diff --git a/0_2_Dataset_construction.py b/0_2_Dataset_construction.py index 6881072..a5816cb 100644 --- a/0_2_Dataset_construction.py +++ b/0_2_Dataset_construction.py @@ -1,5 +1,8 @@ -# Business Data Challenge - Team 1 +# Purpose of the script : Construction of training and test datasets for modelling by company +# Input : KPI construction function and clean databases in the 0_Input folder +# Output : Train and test datasets by compagnies +# Packages import pandas as pd import numpy as np import os @@ -9,12 +12,10 @@ import warnings from datetime import date, timedelta, datetime from sklearn.model_selection import train_test_split - # Create filesystem object S3_ENDPOINT_URL = "https://" + os.environ["AWS_S3_ENDPOINT"] fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL}) - # Import KPI construction functions exec(open('0_KPI_functions.py').read()) @@ -24,50 +25,59 @@ warnings.filterwarnings('ignore') def dataset_construction(min_date, end_features_date, max_date, directory_path): - # Import customerplus + # Import of cleaned and merged datasets df_customerplus_clean_0 = display_input_databases(directory_path, file_name = "customerplus_cleaned") df_campaigns_information = display_input_databases(directory_path, file_name = "campaigns_information", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at']) df_products_purchased_reduced = display_input_databases(directory_path, file_name = "products_purchased_reduced", datetime_col = ['purchase_date']) - - # if directory_path == "101": - # df_products_purchased_reduced_1 = display_databases(directory_path, file_name = "products_purchased_reduced_1", datetime_col = ['purchase_date']) - # df_products_purchased_reduced = pd.concat([df_products_purchased_reduced, df_products_purchased_reduced_1]) + df_target_information = display_input_databases(directory_path, file_name = "target_information") - # Filtre de cohérence pour la mise en pratique de notre méthode + # Dates in datetime format max_date = pd.to_datetime(max_date, utc = True, format = 'ISO8601') end_features_date = pd.to_datetime(end_features_date, utc = True, format = 'ISO8601') min_date = pd.to_datetime(min_date, utc = True, format = 'ISO8601') - #Filtre de la base df_campaigns_information + # Filter for database df_campaigns_information df_campaigns_information = df_campaigns_information[(df_campaigns_information['sent_at'] <= end_features_date) & (df_campaigns_information['sent_at'] >= min_date)] df_campaigns_information['opened_at'][df_campaigns_information['opened_at'] >= end_features_date] = np.datetime64('NaT') - #Filtre de la base df_products_purchased_reduced + # Filter for database df_products_purchased_reduced df_products_purchased_features = df_products_purchased_reduced[(df_products_purchased_reduced['purchase_date'] <= end_features_date) & (df_products_purchased_reduced['purchase_date'] >= min_date)] print("Data filtering : SUCCESS") - # Fusion de l'ensemble et creation des KPI + # Building and merging features - # KPI sur les campagnes publicitaires + # Campaigns features df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_information, max_date = end_features_date) - # KPI sur le comportement d'achat + # Purchasing behavior features df_tickets_kpi = tickets_kpi_function(tickets_information = df_products_purchased_features) - # KPI sur les données socio-démographiques + # Socio-demographic features df_customerplus_clean = customerplus_kpi_function(customerplus_clean = df_customerplus_clean_0) + + # Targets features + df_targets_kpi = targets_KPI(df_target = df_target_information) print("KPIs construction : SUCCESS") - # Fusion avec KPI liés au customer + # Merge - campaigns features df_customer = pd.merge(df_customerplus_clean, df_campaigns_kpi, on = 'customer_id', how = 'left') # Fill NaN values df_customer[['nb_campaigns', 