Update README.md

README.md

@@ -77,7 +77,7 @@ Three datasets were utilized to construct a network plot of the Big Four banks:
       * Successor Bank ID (RSSD).
       * Date of transformation (the date in which the merger/acquisition occurred). [Link](https://www.ffiec.gov/npw/FinancialReport/DataDownload)
  * Attributes.csv: Since the "Relationships.csv" file only has a numerical identifier as an ID for banks, it was necessary to cross reference/map the "Attributes.csv" file (which contains both the numerical identifier and its legal name) to obtain the legal name of the financial entities of interest. [Link]( https://www.ffiec.gov/npw/FinancialReport/DataDownload)
- * "history.csv". Each one of the Big Four banks has a history csv file that can be found by entering the RSSD number in the National Information Center search engine. "history.csv" displays the history of bank, uncluding mergers and acquistions. Since not all RSSD numbers are identifiable through the "Attributes.csv" file, I can rely on the "history.csv" to fill the gaps on the missing pieces. [Link](https://www.ffiec.gov/NPW)
+ * "history.csv". Each one of the Big Four banks has a history csv file that can be found by entering the RSSD number in the National Information Center search engine. "history.csv" displays the history of bank, including mergers and acquistions. Since not all RSSD numbers are identifiable through the "Attributes.csv" file, I can rely on the "history.csv" to fill the gaps on the missing pieces. [Link](https://www.ffiec.gov/NPW)
 
 The Jupyter Notebook *network_MnA.ipynb* is file in the "script" folder that cleans, wrangles, and plots the data by carrying the following tasks:
 *	Adding missing values, such as the legal names of the entities by concatenating “Tranformations.csv”, “Attributes.csv”, “history.csv” files” to develop a data frame that has the necessary data points...