2. Preparing Data for Training

Before training a machine learning model, it is crucial to ensure that the dataset is well-structured, clean, and representative of the problem being solved. Data preparation is one of the most time-consuming yet essential steps in machine learning, as poor-quality data can lead to inaccurate or biased models. The key steps in data preparation include:

• Data Cleaning: Handling missing values, removing duplicate entries, correcting inconsistencies, and filtering out noise in the dataset.
• Feature Engineering: Creating new features or modifying existing ones to improve the predictive power of the model.
• Normalization and Scaling: Many machine learning algorithms perform better when numerical features are scaled to a specific range.
• Data Splitting: A dataset is typically divided into three parts – training set, validation set, and test set.

Proper data preparation reduces the risk of bias, overfitting, and underfitting, making it a crucial foundation for successful model training.