Use EDA to analyze the patterns present in the data. This will ensure that the applicants capable of repaying the loan are not rejected.
The Dataset: https://www.kaggle.com/gauravduttakiit/loan-defaulter
There are some columns with the missing values percentage more than 40%
There are few columns with the invalid data.
It contains some columns with categorical values.
Few features have been derived for analytical purpose.
The Notebook: Jupyter Notebook focusses on:
Data Loading
Data merging
Data validation and cleansing
Data processing
Data Visualization
The Notebook is placed at: https://github.com/GaikwadChinmay/EDA-Case-Study-Bank-Loans/blob/main/Credit_EDA_Case_Study_Final_Notebook.ipynb
Data Visualization: Data is visualized with:
Univariate analysis
Bi-variate analysis
Multi-variate analysis
Data visualization is done with the help of:
pyplot from matplotlib
Seaborn
Conclusion: A report containing the visual summaries and the inferences from the graphs has been placed at : https://github.com/GaikwadChinmay/EDA-Case-Study-Bank-Loans/blob/main/Case%20Study%20Final%20Report.pdf
Thank You!
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