%%name%%, Author at %%sitename%% %%page%%

December 10, 2020 - Knowing Your Model Performance, Simplified with Oracle OAC Machine Learning

Introduction: In our previous blog, we were able to gain deeper insights into our data through exploratory data analysis and as a supplement we were also able to create a Data Visualization project with graphs generated from the Explain feature. In this blog, we will create a model using one of the machine learning algorithms and apply it to the test data to predict customers that are likely to open Term deposits.

Tejaswini

Tejaswini Uppu is one of our Analytics Associate based in Hyderabad, India and has been with Apps Associates for more than 2 years working on multiple Analytics technologies spanning Oracle, Informatica, Snowflake and Python.

December 9, 2020 - Gaining Deeper Insights of Data with Oracle OAC Machine Learning

Tejaswini

Introduction: In our previous blog, OAC Machine Learning we learned about identifying, loading and preparing data using two different data sources in order to achieve the objective of our model. After loading the data, we also looked into the data enrichment feature called “Recommendations” which is data cleaning efficient. In this blog we will gain deeper insights into our data through exploratory data analysis and as a supplement we will create a Data Visualization project with graphs generated from the Explain feature. Later in the third blog we will create a model using one of the machine learning algorithms and apply it to the test data to predict the customers that are likely to open Term deposits.

Tejaswini

October 8, 2020 - Data Preparation with Oracle OAC Machine Learning

Tejaswini

Introduction: Financial institutions want to predict customers that are likely to open Term deposits after a marketing campaign which in turn will help the marketing team with contacting the desired customers.

Tejaswini