How can Hadoop help a data scientist in predictive analysis?

Hadoop has given Big Data Analytics a new dimension. This open-source platform for big data processing helps in capturing, storing and processing massive amounts of unstructured data. With its innovative contribution to business, the real-time data has gained immense credibility. Hadoop predictive analytics is today’s real-time recommendation for reducing cost and market analysis to improve performance.
Do you want to move up in your Hadoop career? Here’s a list of the Top Hadoop Certifications for 2018. Select one to get certified today!
Hadoop predictive analytics provides advanced analytics that gives you better insight into the customer, potential risks, and product portfolios in the market. It is a competitive advantage that an organization can have in:
Detecting fraud
Optimizing marketing campaigns
Operation improvement
Reducing risk
Data scientists can perform predictive analysis efficiently using Hadoop. Predictive analytics is used in almost all business verticals, including Finance, Banks, Retail, Energy, Manufacturing, and Government.
Let’s now move on to the next section to learn how it happens and what the Hadoop role is in it.
What is Predictive Analytics Model?
Data scientists use input data and different statistical methods to determine the outcome or probability of the output data in predictive analysis models. The target model is the output data.
Predictive analytics employs two types of models:
1. Classification Model
The classification model for predictive analytics predicts class membership. This model can be used to predict whether a member will leave or stay with a group. It is a logical representation, and often represents 0 or 1.
2. Regression Model
Regression models for predictive analysis can predict number through analysis. This model can be used to analyze the revenue potential of a business.
These are some of the most popular predictive modeling techniques:
Decision trees
Regression (logistical and linear)
Neural networks
Bayesian analysis
Ensemble models
Gradient boosting
Partial least squares
Incremental response (also known as net lift or uplift models).
K-nearest neighbour (knn).
Analyse of the principal component
Support vector machine
Memory-based reasoning
Time series data mining
Two important factors should be considered regardless of the model an organization follows:
Predictive analysis requires collaboration between different vendors and in-house personnel. The organization’s intellectual property must be protected.
The company’s predictive analytics model must be kept current and in line with market changes. The model’s competitive advantage may be lost over time.
Are you ready for a Hadoop interview? These are the Top 50 Hadoop Interview Questions & Answers that will help get you the job!
Different stages of the Predictive Analytics Lifecycle
Predictive analytics’ core is the following of its life cycle. The lifecycle of a predictive model includes several stages. It starts with the problem statement and ends with its replacement by another model. These are the stages of predictive analytics.
1. Identify the Problem
This is the first step in understanding the problem.
You will need to do a dry run of the predictive analytics steps necessary to solve the problem.
To determine the purpose of the analysis, i.e. What would be the target model that would be based on the input data?
2. Designing the Data Required
To examine the useful predictions made based on input data.
To create a decision model using the insights from analysis
To take the necessary actions based upon the analysis.
3. Data Pre-processing
This is the most difficult phase of the entire cycle.
Analyzing data requires data from multiple sources, such as sensors, transactional systems, logs, and so forth.
Data management is necessary to clean up and prepare the collected data for analysis.
Data preparation also includes analysis of business problems.
4. Analyzing Data
This is the first stage of predictive analytics.
This step can be done using data analytics tools or manually.
The model is deployed, which means that it starts to work with prepared data.
Give the outcome, which can be results or the predictive modeling over data.
5. Visualization of Data
To give you a better understanding of data, the tool allows you to visualize the output result.
The global Hadoop Market is expanding at a rapid pace. Global Hadoop Market is expected reach $84.6 billion in 2021, according to the trend analysis report.
[divider /]
Hadoop and Big Data Predictive Analytics
Hadoop offers several benefits when it comes to managing the data analytics lifecycle of a predictive model.
Data Sourcing
Hadoop distributed file system HDFS (Hadoop Distributed File System) works as the