Top 5 Predictive Analytics Models

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The power of predictive analytics is its ability to predict outcomes and trends before they happen. Predicting future events gives organizations the advantage to understand their customers and their business with a better approach. Predictive analytics tools comprise various models and algorithms, with each predictive model designed for a specific purpose.

Identifying the best predictive analytics model for your business is a crucial part of business strategy. For example, you wish to reduce the customer churn for your business. In that case, the predictive analytics model for your company will be different from the prediction model used in the hospitals for analyzing the behavior of the patients after certain medical operations.

With the advancements in technology, data mining, and machine learning tools, several types of predictive analytics models are available to work with. However, some of the top recommended predictive analytics models developers generally use to meet their specific requirements. Let us understand such key predictive models in brief below:

1. Classification Model

The classification models are the most simple and easy to use among all other predictive analytics models available. These models arrange the data in categories based on what they learn from the historical data.

Classification models provide the solution in “yes” and “no” to provide a comprehensive analysis. For instance, these models help to answer questions like:

    Does the user make the correct request?
    Is the vaccine for certain diseases available in the market?
    Will the stocks for the company get raised in the market?

When looking for any decisive answers, the classification model of predictive modeling is the best choice. The classification models are applied in various domains, especially in finance and retail industries, due to their ability to retrain with the new data and provide a comprehensive analysis to answer business questions.

2. Clustering Model

As data collection may have similar types and attributes, the clustering model helps sort data into different groups based on these attributes. This predictive analytics model is the best choice for effective marketing strategies to divide the data into other datasets based on common characteristics.

For instance, if an eCommerce business plans to implement marketing campaigns, it is quite a mess to go through thousands of data records and draw an effective strategy. At the same time, using the clustering model can quickly identify the interested customers to get in touch with by grouping the similar ones based on the common characteristics and their purchasing history.

You can further divide the predictive clustering modeling into two categories: hard clustering and soft clustering. Hard clustering helps to analyze whether the data point belongs to the data cluster or not. However, soft clustering helps to assign the data probability of the data point when joining the group of data.

3. Forecast Model

The forecast model of predictive analytics involves the metric value prediction for analyzing future outcomes. This predictive analytics model helps businesses for estimating the numeric value of new data based on historical data.

The most important advantage of the forecast predictive model is that it also considers multiple input parameters simultaneously. It is why the forecast model is one of the most used predictive analytics models in businesses. For instance, if any clothing company wants to predict the manufacturing stock for the coming month, the model will consider all the factors that could impact the output, such as: Is any festival coming by? What are the weather conditions for the coming month?

You can apply the forecast model wherever the historical numeric data is applicable. For example, a manufacturing company can predict how many products they can produce per hour. At the same time, an insurance company can expect how many people are interested in their monthly policy.

4. Outliers Model

Unlike the classification and forecast model, which works on the historical data, the outliers model of predictive analytics considers the anomalous data entries from the given dataset for predicting future outcomes.

The model can analyze the unusual data either by itself or by combining it with other categories and numbers present. Because the outliers model is widely helpful in industries and domains such as finance and retail, it helps to save thousands and millions of dollars for the organizations.

As the predictive outliner model can analyze the anomalies so effectively, it is highly used to detect fraud and cyber crimes easily and quickly before it occurs. For example, it helps to find unusual behavior during bank transactions, insurance claims, or spam calls in the support systems.

5. Time Series Model

The time series model of predictive analytics is the best choice when considering time as the input parameter to predict future outcomes. This predictive model works with data points drawn from the historical data to develop the numerical metric and predict future trends.

If the business wishes to foresee future changes in their organization or products over a specific time, the time series predictive model is their solution. This model involves the conventional method of finding the process and dependency of various business variables. Also, it considers the extraneous factors and risks that can affect the business at a large scale with passing time.

Talking about the use cases, this predictive analytics model helps identify the expected number of calls for any customer care center for next week. It can also analyze the number of patients admitted to the hospital within the next week.

As you know, growth is not necessary to be linear or static. Therefore, the time series model helps get better exponential growth and alignment for the company’s trend.

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