'nb_campaigns_opened']] = df_customer[['nb_campaigns', 'nb_campaigns_opened']].fillna(0) + + # Merge - targets features + df_customer = pd.merge(df_customer, df_targets_kpi, on = 'customer_id', how = 'left') - # Fusion avec KPI liés au comportement d'achat - df_customer_product = pd.merge(df_tickets_kpi, df_customer, on = 'customer_id', how = 'outer') + # Fill NaN values + df_customer[['nb_targets', 'target_jeune', 'target_optin', 'target_optout', 'target_scolaire', 'target_entreprise', 'target_famille', 'target_newsletter', 'target_abonne']] = df_customer[['nb_targets', 'target_jeune', 'target_optin', 'target_optout', 'target_scolaire', 'target_entreprise', 'target_famille', 'target_newsletter', 'target_abonne']].fillna(0) + + # We standardise the number of targets closely linked to the company's operations + df_customer['nb_targets'] = (df_customer['nb_targets'] - (df_customer['nb_targets'].mean())) / (df_customer['nb_targets'].std()) + + # Merge - purchasing behavior features + df_customer_product = pd.merge(df_customer, df_tickets_kpi, on = 'customer_id', how = 'outer') # Fill NaN values df_customer_product[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'nb_tickets_internet']] = df_customer_product[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'nb_tickets_internet']].fillna(0) @@ -84,7 +94,7 @@ def dataset_construction(min_date, end_features_date, max_date, directory_path): # 2. Construction of the explained variable df_products_purchased_to_predict = df_products_purchased_reduced[(df_products_purchased_reduced['purchase_date'] <= max_date) & (df_products_purchased_reduced['purchase_date'] > end_features_date)] - # Indicatrice d'achat + # Construction of the dependant variable df_products_purchased_to_predict['y_has_purchased'] = 1 y = df_products_purchased_to_predict[['customer_id', 'y_has_purchased']].drop_duplicates() @@ -103,28 +113,24 @@ def dataset_construction(min_date, end_features_date, max_date, directory_path): return dataset ## Exportation - +# Sectors companies = {'musee' : ['1', '2', '3', '4'], # , '101' 'sport': ['5', '6', '7', '8', '9'], 'musique' : ['10', '11', '12', '13', '14']} +# Choosed sector type_of_comp = input('Choisissez le type de compagnie : sport ? musique ? musee ?') list_of_comp = companies[type_of_comp] -# Dossier d'exportation + +# Export folder BUCKET_OUT = f'projet-bdc2324-team1/Generalization/{type_of_comp}' -# Create test dataset and train dataset for sport companies - - -#start_date, end_of_features, final_date = df_coverage_modelization(list_of_comp, coverage_features = 0.7) - -# start_date, end_of_features, final_date = df_coverage_modelization(list_of_comp, coverage_train = 0.7) - +# Dates used for the construction of features and the dependant variable start_date = "2021-05-01" end_of_features = "2022-11-01" final_date = "2023-11-01" - +# Anonymous customer to be deleted from the datasets anonymous_customer = {'1' : '1_1', '2' : '2_12184', '3' : '3_1', '4' : '4_2', '101' : '101_1', '5' : '5_191835', '6' : '6_591412', '7' : '7_49632', '8' : '8_1942', '9' : '9_19683', '10' : '10_19521', '11' : '11_36', '12' : '12_1706757', '13' : '13_8422', '14' : '14_6354'} @@ -133,33 +139,23 @@ for company in list_of_comp: dataset = dataset_construction(min_date = start_date, end_features_date = end_of_features, max_date = final_date, directory_path = company) - # On retire le client anonyme + # Deletion of the anonymous customer dataset = dataset[dataset['customer_id'] != anonymous_customer[company]] - - # #train test set - # np.random.seed(42) - - # split_ratio = 0.7 - # split_index = int(len(dataset) * split_ratio) - # dataset = dataset.sample(frac=1).reset_index(drop=True) - # dataset_train = dataset.iloc[:split_index] - # dataset_test = dataset.iloc[split_index:] - + # Split between train and test dataset_train, dataset_test = train_test_split(dataset, test_size=0.3, random_state=42) # Dataset Test - # Exportation + # Export FILE_KEY_OUT_S3 = "dataset_test" + company + ".csv" FILE_PATH_OUT_S3 = BUCKET_OUT + "/Test_set/" + FILE_KEY_OUT_S3 with fs.open(FILE_PATH_OUT_S3, 'w') as file_out: dataset_test.to_csv(file_out, index = False) - print("Exportation dataset test : SUCCESS") + print("Export of dataset test : SUCCESS") # Dataset train - # Export FILE_KEY_OUT_S3 = "dataset_train" + company + ".csv" FILE_PATH_OUT_S3 = BUCKET_OUT + "/Train_set/" + FILE_KEY_OUT_S3 @@ -167,7 +163,7 @@ for company in list_of_comp: with fs.open(FILE_PATH_OUT_S3, 'w') as file_out: dataset_train.to_csv(file_out, index = False) - print("Exportation dataset train : SUCCESS") + print("Export of dataset train : SUCCESS") -print("FIN DE LA GENERATION DES DATASETS : SUCCESS") +print("End of dataset generation for ", type_of_comp," compagnies : SUCCESS") diff --git a/0_KPI_functions.py b/0_KPI_functions.py index 18a54a8..2425532 100644 --- a/0_KPI_functions.py +++ b/0_KPI_functions.py @@ -44,7 +44,6 @@ def campaigns_kpi_function(campaigns_information = None, max_date = None): return campaigns_reduced - def tickets_kpi_function(tickets_information = None): tickets_information_copy = tickets_information.copy() @@ -127,4 +126,39 @@ def customerplus_kpi_function(customerplus_clean = None): # customerplus_clean['has_tags'] = customerplus_clean['structure_id'].apply(lambda x: 1 if not pd.isna(x) else 0) return customerplus_clean + +def concatenate_names(names): + return ', '.join(names) + +def targets_KPI(df_target = None): + + df_target['target_name'] = df_target['target_name'].fillna('').str.lower() + + # Target name cotegory musees / + df_target['target_jeune'] = df_target['target_name'].str.contains('|'.join(['jeune', 'pass_culture', 'etudiant', '12-25 ans', 'student', 'jeunesse']), case=False).astype(int) + df_target['target_optin'] = df_target['target_name'].str.contains('|'.join(['optin' ,'opt-in']), case=False).astype(int) + df_target['target_optout'] = df_target['target_name'].str.contains('|'.join(['optout', 'unsubscribed']), case=False).astype(int) + df_target['target_scolaire'] = df_target['target_name'].str.contains('|'.join(['scolaire' , 'enseignant', 'chercheur', 'schulen', 'école']), case=False).astype(int) + df_target['target_entreprise'] = df_target['target_name'].str.contains('|'.join(['b2b', 'btob', 'cse']), case=False).astype(int) + df_target['target_famille'] = df_target['target_name'].str.contains('|'.join(['famille', 'enfants', 'family']), case=False).astype(int) + df_target['target_newsletter'] = df_target['target_name'].str.contains('|'.join(['nl', 'newsletter']), case=False).astype(int) + + # Target name category for sport compagnies + df_target['target_abonne'] = (( + df_target['target_name'] + .str.contains('|'.join(['abo', 'adh']), case=False) + & ~df_target['target_name'].str.contains('|'.join(['hors abo', 'anciens abo']), case=False) + ).astype(int)) + + df_target_categorie = df_target.groupby('customer_id')[['target_jeune', 'target_optin', 'target_optout', 'target_scolaire', 'target_entreprise', 'target_famille', 'target_newsletter', 'target_abonne']].max() + + target_agg = df_target.groupby('customer_id').agg( + nb_targets=('target_name', 'nunique') # Utilisation de tuples pour spécifier les noms de colonnes + # all_targets=('target_name', concatenate_names), + # all_target_types=('target_type_name', concatenate_names) + ).reset_index() + + target_agg = pd.merge(target_agg, df_target_categorie, how='left', on='customer_id') + + return target_agg \ No newline at end of file diff --git a/Exploration_billet_AJ.ipynb b/Exploration_billet_AJ.ipynb index 81c2f22..b8c82e7 100644 --- a/Exploration_billet_AJ.ipynb +++ b/Exploration_billet_AJ.ipynb @@ -50,11 +50,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})\n", - "\n", - "\n", "# Import cleaning and merge functions\n", "\n", "exec(open('0_Cleaning_and_merge_functions.py').read())\n", @@ -97,7 +92,7 @@ "source": [ "def export_in_temporary(df, output_name):\n", " print('Export of dataset :', output_name)\n", - " FILE_PATH_OUT_S3 = \"projet-bdc2324-team1/Temporary\" + \"/\" + output_name + '.csv'\n", + " FILE_PATH_OUT_S3 = \"ajoubrel-ensae/Temporary\" + \"/\" + output_name + '.csv'\n", " with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:\n", " df.to_csv(file_out, index = False)" ] @@ -112,25 +107,60 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "28316e1d-7892-4506-9d53-0695e71aa7bc", + "execution_count": 14, + "id": "fd88e294-e038-4cec-ad94-2bbbc10a4059", "metadata": {}, "outputs": [], "source": [ - "target_example = preprocessing_target_area('1')" + "def concatenate_names(names):\n", + " return ', '.join(names)\n", + "\n", + "def targets_KPI(df_target = None):\n", + " \n", + " df_target['target_name'] = df_target['target_name'].fillna('').str.lower()\n", + "\n", + " # Target name cotegory musees / \n", + " df_target['target_jeune'] = df_target['target_name'].str.contains('|'.join(['jeune', 'pass_culture', 'etudiant', '12-25 ans', 'student', 'jeunesse']), case=False).astype(int)\n", + " df_target['target_optin'] = df_target['target_name'].str.contains('|'.join(['optin' ,'opt-in']), case=False).astype(int)\n", + " df_target['target_optout'] = df_target['target_name'].str.contains('|'.join(['optout', 'unsubscribed']), case=False).astype(int)\n", + " df_target['target_scolaire'] = df_target['target_name'].str.contains('|'.join(['scolaire' , 'enseignant', 'chercheur', 'schulen', 'école']), case=False).astype(int)\n", + " df_target['target_entreprise'] = df_target['target_name'].str.contains('|'.join(['b2b', 'btob', 'cse']), case=False).astype(int)\n", + " df_target['target_famille'] = df_target['target_name'].str.contains('|'.join(['famille', 'enfants', 'family']), case=False).astype(int)\n", + " df_target['target_newsletter'] = df_target['target_name'].str.contains('|'.join(['nl', 'newsletter']), case=False).astype(int)\n", + " \n", + " # Target name category for sport compagnies\n", + " df_target['target_abonne'] = ((\n", + " df_target['target_name']\n", + " .str.contains('|'.join(['abo', 'adh']), case=False)\n", + " & ~df_target['target_name'].str.contains('|'.join(['hors abo', 'anciens abo']), case=False)\n", + " ).astype(int))\n", + " \n", + " df_target_categorie = df_target.groupby('customer_id')[['target_jeune', 'target_optin', 'target_optout', 'target_scolaire', 'target_entreprise', 'target_famille', 'target_newsletter', 'target_abonne']].max()\n", + " \n", + " target_agg = df_target.groupby('customer_id').agg(\n", + " nb_targets=('target_name', 'nunique') # Utilisation de tuples pour spécifier les noms de colonnes\n", + " # all_targets=('target_name', concatenate_names),\n", + " # all_target_types=('target_type_name', concatenate_names)\n", + " ).reset_index()\n", + "\n", + " target_agg['nb_targets'] = (target_agg['nb_targets'] - (target_agg['nb_targets'].mean())) / (target_agg['nb_targets'].std())\n", + " \n", + " target_agg = pd.merge(target_agg, df_target_categorie, how='left', on='customer_id')\n", + " \n", + " return target_agg" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "fd88e294-e038-4cec-ad94-2bbbc10a4059", + "execution_count": 15, + "id": "1b124018-9637-463e-b512-15743ec9480b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_1/target_information.csv\n" + "File path : projet-bdc2324-team1/0_Input/Company_5/target_information.csv\n" ] }, { @@ -154,6 +184,8 @@ " \n", " \n", " \n", + " customer_id\n", + " nb_targets\n", " target_jeune\n", " target_optin\n", " target_optout\n", @@ -161,52 +193,94 @@ " target_entreprise\n", " target_famille\n", " target_newsletter\n", - " \n", - " \n", - " customer_id\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " target_abonne\n", " \n", " \n", " \n", " \n", + " 0\n", + " 160516\n", + " 6.938264\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", + " 1\n", + " \n", + " \n", " 1\n", - " 1\n", - " 1\n", - " 1\n", + " 160517\n", + " 10.357387\n", + " 0\n", " 1\n", " 1\n", " 0\n", " 0\n", + " 0\n", + " 0\n", + " 1\n", " \n", " \n", " 2\n", + " 160518\n", + " 5.228703\n", + " 0\n", " 1\n", " 1\n", - " 1\n", - " 1\n", - " 1\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", " 1\n", " \n", " \n", " 3\n", + " 160519\n", + " 6.083483\n", + " 0\n", " 1\n", " 1\n", " 0\n", " 0\n", - " 0\n", + " 1\n", " 0\n", " 1\n", " \n", " \n", " 4\n", + " 160520\n", + " 2.949288\n", + " 0\n", " 1\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 471205\n", + " 6405875\n", + " -0.754762\n", + " 0\n", + " 0\n", " 1\n", " 0\n", " 0\n", @@ -215,8 +289,50 @@ " 0\n", " \n", " \n", - " 5\n", + " 471206\n", + " 6405905\n", + " -0.469835\n", + " 0\n", + " 0\n", " 1\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 471207\n", + " 6405909\n", + " -0.754762\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 471208\n", + " 6405917\n", + " -0.754762\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 471209\n", + " 6405963\n", + " -0.754762\n", + " 0\n", + " 0\n", " 1\n", " 0\n", " 0\n", @@ -226,61 +342,59 @@ " \n", " \n", "\n", + "

471210 rows × 10 columns

\n", "" ], "text/plain": [ - " target_jeune target_optin target_optout target_scolaire \\\n", - "customer_id \n", - "1 1 1 1 1 \n", - "2 1 1 1 1 \n", - "3 1 1 0 0 \n", - "4 1 1 0 0 \n", - "5 1 1 0 0 \n", + " customer_id nb_targets target_jeune target_optin target_optout \\\n", + "0 160516 6.938264 0 1 0 \n", + "1 160517 10.357387 0 1 1 \n", + "2 160518 5.228703 0 1 1 \n", + "3 160519 6.083483 0 1 1 \n", + "4 160520 2.949288 0 1 0 \n", + "... ... ... ... ... ... \n", + "471205 6405875 -0.754762 0 0 1 \n", + "471206 6405905 -0.469835 0 0 1 \n", + "471207 6405909 -0.754762 0 0 1 \n", + "471208 6405917 -0.754762 0 0 1 \n", + "471209 6405963 -0.754762 0 0 1 \n", "\n", - " target_entreprise target_famille target_newsletter \n", - "customer_id \n", - "1 1 0 0 \n", - "2 1 0 1 \n", - "3 0 0 1 \n", - "4 0 0 0 \n", - "5 0 0 0 " + " target_scolaire target_entreprise target_famille target_newsletter \\\n", + "0 0 1 0 0 \n", + "1 0 0 0 0 \n", + "2 0 0 0 0 \n", + "3 0 0 1 0 \n", + "4 0 0 0 0 \n", + "... ... ... ... ... \n", + "471205 0 0 0 0 \n", + "471206 0 0 0 0 \n", + "471207 0 0 0 0 \n", + "471208 0 0 0 0 \n", + "471209 0 0 0 0 \n", + "\n", + " target_abonne \n", + "0 1 \n", + "1 1 \n", + "2 1 \n", + "3 1 \n", + "4 1 \n", + "... ... \n", + "471205 0 \n", + "471206 0 \n", + "471207 0 \n", + "471208 0 \n", + "471209 0 \n", + "\n", + "[471210 rows x 10 columns]" ] }, - "execution_count": 6, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tenant_id = '1'\n", - "\n", - "def concatenate_names(names):\n", - " return ', '.join(names)\n", - " \n", - "target_example =display_input_databases(tenant_id, \"target_information\")\n", - "\n", - "target_example['target_name'] = target_example['target_name'].fillna('').str.lower()\n", - "\n", - "\n", - "target_example['target_jeune'] = target_example['target_name'].str.contains('|'.join(['jeune', 'pass_culture', 'etudiant', '12-25 ans', 'student', 'jeunesse']), case=False).astype(int)\n", - "target_example['target_optin'] = target_example['target_name'].str.contains('|'.join(['optin' ,'opt-in']), case=False).astype(int)\n", - "target_example['target_optout'] = target_example['target_name'].str.contains('|'.join(['optout', 'unsubscribed']), case=False).astype(int)\n", - "target_example['target_scolaire'] = target_example['target_name'].str.contains('|'.join(['scolaire' , 'enseignant', 'chercheur', 'schulen', 