Predictive Modelling

Predictive Modeling is entirely related with DARA WAREHOUSING, DATA MINING & PROBABILITY. DATA MINING is basically sorting of data using OLAP from the collected EDW DATABASE and after sorting probability is done and as the term is predictive modeling we have to produce a model of a particular topic with prediction with the sorted data.

According to the figure of predictive modeling it is shown that there are several nodes. Each nodes refers to Data. From all of the nodes we should sort datas and predict what can be the model FOR MARKETING DATA.

How To Make Predictive Model?

  • Descriptive analysis of the data
  • Data treatment
  • Data Modeling
  • Estimation of Performance.

Descriptive Analysis

With advanced technology time taken to perform this task can be significantly reduced by using advanced PREDICT TOOLS. For initial analysis, probably need not do any kind of feature engineering. Hence, the time one might need to do descriptive analysis is restricted to know missing values and big features which are directly visible.

Data Treatment

Since, this is considered to be the most time consuming step, smart techniques are needed to fill in this phase for MANAGEMENT OF DATA. Here are two methods which may be implemented

  • Create dummy flags for missing value(s): In general, missing values in variable also sometimes carry a good amount of information.
  • Impute missing value with mean/any other easiest method

Data Modelling :

In case of bigger data, data modeling as well WAREHOUSE TOOLS is needed as sorting needs to be done as analysis of bigger data is very much harder.

Estimation of Performance

Estimation of performance is needed as we need to finish the predictive model within a given amount of time. BEST DATA will be used for the model.

Flow Chart of Predictive Analysis

This data from different sources needs to be cleaned and arranged in a structure so that it can be analyzed easily. This structure needs to be in sync with various business hypothesis. For example, if business hypothesis is that particular age / gender group may have higher likelihood to purchase certain set of products, Age and Gender needs to be attributed at customer level. Once these data sets are ready, we then use various predictive modeling techniques and business understanding to come out with various business insights (nuggets of gold). These insights can then be used in marketing / web site layout to increase efficiency.

Why is predictive analytics important?

Why is predictive analytics important?

Combining multiple analytics methods can improve DATA MINING CONCEPTS for detecting and prevent criminal behavior. As cybersecurity becomes a growing concern, high-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate fraud, zero-day vulnerabilities and advanced persistent threats.

Optimizing marketing campaigns.

By analysing MINING STATISTICS customer responses are determined, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers.

Improving operations

Many companies use predictive models to forecast inventory and manage resources. Airlines use predictive analytics to set ticket prices. Hotels try to predict the number of guests for any given night to maximize occupancy and increase revenue. Predictive analytics enables organizations to function more efficiently.

Reducing risk.

Credit scores are used to assess a buyer’s likelihood of default for purchases and are a well-known example of predictive analytics. A credit score is a number generated by a predictive model that incorporates all data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims and collections.


  • Faster, cheaper computers.
  • Easier-to-use software.
  • Tougher economic conditions and a need for competitive differentiation.
  • Growing volumes and types of data, and more interest in using data to produce valuable insights.

Client Speak

It usually starts as a “symptom” – the need for something more than traditional DB management tools in your enterprise. There are a standard set of activities which when performed, enable the linking of this “symptom” or a “business challenge” to your Big-data needs.

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