linayaseen's repositories
BigDataInDepth
Data Engineering Course
Higgs-Boson-Discovery-Using-Machine-Learning
The Discovery of the long-awaited Higgs boson was announced July 4, 2012, and confirmed six months later. 2013 saw several prestigious awards, including a Nobel prize. But for physicists, discovering a new particle means the beginning of a long and challenging quest to measure its characteristics and determine if it fits the current model of nature. A vital property of any particle is how often it decays into other particles. ATLAS is a particle physics experiment at the Large Hadron Collider at CERN that searches for new particles and processes using head-on collisions of protons of extraordinarily high energy. The ATLAS experiment has recently observed a signal of the Higgs boson decaying into two tau particles, but this decay is a small signal buried in background noise. The goal of the Higgs Boson Machine Learning project is to explore the potential of advanced machine learning methods to improve the discovery significance of the experiment. No knowledge of particle physics is required. Using simulated data with features characterizing events detected by ATLAS, your task is to classify events into "tau decay of a Higgs boson" versus "background."
Seoul-Bike-Trip-Duration-Prediction
Trip duration is the most fundamental measure in all modes of transportation. Hence, it is crucial to predict the trip-time precisely for the advancement of Intelligent Transport Systems (ITS) and traveller information systems. In order to predict the trip duration, data mining techniques are employed in this paper to predict the trip duration of rental bikes in Seoul Bike sharing system. The prediction is carried out with the combination of Seoul Bike data and weather data. The Data used include trip duration, trip distance, pickup-drop-off latitude and longitude, temperature, precipitation, wind speed, humidity, solar radiation, snowfall, ground temperature and 1-hour average dust concentration. Four performance metrics Root mean squared error, Coefficient of Variance, Mean Absolute Error and Median Absolute Error can be used to determine the efficiency of the models