#Why this study ?
Credit cards are an integral part of the growing economy in the Banking, and Financial industry. Credit card utilization adds a prominent value to the banks. Scraping the credit card data will help in identifying interesting patterns and characteristics among different features of the cards that in the future can be used by the banking and financial services to further increase their strength by credit card issuance and acquiring new users.
The data is collected from Banks by scraping their websites. Principal component Analysis (PCA) and K-means have been applied to the collected data, Results indicated how credit cards are grouped based on how different offers and benefits it provides. Moreover, results revealed there are many offers available from banks but not every offer is necessary for analysis and grouping. Further, KNN is applied to identify the closest similar card to provide the credit card recommendation.
Objective of this Study:
The goal of this study is to analyze different credit cards and group them in to clusters based on different offers and benefits.
In order to analyze the cards data, we have to create a corpus of credit card data by Web scraping (using Selenium web driver) the Offers and Benefits of the credit card from official websites and summarize the Offers and Benefits based on different categories/needs and then build a Credit card recommendation system.
Clustering the cards will help us in identifying the group of cards that provide similar set of offers and benefits. Similarly identifying the most similar card using KNN will help in the recommendation.