In this notebook, we will analyze international debt data collected by The World Bank. The dataset contains information about the amount of debt (in USD) owed by developing countries across several categories. We are going to find the answers to questions like: What is the total debt owed by the countries listed in the dataset? Which country owns the maximum amount of debt, and what is the amount? What is the average amount of debt owed by countries across different debt indicators?

In this notebook, we will predict car prices and explore the methods we use to interpret and evaluate the results of our linear regression model. Assess the data. Build a basic linear regression model using a few selected features and run the model summary. Build a full linear regression model using a full range of features available. Plot a correlation matrix and perform feature engineering by reducing the number of independent variables we have. Perform one-hot Encoding on categorical variables. Split data into train and test sets, fit the model to training data and perform test set predictions. Print the summary output of the model, select variables that are statistically significant (p-value < 0.05), and retrain the model. Scale our independent variables and fit the model with our standardized data. Evaluate the Linear Regression model.