Data Preprocessing in Machine learning

• Data pre-processing is a process of preparing the raw data and making it suitable for a machine learning model. Data pre-processing is the first and crucial step while creating a machine learning model.
• When creating a machine learning project, We may not always come across
  clean and formatted data. So, while performing any operation on the data it is mandatory to clean the data and put it in a formatted way.

Why do we need Data Preprocessing?

• real-world data generally contains noises, and missing values, and may be in an unusable format that cannot be directly used for machine learning models.
• Data pre-processing is a required task for cleaning the data and making it suitable for a machine learning model which also increases the accuracy and efficiency of a machine learning model.

Steps involved in Data pre-processing:

• Getting the dataset.
• Importing libraries.
• Importing datasets.
• Finding Missing Data.
• Encoding Categorical Data.
• Splitting dataset into training and test set.
• Feature scaling.