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The rate of penetration (ROP) prediction is carried out using a recorded drilling dataset from 02-Well. The prediction approaches use machine learning random forest regressor model and artificial neural network, especially the MLP regressor. The aim is to make the best machine learning model accurately predict the ROP parameter at 02-Well. The method used is designing the architecture of the machine learning model, which is divided into five stages: exploratory data analysis, data pre-processing, prediction and modeling using the selected algorithm, hyper-parameter tuning, and model evaluation. The 02-Well dataset would be divided into a 70% training set and a 30% test set as the base case. The model evaluation results show that modeling using a random forest regressor has a mean absolute percentage error (MAPE) score of 19.81%, which belongs to the "Good Forecasting" criteria. Meanwhile, modeling using MLP regressors has a MAPE score of 22.84% with the "Reasonable Forecasting" criteria. [ABSTRACT FROM AUTHOR]

Now-a-days, Health care industries are literally playing a major role in curing the diseases that are suffering the people. And this will be one kind of help to health care industries. In present days, people are facing lot of issues related to their health due to their life style and their livelihood. Due to their busy schedule in their lives, people are not at all taking care of their health. They are not having the time to consult doctors and know what they are going through and it may lead to severe risk for them. So, People should be aware of what they are going through at early stage will reduce the high risk. In our proposed system we used logistic regression, Random Forest Classifier and Decision tree classifier in prediction of the disease. Disease Prediction is a supervised model that is used for prediction of diseases from the symptoms or the information provides by the user. This proposed system will process the symptoms entered by the user and provide the predicted disease as an output. [ABSTRACT FROM AUTHOR]