Compute feature correlations with target
Identify predictive features
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Write Python code to compute the correlation between all features and the target variable [target] in DataFrame df. Use Pearson for numerical features, point-biserial for binary vs continuous, and Cramér's V for categorical vs categorical. Output a ranked table of correlation strengths.
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Pensez à convertir les variables catégorielles en type `category` et à gérer les NaN (`dropna()`) avant de lancer vos fonctions. Pour le V de Cramér, utilisez la version corrigée du biais, pas la formule naïve. Enfin, pour trier, créez une colonne avec la valeur absolue : `df_results['abs'] = df_results['corr'].abs()` puis `sort_values('abs', ascending=False)`.
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