Predictive Modelling Training Online Course

Predictive modelling is the process of creating, testing and validating a model. It uses statistics to predict the outcomes. Predictive modelling has different methods like machine learning, artificial intelligence and others. This model is made up of number of predictors which are likely to affect the future results. Predictive modelling is most widely used in information technology.

Uses of Predictive Modelling

Predictive modelling is the most commonly used statistical technique to predict the future behaviour. Predictive modelling analyses the past performance to predict the future behaviour.

Section 1: Introduction

What is Predictive Modelling

Predictive analytics is an emerging strategy across many business sectors and they are used to improve the performance of the companies. Predictive modelling is a part of predictive analytics which is used to create a statistical model to predict the future behaviour. The predictive modelling can be used on any type of event regardless of its occurrence. The predictive model to be used for a particular situation is often selected on the basis of the detection theory. This chapter includes an overview of predictive analytics and predictive modelling. This chapter also includes examples of predictive modelling.

• Pre Processing
• Data Mining
• Results validation
• Understand business and data
• Prepare data
• Model data
• Evaluation
• Deployment
• Monitor and improve

Section 2: Variables

Types of Variables

There are different types of variables in predictive modelling. They are Predicator Variable, Numeric variable, ID variable, Factor variable, categorical variable, extraneous variable, confounding variable and Target Variable.

Difference Between Variables

The difference between each variables is explained in this chapter.

Other Types – Extraneous Variables

Section 3: Steps Included

How to Build a Predictive Model Steps

This chapter contains the basic seven steps involved in predictive modelling

• Defining the objective – This section deals with the ways to define the objective of predictive models with relation to the goals of the business.
• Gathering the data -Collecting data from various sources is another important step in building predictive model. Examples are provided for collection of different types of data from various sources.
• Preparing the data for modelling – This section deals with the segregation of data and how determines how they can be used in predictive modelling.
• Selecting and transforming the variables – This topic deals with the steps for transformation of independent variables to best fit the dependent variable.
• Processing and evaluating the model – In this chapter you will go through several methods of processing and evaluating the model
• Validating the model – The predictive models should perform well on the data. This chapter deals with three powerful methods for ensuring the model fit.
• Implementing and maintaining the model – Effective implementation of Predictive model is another important step. This chapter discusses various auditing procedures and model maintenance practices

Algorithms

Algorithms perform data mining and statistical analysis to find out the trends in data. The predictive analytics has few built in algorithms like regression, time series, outliers, decision trees, k means and neural network.

Forecasting Methods

The forecasting methods used depend mainly on the type of data available. There are different methods of forecasting which are discussed in detail under this lesson

• Qualitative Forecasting
• Quantitative forecasting
• Cross sectional forecasting
• Time Series Forecasting

Section 4: Smoothing Methods

Smoothing Methods – Moving Averages

In moving average smoothing each observation is assigned an equal weight and each observation is forecasted using the average of the previous observations. This method is useful when the item to be forecasted remains unchanged over time. The formula for moving average method is also explained in this chapter.

Smoothing Methods – Double Exponential Smoothing

Exponential smoothing is one of the successful and most widely used forecasting methods. The forecasts that are produced using exponential smoothing methods are weighted averages of the previous observations. There are two types of exponential smoothing – Simple Exponential Smoothing and Double Exponential Smoothing.

Section 5: Regression Algorithms

Regression Algorithms – Exponential

There are different types of statistical, data mining and machine learning algorithms in Predictive Modeling. Each algorithm is used to address the specific needs of the business concern. So choosing the right algorithm for your business is a great task. Regression algorithm is one among them. Regression algorithm is used to forecast continuous data like credit scoring or predicting the next outcome of a time based event. For example regression algorithm can be used to predict the trend of a stock movement with its past prices.

• Linear Regression
• Logistic Regression
• Polynomial Regression
• Stepwise Regression
• Ridge Regression
• Lasso Regression
• ElasticNet Regression

This chapter also lets you learn how to select the right regression model for your business.

Section 6: Clustering Algorithms

Clustering Algorithms – Definition

Clustering can be defined as division of data into groups with similar objects. The clustering technique is not used to predict the value of the target variable in clustering. The clustering algorithm is used to segment the whole data into homogeneous clusters.

Clustering Algorithms – Fuzzy C Means Clustering

Fuzzy C Means Clustering is one of the widely used clustering algorithm. This is a method of clustering which lets a single data to be present in two or more clusters. This algorithm is mostly used in pattern recognition. This algorithm is used for analysis based on distance between various input data points. The formula for Fuzzy C Means Clustering is explained in this chapter. Here you will also learn about the algorithm steps of Fuzzy C Means Clustering. This chapter also contains the comparative study of K means Cluster algorithm and Fuzzy C Means Cluster algorithm.

Section 7: Neural Network Algorithm

Neural Network Algorithm

The neural network algorithm is used for pattern recognition, find out the predictions and learn from the result. An example of neural network is human brain. Neural networks in data mining applies pattern recognition and machine learning algorithms to build predictive models. This chapter explains the two main components of the neural network algorithm – Nodes and Links. Nodes are artificial neurons and Links are the components which connects these nodes. The other topics included in this chapter are

• Kohonen Neural Network
• Steps of algorithm of learning the neural network
• Case studies

Section 8: Support Vector Machines

Support Vector Machines

Support Vector Machine (SVM) is a supervised machine learning algorithm which is used for analyzing data for classification and regression categories. But this algorithm is mostly used in classification problems. The advantages of SVM are it can be effectively used in high dimensional spaces and it is also memory efficient. The SVM is also versatile and different kernels can be used here. The main disadvantage of SVM is that it produces poor performance if the number of features is more than the number of samples. The other topics included in this section are listed below

• Linear Support Vector Machines
• Non Linear SVM
• Basics of Support Vector Machines
• Calculating the SVM classifier
• How is the optimal hyperplane calculated
• How to implement SVM in Python
• Source code and Explanation
• Tuning the parameters of SVM
• Support Vector Regression
• Pros and Cons of Support Vector Machines
• Practice problem and Case Studies

This course is more suitable for students or researchers who are interested in learning about predictive analytics.

Professionals from around the world have benefited from eduCBA’s Predictive Modelling Training courses. Some of the top places that our learners come from include New York, Dubai, San Francisco, Bay Area, New Jersey, Houston, Seattle, Toronto, London, Berlin, UAE, Chicago, UK, Hong Kong, Singapore, Australia, New Zealand, India, Bangalore, New Delhi, Mumbai, Pune, Kolkata, Hyderabad and Gurgaon among many.

FAQ’S General Questions

• What will be the career benefits of Predictive Modelling ?

Due to the huge amount of data present everywhere the importance of analytics is growing abundantly over the last few years. There is a huge demand for predictive modellers and a large number of organisations are looking for persons with predictive modelling skills and experience.

• How Predictive modelling helps the business organisations ?

The Predictive modelling offers a lot of benefits to the organisation. It helps them to improve their business decisions and which in turn will have a huge impact on their business and its profits. It is a measurable impact.

• What skills are needed to deliver predictive modelling ?

To become a successful Predictive modeller you should possess domain knowledge. You should be well educated with the analytics tools and technology like SAS, R, STATA and others. You should also have some basic knowledge statistical and machine learning techniques. If you possess all these characteristics then you will become an expert and you will have a good career start in predictive modelling field.

The pre-requisites for this course includes a basic statistical knowledge and details on software like SPSS or SAS or STATA.

Currently there are no Q&As for this course. Be the first to ask a question.