Credit Risk Analytics

An interactive web dashboard for exploring credit risk patterns across the Plains region (MN, IA, MO, KS, NE, SD, ND) using Lending Club loan data (2007–2018) enriched with macroeconomic indicators (FRED, BLS, BEA).

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Dataset

The raw data file is NOT included in this repository due to its size (original dataset with 2,260,668 rows and 145 columns).

Required columns after preprocessing

The app expects a cleaned CSV at data/cleaned_loan_data (2).csv with at least these columns:

Column	Description
loan_amnt	Loan amount ($)
int_rate	Interest rate (%)
dti	Debt-to-income ratio
revol_util	Revolving line utilization (%)
annual_inc	Annual income ($)
installment	Monthly installment ($)
emp_length_num	Employment length (numeric, years)
grade_num	Loan grade (1=A, 2=B, 3=C, 4=D, 5=E)
income_cat	Income category (0=Low, 1=Mid, 2=High, 3=Very High)
addr_state	Borrower state (MN, IA, MO, KS, NE, SD, ND)
default	Default flag (0 = no default, 1 = default)
open_acc	Number of open credit accounts
issue_d	Loan issue date (parsed as datetime)
unemployment_rate	State-level unemployment rate (FRED)
inflation	Inflation rate (BLS)
gdp_growth	GDP growth rate (BEA)

Preprocessing steps (from Credit_Risk_.ipynb)

Run the Jupyter notebook to reproduce the cleaned dataset from raw Lending Club data:

jupyter notebook Credit_Risk_.ipynb

The notebook handles: filtering to Plains states → feature engineering → macroeconomic data merge → encoding → export to data/cleaned_loan_data (2).csv.

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Steps to Run

1. Clone the repository

git clone https://github.com/vuchau0802/Credit-Risk-Analytics.git
cd Credit-Risk-Analytics

2. Create and activate a virtual environment

# Windows
python -m venv venv
venv\Scripts\activate

# macOS / Linux
python -m venv venv
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Run the app

python app.py

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Demo

Plains region states covered: Minnesota, Iowa, Missouri, Kansas, Nebraska, South Dakota, North Dakota.