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I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More
I have extensive experience in data management and data processing. Over the past few years I saw the data management technology transition into the Big Data ecosystem and I needed to follow suit. I... 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 machine learning project, we will use hundreds of anonymized features to predict if customers are satisfied or dissatisfied for one of the biggest banks - Santander
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.
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.