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aloknsingh / ds_xgboost_clf_4_imbalance_data

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Predicting Bank Client's Cerificate of Deposit Purchase using Scikit Learn and XGBoost for imbalance dataset

This Code Pattern will guide you through how to use XGBoost, Scikit Learn and Python in IBM Watson Studio. The goal is to use a Jupyter notebook and data from the UCI repository for Bank Marketing Data to predict if a client will purchase a Certificate of Deposit (CD) from a banking institution.

Class imbalance is a common problem in data science, where the number of positive samples are significantly less than the number of negative samples. As data scientists, one would like to solve this problem and create a classifier with good performance. XGBoost (Extreme Gradient Boosting Decision Tree) is very common tool for creating the Machine Learning Models for classification and regression. However, there are various tricks and techniques for creating good classification models using XGBoost for imbalanced data-sets that is non-trivial and the reason for developing this Code Pattern.

In this Code Pattern, we will illustrate how the Machine Learning classification is performed using XGBoost, which is usually a better choice compared to logistic regression and other techniques. We will use a real life data set which is highly imbalanced (i.e the number of positive sample is much less than the number of negative samples).

This Code Pattern will walk the user through the following conceptual steps:

Flow

  1. Log into IBM Watson Studio service.
  2. Upload the data as a data asset into Watson Studio.
  3. Start a notebook in Watson Studio and input the data asset previously created.
  4. Pandas are used to read the data file into a dataframe for initial data exploration.
  5. Use Matplotlib and it's higher level package seaborn for creating various visualizations.
  6. Use Scikit Learn to create our ML pipeline to prep our data to be fed into XGBoost.
  7. Use XGBoost to create and train our ML model.
  8. Evaulate their predictive performance.

Included components

Featured technologies

Steps

This Code Pattern consists of following activities:

Run a Jupyter notebook in the IBM Watson Studio

  1. Sign up for the Watson Studio
  2. Create a new Watson Studio project
  3. Create the notebook
  4. Upload data
  5. Run the notebook
  6. Save and Share

1. Sign up for the Watson Studio

Log in or sign up for IBM's Watson Studio.

Note: if you would prefer to skip the remaining Watson Studio set-up steps and just follow along by viewing the completed Notebook, simply:

2. Create a new Watson Studio project

3. Create the Notebook

/notebooks/predict_band_cd_subs_by_xgboost_clf_for_imbalance_dataset.ipynb

Note: If queried for a Python version, select version 3.5.

4. Upload data

5. Run the notebook

When a notebook is executed, what is actually happening is that each code cell in the notebook is executed, in order, from top to bottom.

Each code cell is selectable and is preceded by a tag in the left margin. The tag format is In [x]:. Depending on the state of the notebook, the x can be:

There are several ways to execute the code cells in your notebook:

6. Save and Share

How to save your work:

Under the File menu, there are several ways to save your notebook:

How to share your work:

You can share your notebook by selecting the Share button located in the top right section of your notebook panel. The end result of this action will be a URL link that will display a “read-only” version of your notebook. You have several options to specify exactly what you want shared from your notebook:

Explore, Analyze and Predict CD Subscription for Bank Client

1. Explore the dataset

The imbalanced dataset is from Purtugese banking institutions, and is based on phone calls to bank clients regarding the purchase of financial products offered by the bank (ie. Certificates of Deposit).

2. Prepare the data

For this section we will mostly use Python based libraries such as XGBoost, Scikit-learn, Matplotlib, SeaBorn, and Pandas.

3. Visual Data Exploration to understand the data using Seaborn and Matplotlib

Data scientists typically perform data exploration to gain better insight into data. Here we will explore inputs for distribution, correlation and outliers, and outputs to note any class imbalance issues.

4. Create Scikit learn ML Pipelines for Data Processing

In typical machine learning applications, an ML pipeline is created so that all the steps that are done on a training data set can be easily applied to the test set.

5. Model Training and evaluation

Model Training is a iterative process and we will do several iterations to improve our model performance.

Using XGBoost as our tool of choice, we will highlight classification performance metrics such as ROC curve, Precision-Recall curve, and Confusion Matrix.

We then offer multiple strategies to improve our classifier performance.

6. Inference Discussion (Generalization and Prediction)

In many ML training applications, there is the risk that the model won't generalize enough for unknown data. To mitigate this, it is recommended that data scientists do generalization error testing. This involves running cross validated models to predict on held-out data, to see it's performance on test data. But it's important that we don't look at held-out data or use it in training because this can make our model training biased and result in a large generalization error.

Sample Output

The following screen-shots show that we set the weight on the positive sample to be 1000 and the feature selection threshold to be 0.008. In the third attempt running this tuned classifier, we find that our recall for an imbalanced positive sample has improved to 0.84 on the test data.

Awesome job following along! Now go try and take this further or apply it to a different use case!

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License

Apache 2.0