Welcome to the Bank Loan Case Study project repository! This project is aimed at analyzing patterns in loan application data to improve decision-making processes in a finance company specializing in lending various types of loans to urban customers. By leveraging Exploratory Data Analysis (EDA), we aim to ensure that capable applicants are not rejected while minimizing the risk of loan defaults.
- Excel File: (https://docs.google.com/spreadsheets/d/19LQNdDrnH7HeZ7XlqqIFBMT7kKm04gwW/edit?usp=drive_link&ouid=112575387227847181919&rtpof=true&sd=true)
- Presentation Slides: (https://docs.google.com/presentation/d/1UOgl0ax3_BUCRE4NPIXYPXfd2OflsapN3pMEYG9akDw/edit?usp=drive_link)
- Project Report: https://drive.google.com/file/d/17AECEDQt0VulRXLOFtengLBwZGDXDea6/view?usp=drive_link
- Video Presentation: https://www.awesomescreenshot.com/video/25278608?key=55d9595ab66fd205fcfa340c817d072c
Imagine you're a data analyst at a finance company facing the challenge of minimizing loan defaults without rejecting capable applicants. The dataset provided contains information about loan applications categorized into two scenarios: customers with payment difficulties and all other cases. Our goal is to use EDA techniques to understand how customer and loan attributes influence the likelihood of default, aiding in better decision-making processes.
The primary objectives of this project are:
- Identify patterns indicating difficulty in paying loan instalments.
- Improve decision-making processes, such as loan approval, denial, or adjustment of loan terms.
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Handling Missing Data: Identify missing data and determine appropriate methods for handling them effectively using Excel built-in functions.
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Detecting Outliers: Identify and address outliers in the dataset using Excel statistical functions and features.
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Analyzing Data Imbalance: Assess data imbalance and its impact on analysis accuracy, focusing on binary classification problems.
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Conducting Various Analyses: Perform univariate, segmented univariate, and bivariate analyses to gain insights into factors influencing loan default.
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Identifying Correlations: Identify top correlations between variables and the target variable for different scenarios using Excel functions.
- Review the provided Excel file containing the loan application dataset.
- Explore the presentation slides, project report, and video presentation for detailed insights and findings.
Project Lead and Analyst - @Shraaj1 Linkedin ID - https://www.linkedin.com/in/rajrathod54321/ E-mail ID - rajr65037@gmail.com
We welcome feedback and suggestions! Feel free to open an issue or reach out via email.