Evaluate risk with credit loans based on borrower financial history and loan terms using predictive classifiers and quantify risk with regression modeling. Publicly available data set by Prosper Loan.
This project focus' primarily on machine learning tenchinques in python (Jupyternotebooks). 3 primary files are included in this project:
- Report ('Credit Risk Modeling.pdf')
- Jupyternotebooks file - Models & Analysis ('Loan Risk.ipynb').
- csv of the prosper loan data ('prosperLoanData1.csv')
- Variable Definition File ('Prosper Loan Data - Variable Definitions.xlsx')
Jupyternotebooks is required to run the .ipynb file.
For modeling, plotting, and statistical operations, the following libraries and modules are imported:
- import pandas as pd
- import numpy as np
- from time import time
- import matplotlib.pyplot as plt
- %matplotlib inline
- import seaborn as sb
- import warnings
- from sklearn.model_selection import train_test_split
- from sklearn.preprocessing import StandardScaler
- from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
- from sklearn import model_selection
- from sklearn.metrics import confusion_matrix
- from sklearn.metrics import accuracy_score
- from sklearn import svm
- from sklearn.ensemble import RandomForestClassifier
- from sklearn.linear_model import LinearRegression
- from sklearn.metrics import mean_squared_error
Please note that the data used in this project is comprised of 8 years of data from Prosper Loan. This dataframe consists of over 113,000 rows and 81 columns. Since the prosperLoanData.csv is 100+ mb, a link is provided below to download the original dataset. A modified version of the dataset is provided in the repository for analysis. Please make sure all files are located in the same working directory before running.
https://docs.google.com/document/d/1qEcwltBMlRYZT-l699-71TzInWfk4W9q5rTCSvDVMpc/pub
- Paras Patel