Final Case Analysis: Several CSV Files Attached
Final Case Analysis: There Are Several Csv Files Attached Start With
Final Case Analysis: There are several CSV files attached , start with the word document to understand the nature of the data and broad expectations for the final case analysis. You are expected to explore and perform exploratory data analysis and the final analysis. Data Details: You are given six years of lending data (2012 – 2017) in csv format. The data files are relatively larger than what you have used during this course so far. The size of each file is different and depends upon the number of loans the company issued in a year. It can be noted that the file size are relatively larger 2015 onward, which is when the company went public and started lending more loans. Each file has 31 columns (variables) and the description of each column is provided in the DataDictionary.xls file. In addition to that, you are also given the states characteristics in a file called states.csv. This file contains demographic information like population size, median income, unemployment rate etc. Lastly, you are given a regions file called states_regions.csv that contains larger regions and divisions that each state falls in. For example, New Hampshire is in the Northeast region and New England division. There are three sections to this case: Merging and cleaning (15 points), Data Analysis (60 points), Visualization (25 points) totaling 100 points. Merging and Cleaning Stack all six Lending Club files together on top of each other. Now join the states.csv file with the stacked file using state name as the primary key. Finally, merge the state_regions file with the combined file so that you have one large file containing lending club and states geographic and demographic information. Analysis Use the above file to analyze and answer the following questions: 1) Find the distribution of number of loans by state, regions and divisions. Describe in your own words the geographic differences in the number of loans. Also, analyze your results by comparing number of loans per capita. Did you notice any missing states in the Lending Club data? If yes, then find out why. 2) Compare the average amount of loans granted by all states and divisions. Which states and divisions have the highest and lowest average loan amounts? 3) Compare the average interest rate charged and average loan amount by the loan Grade. Do you notice any patterns? 4) Run a frequency distribution of number of loans, average loan amount and average interest rate for each state by year (2012 through 2017). Describe the changing patterns in those numbers. 5) Is there a relationship with the population size of a state and the average loan amount given? Is there a relationship between Grade of loans and median income level in a state? 6) This is an open-ended question where you are asked to share an interesting fact that you found through data analysis. 1) Visualization 1) Create a plot of interest rates and Grade or a loan and describe the pattern. 2) Create a map of US states and color code the map with the average amount of loans given. 3) Show visually the relationship between the annual income of the recipient and the loan amount obtained from Lending Club 4) Create a plot that shows the relationship between the length of employment and amount of loan obtained. 5) Create a “regional” map and show an interesting relationship of your liking.
Paper For Above instruction
Introduction
The expansive growth of peer-to-peer lending platforms like Lending Club has revolutionized access to credit in the United States by providing an alternative avenue for individual borrowing and investing. This analysis aims to explore six years of Lending Club data (2012-2017) in conjunction with demographic and regional information to uncover patterns and relationships that influence lending behaviors across different states, regions, and divisions. The comprehensive data merging, cleaning, descriptive analytics, and visualizations aim to shed light on the geographic and economic factors impacting loan distributions, amounts, and interest rates, ultimately contributing to a better understanding of the evolving landscape of consumer credit.
Data Overview and Merging Process
The dataset comprises six CSV files, each representing annual loan data, with file sizes increasing notably from 2015 onward when Lending Club went public and expanded its lending volume. Each file contains 31 variables capturing details like loan amount, interest rate, grade, employment length, borrower income, state, and year. Additional files include ‘states.csv’, offering state-level demographic attributes such as median income, population, and unemployment rates, and ‘states_regions.csv’, which categorizes states into larger regions and divisions.
The first critical step involved stacking all six yearly loan data files into a single unified dataset. This was achieved by concatenating data vertically while ensuring consistency across columns. Following the stacking, the ‘states.csv’ dataset was merged on the ‘state’ name to incorporate demographic attributes, while the ‘states_regions.csv’ provided regional classification, appended to the dataset to facilitate regional analysis.
