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I have worked for more than 15 years in Java and J2EE and have recently developed an interest in Big Data technologies and Machine learning due to a big need at my workspace. I was referred here by a... Read More
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In this machine learning project you will build a classification system to classify human activities.
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.
Build a machine learning model that will predict which jobs users will apply to given their past applications, demographics and work history.
In this project, we are going to work on Deep Learning using H2O to predict Census income.
In this project, we are going to predict item-level sales data using different forecasting techniques.