https://github.com/auliannee/new-york-uber-pickups-analysis

This repository contains the projects related to data collecting, quality check, manipulation, analyzing, and visualizations.
https://github.com/auliannee/new-york-uber-pickups-analysis

data-analysis data-science ipython-notebook jupyter-notebook python

Last synced: 5 days ago
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This repository contains the projects related to data collecting, quality check, manipulation, analyzing, and visualizations.

• Host: GitHub
• URL: https://github.com/auliannee/new-york-uber-pickups-analysis
• Owner: auliannee
• Created: 2022-07-17T07:50:58.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-07-17T08:28:04.000Z (over 2 years ago)
• Last Synced: 2024-10-12T14:43:47.127Z (about 1 month ago)
• Topics: data-analysis, data-science, ipython-notebook, jupyter-notebook, python
• Language: Jupyter Notebook
• Homepage:
• Size: 525 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# New-York-Uber-Pickups-Analysis

Hi! So this project inspired by Abraham Prayoga’s project and I’d like to try to analyze the same dataset [(Uber Pickups in NYC)](https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city) from Kaggle using python for study purposes.

In this project, we are looking for insight from Uber pickup dataset in New York City. First, we need to define the objective and business case clearly to guide us in the exploration phase.

The one thing that can be improved is enhance the customer experience by minimizing the waiting time. The longer of waiting time, the bigger probability of cancelation. So, by knowing when-where the rush hour, we can locate the driver nearer to the customer and minimize the cancelation rate. Aside of that, giving discount to the certain busy time and location could boosting the number of pickups. More pickups mean more revenue.

There are tons of questions can be answered by this dataset, but this analysis will be more focused on:

1.  Pickup Distribution by Month and Date

2.  Pickup Distribution by Day of The Week and Hour

Here are the steps I will do for this analysis:

1.	Import Library

2.	Understand The Data

3.	Data Quality Check

4.	Data Manipulation

5.	Data Analysis and Visualization