(2) One-hot encoding: Categorical variables (e.g., gender, region) were converted into binary dummy variables to avoid misinterpreting ordinal relationships. (3) Normalization: Continuous features ...

Normalization ensures that variables like age and anticholinergic burden contribute proportionally to the model during training. Encoding Categorical variables (e.g., gender, neuroleptic class) were ...

After preprocessing, including handling missing values and encoding categorical variables, the dataset was split into training and testing sets.

Many of their points are about the meaning of life and they are all absurdist. They believe that our stress comes from that quest for meaning and the knowledge we will never get. A definitive ...

This study presents a machine learning algorithm aimed at predicting home values in the housing market. By utilizing a comprehensive dataset that includes details such as location, size, number of ...

Data preprocessing The dataset undergoes thorough preprocessing, including handling missing values, encoding categorical variables, normalizing and scaling numerical data, and selecting key features ...

💡 Potential Improvement: Use One-Hot Encoding instead of Label Encoding if categorical features are non-ordinal. python Copy Edit df = pd.get_dummies (df, columns=categorical_cols, drop_first=True) ...