jauny143 / data-preprocessing-

Data preprocessing is a process of preparing the raw data and making it suitable for a machine learning model.

Data-Preprocessing-

Data preprocessing is a process of preparing the raw data and making it suitable for a machine learning model. A real-world data generally contains noises, missing values, and maybe in an unusable format which cannot be directly used for machine learning models. Data preprocessing is required tasks 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. It involves below steps:

1. Getting a dataset
2. Importing the required libraries
3. Importing the dataset
4. Finding missing data
5. Encoding categorical data
6. Spliting dataset into train and test dataset
7. Feature scaling