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Next, I use copyVars to copy the target and _customerID feature over to our new transformed table from the original table. I specify the output table (casout) to be called woe_transform. Then I provide the req_packs list that I created in figure 2 with all the transformations. I first reference our data using the table parameter. Now that we have our data pipeline in place, let’s transform the data (Figure 3).
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We then append those lists together to later pass the transformation outline to our transform action.įigure 2: Set up weight of evidence transformation The cattrans parameter stands for categorical transformation. The second transformation, which I label as req_pack2, is nearly identical except I’m transforming the nominal inputs and therefore need to use cattrans instead of discretize. IV is a common statistic used in classification models to gauge the predictive power of your feature set. This enables a search across those bins to find the optimal bin number using information value (IV). Within that transformation is a regularization parameter where you can specify a range of bins using the min and max NBins parameter. I call discretize in Python to bin the continuous values and specify the WOE transformation. I give that transformation a name, pass the list of features and the target, and specify the event of interest, which is ”bad” in this case. Next, we create the first transformation called req_pack1, which is just short for request package and is the parameter found in the ansform action. Suppose a WOE transformation on income level included income level $100k - $150k, then all observations within that bin would receive the same WOE value which can be computed using the formula below.
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This method attempts to find a monotonic relationship between the input features and your target variable by splitting each feature into bins and assigning a weight to each bin. I first transform my data using the weight of evidence (WOE) method.
#Credit card validator python project code#
The code and Jupyter Notebook are available on GitHub. The CAS server is a distributed in-memory engine where I can do all my heavy lifting or computations. The one main difference and benefit is that the algorithms within these action sets have been highly parallelized to run on a CAS (Cloud Analytic Services) server. CAS Action Sets are synonymous to libraries in Python or packages in R. SWAT acts as a bridge between the python language to CAS Action Sets. The credit scoring codeįor this analysis I’m using the SAS Open Source library called SWAT (Scripting Wrapper for Analytics Transfer) to code in Python and execute SAS CAS Action Sets. Our target variable will be a binary variable with the values ”bad” or ”good” with respect to the customer defaulting given some historical period. The features - what are called characteristics in credit scoring - include the number of children, number in household, age, time at address, time at current job, has a telephone, income, etc. The training data for the credit scoring example in this post is real customer bank data that has been massaged and anonymized for obvious reasons. Using credit scoring can optimize risk and maximize profitability for businesses. In those situations, it might be a long time before you get them back. If turnaround times to approve or deny credit have long lag times or a bank inaccurately denies a good customer credit, they could lose those customers to competitors. If credit is offered when it shouldn’t be, then a future loss is likely. It’s up to the business to assess the credit worthiness and credit scores of consumers to identify optimal product solutions based on risk, turnaround times, incorrect credit denials and more. As consumers we’re bombarded with offers. Application scorecards are used when new customers apply for loans to predict their likelihood to be profitable customers, and to associate a score to them.įor banks, credit scoring helps manage risk.Behavioral scorecards deal more with predicting or scoring current customers and their likelihood to default.There are two basic types of scorecards: behavioral scorecards and application scorecards. Scorecards and the value of credit scoring
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#Credit card validator python project how to#
Since I have previous experience with customer analytics, but not specifically with financial risk, I’ve been learning how to develop a credit scorecard, and I wanted to share what I’ve learned including my thoughts and code implementation. Naturally, this means credit scoring is an important data science topic for banks and any business that works with the banking industry. As part of that process, banks and other lenders use a scorecard to determine your likelihood to pay off that loan. Whether you’re applying for your first credit card or shopping for a second home - or anywhere in between - you’ll probably encounter an application process. Building credit scorecards using SAS and Python