'école']), case=False).astype(int)\n", - "target_example['target_entreprise'] = target_example['target_name'].str.contains('|'.join(['b2b', 'btob', 'cse']), case=False).astype(int)\n", - "target_example['target_famille'] = target_example['target_name'].str.contains('|'.join(['famille', 'enfants', 'family']), case=False).astype(int)\n", - "target_example['target_newsletter'] = target_example['target_name'].str.contains('|'.join(['nl', 'newsletter']), case=False).astype(int)\n", - "\n", - "\n", - "\n", - "target_agg = target_example.groupby('customer_id').agg(\n", - " nb_targets=('target_name', 'nunique'), # Utilisation de tuples pour spécifier les noms de colonnes\n", - " all_targets=('target_name', concatenate_names),\n", - " all_target_types=('target_type_name', concatenate_names)\n", - " ).reset_index()\n", - "\n", - "target_example_categorie = target_example.groupby('customer_id')[['target_jeune', 'target_optin', 'target_optout', 'target_scolaire', 'target_entreprise', 'target_famille', 'target_newsletter']].max()\n", - "\n", - "target_example_categorie.head()" + "targets_KPI(display_input_databases('5', file_name = \"target_information\"))" ] }, { @@ -690,7 +804,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "d91d5895", "metadata": {}, "outputs": [], @@ -708,7 +822,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "6930bff5", "metadata": {}, "outputs": [], @@ -730,567 +844,16 @@ { "cell_type": "code", "execution_count": null, - "id": "08488e43-56e0-461b-8770-c4e68d5c09f4", - "metadata": {}, - "outputs": [], - "source": [ - "pd.set_option('display.max_rows', None)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, "id": "1e7ee1a0", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_1/target_information.csv\n", - "Nombre de ciblage : 768024\n", - "Nombre de client avec étiquette target : 151159\n" - ] - }, - { - "data": { - "text/html": [ - "
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target_namecustomer_idcumulative_customers
161consentement optin mediation specialisee0.9923330.195306
160consentement optin jeune public0.9921940.390585
158consentement optin b2c0.7204930.532390
5Arenametrix_bascule tel vers sib0.2329730.578242
165consentement optout b2c0.2283890.623193
19COM Inscrits NL générale (historique)0.1521910.653146
162consentement optin newsletter generale0.1461710.681915
169consentement optout newsletter generale0.1247360.706465
170consentement optout scolaires0.1041550.726964
166consentement optout dre0.0947880.745620
164consentement optout b2b0.0940670.764134
126Inscrits NL générale (export_291019 + operation_videomaton)0.0931870.782474
157consentement optin b2b0.0842490.799056
216ddcp_visiteurs dps 0106220.0817350.815142
20Contacts_prenomsdoubles0.0770250.830302
115FORMATION _ acheteurs optin last year0.0693640.843954
214ddcp_promo_visiteurs occasionnels_musee_8mois0.0439270.852600
189ddcp_promo_md_musée_dps 0110190.0397590.860425
188ddcp_promo_MD_billet_musée_oct_2019_agarder20.0362660.867563
163consentement optin scolaires0.0320790.873876
159consentement optin dre0.0299490.879771
34DDCP Newsletter enseignants0.0298360.885643
36DDCP Newsletter jeune public0.0255490.890671
127Inscrits NL générale site web0.0246890.895531
145Votre première liste0.0245770.900368
61DDCP billets famille0.0238760.905067
106DRE MucemLab0.0152290.908064
39DDCP Newsletter relais champ social0.0150170.911020
110DRE institutionnels0.0147460.913922
48DDCP PROMO Participants ateliers (adultes et enfants)0.0129270.916466
74DDCP promo Plan B 2019 (concerts)0.0128870.919003
72DDCP promo MD pass musées dps oct 20180.0118090.921327
94DDCP rentrée culturelle 20230.0116240.923614
23DDCP MD Procès du Siècle0.0111410.925807
186ddcp_md_scene_ouverte_au_talent0.0104330.927860
108DRE chercheurs0.0103000.929888
220festival_jean_rouch0.0099370.931843
105DRE Festival Jean Rouch0.0099370.933799
