Last Updated on by admin

How To Build A predictive Model In Data Science?

Predictive Analytics is an advanced concept in Data Science that is completely changing the business landscape across various industries.  As per the stats, Predictive Modelling experts are also very much in demand, just like the Data Scientists. Many companies are readily hiring skilled & certified Predictive Modelling professionals by offering them exclusively high salary packages.

If you are curious to become a skilled predictive modelling expert in the field of Data Science, then join us for our Kelly Technologies best success-rate Data Science Training In Hyderabad program. 

Now, let’s have a look at what exactly is Predictive Modelling & how to build a predictive model.

Defining Predictive Modelling-

Predictive Analytics can be interpreted as a multidisciplinary technique in the field of data analytics that makes use of advanced Machine Learning, Statistical Algorithms, and other data mining techniques to accurately predict the occurrence of future events by analyzing the historical data.

How To Build A Predictive Model?

The sequence of steps that are involved in the process of building a predictive model is as follows

  • Collecting The Data

In the first step, data is collected from various sources & advanced data mining & web scraping tools can be used for this purpose.

  • Build The Sample Data

In the next step, a model is built based on the sample data. The model is then trained with relevant algorithms on the data that is going to be used to fill in the target variable.

In general, predictive models can be classified into two different types

Classification-This is used to make predictions based on a value that is discrete through the category and finite with no order

Regression-This is used to make predictions based on a value that is continuous through numeric quantity & infinite with ordering

While speaking of algorithms that are to be used in the predictive models, linear regression, and neural network are the ones that are most extensively used as they yield precise & accurate results.

  • Testing & Implementing The Model 

Once the model is trained with the algorithm on the data set, it is then tested by comparing the outcomes of the models with the actual values. If the results are satisfactory, then this predictive model is used on the new incoming data & once the predictions are made, accurate steps are taken accordingly.

As the demand for predictive analytics is prevalent across many fields, this is the right time to master the predictive analytics skills & to become a career-ready Data Science expert by joining for the exclusive Data Science training program offered by Kelly Technologies.

Leave a Reply

Your email address will not be published.