Enroll Course: https://www.udemy.com/course/rohitbasistgurjar-data-analysis-project-on-electoral-bond/
The world of political funding can often seem opaque, but thanks to a recent Udemy course, ‘Data Analysis Project on Electoral Bond in Python Colab,’ we can shed some light on the intricate details of India’s Electoral Bond scheme. This comprehensive course, utilizing Python and Google Colab, takes you through a fascinating analysis of data spanning from April 2019 to January 2024, sourced directly from SBI Bank’s disclosures to the Election Commission of India.
The course begins with a thorough data overview, presenting two critical datasets: Purchase Data and Redemption Data. The instructor meticulously guides you through the process of merging and validating these datasets, highlighting potential discrepancies. A key finding immediately emerges: no electoral bond was redeemed more than 15 days after its purchase, suggesting a tight operational window.
The data merging process revealed some interesting, albeit expected, mismatches. After creating a unique identifier using ‘Prefix’ and ‘Bond Number,’ the analysis uncovered 1,680 unmatched entries in the Purchase Data and 130 in the Redemption Data. Beyond mere counting, the course emphasizes essential data cleaning techniques, including standardizing data types and resolving naming inconsistencies, ensuring the integrity of the analysis.
One of the most illuminating aspects of the course is the exploration of top purchasers. Between 2019 and 2024, the data points to Future Gaming and Hotel Services, Megha Engineering and Infrastructure, Qwik Supply Chain Private Limited, Haldi Energy, and Vedanta Limited as the leading entities. The denomination insights are equally striking, with bonds of ₹1 crore denomination dominating across all donations, underscoring the significant financial scale of these transactions.
Time-based trends are also thoroughly examined. The course identifies January, April, and October as peak purchase months, with 2022 and 2023 standing out as peak years for purchases. The redemption patterns reinforce the swiftness of the process, with a majority of bonds redeemed within 5 days of purchase.
Finally, the course delves into the political party encashments, revealing the Bharatiya Janata Party (BJP), Trinamool Congress, and Indian National Congress as the top beneficiaries. Through the addition of new columns for day names, months, and years, and the application of crosstab analysis, the course effectively evaluates purchase trends against political party denomination sums. This analytical approach provides a clear, data-driven perspective on the flow of funds.
Overall, ‘Data Analysis Project on Electoral Bond in Python Colab’ is an exceptional course for anyone interested in data analysis, Python, or understanding the financial mechanisms of political funding. It’s practical, insightful, and equips learners with valuable skills to dissect complex datasets. Highly recommended!
Enroll Course: https://www.udemy.com/course/rohitbasistgurjar-data-analysis-project-on-electoral-bond/