Banco Santander Retail Customer Satisfaction

Contributed by Sricharan Maddineni, Wendy Yu, Matt Samelson, and Michael Todisco. They are currently in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between January 11th to April 1st, 2016. This post is based on their final capstone project  (due on the 12th week of the program).

Banco Santander enlisted the help of the Data Science community in a recently sponsored competition on the Kaggle. The competition objective was to build a predictive model to classify satisfied and dissatisfied customers.

The bank provided both a training and test dataset. The training dataset provided an indicator of client satisfaction. Competition participants were asked to use this set to formulate and tune a model to successfully predict satisfaction of clients in a test dataset for which a satisfaction indicator was not provided.

The training data set consisted of 369 anonymized variables and 76,818 observations.

The Data

Little information was available on the variables. Only the data structure and particular hints in variable names (in Spanish no less) provided insight into how best to pre-process data. Matters were complicated in that it was not immediately clear which variables were categorical and which were continuous. Furthermore, some variables were outright useless in that they consisted of a single value for each observation in the dataset. We observed that the data was largely imbalanced by group. The training set contained approximately 73,000 satisfied customers and approximately 3,000 dissatisfied clients.

Our examination suggested that predictors consisting exclusively of integer values were categorical while the remaining variables were continuous. Utilizing this approach, we separated categorical variables and continuous variables. We identified categorical variables as such and applied centering and standardization processes to continuous variables to remove any unwanted effect in subsequent model fitting due to scaling issues. We also checked categorical variables for zero variance to identify and remove those in which only one value was present for all observations (thereby nullifying their predictive value).

Preprocessing

Pre-processing data is essential to proper model performance in many instances.  Many non-parametric models will not perform properly if data is not standardized.  Furthermore, unprocessed data sets may have variables with missingness and/or values that have no predictive value (so-called "zero-variance" factors with only one specified level, etc.).  Variables with clearly  no predictive value are best removed since they consume valuable machine resources when fitting a model.  Accordingly, working with missingness and appropriate standardization is an essential first-step to any modeling endeavor.

The Santander data set did not have issues with missingness.  Values were specified for all variables. That said, some of the values were meaningless (e.g., "-99999" filler values).  The code below depicts the pre-processing steps taken to pre-process the data prior to the commencement of modelling:

 


The Models

We began by applying high accuracy models to the problem to assess the maximum degree of predictability. Our initial efforts included running boosted tree models and deep learning models.

Xgboost - Sricharan Maddineni

Extreme Gradient boosting is a powerful machine learning algorithm that excels in regression, classification and ranking. Xgboost allows us to achieve a simple and predictive model. Gradient boosting works by creating hundreds of tree models that additively produce one highly predictive model. One deficiency of Xgboost is it can only deal with numeric matrices but this was not an issue in the Santander dataset since all observations were numeric. Xgboost algorithm also appreciates sparse model matrices because observations are denoted as 1’s or 0’s and this simplifies the computational requirements.

The key to xgboost is finding the right parameters by defining the objective function to measure the performance of the model. The equation for the objective function is given as follows:

Obj(Θ)=L(Θ)+Ω(Θ)

where L is the training loss function and Ω is the regularization term.

The regularization term controls the model complexity and helps avoid overfitting. For this specific kaggle competition, the AUC was optimized, but it is more common to use the logistic loss.

Xgboost is an ensembling tree model where the prediction scores of each individual tree are summed up to get the final score. Additionally, we get CART scores rather than classification values which lend to more interpretability than classifications. Mathematically this is represented as:

Screen Shot 2016-03-31 at 11.51.40 AM

where K is the number of trees, F is the set of all CARTS, and f is a function in the functional space F.

