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https://github.com/ghosts-octopus/loan-status-prediction-using-svm-

A Loan Status Prediction system is a machine learning model or application that predicts whether a loan application will be approved or rejected. Using lang. Jupyter Notebook & Python. Below like show the demo >>
https://github.com/ghosts-octopus/loan-status-prediction-using-svm-

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A Loan Status Prediction system is a machine learning model or application that predicts whether a loan application will be approved or rejected. Using lang. Jupyter Notebook & Python. Below like show the demo >>

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# 📉 Loan Status Prediction 📊



## About the Project



A Loan Status Prediction system is a machine learning model or application that predicts whether a loan application will be approved or rejected based on various input features and historical data. The system aims to automate the loan approval process and assist in making informed decisions by assessing the creditworthiness of applicants.



The Loan Status Prediction system typically utilizes a dataset that includes information about loan applicants, such as personal details (gender, age, marital status), financial information (income, debt obligations), credit history (credit score, repayment history), employment details, and other relevant factors.



The system goes through several steps, including data preprocessing, feature engineering, model training, and prediction. 



## Steps involved in the Project



1. **Dataset**: The project utilizes a dataset containing information about loan applications. The dataset includes features such as gender, marital status, number of dependents, education, employment status, income details, loan amount, loan amount term, credit history, property area, and the loan status (whether approved or not). ```Click on the dataset link```[click here](https://loan-status-prediction-using-svm--w2ll4auviaf.streamlit.app/)



2. **Data Preprocessing:** The dataset undergoes several preprocessing steps to handle missing values, convert categorical variables into numerical representations, and ensure consistency with the training data. Missing values are filled using appropriate methods, and categorical variables are encoded using label encoding or mapping.



3. **Exploratory Data Analysis (EDA):** EDA is performed using data visualization techniques. Plots such as count plots are used to analyze the distribution of loan approval based on education, marital status, etc., providing insights into the relationship between various features and the loan status.



4. **Splitting the Data:** The preprocessed dataset is divided into training and testing sets using the train_test_split function from sklearn.model_selection. This splitting allows us to train the SVM classifier on a portion of the data and evaluate its performance on unseen data.



5. **SVM Classifier:** The SVM (Support Vector Machine) classifier is chosen as the predictive model for loan status prediction. The classifier is initialized with a linear kernel, indicating a linear decision boundary between the loan approval classes.



6. **Training and Evaluation:** The SVM classifier is trained using the training data. The fit method is used to train the classifier on the features and the corresponding loan approval labels. The accuracy of the model is evaluated using the training data itself by comparing the predicted labels with the actual labels. The same process is applied to the testing data to evaluate the model's performance on unseen data.



7. **Saving the Model:** The trained SVM classifier is saved using the pickle library. The model is serialized and saved to a file (e.g., loan_status_model.pkl) to be used for future predictions without the need for retraining.



8. **Streamlit App:** A web application is developed using Streamlit to create an interactive interface for users to input loan application details. The input fields include features such as gender, marital status, income, loan amount, etc. After entering the details and clicking the "Predict Loan Status" button, the trained SVM model is loaded, and the loan status is predicted based on the provided information.



## A brief about Support Vector Machine Model



Support Vector Machine,(SVM), falls under the “supervised machine learning algorithms” category. It can be used for classification, as well as for regression. In this model, we plot each data item as a unique point in an n-dimension,(where n is actually, the number of features that we have), with the value of each of the features being the value of that particular coordinate. Then, we perform the process of classification by finding the hyper-plane that differentiates the two classes.



![1](https://github.com/dhrupad17/Loan-Status-Prediction/assets/91726340/2e544eb1-5c8d-4239-a81b-b6001ef8185e)



SVM is preferred over other algorithms when :



1)The data is not regularly distributed.



2)SVM is generally known to not suffer the condition of overfitting.



3)Performance of SVM, and its generalization is better on the dataset.



4)And, lastly, SVM is known to have the best results for classification types of problems.