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The Human Activity Recognition dataset was built from the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The objective is to classify activities into one of the six activities performed.Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist.
Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers were selected for generating the training data and 30% the test data.
In this data science project with Python, we will complete the analysis of what sorts of people were likely to survive.You will learn to use various machine learning tools to predict which passengers survived the tragedy.
The goal of this machine learning project is to predict which products existing customers will use next month based on their past behaviour and that of similar customers.
In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.