This repository has datasets containing information of Uber pickups in NYC from April 2014 to September 2014 and January to June 2015. data Analysis , virtualization and some insights are gathered here

Related tags

Machine LearningUber-pickups
Overview

uber-pickups-analysis

Data Source: https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city

Information about data set

The dataset contains, roughly, TWO groups of files: ● Uber trip data from 2014 (April - September), separated by month, with detailed location information. ● Uber trip data from 2015 (January - June), with less fine-grained location information.

Uber trip data from 2014 There are six files of raw data on Uber pickups in New York City from April to September 2014. The files are separated by month and each has the following columns: ● Date/Time : The date and time of the Uber pickup ● Lat : The latitude of the Uber pickup ● Lon : The longitude of the Uber pickup ● Base : The TLC base company code affiliated with the Uber pickup. These files are named:

● uber-raw-data-apr14.csv ● uber-raw-data-aug14.csv ● uber-raw-data-jul14.csv ● uber-raw-data-jun14.csv ● uber-raw-data-may14.csv ● uber-raw-data-sep14.csv

Uber trip data from 2015

Also included is the file uber-raw-data-janjune-15.csv This file has the following columns: ● Dispatching_base_num : The TLC base company code of the base that dispatched the Uber. ● Pickup_date : The date and time of the Uber pickup ● Affiliated_base_num : The TLC base company code affiliated with the Uber pickup. ● locationID : The pickup location ID affiliated with the Uber pickup These files are named:

  • uber-raw-data-janjune-15.csv

motive of Project

To analyze the data of the customer rides and visualize the data to find insights that can help improve business. Data analysis and visualization is an important part of data science. They are used to gather insights from the data and with visualization you can get quick information from the data.

How to Run the Project

In order to run the project just download the data from above mentioned source then run any file.

Prerequisites

You need to have installed following softwares and libraries in your machine before running this project.

Python 3 Anaconda: It will install ipython notebook and most of the libraries which are needed like sklearn, pandas, seaborn, matplotlib, numpy, scipy.

Installing

Python 3: https://www.python.org/downloads/ Anaconda: https://www.anaconda.com/download/

Authors

DEVA DEEKSHITH and kilari jaswanth(https://github.com/Kilarijaswanth)- combined work

Owner
B DEVA DEEKSHITH
B DEVA DEEKSHITH
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