vishnukanduri/Credit-Risk-Modeling-in-Python
Modeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the feature. Checked for missing values and cleaned the data. Built the probability of default model using Logistic Regression. Visualized all the results. Computed Weight of Evidence and price elasticities.
This project helps credit risk analysts evaluate the likelihood of consumer loan defaults. By taking raw loan application data, it cleans and processes the information to build a probability of default model using logistic regression. The output provides insights into risk factors and visualizes the results, enabling better lending decisions.
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Use this if you are a credit risk analyst needing a clear, explained example of building a consumer loan default prediction model from scratch.
Not ideal if you need a production-ready, highly optimized credit scoring system rather than a detailed analytical example.
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Mar 31, 2020
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