DataScience Term Project
https://archive.ics.uci.edu/ml/datasets/adult Data Set Characteristics: Multivariate Number of Instances: 48842 Area: Social Attribute Characteristics: Categorical, Integer Number of Attributes: 14 Date Donated 1996-05-01 Associated Tasks: Classification Missing Values? Yes Number of Web Hits: 2777169 To determine whether a person makes over 50K a year.
The goal of this project is to predict whether an individual's income exceeds $50,000 per year based on various demographic and employment attributes using machine learning techniques. The project will involve preprocessing, data scaling and encoding, and the application of classification, regression, and clustering algorithms. The team will follow the end-to-end Big Data process (except data curation and deployment) and use k-fold cross-validation for testing classification models.
The Adult Income dataset contains 48,842 instances and 14 attributes, of which 6 are numerical and 8 are categorical. The dataset is derived from the 1994 US Census Bureau database. The attributes in the dataset are:
age: continuous (numerical)
workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked (categorical)
fnlwgt: continuous (numerical). It represents the number of people the census believes the entry represents (sampling weight).
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 (categorical)
education-num: continuous (numerical). It represents the number of years of education in total.
marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse (categorical)
occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces (categorical)
relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried (categorical)
race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black (categorical)
sex: Female, Male (categorical)
capital-gain: continuous (numerical)
capital-loss: continuous (numerical)
hours-per-week: continuous (numerical)
native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands (categorical)
The target variable is 'income', which is binary: '>50K' and '<=50K'. The aim is to predict this variable based on the attributes provided.
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