/Loan-Approval-ML-Model

Primary LanguagePython

Loan Approval Prediction Model

This repository contains the code for a loan approval prediction model. The model is designed to predict whether a loan will be approved based on various features such as education, employment status, and the number of dependents.

Project Overview

The goal of this project is to build a predictive model that can accurately determine the likelihood of a loan being approved. This model can be useful for financial institutions to make informed decisions about loan approvals.

Data

The dataset used for this project is a loan approval dataset, which includes features such as:

  • Education level
  • Employment status
  • Number of dependents
  • Loan status (approved or not)

The dataset is loaded from a CSV file named loan_approval_dataset.csv.

Methodology

The project uses the following steps:

  1. Data Exploration: Initial analysis of the dataset to understand the structure, missing values, and unique values in each column.
  2. Data Preprocessing: Encoding categorical variables using LabelEncoder from sklearn.preprocessing.
  3. Feature Engineering: Visualizing the distribution of categorical features and their relationship with the loan status.
  4. Model Training: Training two models - Logistic Regression and RandomForestClassifier - to predict the loan status.
  5. Model Evaluation: Evaluating the models using accuracy score and comparing their performance.

Models

Two models are used in this project:

  • Logistic Regression: A simple yet powerful model for binary classification problems.
  • RandomForestClassifier: An ensemble learning method that operates by constructing multiple decision trees at training time and outputting the class that is the mode of the classes of the individual trees.

Results

The model's performance is evaluated using the accuracy score. The results are compared between the two models to determine which one performs better on the loan approval prediction task.

How to Run

  1. Clone this repository.
  2. Install the required Python packages: pandas, numpy, matplotlib, seaborn, sklearn.
  3. Run the Jupyter notebook Project1.ipynb to execute the code and see the results.

Contributing

Contributions are welcome. Please feel free to submit a pull request or open an issue to discuss potential improvements.

License

This project is licensed under the MIT License. See the LICENSE file for details.