This lesson will focus on tackling redundant or correlated features in a dataset. These features provide similar, overlapping information that can potentially affect the performance of machine learning models. Learning to handle such features is critical to data cleaning and preprocessing.

Why is handling redundant or correlated features necessary, you ask? Here's the reason: Machine learning models are grounded in mathematics, and we need to ensure that the input data doesn't contain multicollinearity, meaning predictors are not independent, as it may cause issues with mathematical calculations. By identifying and eliminating redundant or correlated features, we can ensure that each feature in our dataset offers unique and valuable information that improves the predictive model's performance.

In statistics, correlation is a term that indicates the degree to which two variables move in relation to each other. If two features are highly correlated, they carry similar information.

In the context of our Titanic dataset, let's consider the pclass (passenger class) and fare (ticket cost) columns. Intuitively, passengers belonging to higher class (1st) would have paid a higher fare. Therefore, these two columns are likely to be strongly correlated.

To quantify this relationship, we use the correlation coefficient, a value between -1 and 1. If the correlation coefficient is close to 1, it indicates a strong positive correlation. Conversely, a coefficient near -1 indicates a strong negative correlation. A coefficient close to zero suggests no correlation.

To calculate the correlation between features in our dataset, we use the corr() function from the Pandas library. Let's see how it's done: