['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']
StandardScaler()
['opt_in']
OneHotEncoder(handle_unknown='ignore', sparse_output=False)
LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
+ " ['opt_in'])])['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet', 'is_email_true', 'opt_in', 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']
StandardScaler()
['opt_in']
OneHotEncoder(handle_unknown='ignore', sparse_output=False)
LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
" 1.0: 3.486549107420539},\n",
" max_iter=5000, solver='saga')['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']
StandardScaler()
['opt_in']
OneHotEncoder(handle_unknown='ignore', sparse_output=False)
LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
+ " ['opt_in'])])['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet', 'is_email_true', 'opt_in', 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']
StandardScaler()
['opt_in']
OneHotEncoder(handle_unknown='ignore', sparse_output=False)
LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
" 1.0: 3.486549107420539},\n",
" max_iter=5000, solver='saga')Pipeline(steps=[('preprocessor',\n",
+ " ColumnTransformer(transformers=[('num',\n",
+ " Pipeline(steps=[('scaler',\n",
+ " StandardScaler())]),\n",
+ " ['nb_tickets', 'nb_purchases',\n",
+ " 'total_amount',\n",
+ " 'nb_suppliers',\n",
+ " 'vente_internet_max',\n",
+ " 'purchase_date_min',\n",
+ " 'purchase_date_max',\n",
+ " 'time_between_purchase',\n",
+ " 'nb_tickets_internet',\n",
+ " 'is_email_true', 'opt_in',\n",
+ " 'gender_female',\n",
+ " 'gender_male',\n",
+ " 'gender_other',\n",
+ " 'nb_campaigns',\n",
+ " 'nb_campaigns_opened']),\n",
+ " ('cat',\n",
+ " Pipeline(steps=[('onehot',\n",
+ " OneHotEncoder(handle_unknown='ignore',\n",
+ " sparse_output=False))]),\n",
+ " ['opt_in'])])),\n",
+ " ('randomF',\n",
+ " RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n",
+ " 1.0: 3.486549107420539},\n",
+ " n_jobs=-1))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('preprocessor',\n",
+ " ColumnTransformer(transformers=[('num',\n",
+ " Pipeline(steps=[('scaler',\n",
+ " StandardScaler())]),\n",
+ " ['nb_tickets', 'nb_purchases',\n",
+ " 'total_amount',\n",
+ " 'nb_suppliers',\n",
+ " 'vente_internet_max',\n",
+ " 'purchase_date_min',\n",
+ " 'purchase_date_max',\n",
+ " 'time_between_purchase',\n",
+ " 'nb_tickets_internet',\n",
+ " 'is_email_true', 'opt_in',\n",
+ " 'gender_female',\n",
+ " 'gender_male',\n",
+ " 'gender_other',\n",
+ " 'nb_campaigns',\n",
+ " 'nb_campaigns_opened']),\n",
+ " ('cat',\n",
+ " Pipeline(steps=[('onehot',\n",
+ " OneHotEncoder(handle_unknown='ignore',\n",
+ " sparse_output=False))]),\n",
+ " ['opt_in'])])),\n",
+ " ('randomF',\n",
+ " RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n",
+ " 1.0: 3.486549107420539},\n",
+ " n_jobs=-1))])ColumnTransformer(transformers=[('num',\n",
+ " Pipeline(steps=[('scaler', StandardScaler())]),\n",
+ " ['nb_tickets', 'nb_purchases', 'total_amount',\n",
+ " 'nb_suppliers', 'vente_internet_max',\n",
+ " 'purchase_date_min', 'purchase_date_max',\n",
+ " 'time_between_purchase',\n",
+ " 'nb_tickets_internet', 'is_email_true',\n",
+ " 'opt_in', 'gender_female', 'gender_male',\n",
+ " 'gender_other', 'nb_campaigns',\n",
+ " 'nb_campaigns_opened']),\n",
+ " ('cat',\n",
+ " Pipeline(steps=[('onehot',\n",
+ " OneHotEncoder(handle_unknown='ignore',\n",
+ " sparse_output=False))]),\n",
+ " ['opt_in'])])['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet', 'is_email_true', 'opt_in', 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']
StandardScaler()
['opt_in']
OneHotEncoder(handle_unknown='ignore', sparse_output=False)
RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n",
+ " 1.0: 3.486549107420539},\n",
+ " n_jobs=-1)