a3x3k / uber-data-analysis

Exploratory Data Analysis on Uber Pickups

Table of Contents

	Table Of Contents
1	About
2	Setup
3	Libraries
4	Data Set
5	Contributors

Directory Structure

├── Data Set
│   ├── Apr.csv
│   ├── May.csv
│   ├── Jun.csv
│   ├── Jul.csv
│   ├── Sep.csv
│   ├── Uber - 2016.csv
│ 
└── README.md

About

• Taxis are essential, and taxi-related information can provide ground-breaking insight into different facets of city life, from economic activity and human behavior to mobility trends. Taxi data involves geographical components as well as several variables associated with each ride.

• Data insight is gain through data pre-processing, feature engineering and data exploratory analysis. This transformation of raw data will enable us to have the meaningful insight into the data and understand the mobility pattern of New York City.

• The primary methodology behind this study is to analyze and find the accuracy of the most frequent category of trip among all trips taken by a customer in a region using data analysis. Uber Data Analysis task permits us to recognize the complicated factual visualization of this large organization.

• We will be using Python programming language. Here we analyze the Daily, Monthly and Yearly Uber Pickups in New York City. There are many questions that can be answered but here we will be focusing on,

  • Uber Pickups and distribution in NYC
  • Time when Uber pickups happen regularly.
  • Days when pickup happens regularly.
  • Pickup distribution in the zones.
  • Finding out the hotspot areas

Setup

• To run this project, install and setup the following Libraries,

!pip install numpy
!pip install scipy
!pip install matplotlib
!pip install pandas
!pip install seaborn
!pip install pillow
!pip install scikit-learn
!pip install plotly
!pip install mplleaflet
!pip install chart_studio
!pip3 install https://github.com/matplotlib/basemap/archive/master.zip

Libraries

• Project is created with,

  • Plotly
  • Datetime
  • Stockstats
  • Matplotlib
  • Pandas
  • Numpy
  • Seaborn
  • Mpl_toolkits
  • Sklearn
  • Geopy
  • Shapely

Data-Set

Name	Link
Kaggle	Kaggle

Other Contributors

Harsha Sathish	Navneet Kumar Singh

For Project Code and Report feel free to Mail me!