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.
The project utilizes a dataset containing customer information that includes:
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Loan_ID : Unique Loan ID
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Gender : Male/ Female
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Married : Applicant married (Y/N)
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Dependents : Number of dependents
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Education : Applicant Education (Graduate/ Under Graduate) Self_Employed : Self employed (Y/N)
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ApplicantIncome : Applicant income
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CoapplicantIncome : Coapplicant income
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LoanAmount : Loan amount in thousands of dollars
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Loan_Amount_Term : Term of loan in months
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Credit_History : Credit history meets guidelines yes or no
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Property_Area : Urban/ Semi Urban/ Rural Loan_Status : Loan approved (Y/N) this is the target variable
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.
Includes trained machine learning models for loan eligibility prediction.
Contains the results and evaluation metrics of the models.
Ensure the dataset is in the correct format and contains all necessary features. Run Data_Preprocessing.ipynb to clean and preprocess the data.
Explore Exploratory_Data_Analysis.ipynb to understand the dataset better.
Execute Model_Training.ipynb to train machine learning models for loan eligibility prediction.
Evaluate the performance of the trained models.
- Python 3.x
- Jupyter Notebook
- Pandas
- NumPy
- Scikit-learn
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.