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About KYC Passing Rate Data Analysis Using Feature Engineering Clustering and Data Visualization
This document is a dissertation submitted for a Master's degree that analyzes the Know Your Customer (KYC) passing rate at a bank called N26 using machine learning and data visualization techniques. The dissertation aims to identify factors that influence the KYC passing rate and find patterns in the data through clustering. Random forest is used to determine important factors, and K-means clustering is applied to group similar documents. Tableau is then used to visualize the clusters and help assess risks and improve processes. The results indicate that image integrity plays a vital role in the KYC pass rate, and failed documents show a lack of image integrity.
Detailed Information
| Author: | ['SIDDESH SANNA GOWDRU'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 56 |
| Language: | English |
| Format: | |
| Price: | FREE |
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