![\"tiny](\"http://www.oracle.com//technetwork/database/options/advanced-analytics/creditscore60-5013102.jpg\" "\\"tiny")
You have a hunch that weakening profits may be due to the company selling to non-optimal customers; customers who have good enough credit to purchase “big ticket items” e.g. large televisions, appliances and durable goods on payment credit cards and payment plans but then fail to complete all their payments and have to return the purchased goods. The company takes a loss and has to resell the items at a discount as “used”.
\nIn your data assembled inside Oracle Autonomous Database, you have over 100 variables to consider to understand your data and to find these hidden patterns and eventually to build predictive models that target these “Good_Credit” better customers. You wants to first explore data using simple charts and graphs, but then move onto using Oracle Machine Learning’s powerful algorithms to automatically sift through her data to find patterns, new insights and to make predictions that target her best customers–those who have both good enough credit and are able to complete all the payments.
\nLet’s take a look.
\nCopyright (c) 2020 Oracle and/or its affiliates. All rights reserved.
\n
You want to avoid customers who fail to make payments and involuntarily churn. You will be using Oracle Machine Learning's classification models in Autonomous Database. In the CREDIT_SCORING_100K dataset, you have tagged the disearable customers as “Good_Credit” in the CREDIT_SCORING_100K table. Those who either have lower credit or have failed to keep up with their payments and have involuntarily churned you have labeled as “Other_Credit” customers. You want to build Oracle Machine Learning predictive models that target “Good_Credit” customer and can generate insights about them.
\nLet's get started!
\n"}]},"apps":[],"jobName":"paragraph_1586357597946_1321432600","id":"20200408-131450_644132069","dateCreated":"2020-04-08T14:53:17+0000","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:35330"},{"title":"Additional information...","text":"%md\nOracle Autonomous Database DocumentationOracle Autonomous Database Documentation https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\n
Oracle Machine Learning folder in Github https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\n
Oracle Machine Learning on Oracle.com: https://www.oracle.com/machine-learning
![\"tiny](\"https://upload.wikimedia.org/wikipedia/commons/thumb/b/b9/CRISP-DM_Process_Diagram.png/330px-CRISP-DM_Process_Diagram.png\" "\\"tiny")
\n
Cross-industry standard process for data mining From Wikipedia, the free encyclopedia\n
https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining
For simplification purposes, the data in this example has already been prepared adequately to go directly to building machine learning models. The Oracle Machine Learning Documentation covers in detail Automatic Data Preparation and related data preparation steps.\n
See https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/index.html
In this notebook, we will build attribute importance and classification models. Please refer to the Oracle Machine Learning Documentation for additional information on the algorithms and related topics.\n
https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/index.html
Success! You have used the Oracle Autonomous Database and Oracle Machine Learning to build and apply models that help your business proactively target customers who can complete all payments for theirpurchases. Oracle Autonomous Database and Oracle Machine Learning simplify model building and model deployment to accelerate time from data to insights and predictions and help you better compete using data-driven AI/ML.
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