Predictive modeling is a type of data mining that is used in a variety of situations and industries. This process involves the creation of statistical models that can make predictions about future events based on historical data.
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Predictive modeling is often used along with other data analysis processes like other types of data exploration, analysis, and mining. Read on to learn about the different types of predictive modeling and how each type can be used most effectively.
Introduction to predictive modeling
As we mentioned in our introduction, predictive modeling focuses on building statistical models that predict future events based on historical data. Predictive modeling can be applied in many industries and used in various applications.
For example, you can use predictive modeling to analyze credit card data to determine if customers are likely to repay their debts. Or you can use predictive modeling to predict whether a machine part will fail due to excessive wear.
Data professionals can also use predictive modeling to explore data for new trends, patterns, and insights. Predictive modeling is used in many fields, including marketing, health, finance, and sports.
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Predictive modeling can be grouped into two broad categories: supervised and unsupervised. Supervised predictive modeling usually starts with a set of training data, also known as a training set or training corpus, labeled or tagged with correct answers. Unsupervised predictive modeling does not have this labeled data. Instead, it involves analyzing the properties of the dataset to uncover hidden patterns with no correct answers.
Different types of predictive models
There are several types of predictive modeling and each model is useful in certain situations. When deciding which model to use, it is important to consider what the model will do, the type of data you have, and the questions you would like it to answer. This ensures that you choose a model that can give you the best results.
Forecasting models are one of the most important types of predictive models. They predict future values based on historical data. Additionally, these models handle metric value predictions by estimating the numerical value of new data based on insights gained from historical data.
Common use cases for forecasting models include forecasting sales, costs, and inventory. Forecasting is a crucial part of business planning because it helps businesses make informed decisions about how to allocate resources.
Forecasts are also useful as they help businesses decide how much of their inventory they need to carry at any given time based on consumer demand. The most common forecasting models are exponential smoothing, autoregressive moving averages, seasonal adjustment, and statistical regression models.
One of the disadvantages of forecasting models is that they can produce inaccurate forecasts if insufficient historical data is used as input data.
A classification model is used to assign classes to data. Classification models are generally easier and more cost effective to implement than continuous value prediction. Examples of these types of models include binary, multi-class, and regression models.
This type of model is ideal for making decisions when the output variable is either categorical (nominal) or ordinal. For example, a lender may wish to use a classification model to determine whether to extend credit to an applicant. Input variables can be factors such as the amount of money in their bank account, their debt to income ratio, and whether they have any outstanding loans.
The output variable could be a yes/no answer: will this person not repay his loan? These patterns can also predict how someone will behave by measuring how they have behaved in the past.
The most common types of classification models are logistic regression, support vector machines, artificial neural networks, linear discriminant analysis, decision trees, K-nearest neighbors, support and naive Bayes classifier models.
An outlier pattern is used to identify abnormal data points that do not fit the pattern of the rest of the data. For example, an outlier pattern can be used to identify incorrect credit card charges or other fraudulent numbers. It would look at the individual data points to determine if they are incorrect compared to the rest of the data.
If a data point looks very different from the rest of the data, it is an outlier. It might seem simple enough to identify these errors without a model, but especially with larger data sets, outlier modeling can help you find unusual data points and predict future issues with those numbers.
There are different types of outliers that outlier models can work with. Here are some of the most common:
- Flattening: When a large number of data points have extreme values.
- Asymmetry: when there are more data points than expected on one side of the distribution.
- Heteroscedasticity: When some groups have more variability in their measurements than others.
- Bimodal distribution: When a graph has two peaks instead of one.
Time series model
A time series model is used to predict future events based on past data ordered in a sequence. It is an econometric technique used to predict future values based on past values. A time series model uses a system’s trends, seasonality and cyclicality, and other factors to predict future behavior.
Time series models are particularly useful for companies that operate on seasonal cycles or other types of cycles. For example, if you have a retail store, you would want to know when your busiest months are so you can allocate more work resources to those times.
The most common type of time series model is the auto-regressive integrated moving average model. ARIMA combines two other models: exponential smoothing and a moving average. Exponential smoothing is used to smooth extreme values in the data, while moving average generates a constant value.
A clustering model is used to identify groups of data points that are very similar to each other. The clustering model is used to group similar items together, which can make things like customer segmentation and finding the best way to market products easier.
An example of a clustering algorithm is k-means, which iteratively assigns observations into clusters until all observations have been assigned or until no observations need to be reassigned . The result is that each observation will be assigned to a group.
Predictive Modeling vs Predictive Analytics
Predictive modeling and predictive analytics are often used interchangeably, but they are different processes used for distinct business purposes.
Predictive modeling uses a statistical model to predict a future event or outcome based on known data. For example, you can use predictive modeling in a marketing campaign by targeting customers who have purchased a certain product in the past and sending them an advertisement for that same product. Predictive modeling almost always has a visual element to help users better understand their data.
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Predictive analytics is the analysis of data to uncover hidden patterns, insights, and opportunities for further research. It refers to a broader set of techniques, including statistical methods and techniques from other fields like machine learning, text mining, social network analysis, and bioinformatics. Predictive analytics generally refers to analyzing historical data about events to make predictions about the future.
Why is predictive modeling used?
Predictive modeling is used in many industries, all for the same purpose: to help organizations make better decisions. This type of model is useful in many business situations where you have a lot of data but no clear answers about what it means for future business processes and performance. In these situations, big data modelers and other data professionals can use predictive models as resources to accurately predict future outcomes.