What Is Predictive Modeling?

Predictive modeling uses known results to create, process, and validate a model to forecast future outcomes. It is a tool used in predictive analytics, a data mining technique. Companies may use💝 predictive modeling when creating marketing campaigns to gauge customer responses, and financial analysts may use it to estimate trends and events in the stock market.

Compiling Data

Modern predictive modeling is rumored to have started in the 1940s, with governments using early computers to analyze weather data. As software and hardware capabilities increased, large am🃏ounts of data b▨ecame storable and more easily accessed for analysis.

By analyzing historical events, companies can use predictive modeling to increase the probability of forecasting events, 澳洲幸运5官方开奖结果体彩网:customer behavior, and financial, economic, and market risks. Companies can compile real-time data from social media, internet browsing history, cell phone data, and 澳洲幸运5官方开奖结果体彩网:cloud computing platforms.

Due to the sheer volume of information, companies use predictive modeling tools—often via computer software programs. The programs process vast amounts of historical data to assess and identify patterns. Predictive analytics is a branch of advanced analytics that combines historical data with statistical modeling, data mining techniques, and 澳洲幸运5官方开奖结果体彩网:machine learning.

Classification Models

Classification models use machine learning to place data into categories or classes based on criteria set by a user. There are several types of classifi🃏cation algorithms, some of which are:

• Logistic regression: A binary classification such as a yes or no answer.
• Decision trees: A series of yes/no, if/else, or other binary results placed into a visualization known as a decision tree.
• Random forest: An algorithm that combines unrelated decision trees using classification and regression.
• Neural networks: Machine learning models that review large volumes of data for correlations that emerge only after millions of data points are considered.
• Naïve Bayes: A modeling system based on Bayes' Theorem, which determines conditional probability.

Clustering Models

Clustering is a technique that groups data points. It assumes data in similar groups have the same characteristics and data in different groups have different properties. Some popular clustering a෴lgorithms are:

• K-Means: A modeling technique that uses groups to identify central tendencies of different groups of data.
• Mean-Shift: The algorithm shifts the mean so that "bubbles," or maxima of a density function, are identified. When the points are plotted on a graph, data appear to be grouped around central points called centroids.
• Density-based Spatial Clustering With Noise (DBSCAN): DBSCAN is an algorithm that groups data points together based on an established distance between them. This model establishes relationships between different groups and identifies outliers.

Outlier Models

A dataset always has outliers or values outside its normal val♔ues. In a given set of numbers: 21, 32, 46, 28, 37, and 299, the firs❀t five numbers are similar, but 299 is an outlier. Some algorithms used to identify outliers are:

• Isolation Forest: An algorithm that detects the differing data points in a sample.
• Minimum Covariance Determinant (MCD): Covariance is the relationship of change between two variables. The MCD measures the mean and covariance of a dataset that minimizes the influence outliers have on the data.
• Local Outlier Factor (LOF): An algorithm that identifies the nearest neighboring data points and assigns scores, allowing those furthest away to be identified as outliers.

Time Series Models

Time series model💜ing uses historical data to forecast events. A few🅠 of the common time series models are:

• ARIMA: The autoregressive integrated moving average model uses autoregression, integration (differences between observations), and moving averages to forecast trends or results.
• Moving Average: The moving average uses the average of a specified period, such as 50 or 200 days, which smooths out fluctuations.

How Companies Use Predictive Modeling

A company that knows what to expect based on historical data can better manage inventories, marketing campaigns, and human resources. Companies that take advantage of big data through predictive modeling can better understand how their customers engage with their product⭕s and identify potential risks and opportunities for the company.

Patterns help a company to recognize breaches in security or fraudulent behavior. The insurance and banking industry can use 💙data analytics to monitor risk patterns for loan default or coverage amounts.

Predictive models are evident in 澳洲幸运5官方开奖结果体彩网:artificial intelligence (AI). Neural networks create a web of interconnected nodes in hierarchical levels, representing the foundation for AI, making relationships and patterns between variables that would prove impossible or too time-consuming for human analysts.

Advantages and Disadvantages

The Bottom Line

Predictive mode♌ling is a statistical analysis of data used to generate future scenarios for organizations and companies. It can be used in any industry, enterprise, or endeavor in which data is collected. Predictive modeling is based on historical data and does not guarantee future results.