Data preprocessing in Machine Learning refers to the process of preparing the raw data to make it appropriate for a building and training Machine Learning models. In simple words, data preprocessing in Machine Learning nothing but a data mining techniquethat transforms raw data into an understandable and readable format.

Why Data Preprocessing in Machine Learning?

Generally, real-world data is inconsistent, incomplete, inaccurate and often lacks specific attribute values/trends. Here is where data preprocessing comes into scenario. In Machine Learning Data Preprocessing is a crucial step that helps improve the quality of data. Data Preprocessing promotes the extraction of meaningful information from the data.

Data preprocessing involves:

1. Getting the dataset
2. Importing libraries
3. Importing datasets
4. Finding Missing Data
5. Encoding Categorical Data
6. Splitting dataset into training and test set
7. Feature scaling

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