Entropy

Data Collection and Preprocessing

Data is the backbone of any Machine Learning (ML) model. The first step in ML practice involves collecting high-quality data from various sources such as databases, APIs, web scraping, or sensors. Once collected, data must be cleaned and preprocessed to remove noise, handle missing values, and ensure consistency.

Preprocessing includes normalization, standardization, feature scaling, and categorical encoding. Tools like Pandas, NumPy, and Scikit-Learn are widely used for data manipulation. Effective data preprocessing leads to better model performance and reduces the risk of biased predictions.