https://github.com/agnivchtj/us-census-classifier

Find the optimal classification algorithm that can predict salaries above $50k, based on US Census data.
https://github.com/agnivchtj/us-census-classifier

census-data decision-tree-classifier jupyter-notebooks knn-classifier logistic-regression naive-bayes-classifier python scikit-learn svm-classifier

Last synced: 2 days ago
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Find the optimal classification algorithm that can predict salaries above $50k, based on US Census data.

• Host: GitHub
• URL: https://github.com/agnivchtj/us-census-classifier
• Owner: agnivchtj
• Created: 2020-11-08T22:39:39.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2020-11-08T22:49:47.000Z (about 4 years ago)
• Last Synced: 2024-11-21T12:47:26.523Z (2 months ago)
• Topics: census-data, decision-tree-classifier, jupyter-notebooks, knn-classifier, logistic-regression, naive-bayes-classifier, python, scikit-learn, svm-classifier
• Language: Jupyter Notebook
• Homepage:
• Size: 316 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# US Census Classifier

For this project, a dataset from the American 1994 Census is explored and used to train a classifier. The data is stored in tabular format and contains over 16,000 samples.

The objective is to implement a binary classifier that can predict whether a person makes over $50,000 a year using the dataset.

The dataset has the following 11 attributes: Age, Education-num, Hours-per-week, Work class, Education, Marital-status, Occupation, Relationship, Race, Sex, Native-country with Salary as target variable.