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https://github.com/guillaume-chevalier/predict-if-salary-is-over-50k-with-keras

Predict whether income exceeds $50K/yr based on census data of the "Adult Dataset". Also known as "Census Income" dataset.
https://github.com/guillaume-chevalier/predict-if-salary-is-over-50k-with-keras

keras machine-learning neural-network

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Predict whether income exceeds $50K/yr based on census data of the "Adult Dataset". Also known as "Census Income" dataset.

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# Predict if salary is >50k with Keras (machine learning)



A neural network with two layers (sigmoid activation) is used to predict if someone makes more than 50k or less based on training and test data from the UCI Machine Learning Repository.



Percentage right: 85.333% on 16281 test entries on which we did not trained the neural network.

Therefore, the error is of 14.667%.



## Dependencies



- Python 3.0 (Anaconda distribution is preferred),

- Theano (requires linux),

- Keras (requires Theano),

- iPython notebook (included in Anaconda bundle, run `ipython notebook` in terminal from the project's directory).



## Comparison with other regression methods on the dataset



__Algorithm__              | __Error (%)__

----------------       | ---------

__My neural network__  | __14.67__

C4.5                   | 15.54

C4.5-auto              | 14.46

C4.5 rules             | 14.94

Voted ID3 (0.6)        | 15.64

Voted ID3 (0.8)        | 16.47

T2                     | 16.84

1R                     | 19.54

NBTree                 | 14.10

CN2                    | 16.00

HOODG                  | 14.82

FSS Naive Bayes        | 14.05

IDTM (Decision table)  | 14.46

Naive-Bayes            | 16.12

Nearest-neighbor (1)   | 21.42

Nearest-neighbor (3)   | 20.35

OC1                    | 15.04

Pebls                  | Crashed.



## Dataset's structure



### 2 Outputs (classification)

- >50K,

- <=50K.



### 14 Inputs (string and continuous)

- 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.



_Note: some data contain "?", so this value is possible for each of those 14 input fields_



## Presentation's slides



The slides of the seminar are available [here](https://docs.google.com/presentation/d/1Ynz-IN8iMyD1Fp1gqUuX5_0SOxSUhS1LFzO_bB9Di68/present?usp=sharing) (in French).



## References



[Dataset infos](http://archive.ics.uci.edu/ml/datasets/Adult)



Source:

> Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.