Adult data set income classification

Python 3 project for classifying the adult data set using Random Forest and a Decision Tree classifier.

The goal of this project is to train a sample from the original adult dataset with a classifier model (using csv/training.csv) and predict in the best way logs from csv/testing.csv.

Required modules

sklearn
pandas
category-encoders
imblearn
joblib

In case of missing one, do the following for each missing, for example:

pip install sklearn

Run example

To run the whole process simply run

python randomForest.py

or

python decisionTree.py

To test new models you can reuse the randomForest.py template or decisionTree.py

Final results

Random Forest

Accuracy (cross-validation: 0.91

Accuraccy XtrainS: 0.93

Accuraccy XtestS: 0.90

Decision Tree

Accuracy (cross-validation: 0.87

Accuraccy XtrainS: 0.91

Accuraccy XtestS: 0.89