RADACAD Blog

Business Understanding for Machine Learning – Predictive and Prescriptive Analysis

Facebooktwittergoogle_plusredditpinterestlinkedintumblrmailFacebooktwittergoogle_plusredditpinterestlinkedintumblrmail

 

In the last Post, the explanation about machine learning and what is descriptive analysis has been provided. In this post, I am going to provide some overview of the Predictive and Prescriptive analysis

Predictive Analysis

Another analysis in machine learning in predictive analysis. Predictive analysis is about supervised learning. That means we want to learn from past data to predict the future trends. Predictive analysis forecast future based on some probability. There is no way to predict future with 100% probability. Predictive analysis mainly based on the probability of the statistic. We can classify predictive analysis into two main approaches:

·        Regression

·        Classification

In regression approaches, we predict a value. For instance, we may predict the sales for the next three months, or predict the number of website followers in the next three year.

While classification is about the predicting the group. Classification is about to predict the new item belong to which categories. For instance, in customer churn problem, we are going to predict a new custom belong to a group that may leave us or stays with us.

For both regression and classification, there are many algorithms that able to help us.

For regression algorithm, we have different algorithms such as Linear Regression, Logistic Regression, Polynomial Regression, Stepwise Regression, Ridge Regression, Lasso Regression, and ElasticNet Regression. Moreover, some other techniques such as Neural Network, decision tree and so forth.

 

For classification, there are many approaches such as SVM, Decision tree algorithms, KNN and so forth.Time series Forecasting is a subdomain of the predictive analysis and has some similarity with regression algorithms.  Time series analysis forecast values based on time, time series data may have a trend or/and seasonality. Time series forecasts values based the past data trend, seasonality and existing residuals.

Prescriptive Analysis

The prescriptive analysis is about the recommendation system. Recommendation system predicts the rating or preference users would give to an item. Recommendation system can be categorized into two main class:

·         Content-Based System is the finding similarity between items. Content-based recommendation analyze item descriptions. And recommend an item that has more similarity to the user interest. For instance, we want to recommend new movies to a person, content-based filtering check the similarity of the new movies with other movies that the person already watched.

 

  • Collaborative-filtering recommends items based on how similar users liked the items. Collaborative filtering recommends items based on how similar users liked the item

http://www.informationbuilders.es/sites/www.informationbuilders.com/files/intl/co.uk/presentations/four_types_of_analytics.pdf?redir=true

 https://cartesianfaith.com/2012/02/16/r-for-quants-part-ii-a/

 http://www.fxpal.com/publications/FXPAL-PR-06-383.pdf

 https://blog.dominodatalab.com/recommender-systems-collaborative-filtering/

Facebooktwittergoogle_plusredditpinterestlinkedintumblrmailFacebooktwittergoogle_plusredditpinterestlinkedintumblrmail
rssyoutuberssyoutube
Leila Etaati on LinkedinLeila Etaati on TwitterLeila Etaati on Youtube
Leila Etaati
Trainer, Consultant, Mentor
Leila is the first Microsoft AI MVP in New Zealand and Australia, She has Ph.D. in Information System from the University Of Auckland. She is the Co-director and data scientist in RADACAD Company with more than 100 clients in around the world. She is the co-organizer of Microsoft Business Intelligence and Power BI Use group (meetup) in Auckland with more than 1200 members, She is the co-organizer of three main conferences in Auckland: SQL Saturday Auckland (2015 till now) with more than 400 registrations, Difinity (2017 till now) with more than 200 registrations and Global AI Bootcamp 2018. She is a Data Scientist, BI Consultant, Trainer, and Speaker. She is a well-known International Speakers to many conferences such as Microsoft ignite, SQL pass, Data Platform Summit, SQL Saturday, Power BI world Tour and so forth in Europe, USA, Asia, Australia, and New Zealand. She has over ten years’ experience working with databases and software systems. She was involved in many large-scale projects for big-sized companies. She also AI and Data Platform Microsoft MVP. Leila is an active Technical Microsoft AI blogger for RADACAD.

Leave a Reply

Your email address will not be published. Required fields are marked *