Electoral Bonds Scheme Data Analysis Portfolio Project

Recently I stumbled upon a news published in the daily newspaper about the scrapping of the Electoral Bond Scheme in India. It immediately caught my attention. I wanted to take a deep dive into the data released by the Election Commission of India in association with the State Bank of India to find some meaningful insights and create a beautiful dashboard with visuals.

On 15th February, the Supreme Court of India scrapped the Electoral Bond Scheme introduced in 2018, which allowed anonymous political funding, and ordered the disclosure of donors, the amount they donated, and the recipients. The Election Commission published the electoral bonds data shared by SBI on its website. The data released showed the details of the purchase of electoral bonds by companies and individuals of denominations between Rs 1,000 to Rs 1 crore since April 12, 2019.

What is an Electoral Bond?

• Electoral bonds are instruments/securities, including data used to donate funds to political parties. Electoral Bonds may be purchased by a person who is a citizen of India or incorporated or established in India.
• A person being an individual can buy Electoral Bonds, either singly or jointly with other individuals.
• Only the Political Parties registered under Section 29A of the Representation of the People Act, 1951 (43 of 1951) and which secured not less than one percent of the votes polled in the last General Election to the House of the People or the Legislative Assembly of the State, shall be eligible to receive the Electoral Bonds.

In this Data Analysis Project, I have delved deep into the data and found some insights published in public by the State Bank of India right after that.

Tools used: Excel, PostgreSQL, and Power BI

The data was presented in two PDF files containing the donor data, including data of companies and individual donors who donated and the Political parties data that collected those donations.

After converting those files into Excel format I did all the data cleaning. Some duplicate values were corrected. Also, I divided the donors’ data into two separate Excel files. One, with the companies’ data and the other one with the individual donors’ data. Also, I included the Head Quarter City, Head Quarter State/ Province, and Country columns in the company’s Excel file. Lastly, I converted all files into CSV format for simplicity of usage.

In PostgreSQL, I answered 12 questions to showcase the skills I learned in SQL.

Basic commands: SELECT Aggregate functions: SUM, MAX Intermediate SQL functions: String handling functions, Numeric calculations, subqueries Advanced SQL functions: CTEs and Windows functions

In Power BI I crafted a compelling dashboard with visuals to showcase insights that were derived from the data.

N.B.: This project is only for study and practice purposes. There is no intention to misuse the data as well as malign any individual, company, or political party.