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Our main objective is to predict number of pickups, given location co-ordinates (latitude and longitude) and time, in the query region and surrounding regions. And to solve this we would be using data collected in Jan 2015 to predict the pickups in Jan - Mar 2016. Main attraction of this project was use Fourier transform as feature. We have chos…

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Taxi-demand-prediction-analysis

Our main objective is to predict number of pickups, given location co-ordinates (latitude and longitude) and time, in the query region and surrounding regions. And to solve this we would be using data collected in Jan 2015 to predict the pickups in Jan - Mar 2016. Main attraction of this project was use Fourier transform as feature. We have chosen our error metric for comparison between models as MAPE (Mean Absolute Percentage Error) so that we can know that on an average how good is our model with predictions

Data: Yellow taxi data for 2015 whole year from TLC

Performance matrix: 1.MAPE

Results of different applied models can be noticed in each corresponding file.

NB: This project was done as a part of assignment for machine Learning course taken at appliedaicourse.com/

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Our main objective is to predict number of pickups, given location co-ordinates (latitude and longitude) and time, in the query region and surrounding regions. And to solve this we would be using data collected in Jan 2015 to predict the pickups in Jan - Mar 2016. Main attraction of this project was use Fourier transform as feature. We have chos…

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