Predictive Modeling

Predictive modeling is the development of models that can forecast future events, trends, or patterns based on historical data. Businesses use these models to make informed decisions for future endeavors.

Businesses use predictive models to detect future risks and promising opportunities. Popular predictive modeling techniques include linear regression, multiple regression, logistic regression, decision trees, random forests, data mining, and neural networks.

• Predictive modeling is a subset of data analytics. A proven model is created which analyzes historical data and current data to forecast future events, anomalies, outcomes, trends, patterns, and behaviors.
• Predictive modeling utilizes various statistical and data science techniques.
• Predictive modeling is extensively applied in business, manufacturing, marketing, insurance, banking, finance, healthcare, retail, and weather forecasts.
• Gathering relevant data is the biggest challenge encountered in predictive modeling.

Predictive modeling is a predictive analysis tool. It is widely used by companies to determine the viability of a new venture, project, or proposal. It applies statistical and analytical tools for analyzing current data and historical data and determines future outcomes.

Businesses employ this tool to forecast earnings, sales, expenses, commercial success, and economic growth. This tool facilitates the identification of future risks and the formulation of corrective measures to limit the damage.

Some of the common predictive models used by analysts are as follows:

1. Clustering Model: This method groups gathered data into clusters based on similar attributes or characteristics. Analysts analyze the behavior of the whole group to determine future outcomes.
2. Classification Model: Analysts classify new data into a similar pre-defined category to predict results. 
3. Outliers Model: In the outlier model, analysts check whether certain data falls outside the usual pattern of behavior to detect frauds and abnormal behaviors.
4. Forecast Model: It is one of the most common predictive analytics models; analysts perform various mathematical calculations and scan through historical records to predict future outcomes.
5. Time Series Model: Time series models look at historical data and identify patterns in the past data to arrive at a point in the future. Since historical data show patterns, it becomes evident that future data must also depict a pattern. A time series is used to crack future patterns; to ensure minimal deviance between calculations and real-world outcomes.

The biggest challenge in predictive analytics is gathering relevant data. It involves extensive analysis of the available information—analysts require advanced skills, adequate experience, and constant learning. It is important to note that forecasts are prone to human error and personal bias.

The following techniques are used in predictive modeling:

1. Linear Regression: When two continuous variables depict a linear relationship, a linear regression can be used to determine the value of the dependent variable—based on the independent variable.
2. Multiple Regression: This is similar to linear regression, except the value of the dependent variable is evaluated by analyzing multiple independent variables.
3. Logistic Regression: It is used for ascertaining dependent variables when the data set is large—requiring categorization.
4. Decision Tree: This method is commonly used for data mining. A flowchart representing an inverted tree is formulated. Here the internal node splits into branches that list out two or more possible decisions, and each decision is further subdivided—to show other possible outcomes. This technique helps in selecting the best option.
5. Random Forest: It is a popular regression and classification model. It is used for solving machine learning algorithms. It comprises multiple decision trees—not correlated to each other. These decision trees collectively facilitate the analysis.
6. Boosting: As the name suggests, this method facilitates learning from the results of other models—decision tree, logistic regression, neural network, and support vector machine.
7. Neural Networks: It is a problem-solving mechanism used in machine learning and artificial intelligence. It develops a set of algorithms for a computational learning system. These algorithms comprise three layers—input, processing, and output.

Let us look at some examples to understand predictive modeling.

#1 - Insurance Sector

Insurance companies use various predictive techniques to evaluate premium values, maximize profits, identify frauds, and improve claim settlement processes. For instance, a vehicular insurance company analyzes vehicles’ conditions and applies various algorithms to determine the applicable premium amount.

#2 - Finance and Banking Industry

Before extending a loan, banks use prediction models to review borrowers’ credit scores—to verify credibility, background, and previous defaults. It helps predict the chances of fraud, misrepresentation, and risks involved with a particular client.

#3 - Marketing and Retail Sector

When a business runs a marketing campaign, it uses predictive modeling techniques to anticipate campaign success. Predictive analysis also gauges target audience and future sales. In the retail sector, predictive analyses provide, forecasts based on which businesses decide the required inventory for each certain product. Projections help decide how much stock volume is required to meet future demands—pertaining to a particular product.

#4 - Weather Forecast

Predictive modeling methods like a decision tree and linear regression forecast weather changes and natural calamities—thunderstorms, cyclones, and tsunamis. These models can ascertain the wind direction and wind speed of storms. Thus, these models are used to alert inhabitants of an area.

This is a guide to what is Predictive Modeling & definition. We explain predictive modeling techniques applied to machine learning, data mining, & other examples. You can learn more about economics from the following articles –

• Statistical Analysis
• Monte Carlo Simulation
• Multiple Linear Regression