AdultSalaryDataUS
Beginner ML project on the UCI Adult Dataset here
A total of 4 classifiers were used, Namely -
- Random Forest Classifer.
- AdaBoost Classifier.
- KNN Classifier.
- SVM Classifier.
The details is given in the jupyter notebook of each classifier in the Classifiers Folder.
The result and peroformance on the training set:
- Random Forest gave the best accuracy and performed well on the negative label, but couldn't acheive a good recall or f1-score.
- AdaBoost had an average accuracy but the best ROC AUC score and the best recall and f1-score.
- KNN performed worse than Random Forst or AdaBoost, but better than SVM.
- SVM classifier was not able to do a good job, maye because of Randomised Search Cross Validation