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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
I have 11 years of experience and work with IBM. My domain is Travel, Hospitality and Banking - both sectors process lots of data. The way the projects were set up and the mentors' explanation was... Read More
It is important to predict the final destination of a taxi to enhance the efficiency of electronic taxi dispatching systems. When there is high demand, often there could be a taxi whose current ride can end near or exactly at the requested pick up location from a new rider. Predicting the final destination of taxi will help the dispatcher know where the driver would be ending their current ride so they can identify which taxi should be assigned for the next pickup request.
In this data science project, you will predict the destination of a taxi given the variable-length sequence of GPS points which represent the beginnig of its trajectory and other information in the taxi trajectory dataset such as taxi id, client information, date and time. The training data has details of all the taxi rides from 2013-2014 in the city of Porto, Portugal. The taxi trajectory dataset represent around 1.7 million rides run by 442 taxis.
In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.
Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.
Music Recommendation Project using Machine Learning - Use the KKBox dataset to predict the chances of a user listening to a song again after their very first noticeable listening event.