A lot of Predictive Analytics tools are powered by many Predictive Analytics algorithms, which are applied within a wide range of cases. If you determine the correct technique, you can leverage data to take out insightful decisions. Since Predictive Analytics have helped a lot of companies in various ways, the field is going to grow bigger and better, so if you are looking to take it as a professional career and searching for Online Courses on Data Science in India, Online Predictive Analytics Course, and Online Python Training in India, check out courses offered by Teksands.
Here are the top 5 most popular Predictive Analytics algorithms that you must know about:
Classification model
The classification model is one of the most simple types of Predictive Analytics models, which puts categories based on historical data. This type of algorithm is best suited to answer yes or no questions, and it is helpful for making a decisive decision. Since the model can be easily retrained with new data, the classification model can be used in a lot of different industries.
Clustering model
In the clustering model, data is separated and nested in smaller groups that are based on similar attributes. For example, any company can take all customer data and separate customers into similar groups, which are based on common characteristics using the clustering model. In this way, the company can quickly devise strategies to target each group.
Forecast model
The forecast model of Predictive Analytics deals in estimating numeric value based on learning from past data. Wherever historical numerical data is involved, it’s best to use the forecast model as it also considers several input parameters.
Outliers model
This type of Predictive Analytics algorithm revolves around anomalous data entries within datasets. The unique thing about the outliers model is that it can easily identify anomalous figures in conjunction with other numbers or by themselves. Outliers model is best for the retail and the finance sector as apart from identifying fraudulent transactions, it can also determine purchase history, nature of the purchase, time, location, among others.
Time series model
As the name suggests, the time series model takes time as the input parameter and comprises a sequence of data points captured. This type of Predictive Analytics model uses past year data to create a numerical metric and then predicts the next 3 to 6 weeks using that metric. The Time series model helps understand how a particular metric is behaving over time with accuracy and beyond averages. Since it takes time as the parameter, the model also takes into account any event that can affect the metric.
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