/bank_loan_prediction

bank loan prediction using Machine Learning

Primary LanguageHTML

Loan Eligibility Prediction Project

Overview:

This project focuses on predicting the loan eligibility of customers in a bank using machine learning techniques. The goal is to assist the bank in automating the loan approval process by analyzing various customer features and determining their eligibility for a loan.

Project Structure:

Data:

The project utilizes a dataset containing customer information that includes:

  • Loan_ID : Unique Loan ID

  • Gender : Male/ Female

  • Married : Applicant married (Y/N)

  • Dependents : Number of dependents

  • Education : Applicant Education (Graduate/ Under Graduate) Self_Employed : Self employed (Y/N)

  • ApplicantIncome : Applicant income

  • CoapplicantIncome : Coapplicant income

  • LoanAmount : Loan amount in thousands of dollars

  • Loan_Amount_Term : Term of loan in months

  • Credit_History : Credit history meets guidelines yes or no

  • Property_Area : Urban/ Semi Urban/ Rural Loan_Status : Loan approved (Y/N) this is the target variable

Notebooks:

loan_prediction.ipynb

  • Contains data cleaning and preprocessing steps.
  • Explores the dataset to gain insights.
  • Implements machine learning models for loan eligibility prediction.
  • Evaluates the performance of the trained models.

Models:

Includes trained machine learning models for loan eligibility prediction.

Results:

Contains the results and evaluation metrics of the models.

Instructions:

Data Preparation:

Ensure the dataset is in the correct format and contains all necessary features. Run Data_Preprocessing.ipynb to clean and preprocess the data.

Exploratory Data Analysis:

Explore Exploratory_Data_Analysis.ipynb to understand the dataset better.

Model Training:

Execute Model_Training.ipynb to train machine learning models for loan eligibility prediction.

Model Evaluation:

Evaluate the performance of the trained models.

Dependencies:

  • Python 3.x
  • Jupyter Notebook
  • Pandas
  • NumPy
  • Scikit-learn

Conclusion:

This project aims to provide a predictive model that can assist the bank in making informed decisions regarding loan approvals. By leveraging machine learning techniques, the bank can streamline its loan approval process and improve efficiency.