Skip to content

Latest commit

 

History

History
125 lines (75 loc) · 6.2 KB

ReadMe.md

File metadata and controls

125 lines (75 loc) · 6.2 KB
Raw

Bank Loan Case Study - Exploratory Data Analysis(EDA)

Final Project 2

Difficulty Level: Advanced

Project Overview:

In today’s fast-paced financial services industry, risk assessment and loan default prediction are critical elements in maintaining the financial health of lending institutions. This case study takes us into the role of a data analyst at a finance company that specializes in lending various types of loans to urban customers. The primary objective of this project is to analyze patterns in loan applications and identify factors that contribute to loan default, enabling the company to make more informed decisions during the loan approval process.

This study aims to ensure that:

  • Capable applicants are not rejected, safeguarding potential business opportunities.
  • Risky applicants are identified early, reducing potential financial losses.

Using Exploratory Data Analysis (EDA), we dive into a dataset of loan applications to extract valuable insights and inform the company's lending strategies.

Business Objectives:

The company faces two key risks during loan processing:

  1. Lost business: When a capable applicant who can repay the loan is denied.
  2. Financial loss: When a risky applicant who cannot repay the loan is approved.

The goal of this project is to:

  • Understand customer and loan attributes that influence loan default.
  • Identify patterns that signal difficulty in repayment to make better decisions about loan approval, such as denying risky applicants, reducing the loan amount, or increasing interest rates for higher-risk customers.

Key Data Analytics Tasks:

A. Identify Missing Data and Deal with It Appropriately:

Handling missing data is crucial for ensuring the accuracy and integrity of our analysis. We begin by identifying missing values in the dataset and employing suitable strategies to handle them.

Task:

  • Identify missing data using Excel functions (e.g., COUNT, ISBLANK, IF).
  • Apply appropriate methods like imputation (e.g., using AVERAGE or MEDIAN) to fill in missing values.

Graph Suggestion:

  • Create a bar chart or column chart to visualize the proportion of missing values for each variable.

B. Identify Outliers in the Dataset:

Outliers can skew the analysis and affect decision-making. Our objective is to detect and handle outliers in key numerical variables.

Task:

  • Detect outliers using Excel statistical functions like QUARTILE and Interquartile Range (IQR).
  • Use conditional formatting and thresholds to highlight and investigate outliers.

Graph Suggestion:

  • Create scatter plots to visualize the distribution of numerical variables and highlight potential outliers.

C. Analyze Data Imbalance:

Understanding the distribution of classes (e.g., loan default vs. non-default) is essential, especially for binary classification problems. Data imbalance can lead to biased models and incorrect insights.

Task:

  • Calculate the proportion of each class (e.g., default vs. non-default) using Excel functions (COUNTIF, SUM).
  • Assess the data imbalance and understand its implications.

Graph Suggestion:

  • Create a pie chart to visualize the distribution of the target variable and highlight data imbalance.

D. Perform Univariate, Segmented Univariate, and Bivariate Analysis:

To gain deep insights into the driving factors behind loan default, we perform:

  • Univariate analysis to explore the distribution of individual variables.
  • Segmented univariate analysis to compare variable distributions across different scenarios.
  • Bivariate analysis to examine relationships between variables and the target variable (loan default).

Task:

  • Use Excel functions like COUNT, AVERAGE, MEDIAN for descriptive analysis.
  • Apply filters, sorting, and pivot tables for segmented and bivariate analysis.

Graph Suggestion:

  • Use histograms, bar charts for visualizing distributions.
  • Create stacked bar charts or grouped bar charts for segmented comparisons.

E. Identify Top Correlations for Different Scenarios:

Understanding the correlation between customer/loan attributes and the likelihood of loan default helps in identifying key indicators for risk assessment.

Task:

  • Segment the dataset into different scenarios (e.g., customers with payment difficulties and all other cases).
  • Use Excel functions like CORREL to compute correlation coefficients between variables and the target variable for each segment.
  • Rank correlations to determine the top indicators of loan default for each scenario.

Graph Suggestion:

Create correlation matrices or heatmaps to visualize correlations.

Tools Used:

  • Microsoft Excel: for data cleaning, analysis, and visualization.
  • Excel functions such as COUNTIF, AVERAGE, CORREL, IF, QUARTILE, and others for handling missing data, outliers, correlations, and performing EDA.

Deliverables:

The final deliverable is a comprehensive Bank Loan Case Study Report that provides the following:

  • Insights into missing data, outliers, and data imbalance.
  • Univariate, segmented univariate, and bivariate analysis of key variables.
  • Top correlations for different customer and loan scenarios.

Conclusion:

This project provides a structured approach to performing Exploratory Data Analysis (EDA) on loan application data. By identifying patterns in customer and loan attributes, we aim to help the company minimize risk and optimize its loan approval decisions. The insights derived from this analysis can be used to:

  • Refine loan approval criteria.
  • Design targeted loan products for different customer segments.
  • Implement better risk management practices to prevent financial losses.

How to Run the Project:

  • Load the dataset into Microsoft Excel.
  • Follow the steps outlined for identifying missing data, handling outliers, and performing analysis using Excel functions.
  • Visualize the findings using the suggested charts and graphs.
  • Interpret the insights to make informed decisions about loan approval.