Press "Enter" to skip to content

Category: Data Science

Understanding The Problem: Churn Edition

Published 2017-04-18 by Kevin Feasel

Emre Yazici points out the importance and difficulty of nailing down good definitions, using bookings churn as an example:

WHEN: Let’s say, we have made our design, constructed a model and obtained a good accuracy. However our model predicts (even with 95% accuracy) the customer who are going to churn in next day! That means our business department have to prevent (somehow, as explained before) those customers to churn in “one day”. Because next day, they will not be our customers. Taking an action to “3000” customers (let’s say) in one day only is impossible. So even our project predicts with very high accuracy, it will not be usefull. This approach also creates another problem: Consider that N months ago, a customer “A” was a happy customer and was working (providing us) with us (let’s say, it is a customer with %100 efficiency – happiness) and tomorrow it will be a customer who is not working with us (a customer with %100 efficiency – happiness). And we can predict the result today. So most probably, the customer has already got the idea to leave from our company in the last day. This is a deadend and we can not prevent the customer to churn at this point – because it is already too late.

So we need to have a certain time limit… Such that we need to be able to warn the business department “M months” before (customer churn) thus they can take action before the customers leave. Here comes another problem, what is the time limit… 2 months, 2.5 months, 3 months…? How do we determine the time, that we need to predict customers churn before (they leave)?

There’s a lot more to a good solution than “I ran a regression against a data set.”

Comments closed

When Binomials Converge

Published 2017-04-18 by Kevin Feasel

Mala Mahadevan shows an example of the central limit theorem in action, as a large enough sample from a binomial distribution approximates the normal:

An easier way to do it is to use the normal distribution, or central limit theorem. My post on the theorem illustrates that a sample will follow normal distribution if the sample size is large enough. We will use that as well as the rules around determining probabilities in a normal distribution, to arrive at the probability in this case.
Problem: I have a group of 100 friends who are smokers.  The probability of a random smoker having lung disease is 0.3. What are chances that a maximum of 35 people wind up with lung disease?

Click through for the example.

Comments closed

Logistic Regression With R

Published 2017-04-17 by Kevin Feasel

Raghavan Madabusi runs through a sample logistic regression:

Input Variables: These variables are called as predictors or independent variables.

  • Customer Demographics (Gender and Senior citizenship)
  • Billing Information (Monthly and Annual charges, Payment method)
  • Product Services (Multiple line, Online security, Streaming TV, Streaming Movies, and so on)
  • Customer relationship variables (Tenure and Contract period)

Output Variables: These variables are called as response or dependent variables. Since the output variable (Churn value) takes the binary form as “0” or “1”, it will be categorized under classification problem in the supervised machine learning.

One of the interesting things in this post was the use of missmap, which is part of Amelia.

Comments closed

Linear Regression In SQL

Published 2017-04-17 by Kevin Feasel

Phil Factor shows how to generate a quick linear regression using SQL, Powershell, and Gnuplot:

It looks a bit like someone has fired a shotgun at a wall but is there a relationship between the two variables? If so, what is it? There seems to be a weak positive linear relationship between the two variables here so we can be fairly confident of plotting a trendline.

Here is the data, and we will proceed to calculate the slope and intercept. We will also calculate the correlation.

It’s good to know that this is possible, but I’d switch to R or Python long before.

Comments closed

Text Normalization With Spark

Published 2017-04-14 by Kevin Feasel

Engineers at Treselle Systems have put together a two-part series on text normalization using Apache Spark.  First, they walk through normalizing the text:

We have used Spark shared variable “broadcast” to achieve distributed caching. Broadcast variables are useful when large datasets need to be cached in executors. “stopwords_en.txt” is not a large dataset but we have used in our use case to make use of that feature.

What are Broadcast Variables?
Broadcast variables in Apache Spark is a mechanism for sharing variables across executors that are meant to be read-only. Without broadcast variables, these variables would be shipped to each executor for every transformation and action, which can cause network overhead. However, with broadcast variables, they are shipped once to all executors and are cached for future reference.

From there, they dig into details on what the Spark engine did and why we see what we do:

Note: Stage 2 has both reduceByKey() and sortByKey() operations and as indicated in job summary “saveAsTextFile()” action triggered Job 2. Do you have any guess whether Stage 2 will be further divided into other stages in Job 2? The answer is: yes Job 2 DAG: This job is triggered due to saveAsTextFile() action operation. The job DAG clearly indicates the list of operations used before the saveAsTextFile() operations.Stage 2 in Job 1 is further divided into another stage as Stage 2. In Stage 2 has both reduceByKey() and sortByKey() operations and both operations can shuffle the data so that Stage 2 in Job 1 is broken down into Stage 4 and Stage 5 in Job 2. There are three stages in this job. But, Stage 3 is skipped. The answer for the skipped stage is provided below “What does “Skipped Stages” mean in Spark?” section.

