Facebook-Stock-Analysis

For the Code Louisville data analysis final project, I wanted to extend my work from the last cohort. This desire drove me to investigate Facebook.

In my data breaches analysis, I found that Facebook had experienced the most reported data breaches. In order to pursue this investigation into Facebook, I located and downloaded a dataset of their stock history found on Kaggle. I wanted to see if there were any effects from a financial perspective.

Table of Contents

1. Project Requirements
2. Project Hierarchy
3. Project Instructions
4. Investigation
5. Features
6. Data
7. Conclusion

Project Requirements

Note: I started working on this project using Anaconda which utilized python 3.9.13. After careful consideration and a failed update, I decided to try python 3.10.9.

Special Instructions: The pre-release version of Jupyter needs to be installed within Visual Studio Code.

Use the following syntax to install the required packages:

pip install <package>

Required Packages to Install

• pandas
• seaborn
• matplotlib
• statistics

Project Hierarchy

Data Breach Analysis

This notebook is an altered version of the original analysis, where it has been appended to build a pivot table of annual averages of compromised records pertaining to Facebook. Then, it outputs the derived pivot table to a CSV file.

Facebook Stock Analysis

This notebook builds a pivot table of monthly stock averages and outputs it to a CSV file.

Effects on Facebook Stocks

This notebook merges the pivot tables built from the Data Breach Analysis and Facebook Stock Analysis notebooks. Then, it outputs the merged data to a CSV file.

Looker Studio

The dynamic visualization tool that I utilized to construct an interactive dashboard of the data. There is a link under the data section.

Project Instructions

Please build and run the jupyter notebooks in the following order:

1. Data Breaches Analysis

   • Made a pivot table
   • Wrote in Jupyter's markdown cells explaining my thought process and code.

2. Facebook Stock Analysis

   • Made a pivot table
   • Wrote in Jupyter's markdown cells explaining my thought process and code.

3. Effects on Facebook Stocks

   • Performed a pandas merge with two data sets,then calculated some new values based on the new data set.
   • Wrote in Jupyter's markdown cells explaining my thought process and code.

Investigation

Upon investigation, I found some questions to ask against the dataset:

• What are the highest and lowest prices of stock?
• What are the highest and lowest numbers of traded stocks?
• Is there a relationship between the price of a stock and the number of traded stocks?

Features

• Read two data files (JSON,CSV, Excel, etc.)
• Performed a pandas merge with two data sets, then calculated some new values based on the new data set.
• Made a dashboard to display the data.
• Made a pivot table.
• Built a custom data dictionary.
• Wrote in Jupyter's markdown cells explaining my thought process and code.

Data

Sources

• https://www.kaggle.com/datasets/hishaamarmghan/list-of-top-data-breaches-2004-2021

• https://www.kaggle.com/datasets/kalilurrahman/facebook-stock-data-live-and-latest

Facebook Data Dictionary

Column	Description	Data Type	Field type
Date	Time Format: MM/DD/YYYY	Object	Origin
Year	Component of the Date	int32	Origin
Month	Component of the Date	int64	Origin
Formatted Date	Combination of Year and Month	object	Formulated
Breach	Indication that a breach occurred	int64	Formulated
Records	The number of records that were compromised in a breach	object	Origin
Open	The opening price of stock	float64	Origin
High	The highest price of stock	float64	Origin
Low	The lowest price of stock	float64	Origin
Close	The closing price of stock	float64	Origin
Volume	The number of traded stocks	float64	Origin

Facebook Stock Dashboard

https://lookerstudio.google.com/s/h-TjNVlXEvk

Conclusion

Results