Loan prediction is a crucial task in the financial industry as it helps lenders assess the creditworthiness of loan applicants and make informed decisions. In this project, we explore the application of various machine learning (ML) algorithms to predict loan repayment probabilities. By leveraging the power of ML models, we aim to improve accuracy, efficiency, and risk management in lending practices.

This report provides a step-by-step analysis of loan prediction using ML algorithms, culminating in the selection of the most suitable model for production deployment. The following sections outline the structure of the project:

• Exploratory Data Analysis (EDA)
• Data Cleaning
• ML Model Selection and Evaluation
• Choosing the Best Model for Production

You can find the complete code and analysis of this project in the following Kaggle notebook:

To clone and run this project locally, follow the steps below:

• Clone the repository:

git clone https://github.com/pooranjoyb/LoanPrediction.git

cd LoanPrediction

• Install the required dependencies.

pip install -r requirements.txt

• Run the Streamlit Application

python -m loan_predictor

The loan prediction project utilizes the following technologies and libraries:

• Python
• Streamlit
• NumPy
• scikit-learn
• pandas

Feel free to explore the code and adapt it to your specific needs.