There’s some good information here if you want to become more familiar with how Spark works.

Comments closed

Measuring Correlation In SQL

Published 2017-04-13 by Kevin Feasel

Phil Factor shows how to calculate Kendall’s Tau and Spearman’s Rho in SQL:

Kendall’s Tau rank correlation is a handy way of determining how correlated two variables are, and whether this is more than chance. If you just want a measure of the correlation then you don’t have to assume very much about the distribution of the variables. Kendall’s Tau is popular with calculating correlations with non-parametric data. Spearman’s Rho is possibly more popular for the purpose, but Kendall’s tau has a distribution with better statistical properties (the sample estimate is close to a population variance) so confidence levels are more reliable, but in general, Kendall’s tau and Spearman’s rank correlation coefficient are very similar. The obvious difference between them is that, for the standard method of calculation,  Spearman’s Rank correlation required ranked data as input, whereas the algorithm to calculate Kendall’s Tau does this for you.  Kendall’s Tau consumes any non-parametric data with equal relish.

Kendall’s Tau is easy to calculate on paper, and makes intuitive sense. It deals with the probabilities of observing the agreeable (concordant) and non-agreeable (discordant) pairs of rankings. All observations are paired with each of the others, A concordant pair is one whose members of one observation are both larger than their respective members of the other paired observation, whereas discordant pairs have numbers that differ in opposite directions. Kendall’s Tau-b takes tied rankings into account.

I appreciate Phil putting this series together.  I’d probably stick with R, but it’s good to have options.

Comments closed

Probabilistic Record Linking In Spark

Published 2017-04-10 by Kevin Feasel

Tom Lous builds a solution to link similar companies together by address:

Recently a colleague asked me to help her with a data problem, that seemed very straightforward at a glance.
She had purchased a small set of data from the chamber of commerce (Kamer van Koophandel: KvK) that contained roughly 50k small sized companies (5–20FTE), which can be hard to find online.
She noticed that many of those companies share the same address, which makes sense, because a lot of those companies tend to cluster in business complexes.

Read on for the solution.  Like many data problems, it turns out to be a lot more complicated than you’d think at first glance.

Comments closed

Understanding Boosted Trees

Published 2017-04-06 by Kevin Feasel

Maria Jesus Alonso explains decision trees and their subsequent improvements:

Bagging (or Bootsrap Aggregating), the second prediction technique brought to the BigML Dashboard and API, uses a collection of trees (rather than a single one), each tree built with a different random subset of the original dataset for each model in the ensemble. Specifically, BigML defaults to a sampling rate of 100% (with replacement) for each model. This means some of the original instances will be repeated and others will be left out. Bagging performs well when a dataset has many noisy features and only one or two are relevant. In those cases, Bagging will be the best option.

Random Decision Forests extend the Bagging technique by only considering a random subset of the input fields at each split of the tree. By adding randomness in this process, Random Decision Forests help avoid overfitting. When there are many useful fields in your dataset, Random Decision Forests are a strong choice.

Click through for how boosted trees change this model a bit.

Comments closed

Using OLS To Fit Rational Functions

Published 2017-04-06 by Kevin Feasel

Srini Kumar and Bob Horton show how to use the lm function to fit functions using the Pade Approximation:

Now we have a form that lm can work with. We just need to specify a set of inputs that are powers of x (as in a traditional polynomial fit), and a set of inputs that are y times powers of x. This may seem like a strange thing to do, because we are making a model where we would need to know the value of y in order to predict y. But the trick here is that we will not try to use the fitted model to predict anything; we will just take the coefficients out and rearrange them in a function. The fit_pade function below takes a dataframe with x and y values, fits an lm model, and returns a function of x that uses the coefficents from the model to predict y:

The lm function does more than just fit straight lines.

Comments closed

The Central Limit Theorem

Published 2017-04-05 by Kevin Feasel

Mala Mahadevan explains the Central Limit Theorem with an example:

The central limit theorem states that the sampling distribution of the mean of any independent,random variable will be normal or nearly normal, if the sample size is large enough. How large is “large enough”? The answer depends on two factors.

  • Requirements for accuracy. The more closely the sampling distribution needs to resemble a normal distribution, the more sample points will be required.
  • The shape of the underlying population. The more closely the original population resembles a normal distribution, the fewer sample points will be required. (from stattrek.com).

The main use of the sampling distribution is to verify the accuracy of many statistics and population they were based upon.

Read on for an example and to see how to calculate this in T-SQL.

Comments closed