How do I prepare data for machine learning?

How do I prepare data for machine learning?

Preparing data for machine learning involves several steps, including data cleaning, normalization, and splitting the dataset.

What is data cleaning and why is it important?

Data cleaning is the process of correcting or removing inaccurate records from a dataset. It's crucial because poor quality data can lead to inaccurate models.

Can you explain normalization?

Normalization is the process of scaling data to a specific range, usually 0 to 1, to ensure that no single feature dominates the model.

How do I split my dataset for training and testing?

You can split your dataset using techniques like random sampling or stratified sampling, typically allocating 70-80% for training and 20-30% for testing.