https://github.com/robinmillford/crystalrisk-a-transparent-view-of-financial-stability

In the "Comprehensive Loan Analysis" project, I utilized SQL queries to thoroughly investigate and gain insights from a dataset containing loan information
https://github.com/robinmillford/crystalrisk-a-transparent-view-of-financial-stability

data-preprocessing data-science data-visualization mysql pandas sql tableau

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In the "Comprehensive Loan Analysis" project, I utilized SQL queries to thoroughly investigate and gain insights from a dataset containing loan information

• Host: GitHub
• URL: https://github.com/robinmillford/crystalrisk-a-transparent-view-of-financial-stability
• Owner: RobinMillford
• Created: 2024-01-13T10:27:32.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-01-13T11:19:55.000Z (10 months ago)
• Last Synced: 2024-01-13T21:01:43.205Z (10 months ago)
• Topics: data-preprocessing, data-science, data-visualization, mysql, pandas, sql, tableau
• Language: Jupyter Notebook
• Homepage:
• Size: 12 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
**Title: Comprehensive Loan Analysis for Risk Assessment and Strategic Decision-Making**

**Dataset -** https://www.kaggle.com/datasets/henryokam/prosper-loan-data

**Summary:**

In the "Comprehensive Loan Analysis" project, I utilized SQL queries to thoroughly investigate and gain insights from a dataset containing loan information. The dataset included various attributes such as loan duration, status, interest rate, yield, Prosper rating, employment status, home ownership, loan amount, monthly payment, and the number of investors. The goal of the project was to perform a comprehensive analysis to aid in risk assessment and strategic decision-making.

**Key SQL Queries and Findings:**

1. **Loan Performance and Risk Analysis:**

   - Explored default rates, average interest rates, and loan amounts based on Prosper ratings.

   - Identified the correlation between loan status, return rates, and employment status.

   - Investigated how loan statuses transitioned over time.

2. **Loan Characteristics and Borrower Profiles:**

   - Analyzed the distribution of loan durations, loan amounts, and monthly payments.

   - Explored the relationship between loan amounts, returns, and home ownership status.

   - Examined how loan amounts and interest rates correlated.

3. **Investor Engagement and Diversity Analysis:**

   - Investigated the diversity of investors by analyzing the distribution of unique investors across loans.

   - Explored the correlation between the number of investors and Prosper ratings.

**Data Visualization in Tableau:**

After extracting valuable insights from the SQL queries, we created interactive and visually appealing dashboards in Tableau to further enhance the understanding of the data.

Tableau - https://public.tableau.com/app/profile/yamin3547/viz/CrystalRiskATransparentViewofFinancialStability/Dashboard3#3

These visualizations provide a dynamic and accessible way to communicate the findings and enable stakeholders to make informed decisions based on the comprehensive loan analysis.

By combining SQL analysis with data visualization, this project delivers actionable insights for risk mitigation, strategic decision-making, and optimizing lending practices.