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Problem:

 

We have been given census data about attributes of US citizens (occupation, education, gender, race). With organizations working to ensure equal pay, we want to build a model to see how accurately we can classify low income from high income citizens. Building this model will allow us to understand which attributes contribute to affluency and how we can improve policies in the US.

 

Part 1:

 

Use proper data cleansing techniques to ensure that you have the highest quality data to model this problem. Detail your process and discuss the decisions you made to clean the data.

 

Part 2:

 

Build a nearest neighbors model with the given data, interpret the results, and convey those results to stakeholders. Highlight key learning points such as feature importance of variables, how those variables explain the scenario, how you determined K, why you choose that final value of K, and the overall accuracy of your model and accompanying models. 

  1. Dataset link: https://drive.google.com/file/d/1gGZADHDC5K7rGQuc_7v-KK51sFFzE69J/view?usp=drivesdk

The paper needs to be five pages

 

 

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