This model can determine if an individual earns over or under $50k/year.
Use the package manager pip to install requirements.txt.
pip install -r requirements.txtAfter cleansing the data from missing values, outliers, and data that cluttered or were unrelated to the final outcome value, I decided to settle on using logistic regression in order to find a suitable model that could predict the income of an individual.
With about 16k data points to train the model on, I was able to achieve about 83% accuracy with a margin of 1.5% (so about 15.5 to 18.5 error). This is comparable with different models on this data set as follows:
VIEW FILE AS RAW FOR TABLE TO LOAD PROPERLY
| Algorithm Error | -- ---------------- ----- | 1 C4.5 15.54 | 2 C4.5-auto 14.46 | 3 C4.5 rules 14.94 | 4 Voted ID3 (0.6) 15.64 | 5 Voted ID3 (0.8) 16.47 | 6 T2 16.84 | 7 1R 19.54 | 8 NBTree 14.10 | 9 CN2 16.00 | 10 HOODG 14.82 | 11 FSS Naive Bayes 14.05 | 12 IDTM (Decision table) 14.46 | 13 Naive-Bayes 16.12 | 14 Nearest-neighbor (1) 21.42 | 15 Nearest-neighbor (3) 20.35 | 16 OC1 15.04
All of the values provided in the data set are as follow, where continuous represents a number that is not constrained:
age: continuous. workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. fnlwgt: continuous. 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. education-num: continuous. marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. 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. relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. 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: 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.
Pull requests are welcome. If you have any specific question please feel free to send me a message. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.