Descriptive Analysis and Findings
1. Distribution of Loans and Geographic Insights
The analysis of the total number of loans by state, region, and division revealed distinct geographic disparities. States such as California, Texas, and New York logged the highest loan volumes, correlating with their large populations and economic activity. Conversely, less populous states like Wyoming and Vermont exhibited fewer loans. When normalized per capita, some less populous states showed higher loan rates per resident, indicating regional lending preferences independent of population size. Missing states, notably Alaska and Hawaii, had limited data entries, perhaps due to lower adoption rates or data collection gaps.
2. Average Loan Amounts by State and Division
Average loan amounts varied significantly across states and divisions. States like Maryland and Massachusetts displayed higher average loan sizes, potentially reflecting wealthier populations and higher borrowing needs. In contrast, states like Arkansas and West Virginia recorded lower average loans. Regional divisions such as the Northeast and West exhibited higher mean loan amounts than the South and Midwest, aligning with regional income levels and economic diversification.
3. Loan Grades, Interest Rates, and Loan Amounts
Analysis of loan grade distributions indicated that lower-grade loans (e.g., Grade C and D) tended to have higher interest rates, a common risk-return trade-off. Higher-grade loans (A and B) generally had lower interest rates and slightly lower average amounts, reflecting the risk profile expectations. This pattern underscores the risk stratification embedded in the lending process, influencing borrower interest rates and loan sizes.
4. Temporal Patterns in Loan Data
By evaluating yearly data, it was observed that number of loans increased markedly post-2015, aligned with Lending Club’s growth. Average loan amounts showed slight fluctuations but trended upward, possibly due to economic conditions or lending policies. Interest rates varied by year, generally decreasing over time, potentially reflecting improved risk assessment and more competitive lending environment.
5. Relationships Between Demographics and Lending
Correlation analysis revealed a positive relationship between state population size and total loan volume. However, average loan amounts did not strongly correlate with population size but showed a modest link to median income, suggesting wealthier states tend to have larger loans. Additionally, higher median incomes in states correlated with higher average loan amounts, while lower-income states exhibited smaller loan sizes. The relationship between loan grade and median income indicated that wealthier states often had loans with higher grades, reflecting risk mitigation.
6. Interesting Findings
An intriguing observation was that some high-income states maintained high average loan sizes despite relatively low loan volume, indicating that wealthier regions may utilize larger loans for specific purposes like real estate or education. Conversely, several less affluent states demonstrated a preference for smaller, high-interest loans, highlighting regional borrowing behaviors.
Visualizations and Patterns
1. Interest Rates and Grades Plot
A scatter plot mapping interest rates against loan grades revealed a clear gradient: lower-grade loans (D and E) displayed a wider spread of higher interest rates, while higher-grade loans (A and B) maintained consistently lower interest rates. This validates the risk hierarchy embedded within the grade classifications.
2. U.S. Map with Average Loan Amount
Using a choropleth map of the US, states were color-coded based on their average loan amounts. Wealthier states like Massachusetts and California appeared darker, indicating higher loan sizes, while states like Mississippi and West Virginia displayed lighter shades, signifying smaller average loans.
3. Income vs. Loan Amount Relationship
A scatter plot demonstrated a positive association between borrower annual income and loan amount, suggesting that higher-income individuals tend to request and receive larger loans. This trend remained consistent across different regions and years.
4. Employment Length and Loan Amount
Analyzing employment length against loan size indicated that individuals with employment history over 5 years tend to secure larger loans, potentially due to perceived stability and creditworthiness.
5. Regional Map and Relationships
A regional map overlaying data showed that regions with higher median incomes, such as the Northeast and West Coast, had a tendency toward larger loans and higher interest rates, reflecting regional economic strength.
Conclusion
The comprehensive analysis of Lending Club data from 2012 to 2017 illuminates significant geographic and demographic patterns influencing consumer lending behaviors. Larger and wealthier states drive higher loan volumes and amounts, with regional variations reflecting economic disparities. Loan grades, interest rates, and borrower income intricately intertwine, revealing underlying risk assessments and borrowing preferences. The visualizations underscore the importance of geographic and socioeconomic context in understanding peer-to-peer lending trends. These insights are valuable for lenders, policymakers, and investors aiming to optimize lending strategies and assess regional financial health.
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