Analyzing credit default

This repository contains a Jupyter notebook for analyzing credit risk. The notebook is written in Python and uses various libraries and packages such as scikit-learn, pandas, and numpy. Additionally, the repository includes a dataset, a requirements.txt file, and an Image file.

Getting Started

To get started with the analysis, you'll need to have Python 3.8 or higher installed on your machine. Clone this repository to your local machine using the following command:

Copy code:

git clone https://github.com/your_username/credit-risk-analysis.git Once you have cloned the repository, you can install the required packages by running the following command in your terminal:

Copy code:

pip install -r requirements.txt

Data

The data used for this analysis is located in the data directory. The credit-data.csv file contains information about credit applicants, including their age, income, and credit score.

Notebook

The analysis is contained in the Analyzing Credit Default.ipynb notebook, which is located in the root directory. The notebook is organized as follows:

• Introduction: A brief overview of the analysis and its objectives.
• Import Libraries: Importing the required libraries for the analysis.
• Data Preprocessing: Loading the dataset, handling missing values, encoding categorical variables, and scaling the features.
• Exploratory Data Analysis: Visualizing and analyzing the distribution of features in the dataset to gain insights into the data.
• Cross Validation: Splitting the dataset into training and testing sets using cross-validation techniques.
• Model Implementation: Building a logistic regression model to predict credit risk and tuning its hyperparameters using grid search.
• Evaluation: Evaluating the performance of the model using various metrics such as accuracy, precision, recall, and F1-score.

Conclusion

This repository provides a notebook for analyzing credit risk using machine learning techniques. The code and data can be used as a starting point for more complex analyses or can be adapted to suit specific use cases. If you have any questions or feedback, feel free to contact me. Additionally, you can find a visual representation of the data exploration in Images file.