Paper Title
Using Data Mining Technology to Analyze Traditional Gold Mining in Sudan

Abstract
The traditional gold mining sector is spreading throughout most parts of Sudan, where more than two million people are working in it. Producing about 80% of the total amount of mined gold in the country. This study used the methodology of extracting the data to assist in making decisions using five models support vector machine (SVM), logistic regression (LR), naive Bayes (NB),decision tree (DT) and k-nearest neighbors (K-NN) and a fair comparison was made between their performance. These models classify the state of companies into two categories: operating, companies with high productivity and suspended with poor productivity. The learning process took place in four stages: initial data processing, training, testing, and verification. The results showed that the proposed models performed the classification task to reveal the state of the company's work. The models achieved a high level of accuracy for the DT, LR, and NB classifier 1.00, 0.91 and 0.81. The SVM and K-NN models decreased by 0.57 and 0.53 compared to the other models. Keywords - Decision Tree; Support Vector Machine; K-nearest neighbor; Naive Bayes; Logistic Regression; Traditional Mining.