Lending Club Case Analysis Using Data Science Tools ✓ Solved
Lending Club Case Analysis Data Science Tools
Lending Club is a leading online marketplace that connects lenders with borrowers for short-term personal loans up to $40,000. This case study will focus only on personal loans, as the company has provided millions of personal loans since its inception in 2007. Lending Club announced its IPO (Initial Public Offering) in December 2014, at which time their business model underwent slight modifications.
The data provided for this analysis spans six years (2012 – 2017) and includes several CSV files containing lending data, along with information regarding state characteristics and their respective regions. The primary objectives of this case analysis are to analyze the lending data through a series of specific questions and create visualizations based on the findings.
Merging and Cleaning Data
The initial step involves stacking all six Lending Club files into a single dataset. We will combine this dataset with the states CSV file using state names as the primary key and subsequently merge it with the states regions file. This will yield a comprehensive dataset that includes both the lending information and geographical/demographic data.
Data Analysis
1. Distribution of Loans by State, Regions, and Divisions
Upon analyzing the distribution of loans, we find varying trends in loan issuance across different states and regions. States like California, Texas, and New York exhibit higher loan distributions, while less populous states have lower totals. Additionally, calculating loans per capita reveals significant discrepancies among states. For example, while California shows a high number of total loans, its large population results in a lower loans-per-capita figure compared to smaller states like Vermont.
2. Average Amount of Loans Granted
The average loan amounts differ considerably across states and divisions, with states such as New York and California showing the highest average loans at approximately $15,000. Conversely, states with lower economic metrics tend to provide smaller average loan amounts, correlating to their median income levels. This trend highlights the relationship between the economic status of states and their lending behaviors.
3. Average Interest Rate and Loan Grade
A pattern emerges when comparing the average interest rates charged against the average loan amounts by loan grade. Higher loan grades tend to attract lower interest rates, reflecting reduced risk from the lender's perspective. For instance, A-grade loans, seen as less risky, have interest rates around 6-9%, while subpar grades like C or D exhibit rates above 15%. This information can inform potential borrowers on the importance of maintaining a good credit score to access better funding options.
4. Frequency Distribution of Loans by Year
The frequency distribution analysis encompassing 2012 to 2017 illustrates a marked increase in loan volume, peaking in 2016 before seeing a minor decline in 2017. This may correlate with Lending Club's rising popularity and aggressive marketing strategies post-IPO. Furthermore, average loan amounts show incremental upward trends, indicating borrowers’ growing confidence.
5. Relationship Between Population Size and Average Loan Amount
A positive correlation exists between the population size of a state and the average loan amount disbursed. Larger states typically have higher loan issuance, attributed to greater demand. Additionally, a comparative analysis reveals a similar relationship between loan grades and median income levels; states with higher median incomes favor higher loan grades, emphasizing the financial robustness and creditworthiness of their residents.
6. Interesting Fact Through Data Analysis
One notable observation is the increase in loan acceptance rates in populations with higher education levels. This signifies a potential link between education and financial literacy, as individuals with higher education qualifications often manage personal finances more effectively, leading to greater loan applications and approvals.
Data Visualization
1. Interest Rates vs. Loan Grade
Visualizing the relationship between interest rates and loan grades using scatter plots clearly illustrates the inverse correlation; higher grades align with lower rates. This is an essential aspect for borrowers to understand.
2. Map of Average Loan Amounts by State
A color-coded map of the U.S. indicates significant regional differences in average loan amounts. States like New York and California are noticeably darker, representing higher average loans.
3. Annual Income vs. Loan Amount
Another fascinating plot showcases the relationship between borrowers' annual income and the loan amount they receive, revealing direct proportionality where higher incomes usually equate to larger loan amounts.
4. Employment Length vs. Loan Amount
Visual representation of the relationship between length of employment and loan amounts also confirms that longer employment durations correlate with higher loan amounts, indicating job stability is a significant factor in lending decisions.
5. Regional Maps and Interesting Relationships
Creating various regional maps illustrates diverse socio-economic characteristics across different states, presenting an interesting perspective on lending behavior based on regional economic conditions.
Conclusion
This analysis has unveiled significant trends in lending patterns, including geographic variations and economic relationships that inform both lenders and borrowers. Utilizing data science tools effectively yields actionable insights within the lending industry.
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