When embarking on a machine learning journey, one of the most pivotal stages is data preprocessing. As every machine learning enthusiast knows, the quality of your input data can make or break your model's performance. Below, we present some essential tips for effective data preprocessing:
Handling missing values is crucial. Consider using techniques such as imputation to fill in missing data, or remove records with too many null entries.
Normalize or standardize features to ensure each feature contributes equally to the analysis, preventing some features from dominating due to differing scales.
Transform categorical variables into a numerical format using techniques like one-hot encoding to facilitate model training.
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