Loan status prediction using machine learning and logistic regression involves forecasting whether a loan will be approved or not based on various applicant features and historical data. This project aims to build a predictive model to help financial institutions in making informed lending decisions.
The dataset used for this project includes features such as applicant income, loan amount, credit history, and other relevant information. The dataset can be obtained from various sources, such as Kaggle.
Python 3.x pandas numpy scikit-learn matplotlib (for visualization) seaborn (for visualization)
Prepare the dataset and save it as a CSV file in the project directory. Run the script to preprocess the data, train the model, and make predictions.
The model is trained using the logistic regression algorithm from the scikit-learn library. The script includes data preprocessing steps, such as handling missing values, encoding categorical variables, and splitting the data into training and testing sets.
Once the model is trained, it can be used to predict the loan status for new applicants based on their features. The script includes a function to make predictions and output the results.
The model's performance is evaluated using metrics such as accuracy. Train Data Accuracy : 77.31% Test Data Accuracy : 75%
The application is deployed using Streamlit. You can access it here = https://whwx7rd4djvybbssmelb6b.streamlit.app/
Contributions are welcome! If you have any suggestions or improvements, please create a pull request or open an issue.