akashkathole7/loan_prediction

Loan Prediction

This project aims to predict whether a loan application will be approved or not based on various factors, such as applicant information, loan amount, and credit history. The goal is to develop a model that can accurately predict loan approval or rejection, which can be helpful for banks or other financial institutions in their decision-making processes.

The dataset used in this project contains information on loan applicants, including their gender, marital status, education level, income, loan amount, loan term, credit history, and other factors. The dataset is divided into a training set and a test set, with the training set used to train the model and the test set used to evaluate its performance.

The project is implemented in Python using various libraries, including Pandas, NumPy, Matplotlib, and Scikit-learn. The data is preprocessed, cleaned, and transformed to prepare it for modeling. The model is trained using various algorithms, including Logistic Regression, Random Forest, and XGBoost, and evaluated using metrics such as accuracy, precision, recall, and F1-score.

To use this project, users can download the dataset and run the Python code provided. The project is organized into different modules, including data preprocessing, feature engineering, model selection, and evaluation. Users can modify the code to experiment with different algorithms, hyperparameters, and feature combinations.

In conclusion, this project provides a useful tool for predicting loan approval or rejection based on various factors, and can be helpful for banks and other financial institutions in their decision-making processes.