Data Science Training Institute in Noida


Posted December 28, 2019 by jitendraksingh

Apart from in-depth training SkyWebcom also gives 100% placement assistance in top companies.
 
Predictive Analytics Using R

Introduction to Predictive Analytics
It is a procedure that uses historical data, AI and machine learning in order to predict the future. In other words, predictive analytics make predictions about the future by studying the presently available data.

What is the process of predictive analytics?
The past data is nourished into an algorithm or mathematical model and considers the key patterns & trends in the available data. After the consideration of the model, it is then applied to the existing data to predict the possible outcome. Top companies implement or make use of predictive analytics in order to grow the revenue of their business.

PURPOSE OF USING PREDICTIVE ANALYTICS
Analysts use predictive analytics for three main reasons and those reasons are;
• To reduce the risk
• Improve operations
• Increase revenues

Different stages in Predictive Analytics
Predictive analytics involves some kind of business problem that is needed to be solved or a capable data that can be used for analysis and recover the predictions which can be converted into a positive outcome. Predictive analytics includes seven steps that are listed below.

1. Define the problem statement
In order to solve a problem the first thing that needs to be done is to understand the problem correctly. In this stage, the analyst will first identify the objective of the project by identifying the variables that are needed to predict.

2. Data Collection
Once the objective of data is identified it is time to collect all the relevant data and analyst must answer some of the questions in order to achieve the accurate data for example what type of data will be required for the project, where does that data exist, how it can be obtained or what are the ways of accessing the data.

3. Data Cleaning
After the collection of the relevant data, it needs to get cleaned so that the correct prediction can take place. Data cleaning helps in identifying the missing values which result in minimized inconsistency.

4. Data Analysis
Data analyzing is a process of transforming, modeling & inspecting the data. The main agenda of data analysis is to obtain useful information and it is done by evaluating all the available variables in order to have a better understanding of the population, construction, quality and relationship among different variables & data to retrieve the information.

5. Build Predictive Models
Once the information is retrieved next thing that takes place is building the predictive models. In this stage mapping of the data takes place into the machine learning algorithm. Algorithms of machine learning are used to solve a series of the problem be it classification problem, clustering or the regression problem. The model is built by splitting the input randomly for modeling purposes to transform it into the training & the testing dataset.

6. Validate Model
After building a model it is mandatory to validate the precision of the model. It validates how effectively the model predicts the outcome and this is done with the help of testing dataset on a model and the evaluation takes place in order to check the accuracy of the prediction.

7. Deployment
This is the last stage where the models are deployed into production or production-like environment for its final use. Any issue or any dismal is fixed at this stage.
What languages are used for predictive analytics?
Several languages are used for predicting analytics such as Python, R, Matlab, and Java. In this article, we’ll particularly discuss ‘R’ language that why it should be used to predict the analytics. Some of the reasons are;

• R is a programming language that is simple & easy to learn and since it has a flexible & simple syntax.
• For predictive analytics like topic statistical language is really important so another reason for using R is that it is a programming & a statistical language.
• R allows you to shape the dataset into any format which is easy to asses and can be easily analyzed as well and there are many other features of R that tell the importance of R in predictive analytics.

SkyWebcom is the credible IT institute of training that offers comprehensive data science training in Noida on real-time projects with 100% placement assistance. Data science course at SkyWebcom is extensive covering all the major topics. SkyWebcom is probably the best data science training institute in Delhi/NCR.
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Issued By Jitendra Singh
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Categories Blogging , Computers , Education
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Last Updated December 28, 2019