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| import pandas as pd | |
| import numpy as np | |
| def encode_categorical_features(df: pd.DataFrame) -> pd.DataFrame: | |
| df = df.copy() | |
| df["genre"] = df["genre"].str.upper().map({"F": 0, "M": 1}) | |
| df["heure_supplementaires"] = ( | |
| df["heure_supplementaires"].str.capitalize().map({"Non": 0, "Oui": 1}) | |
| ) | |
| df["frequence_deplacement"] = ( | |
| df["frequence_deplacement"].str.capitalize().map({ | |
| "Aucun": 0, | |
| "Occasionnel": 1, | |
| "Frequent": 2 | |
| }) | |
| ) | |
| return df | |
| def features_engineering(df: pd.DataFrame) -> pd.DataFrame: | |
| df = df.copy() | |
| df['stabilite_management'] = df['annees_sous_responsable_actuel'] / (df['annees_dans_le_poste_actuel'] + 1) | |
| df['flag_surcharge_et_deplacement'] = ( | |
| (df['heure_supplementaires'] == 1) & (df['frequence_deplacement'] == 2) | |
| ).astype(int) | |
| df['ratio_promotion_anciennete'] = ( | |
| df['annees_depuis_la_derniere_promotion'] / (df['annees_dans_l_entreprise'] + 1) | |
| ) | |
| sat_cols = [ | |
| 'satisfaction_employee_environnement', | |
| 'satisfaction_employee_nature_travail', | |
| 'satisfaction_employee_equipe', | |
| 'satisfaction_employee_equilibre_pro_perso', | |
| ] | |
| df['satisfaction_globale'] = df[sat_cols].sum(axis=1) | |
| df['satisfaction_min'] = df[sat_cols].min(axis=1) | |
| df['log_revenu'] = np.log1p(df['revenu_mensuel']) | |
| return df | |
| def remove_redundant_columns(df: pd.DataFrame) -> pd.DataFrame: | |
| df = df.copy() | |
| df = df.drop(columns=[ | |
| 'niveau_hierarchique_poste', | |
| 'annees_sous_responsable_actuel', | |
| 'annees_depuis_la_derniere_promotion', | |
| 'departement', | |
| 'revenu_mensuel' | |
| ], errors='ignore') | |
| return df |