This repository contains a machine learning project that focuses on predicting loan default using historical lending data. The goal of this project is to create predictive models that can assist financial institutions in making informed lending decisions.
The loan prediction project aims to address the challenge of assessing the creditworthiness of loan applicants. It utilizes various machine learning algorithms to build predictive models that evaluate the likelihood of a loan being paid back or defaulted. These models can help financial institutions mitigate risks and make lending decisions that are both profitable and secure.
The project uses a dataset that contains historical information about loans, including applicant details, loan terms, and loan status (whether the loan was fully paid or charged off). This dataset is crucial for training and testing the machine learning models.
Dataset Link: https://www.kaggle.com/datasets/wordsforthewise/lending-club?datasetId=902&sortBy=voteCount
To run the project locally, follow these steps:
- Clone this repository to your local machine.
- Install the required dependencies listed in the
requirements.txtfile.pip install -r requirements.txt
The project is organized as follows:
CSV/: Contains the dataset files. Notebooks/: Jupyter notebooks for data exploration and model development. ipynb/: Python source code for data preprocessing, model training, and evaluation.
I want you to know that contributions to this project are welcome. If you have any suggestions, find a bug, or want to add new features, please create an issue or submit a pull request.