
4. Clustering algorithms: Algorithms which group observations into similar groups are known as clustering algorithms. Examples include K-Means, Kohonen and Two Step.
5. Decision Trees Algorithms: These algorithms predict and classify one or more discrete variables which are based on other variables in the data set. CNR Tree is an example.
6. Outlier Detection Algorithms: The outlying values are detected in the data sets. Examples include Inter Quartile Range and nearest neighbour outlier.
7. Neural Network algorithms: This includes classification, forecasting and statistical pattern recognition. NNett Neural Network and MONMLP neural network are some of the examples.
8. Factor analysis: This algorithm deals with correlated variables in terms of a lower number of variables unobserved called factors. An example is the maximum likelihood algorithm.

## 3) Predictive modelling process

The process involves running algorithms on the data set in which the prediction is going to take place. The process involves training the model, multiple models being used on the same data set and finally arriving on the model which is the best fit based on the business data understanding. The predictive models’ category includes predictive, descriptive, and decision models.

The predictive modelling process goes as follows:

1. Pre-processing.
2. Data mining.
3. Results validation.
5. Prepare data.
6. Model data.
7. Evaluation.
8. Deployment.
9. Monitor & improve.

## 4) Features of predictive modelling:

• Data analysis & manipulation: Create new data sets, tools for data analysis, categorize, club, merge and filter data sets.
• Visualization: This includes interactive graphics and reports.
• Statistics: To confirm and create relationships between variables in the data.
• Hypothesis testing: Creating models, evaluating and choosing the right models.

## 5) How to make a predictive model?

The following steps must be understood to know how to build a predictive model?

• The first step is to clean up all the data by eliminating outliers and treating missing data.
• Determine whether non-parametric or parametric predictive modelling is more effective.
• Reprocess the data into an appropriate format for modelling algorithm.
• Specify a subset of the data which is to be used for training of the model.
• Train the model parameters to form the trained data-set.
• Conduct tests to assess model efficacy.
• Validate the accuracy of predictive modelling on the data which is not calibrated.
• Send the model for prediction.

Whether you are competing in a competition or predicting data in an office setting it is important to test out different models to choose the most suitable one and the best fit for the data you are working with. Some of the best predictive models are Logistic Regression, Random Forest, Ridge regression, K-nearest neighbours, and XGBoost.

## Conclusion

Predictive analysis uses predicators (known features) to create predictive models using in obtaining future outputs. There are many applications of predictive modelling be it healthcare insurance or finance. Predictive modelling is associated with meteorology throughout a wide variety of disciplines. The benefits of predictive models include demand forecasting, workforce planning and churn analysis, forecasting of external factors, analysis of competitors, equipment or fleet maintenance, modelling credit or other financial risks. The future of predictive models is undoubtedly closely related to artificial intelligence.

Above all these benefits predictive analytics suffers a few disadvantages like data labelling, obtaining massive training data sets, the explainability problem, the generalizability of learning, and bias in data and algorithms. Some of the predictive modelling tools include Apache Hadoop, R, and Python. Predictive models analyse past performances to assess customer’s likeliness to exhibit a specific behaviour in the future. Companies must take advantage of big data through predictive models to understand their customer’s engagement with their products and identify potential risks and opportunities of the company.

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