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This data was extracted from the census bureau database found at:
Donor: Ronny Kohavi and Barry Becker,
Data Mining and Visualization
e-mail: firstname.lastname@example.org for questions.
Split into train-test using MLC++ GenCVFiles (2/3, 1/3 random).
48842 instances, mix of continuous and discrete (train=32561, test=16281)
45222 if instances with unknown values are removed (train=30162, test=15060)
Duplicate or conflicting instances : 6
Class probabilities for adult.all file
Probability for the label '>50K' : 23.93% / 24.78% (without unknowns)
Probability for the label '<=50K' : 76.07% / 75.22% (without unknowns)
Extraction was done by Barry Becker from the 1994 Census database.
A set of reasonably clean records was extracted using the following conditions:
((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))
Prediction task is to determine whether a person makes over 50K a year.
In this project, we are going to predict item-level sales data using different forecasting techniques.
In this R data science project, we will explore wine dataset to assess red wine quality. The objective of this data science project is to explore which chemical properties will influence the quality of red wines.
In this machine learning project, you will build predictive models to identify wine preferences of people using physiochemical properties of wines and help restaurants recommend the right quality of wine to a customer.