Transactions Data Analysis

Table of Contents

1. Installation
2. Project Motivation
3. File Descriptions
4. Results
5. Licensing, Authors, and Acknowledgements

Installation

There's no need to install any libraries to run this code on the Anaconda environment. The code should run with no issues using Python versions 3.*.

Project Motivation

For this project, the main goal is to do an exploratory data analysis on some transaction data and to better understand:

1. What kind of transactions do the users mostly do?
2. Do these transactions bring profit?
3. How is the number of transactions behaving over time?
4. Is the number of transactions related to the number of new users?
5. When would be the least chaotic day of the week and time to take down the server to do a maintanence job on the database?

File Descriptions

The analysis here was divided into 3 notebooks with their respective questions and demonstration of the analysis that led to the answer. They are divided this way for easier readability:

transactions_Q1.ipynb

transactions_Q2.ipynb

transactions_Q3.ipynb

There's also the dataset csv file with the data used on the notebooks.

Results

Each notebook brings the answers to the proposed questions.

A blog post of the finding is available here.

Licensing, Authors, Acknowledgements

The data was made for this project. It's a simulation dataset.