The objective to optimize function can therefore be written as:

Screen Shot 2016-03-31 at 11.56.10 PM

Creating the sparse model matrix:

Xgboost Model Code:

Making the prediction:

xgbplot5

Cross Validation

Feature Importance from Xgboost:

sant

GBM Model - Mike Todisco

GBM is a predictive modeling algorithm can be used for both classification and regression. In this instance, we used decision trees as a basis, which is the dominant usage, but GBM can take on other forms such as linear. The model is ‘boosted’ in that it algorithmically combines multiple weak models and it is ‘gradient boosted’ in that it iteratively solves the residuals to improve accuracy. GBM is competitive with other high-end algorithms and has reliable performance. We didn’t have any missing data, but GBM is robust enough to handle NA’s. GBM also makes any scaling or normalizing unnecessary.

The GBM package has several loss functions that it can run with. We chose to look at two of the loss functions; Bernoulli and Adaboost. Bernoulli is a logistic loss function for 0’s and 1’s. Adaboost is an exponential loss function for 0’s and 1’s.

There are many parameters to tune in the GBM model. Here are a few of the more important ones:

• Number of trees

• Shrinkage – this is also know as the learning rate, dictating how fast/aggressive the algorithm moves across the loss gradient

• Depth - the number of decisions that each tree will evaluate

• Minimum Observations – dictates the number of observations that must be present to yield a terminal node

• Cv.Folds – the number of cross-validations to run

Adaboost Code:

download

Bernoulli Code:

download (1)

Random Forest - Matt Samelson

We were able to run a random forest model with optimized parameters in the R Caret package using the train function. This packaging function have some beneficial aspects in that they afford powerful analysis and optimization characteristics not found in underlying packages.

In this instance we elected to utilize the model drawing on the R-based ranger package to generate the forest.

To maximize computational resources we elected to tune three parameters: eta, sample by tree, number of trees per round, and maximum tree depth. The range of parameters can be found in the code displayed below.  This was done by means of a grid search in which values were specified for each tuning parameter.  The process ran for an astounding 12 hours before yielding the optimal model.

We also employed five-fold cross validation to further optimize the forest construction. Overall model results can be seen in the summary table located at the end of the blog post.

Random Forrest Code:

Neural Net

Random Hyper Parameter Search Random Parameters:

  • Activation function to be used in the hidden layers
  • The number and size of each hidden layer in the model
  • L1 Regularization: constrains the absolute value of the weights and has the net effect of dropping some weights (setting them to zero) from a model to reduce complexity and avoid overfitting.
  • L2 Regulation: constrains the sum of the squared weights. This method introduces bias into parameter estimates, but frequently produces substantial gains in modeling as estimate variance is reduced.
  • Input Dropout Ratio: A fraction of the features for each training row to be omitted from training in order to improve generalization
  • Hidden Dropout Ratios: A fraction of the inputs for each hidden layer to be omitted from training in order to improve generalization.

Neural Net Code:


 Model Results

Model and Parameter XGBoost Adaboost Neural Net Bernoulli RF
AUC 0.840771 0.839205 0.821 0.820872 0.787

Model results were highly predictive as expected given the use of non-parametric modeling techniques that emphasize accuracy over interpretation.  The project objective was one emphasizing accuracy.

R modeling packages are increasingly improving and providing insight into the construction of complex models.  As indicated above under the factor importance chart easily produced after boosted tree modeling in the XGBoost package, 3, 184, 370, and 168 were responsible for a substantial amount of gain in the boosted tree model and therefore have substantial predictive value.

Santander management will hopefully be able to take the results of this endeavor to implement processing that provides both high-level predictive value in identifying dissatisfied clients as well as garnering insight into the factors that drive dissatisfaction.

