This project involves building a Decision Tree model to classify loan applications based on various applicant data. The goal is to predict whether a loan application should be approved or not.
The dataset used for this project contains the following key features:
- ApplicantIncome: Income of the applicant.
- CoapplicantIncome: Income of the coapplicant (if any).
- LoanAmount: Loan amount requested by the applicant.
- Loan_Amount_Term: Term of the loan in months.
- Credit_History: Credit history of the applicant.
- Property_Area: Urban, Semi-Urban, or Rural area of the applicant.
- Education: Education level of the applicant (Graduate/Not Graduate).
- Self_Employed: Whether the applicant is self-employed or not.
- Loan_Status: Target variable indicating loan approval status (Y/N).
- Data Preprocessing: Handling missing values, encoding categorical variables, and splitting the dataset into training and testing sets.
- Model Selection: Implementing a Decision Tree classifier to build the model.
- Model Evaluation: Evaluating the model using accuracy.
The project includes a Tkinter-based graphical user interface (GUI) to:
- Display the input fields for user data.
- Allow users to enter applicant details.
- Display the model's prediction of whether a loan should be approved or not.
- Input Fields: Text boxes and dropdowns for all relevant features (e.g., income, loan amount, credit history).
- Submit Button: Triggers the prediction based on user inputs.
- Result Display: Shows the loan approval result on the GUI window.
- Python 3.x
- Pandas
- NumPy
- Tkinter (built-in with Python)
- Scikit-learn
- Matplotlib
To get started with this project, clone the repository and install the required dependencies: