Big Data Analytics - Problem Definition
Through this tutorial, we will develop a project. Each subsequent chapter in this tutorial deals with a part of the larger project in the mini-project section. This is thought to be an applied tutorial section that will provide exposure to a real-world problem. In this case, we would start with the problem definition of the project.
Project Description
The objective of this project would be to develop a machine learning model to predict the hourly salary of people using their curriculum vitae (CV) text as input.
Using the framework defined above, it is simple to define the problem. We can define X = {x1, x2, …, xn} as the CV’s of users, where each feature can be, in the simplest way possible, the amount of times this word appears. Then the response is real valued, we are trying to predict the hourly salary of individuals in dollars.
These two considerations are enough to conclude that the problem presented can be solved with a supervised regression algorithm.
Problem Definition
Problem Definition is probably one of the most complex and heavily neglected stages in the big data analytics pipeline. In order to define the problem a data product would solve, experience is mandatory. Most data scientist aspirants have little or no experience in this stage.
Most big data problems can be categorized in the following ways −
- Supervised classification
- Supervised regression
- Unsupervised learning
- Learning to rank
Let us now learn more about these four concepts.
Supervised Classification
Given a matrix of features X = {x1, x2, ..., xn} we develop a model M to predict different classes defined as y = {c1, c2, ..., cn}. For example: Given transactional data of customers in an insurance company, it is possible to develop a model that will predict if a client would churn or not. The latter is a binary classification problem, where there are two classes or target variables: churn and not churn.
Other problems involve predicting more than one class, we could be interested in doing digit recognition, therefore the response vector would be defined as: y = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, a-state-of-the-art model would be convolutional neural network and the matrix of features would be defined as the pixels of the image.
Supervised Regression
In this case, the problem definition is rather similar to the previous example; the difference relies on the response. In a regression problem, the response y ∈ ℜ, this means the response is real valued. For example, we can develop a model to predict the hourly salary of individuals given the corpus of their CV.
Unsupervised Learning
Management is often thirsty for new insights. Segmentation models can provide this insight in order for the marketing department to develop products for different segments. A good approach for developing a segmentation model, rather than thinking of algorithms, is to select features that are relevant to the segmentation that is desired.
For example, in a telecommunications company, it is interesting to segment clients by their cellphone usage. This would involve disregarding features that have nothing to do with the segmentation objective and including only those that do. In this case, this would be selecting features as the number of SMS used in a month, the number of inbound and outbound minutes, etc.
Learning to Rank
This problem can be considered as a regression problem, but it has particular characteristics and deserves a separate treatment. The problem involves given a collection of documents we seek to find the most relevant ordering given a query. In order to develop a supervised learning algorithm, it is needed to label how relevant an ordering is, given a query.
It is relevant to note that in order to develop a supervised learning algorithm, it is needed to label the training data. This means that in order to train a model that will, for example, recognize digits from an image, we need to label a significant amount of examples by hand. There are web services that can speed up this process and are commonly used for this task such as amazon mechanical turk. It is proven that learning algorithms improve their performance when provided with more data, so labeling a decent amount of examples is practically mandatory in supervised learning.
