So here, i tried to create a machine learning model that uses the random forest algorithm to predict the chances of a machine becoming faulty using sensory data. Being a beginner to ML concepts, this basic project classifies whether a Machine would get faulty very soon or the machine is safe (no fault will be there in the near future. The output mainly consisted of 2 possibilities [0,1] 1 being the machine would become faulty very soon or the chances of this machine being faulty is high 0 being the machine is safe in the near future or chances of this machine being faulty is low Knowing that the possibilities of the output is only 2, I used the Desicion tree algorithm to evaluate this dataset.BUT, it turned out that my training set was overfitting and my testset accuracy was not great. Considering all this, I used Random forest algorithm and evaluated this dataset. This comes under supervised learning thus the dataset is splitted into 2 (I/O). Also there is seperate allocation of the data for training and testing in the ratio of 7:3. Also this model gave me an accuracy of more than 90% in almost all the cycles. Since this is just the beginning of me making projects, help me and correct me if there's something to be done. Dataset link from kaggle:https://www.kaggle.com/datasets/umerrtx/machine-failure-prediction-using-sensor-data