https://github.com/somjit101/nyc-taxi-demand-prediction

This is a Time Series Forecasting and Regression solution to project the no. of pick-ups at and around a given region at a given time in the city of New York, USA.
https://github.com/somjit101/nyc-taxi-demand-prediction

baseline-model clustering dask dask-dataframes data-cleaning data-visualization exponential-moving-average fft-analysis fourier-transform k-means-clustering new-york-taxi-demand-prediction performance-analysis random-forest-regression scikit-learn time-series-analysis time-series-forecasting xgboost-regression

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This is a Time Series Forecasting and Regression solution to project the no. of pick-ups at and around a given region at a given time in the city of New York, USA.

• Host: GitHub
• URL: https://github.com/somjit101/nyc-taxi-demand-prediction
• Owner: somjit101
• Created: 2021-09-02T10:57:47.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-09-08T18:31:29.000Z (over 3 years ago)
• Last Synced: 2024-11-16T03:29:39.422Z (3 months ago)
• Topics: baseline-model, clustering, dask, dask-dataframes, data-cleaning, data-visualization, exponential-moving-average, fft-analysis, fourier-transform, k-means-clustering, new-york-taxi-demand-prediction, performance-analysis, random-forest-regression, scikit-learn, time-series-analysis, time-series-forecasting, xgboost-regression
• Language: Jupyter Notebook
• Homepage:
• Size: 12.5 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# NYC-Taxi-Demand-Prediction

This is a Time Series Forecasting and Regression solution to project the no. of pick-ups at and around a given region at a given time in the city of New York, USA.

## Dataset Information

We can get the data from [here](http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml) (2016 data). 

The data used in the attached datasets were collected and provided to the NYC Taxi and Limousine Commission (TLC) 

### Information on taxis:

**
 Yellow Taxi: Yellow Medallion Taxicabs
**

 These are the famous NYC yellow taxis that provide transportation exclusively through street-hails. The number of taxicabs is limited by a finite number of medallions issued by the TLC. You access this mode of transportation by standing in the street and hailing an available taxi with your hand. The pickups are not pre-arranged.


**
 For Hire Vehicles (FHVs) 
**

 FHV transportation is accessed by a pre-arrangement with a dispatcher or limo company. These FHVs are not permitted to pick up passengers via street hails, as those rides are not considered pre-arranged. 


**
 Green Taxi: Street Hail Livery (SHL) 
**

  The SHL program will allow livery vehicle owners to license and outfit their vehicles with green borough taxi branding, meters, credit card machines, and ultimately the right to accept street hails in addition to pre-arranged rides. 

 Credits: Quora


**
Note:
**

In the given notebook we are considering only the yellow taxis for the time period between Jan - Mar 2015 & Jan - Mar 2016


## Data Collection

We Have collected all yellow taxi trips data from jan-2015 to dec-2016(Will be using only 2015 data)

 file name 

 file name size

 number of records 

 number of features 

 yellow_tripdata_2016-01 

 1. 59G 

 10906858 

 19 

 yellow_tripdata_2016-02 

 1. 66G 

 11382049 

 19 

 yellow_tripdata_2016-03 

 1. 78G 

 12210952 

 19 

 yellow_tripdata_2016-04 

 1. 74G 

 11934338 

 19 

 yellow_tripdata_2016-05 

 1. 73G 

 11836853 

 19 

 yellow_tripdata_2016-06 

 1. 62G 

 11135470 

 19 

 yellow_tripdata_2016-07 

 884Mb 

 10294080 

 17 

 yellow_tripdata_2016-08 

 854Mb 

 9942263 

 17 

 yellow_tripdata_2016-09 

 870Mb 

 10116018 

 17 

 yellow_tripdata_2016-10 

 933Mb 

 10854626 

 17 

 yellow_tripdata_2016-11 

 868Mb 

 10102128 

 17 

 yellow_tripdata_2016-12 

 897Mb 

 10449408 

 17 

 yellow_tripdata_2015-01 

 1.84Gb 

 12748986 

 19 

 yellow_tripdata_2015-02 

 1.81Gb 

 12450521 

 19 

 yellow_tripdata_2015-03 

 1.94Gb 

 13351609 

 19 

 yellow_tripdata_2015-04 

 1.90Gb 

 13071789 

 19 

 yellow_tripdata_2015-05 

 1.91Gb 

 13158262 

 19 

 yellow_tripdata_2015-06 

 1.79Gb 

 12324935 

 19 

 yellow_tripdata_2015-07 

 1.68Gb 

 11562783 

 19 

 yellow_tripdata_2015-08 

 1.62Gb 

 11130304 

 19 

 yellow_tripdata_2015-09 

 1.63Gb 

 11225063 

 19 

 yellow_tripdata_2015-10 

 1.79Gb 

 12315488 

 19 

 yellow_tripdata_2015-11 

 1.65Gb 

 11312676 

 19 

 yellow_tripdata_2015-12 

 1.67Gb 

 11460573 

 19 

## Features in the dataset:

Field Name

Description

  

VendorID

A code indicating the TPEP provider that provided the record.



Creative Mobile Technologies

VeriFone Inc.



  

tpep_pickup_datetime

The date and time when the meter was engaged.

  

tpep_dropoff_datetime

The date and time when the meter was disengaged.

  

Passenger_count

The number of passengers in the vehicle. This is a driver-entered value.

  

Trip_distance

The elapsed trip distance in miles reported by the taximeter.

  

Pickup_longitude

Longitude where the meter was engaged.

  

Pickup_latitude

Latitude where the meter was engaged.

  

RateCodeID

The final rate code in effect at the end of the trip.



 Standard rate 

 JFK 

 Newark 

 Nassau or Westchester

 Negotiated fare 

 Group ride



  

Store_and_fwd_flag

This flag indicates whether the trip record was held in vehicle memory before sending to the vendor,
 aka “store and forward,” because the vehicle did not have a connection to the server.


Y= store and forward trip


N= not a store and forward trip

Dropoff_longitude

Longitude where the meter was disengaged.

Dropoff_ latitude

Latitude where the meter was disengaged.

  

Payment_type

A numeric code signifying how the passenger paid for the trip.



 Credit card 

 Cash 

 No charge 

 Dispute

 Unknown 

 Voided trip



  

Fare_amount

The time-and-distance fare calculated by the meter.

  

Extra

Miscellaneous extras and surcharges. Currently, this only includes. the $0.50 and $1 rush hour and overnight charges.

  

MTA_tax

0.50 MTA tax that is automatically triggered based on the metered rate in use.

  

Improvement_surcharge

0.30 improvement surcharge assessed trips at the flag drop. the improvement surcharge began being levied in 2015.

  

Tip_amount

Tip amount – This field is automatically populated for credit card tips.Cash tips are not included.

  

Tolls_amount

Total amount of all tolls paid in trip.

  

Total_amount

The total amount charged to passengers. Does not include cash tips.

  

 ## ML Problem Formulation

  

 Time-series forecasting and Regression




- To find number of pickups, given location cordinates(latitude and longitude) and time, in the query reigion and surrounding regions.

 

To solve the above we would be using data collected in Jan - Mar 2015 to predict the pickups in Jan - Mar 2016.



  

## Performance metrics

1. Mean Absolute percentage error.

2. Mean Squared error.