Loan Amount Prediction using Linear Regression

This project utilizes Linear Regression, a fundamental machine learning algorithm, to predict loan amounts based on various applicant and loan characteristics. The sklearn library is employed to implement the Linear Regression model, which is trained on a dataset of historical loan data.

Key Components:

Data Preprocessing: pandas is used to load, manipulate, and preprocess the dataset.

Model Training: sklearn is used to implement the Linear Regression model, which is trained on the preprocessed data.

Model Evaluation: The performance of the model is evaluated using Mean Squared Error (MSE) as the metric.

Visualization: matplotlib and seaborn are used to visualize the predicted loan amounts against the actual values, providing insights into the model's performance.

Model Deployment: The trained model is saved using pickle for future use.

By leveraging the strengths of Linear Regression and the sklearn library, this project demonstrates a practical application of machine learning in the financial sector.