LoanPrediction

This project involves predicting loan approval status based on various applicant attributes. It uses a dataset of loan applications and applies machine learning algorithms to classify whether a loan will be approved or not.

Dataset

The dataset used in this project is stored in a CSV file named dataset.csv. It contains information about loan applicants, including attributes such as gender, marital status, education, income, loan amount, and more.

Getting Started

To get started with the project, follow these steps:

1. Clone the repository to your local machine.
2. Install the required dependencies, including Pandas and scikit-learn.
3. Ensure that the dataset.csv file is in the project directory.
4. Run the LoanPrediction.ipynb script to load and preprocess the dataset, train the machine learning models, and make predictions.

Requirements

To run the Jupyter Notebook and execute the code in this project, make sure you have the following dependencies installed:

• Python 3.x
• Jupyter Notebook
• Pandas
• scikit-learn

You can install the required dependencies by running the following command:

pip install jupyter pandas scikit-learn

Usage

To use this project, follow these steps:

1. Ensure that the dataset file dataset.csv is present in the project directory.
2. Open the LoanPrediction.ipynb script in a Python IDE or text editor.
3. Configure any desired parameters or settings, such as model hyperparameters.
4. Run the script to perform data preprocessing, model training, and prediction.

Results

After running the script, the trained models will output classification predictions for a new set of loan applications stored in the predict.csv file. The predictions will be displayed in the console or saved to an output file.