 

About Authors

Sricharan Maddineni

Sricharan Maddineni

Sricharan Maddineni was a Neuroscience undergrad at Rutgers university. He is a professional music producer turned Data Scientist who has worked with major artists like Kid Ink, Dj Mustard, BMG and garnered over 18 million plays. He has...
View all posts by Sricharan Maddineni >
Wendy Yu

Wendy Yu

As a biologist, Wendy believes in evidence-base analysis, and is passionate about data. Wendy graduated from the University of Pennsylvania in 2013 with a Masters in Biotechnology. While pursuing a career as a biologist Wendy quickly realized that...
View all posts by Wendy Yu >
Matt Samelson

Matt Samelson

Matt Samelson is a data scientist and leader passionate about "hands-on" problem-solving using statistical analysis, predictive analytics, and visualization. He has a track record of driving incremental business improvements and a background in management, consulting, and quantitative research....
View all posts by Matt Samelson >
Michael Todisco

Michael Todisco

Michael has a B.A. in economics from Johns Hopkins University. Over the last four years he has been in the professional world, working at two NYC start-up companies. First, at JackThreads where he was a data analyst for...
View all posts by Michael Todisco >

Related Articles

Leave a Comment

Avatar
U.S. Bank puts customer first with Salesforce Einstein - Enterprise Times July 24, 2017
[…] of predictive analytics supporting retail banking has become increasingly popular in recent years Banco Santander sponsored a competition in 2016 to see how data science could improve customer relationships. This was later developed further with […]
Avatar
coche September 25, 2016
That is ѵery attention-grabbing, Уou are аn excessively professional blogger. І've joined үour rss feed ɑnd sit up for in the hunt for mоre of your excellent post. Additionally, ӏ've shared үοur web site in my social networks
Avatar
Rajendra May 20, 2016
Sricharan Maddineni , The information is very helpful. Thank you. I did not get any mail notification over this reply. I have some questions : 1) If we apply some Boosting or bagging algorithms using Neural net based model, what can we say about the performance of the model? 2) As I can understand from your code, You have made 20 different random models which will run for " 1 epoch ". If you increase no. of epochs, what will be models performance? Thanks , Rajendra
Avatar
Sricharan Maddineni April 28, 2016
Yeah we are Pandas Nyc Data Science Academy team name.
Avatar
Albert April 20, 2016
Are the results shown here produced by Kaggle after submitting the output file?
Avatar
Sricharan Maddineni April 12, 2016
Rajendra, Thanks for reading! I've added info on our Neural Nets Model, hope this is helpful! Let me know if you have more questions. - Sri
Avatar
Rajendra April 12, 2016
A very informative post. Thanks. I wanted to know the approach for "Neural Net " as well. I could not find in this post. My best result till now is 0.8153 . I am not able to improve it any further using "Neural Net". Thanks
Avatar
Rajendra April 12, 2016
A very informative post. Thanks. but I could not find the "Neural Net" model. I have used "neural Net" for this competition. My best score is 0.815103. After this I am not able to improve it. It would be great if you can share the approach for "Neural net" model too. Thanks , Rajendra

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

2019 airbnb Alex Baransky alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus API Application artist aws beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep Bundles California Cancer Research capstone Career Career Day citibike clustering Coding Course Demo Course Report D3.js data Data Analyst data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization Deep Learning Demo Classes Demo Day Demo Lesson Discount dplyr employer networking feature engineering Finance Financial Data Science Flask gbm Get Hired ggplot2 googleVis Hadoop higgs boson Hiring hiring partner events Hiring Partners Industry Experts Instructor Blog Instructor Interview Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter lasso regression Lead Data Scienctist Lead Data Scientist leaflet Lectures linear regression Live Chat Live Online Bootcamp Logistic Regression machine learning Maps matplotlib Medical Research Meet the team meetup Networking neural network Neural networks New Courses nlp NYC NYC Data Science nyc data science academy NYC Open Data NYCDSA NYCDSA Alumni Online Online Bootcamp Online Lectures Online Training Open Data painter pandas Part-time Portfolio Development prediction Prework Programming PwC python python machine learning python scrapy python web scraping python webscraping Python Workshop R R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking Realtime Interaction recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn Selenium sentiment analysis Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau team TensorFlow Testimonial tf-idf Top Data Science Bootcamp twitter visualization web scraping Weekend Course What to expect word cloud word2vec XGBoost yelp