We are asked to predict the probability of the event that a student will drop out a course. We firstly extracted many features from the huge dataset. Then we used ensemble learning machine and model stacking technique to get the final result, which ranked the 1st in 68 teams.
This Project posits elementary analogies of existing Probabilistic and Machine Learning models that have been used to find solutions to the problem of the Structural Segmentation of Musical audio. I have tried to use the idea that the chord of a given beat or frame of a song is an analogous representation of the states generated by trained Hidden Markov Models in generating feature vectors for the aforementioned problem; and that the knowledge of the temporal boundaries within which, a group of frames lie, can be used as constraints in creating the feature vectors that are eventually clustered to identify the pattern in which the various segments of a song repeat.
This report provides insight into the magnetic phenomenon of Hysteresis. Hysteresis is defined as a retardation effect where the magnetisation of a magnetic material lags behind the magnetizing force. Here we will explore the hysteresis loop for a silver steel ferromagnet and use this to discover it’s magnetic properties. The method used will be to place a ferromagnet inside a solenoid with an alternating voltage which will continually reverse the magnetic field and magnetism direction. The relation between these two quantities will be used to produce a hysteresis loop from which magnetic properties can be deduced. The results obtained were: saturation magnetisation = (8.4±0.5)(105)Am-1; remnant magnetisation = (5.9±0.5)(105)Am-1; coercive field: (4.3±0.5)(104)Am-1; energy expended per cycle per unit volume of material: (1.55±0.05)(103)Jm-3s-1; energy product: (8.7±3.0)(104)Jm-3.