Getting Started with Predictive Analytics

Traditional analytical tools have been supporting businesses for many years now, but with increased ability to collect and store data, learning from past behavior is not enough. It is now imperative for businesses to have a forward-looking approach.

This is where predictive analytics comes in!

What really is predictive analytics? Let’s have a look!

Predictive analytics is an information extraction and derivation process. It doesn’t define what will happen in the near future but helps you identify the pattern of behavior. Predictive analytics can be leveraged to make decisions as more often than not the trends gathered through predictive reports do not change very quickly. Predictive analytics also helps in fair assessment of risk and helps you prepare for eventualities.

Different predictive models:

Naïve Bayes Classifier

Naive Bayes Classifier is a machine learning algorithm. It takes advantage of Bayes Theorem and probability theory to predict the category of a sample.

Decision Trees

Decision tree builds classification or regression models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes.

Logistic Regression

Logistic Regression is a classification algorithm. It is used to predict a binary outcome (1 / 0, Yes / No, True / False) given a set of independent variables. To represent binary / categorical outcome, we use dummy variables. It is a special case of linear regression when the outcome variable is categorical, where we are using log of odds as dependent variable. It predicts the probability of occurrence of an event by fitting data to a logit function.

Artificial Neural Networks

Artificial Neural Networks are commonly applied machine learning algorithms that are based on biological neural networks. The goal of these networks is to solve problems the same way as a human brain would.

K-Means Clustering

K-means clustering is a type of unsupervised learning, which is used when a user has unlabelled data (i.e., data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K. The algorithm works iteratively to assign each data point to one of K groups based on the features that are provided. Data points are clustered based on feature similarity.

Advantages of predictive analytics:

Generate more Revenue:
Using predictive analytics, businesses can keep a close watch on potential customers and their buying patterns, in turn offering the right people, the right product(s)/service(s) at the right time. Get a 360-degree understanding of users:

With true analytics reports, a business can understand their users and influence them with a greater probability of success. The market today is customer oriented and higher the level of customer satisfaction, better the retention. Prioritize your work:

Based on predictive reports, businesses can enhance their planning process and prioritize work according to the relevant trends. Detect Fraud:

Predictive analytics is the key to detecting frauds which are likely to occur. Unusual behavior or trends can be given timely attention and loss can be prevented.

To learn more about predictive analytics, meet us at Alteryx Inspire 2018.