📊 This program utilizes a linear regression model to analyze an insurance dataset and predict insurance charges based on various input features. It makes use of the programming language Prolog to perform the computations.

The program performs the following steps:

1. Importing the necessary libraries for data manipulation and visualization.
2. Uploading the insurance dataset file.
3. Reading and displaying the dataset.
4. Converting string values in some columns to binary representation (0 or 1).
5. Separating the true output values (charges) from the input features.
6. Splitting the dataset into training and testing sets.
7. Creating a linear regression model.
8. Training the model using the training dataset.
9. Predicting the output values using the testing dataset.
10. Calculating the root mean squared error (RMSE) between the predicted and actual output values.
11. Evaluating the accuracy of the model.
12. Plotting a graph to visualize the relationship between the true and predicted output values.

1. Ensure that you have Prolog installed on your system.
2. Open a Prolog editor or compiler.
3. Copy the provided code into the editor.
4. Execute the program.

Please note that this program assumes you have the necessary dataset file named "Q2_insurance_dataset.csv" in the same directory as the program file.

Enjoy analyzing the insurance dataset and making predictions with the linear regression model!