Minor Project for approving loans to customers
This repository contains a machine learning project focused on predicting loan approvals using a dataset of historical loan application data. The goal of this project is to develop a predictive model that can assist in automating the loan approval process, making it more efficient and accurate.
The dataset used for this project is sourced from XYZ Dataset Source. It includes various features such as applicant's age, income, credit score, loan amount, etc., and their corresponding loan approval status (0 for not approved, 1 for approved). The dataset is available in the data directory.
─ data/ # Dataset files ─ notebooks/ # Jupyter notebooks for data exploration, preprocessing, and model development ─ src/ # Source code for the machine learning model ─ data_preprocessing.py ─ model_training.py─ loan_approval_app.py # Example application for using the trained model ─ requirements.txt # Python dependencies
- Clone this repository:
git clone https://github.com/your-username/loan-approval.git - Navigate to the project directory:
cd loan-approval - Install the required dependencies:
pip install -r requirements.txt
- Explore the Jupyter notebooks in the
notebooksdirectory to understand the data preprocessing and model development steps. - Use the provided scripts in the
srcdirectory to preprocess the data and train the model. - Deploy the trained model using the example application in
src/loan_approval_app.py.
We have used a Decision Tree classifier for this loan approval prediction task. The model is trained on preprocessed data and is capable of predicting whether a loan application will be approved or not.
The model's performance is evaluated using various metrics such as accuracy, precision, recall, and F1-score. These metrics provide insights into how well the model predicts loan approvals based on the dataset.
Contributions to this project are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request. For major changes, please discuss them in advance.