The research work is entitled supervised machine learning classification algorithms for detection of fracture location in dissimilar friction similar joints. So the main objective of the research work is to implement four supervised machine learning algorithms that is decision tree algorithms, key and irish algorithms, support vector machine algorithm, and adabust algorithm for on the data set of dissimilar friction similar joints for detecting the fracture location. So in the given data set, the rotational speed, building speed, pin profile, and axial force where the input parameters while the fracture location is the output parameter. So the methodology is to import the Python library first, then you have to load the friction ceiling data set, then we have to perform some expertly data analysis on the available data set. And then we are going to split the data set in 80 to 90 ratio. And then we are going to implement the machine learning algorithms on that for training and testing purpose. So while doing the feature importance calculation, we have noticed that the axial force doesn't contribute to the output parameters. So we have neglected the axial force parameter from our study. Now moving to the dissimilar algorithm results. So for this algorithm, we have obtained the accuracy score of the accuracy score of 0.78. Now moving to the next algorithm, that is the K nearest neighbor algorithm. We have we have obtained the accuracy score for 0.67. We have obtained the accuracy score for 0.67 accuracy score. Okay, so now moving to the SVM algorithm. So we have in this case, we have obtained the accuracy score for 0.89, 0.89, which is higher than the all algorithms implemented. Now moving to the next algorithm, that is the W's algorithm. So for this algorithm, we have got the accuracy score of 0.56, 0.56, which is lowest. Now, the conclusion is that the, we have implemented successfully the force to provide maximum algorithms for determining the fracture location in dissimilar friction instead of joints. And the support of a machine algorithm resulted in highest accuracy score of 0.89 in comparison to the other algorithms. While at the boost algorithm, result at in lowest accuracy score of 0.56.