This repository contains code for a machine learning project focused on predicting loan approval status based on various applicant attributes. The project encompasses data preprocessing, exploratory data analysis (EDA), feature engineering, model building, hyperparameter tuning, and model evaluation.
The ability to predict whether a loan application will be approved is crucial for financial institutions to manage risk effectively. This project aims to build machine learning models that can accurately predict the approval status of loan applications based on applicant information such as income, credit history, and property area.
The project follows a typical machine learning workflow, including the following steps:
- Data Loading and Preprocessing: The dataset is loaded, and initial preprocessing steps are performed, such as handling missing values and dropping unnecessary columns.
- Exploratory Data Analysis (EDA): Exploratory data analysis is conducted to gain insights into the data and understand the relationships between different variables. This includes visualizations such as histograms, scatter plots, and correlation matrices.
- Feature Engineering: New features are created from existing ones to enhance the predictive power of the models. Techniques such as feature scaling, one-hot encoding, and creating interaction terms may be employed.
- Model Building: Several machine learning models are trained on the preprocessed data. Models such as logistic regression, random forest, and XGBoost are commonly used for binary classification tasks like loan approval prediction.
- Hyperparameter Tuning: Hyperparameters of the models are tuned using techniques like grid search and randomized search to optimize performance.
- Model Evaluation: The performance of the trained models is evaluated using metrics such as accuracy, precision, recall, and F1-score. Confusion matrices and ROC curves may also be used for evaluation.
To run the code in this repository, follow these steps:
Clone the repository to your local machine:
git clone https://github.com/your-username/loan-approval-prediction.git
Install the required dependencies using pip:
pip install -r requirements.txt
Run the Jupyter notebooks or Python scripts in the src directory to execute different parts of the project.
The code in this repository can be used as a guide for building similar machine learning projects or for understanding the loan approval prediction process. You can modify the code, datasets, and parameters to fit your specific requirements.
Contributions to this project are welcome. If you have any suggestions, improvements, or bug fixes, please feel free to open an issue or submit a pull request