codecademy_income_logistic_regression

Income Classification using Logistic Regression

In this project, we will be using a dataset containing census information from the 1994 Census database to create a logistic regression model that predicts whether or not a person makes more than $50,000.
Let’s get started!

Datasets:
The original data set is available at the UCI Machine Learning Repository here.
The dataset has been loaded for you in script.py and saved as a DataFrame named df. Some of the input and output features of interest are:

• age: continuous
• workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
• education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool
• race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black
• sex: Female, Male
• capital-gain: continuous
• capital-loss: continuous
• hours-per-week: continuous
• native country: discrete
• income: discrete, >50K, <=50K