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This case study aims to give you an idea of applying EDA in a real business scenario. In this study, apart from applying the techniques from EDA module, you will also develop a basic understanding of risk analytics in banking and financial services and understand how data is used to minimize the risk of losing money while lending to customers.

Jupyter Notebook 100.00%
case-study data data-analysis data-cleaning data-visualization exploratory-data-analysis exploratory-data-visualizations jupyter-notebook loans-analysis matplotlib

eda_bank_loan's Introduction

EDA_BANK_LOAN

Use EDA to analyze the patterns present in the data. This will ensure that the applicants capable of repaying the loan are not rejected.

 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

To View the Notebook check Here.

Data Visualization: Data is visualized with:

    Univariate analysis
    Bi-variate analysis
    Multi-variate analysis
    Correlation

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 Here.

Thank You.

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