  Traditional statistical and measurements are unable to solve all industrial
data in the right way and appropriate time. Open markets mean the customers are
increased, and production must increase to provide all customer requirements.
Nowadays, large data generated daily from different production processes and
traditional statistical or limited measurements are not enough to handle all
daily data. Improve production and quality need to analyze data and extract the
important information about the process how to improve. Data mining applied
successfully in the industrial processes and some algorithms such as mining
association rules, and decision tree recorded high professional results in
different industrial and production fields. The study applied seven algorithms
to analyze production data and extract the best result and algorithm in the
industry field. KNN, Tree, SVM, Random Forests, ANN, Na\"ive Bayes, and
AdaBoost applied to classify data based on three attributes without neglect any
variables whether this variable is numerical or categorical. The best results
of accuracy and area under the curve (ROC) obtained from Decision tree and its
ensemble algorithms (Random Forest and AdaBoost). Thus, a decision tree is an
appropriate algorithm to handle manufacturing and production data especially
this algorithm can handle numerical and categorical data.
