Predictive Modeling Notes
In these “Predictive Modeling “, we will introduce Predictive Modeling concepts and that is apply analytical skills and problem solving tools to analyze business decision problems. These notes focus on Predictive Modeling using rich tool set of for pattern discovery such as segmentation, association, and sequence analyses and predictive modeling such as decision tree, regression, and neural network models.
Therefore, We are providing Predictive Modeling Notes for MBA, BBA and CSE branch for any university students. In other word these type of notes will increase knowledge about the subject and to get good score marks in the exam. Any students can easily view and refere this notes from my sites infokhajana.com.
Predictive Modeling Notes
The topics we will cover in these Predictive Modeling Notes will be taken from the following course contents:
Introduction and Accessing and Assaying Prepared Data: Introduction to Applied and Advanced Analytics, creating project, library, and diagram, defining a data source, exploring a data source.
Decision Trees: cultivating decision trees, optimizing the complexity of decision trees, understanding additional diagnostic tools, autonomous tree growth options
Regressions: selecting regression inputs, optimizing regression complexity, interpreting regression models, transforming inputs, categorical inputs, polynomial regressions
Neural Networks and Other Modeling Tools: introduction to neural network models, input selection stopped training, other modeling tools.
Model Assessment and Implementation: model fit statistics, statistical graphics; adjusting for separate sampling, profit matrices, internally scored data set, score code modules.
Introduction to Pattern Discovery: cluster analysis, market basket analysis.
Special Topics & Case Study: ensemble models, variable selection, categorical input consolidation, surrogate models, Case Studies based onsegmenting, association analysis, simple credit risk model, predicting college admission to management institute shall be developed.
Software Requirements: R
TEXT READINGS
1.Applied Analytics using E-Miner, Global Courseware, Latest Edition
2.Olivia Parr-Rud, Business Analytics Using Enterprise Guide and Enterprise Miner A Beginner’s Guide.Latest Edition.
3.Predictive Analytics and Data Optimization Hardcover MickBenson (Editor) will ford press. Latest Edition
4.Mastering Predictive Analytics with R Paperback -import. RuiMiguelForte PACKT publishing, Latest Edition.
Predictive Modeling Notes FAQs
What is a Predictive Modelling?
- Predictive modeling is the process of using data models to find out known results to create, process, and validate that can be used to make future predictions.
- In this case three technique are used in predictive modeling firstly regression, secoundly decision tree and thirdly neural networks.
- As you know that Companies can use predictive modeling to find forecast events, customer behavior, as well as financial, economic, and stock market.
What are the Benefits of Predictive Modeling?
At its core, predictive modeling significantly reduces the costs required for companies to forecast business outcomes, environmental factors, competitive intelligence, and market conditions. Here we are using some methods that can provide value using predictive modeling:
1. demand forecasting
2. Workforce planning and brainstorm analysis
3. External factors forecast Analysis of competitors
4. Fleet or equipment maintenance
6. Modeling credits or other financial risk