Description: This repository contains the code for a credit risk assessment model built using Python. The model is trained on a dataset of past credit applications, and it predicts the risk level of a new credit application based on various features such as income, age, employment status, and more.
The project is implemented using popular Python libraries like pandas, scikit-learn, and matplotlib. The project includes the following steps:
Exploratory data analysis: Analysis of the dataset, feature engineering, and visualization of the data. Data preprocessing: Cleaning the data, handling missing values, and feature scaling. Model training: Implementing various machine learning algorithms and selecting the best one using grid search. Model evaluation: Evaluating the model's performance using various metrics such as accuracy, precision, recall, and F1 score. Model deployment: Using the trained model to predict the credit risk level of new credit applications. The project also includes detailed explanations and comments to help understand the code and the underlying concepts.
Feel free to use this code as a reference for your own credit risk assessment projects.
We'll be using this German dataset that has the information on the different individuals and determines whether the individual has a good or bad risk data = 'german_credit_data.csv'