Because the quality of the data and the useful information that can be derived from it directly affect our model's ability to learn, data pre-processing is an essential step in machine learning; As a result, before we can feed our model the data, we need to pre-process it. Many steps are involved in pre-processing in machine learning. The main importance of pre-processing is it results in high accuracy and reduces time.
1. How to Handle Null Values: We must intervene because no model can handle these NULL or NaN values on its own. We must determine whether our dataset contains null values. The isnull() method enables us to accomplish that.
2. Standardization: It is yet another essential step in the pre-processing process. In standardization, we transform our values so that the mean and standard deviation are equal to zero.
3. How to Deal with Categorical Variables: Another crucial aspect of machine learning is the management of categorical variables. The variables that are discrete and not continuous are referred to as categorical variables.
4. Single-Hot Coding: Therefore, what we do in One-Hot Encoding is to create 'n' columns, where n is the number of distinct values that the nominal variable can take.
5. Multicollinearity: In our dataset, multicollinearity occurs when features are highly dependent on one another.
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