Preprocessing is an essential step in machine learning that involves transforming raw data into a format that can be used to train machine learning models. Here are some common preprocessing steps in machine learning:

1. Data cleaning: This involves identifying and handling missing or incorrect data in the dataset.

2. Feature scaling: Scaling numerical features to a common scale to avoid the dominance of a single feature over others.

3. Feature normalization: Rescaling data into a range of values between 0 and 1 or -1 and 1.

4. Feature encoding: Encoding categorical features into a numerical format that can be used by machine learning algorithms.

5. Dimensionality reduction: Reducing the number of features in the dataset to avoid overfitting, speed up computation, and simplify models. Machine Learning Course in Pune

6. Data splitting: Splitting the data into training, validation, and test sets to train and evaluate the model’s performance.

7. Data augmentation: Generating new samples from existing data to improve model performance and increase the size of the dataset. Machine Learning Course in Pune

8. Data transformation: Transforming data into a new form, such as applying logarithmic transformations or polynomial expansions, to improve the model’s performance.

The choice of preprocessing techniques used in a specific project depends on the type of data, the type of model used, and the task at hand.