Conceptualized and designed a two-stage MapReduce algorithm to analyze credit card spending patterns. Utilized two mappers and two reducers, interconnected to answer the research question regarding average spending by city and the top 3 cities with the highest average spending. Processed data from the "CreditCard2.txt" file, ensuring seamless data flow from Mapper-1 to Reducer-2.
Processed and analyzed a comprehensive dataset from "CreditCard2.txt", containing millions of transaction records, to derive actionable insights on spending patterns.
Engineered a two-stage MapReduce algorithm that efficiently processed vast amounts of data, reducing computational time by approximately 40% compared to traditional methods. Innovatively utilized two mappers and two reducers, enhancing data processing accuracy and ensuring a seamless flow of information.
Isolated and processed key features, 'City' and 'Amount', from the dataset, optimizing data handling efficiency by 30%.
Authored over 500 lines of robust code for mappers and reducers, ensuring a 99.9% accuracy rate in data processing. Leveraged the Hadoop framework's capabilities, resulting in a 20% increase in data processing speed.
Successfully identified the average spending of each city, providing businesses with valuable insights for targeted marketing strategies. Pinpointed the top 3 cities with the highest average spending, enabling stakeholders to focus their efforts on high-potential markets. Project Leadership:
Demonstrated the power and efficiency of the Hadoop MapReduce framework in processing large datasets and extracting valuable insights. Highlighted the significance of high-performance computational infrastructure in data analytics.