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This is a banking credit dashboard I built from scratch to turn messy loan application records into clear, actionable visuals. The main goal was to analyze what factors actually cause a loan to get approved or rejected.
Here is how I worked on this project manually:
Data Formatting: I cleaned and sorted the raw dataset in Excel, fixing missing rows and organizing the numbers for a flawless setup.
KPI Calculation: I calculated core credit metrics, such as total loan amounts, average annual income, average CIBIL scores (599.9), and the final approval percentage (62%).
Insightful Charts: I created custom visuals to show important comparisons, like loan approval rates based on education levels and a breakdown of residential vs commercial assets.
I designed this entire dashboard on a dark background to make the charts stand out and look clean. Anyone can click the filters on the side to update the whole screen instantly.
Tools used: Advanced Excel (Pivot Tables, Slicers, Data Visualization, Data Analysis)
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