275structures_etiquette champ social0.0098440.935736
86DDCP promo spectateurs prog 21-22 (spectacles, ciné, ateliers)0.0085540.937420
128Inscrits NL jeune public site web0.0082630.939046
260rappel po barvalo0.0082560.940671
104DDCP_marseille_jazz_20230.0069000.942029
32DDCP Newsletter centres de loisirs0.0068270.943373
13Autres_interet_exposition0.0067540.944702
228import_arenametrix_contactstousecardouv_expo0.0062120.945925
117Formation clients fidèles0.0060470.947115
22DDCP Cine 20230.0056560.948228
40DDCP OLBJ! 20230.0054640.949304
240journee-de-l-inclusion_20230601_21h250.0053260.950352
137Questionnaire 2 satisfaction scolaire0.0052590.951387
93DDCP rendez-vous de septembre offre spéciale0.0052530.952421
135Plan B 2018 (électro)0.0050810.953421
270save_the_date_populaire0.0049480.954395
132Newsletter CCR (passerelle)0.0047830.955336
116Fichier institutionnel (ne pas utiliser sans autorisation)0.0045380.956229
222fichier institutionnel_ne_pas_toucher0.0045320.957121
266reservations_payees_pass_culture_190422_au_3101230.0044920.958005
102DDCP spectateurs Marseille Jazz 18-19-210.0044320.958878
147acid arab0.0044130.959746
\n", - "
" - ], - "text/plain": [ - " target_name \\\n", - "161 consentement optin mediation specialisee \n", - "160 consentement optin jeune public \n", - "158 consentement optin b2c \n", - "5 Arenametrix_bascule tel vers sib \n", - "165 consentement optout b2c \n", - "19 COM Inscrits NL générale (historique) \n", - "162 consentement optin newsletter generale \n", - "169 consentement optout newsletter generale \n", - "170 consentement optout scolaires \n", - "166 consentement optout dre \n", - "164 consentement optout b2b \n", - "126 Inscrits NL générale (export_291019 + operation_videomaton) \n", - "157 consentement optin b2b \n", - "216 ddcp_visiteurs dps 010622 \n", - "20 Contacts_prenomsdoubles \n", - "115 FORMATION _ acheteurs optin last year \n", - "214 ddcp_promo_visiteurs occasionnels_musee_8mois \n", - "189 ddcp_promo_md_musée_dps 011019 \n", - "188 ddcp_promo_MD_billet_musée_oct_2019_agarder2 \n", - "163 consentement optin scolaires \n", - "159 consentement optin dre \n", - "34 DDCP Newsletter enseignants \n", - "36 DDCP Newsletter jeune public \n", - "127 Inscrits NL générale site web \n", - "145 Votre première liste \n", - "61 DDCP billets famille \n", - "106 DRE MucemLab \n", - "39 DDCP Newsletter relais champ social \n", - "110 DRE institutionnels \n", - "48 DDCP PROMO Participants ateliers (adultes et enfants) \n", - "74 DDCP promo Plan B 2019 (concerts) \n", - "72 DDCP promo MD pass musées dps oct 2018 \n", - "94 DDCP rentrée culturelle 2023 \n", - "23 DDCP MD Procès du Siècle \n", - "186 ddcp_md_scene_ouverte_au_talent \n", - "108 DRE chercheurs \n", - "220 festival_jean_rouch \n", - "105 DRE Festival Jean Rouch \n", - "275 structures_etiquette champ social \n", - "86 DDCP promo spectateurs prog 21-22 (spectacles, ciné, ateliers) \n", - "128 Inscrits NL jeune public site web \n", - "260 rappel po barvalo \n", - "104 DDCP_marseille_jazz_2023 \n", - "32 DDCP Newsletter centres de loisirs \n", - "13 Autres_interet_exposition \n", - "228 import_arenametrix_contactstousecardouv_expo \n", - "117 Formation clients fidèles \n", - "22 DDCP Cine 2023 \n", - "40 DDCP OLBJ! 2023 \n", - "240 journee-de-l-inclusion_20230601_21h25 \n", - "137 Questionnaire 2 satisfaction scolaire \n", - "93 DDCP rendez-vous de septembre offre spéciale \n", - "135 Plan B 2018 (électro) \n", - "270 save_the_date_populaire \n", - "132 Newsletter CCR (passerelle) \n", - "116 Fichier institutionnel (ne pas utiliser sans autorisation) \n", - "222 fichier institutionnel_ne_pas_toucher \n", - "266 reservations_payees_pass_culture_190422_au_310123 \n", - "102 DDCP spectateurs Marseille Jazz 18-19-21 \n", - "147 acid arab \n", - "\n", - " customer_id cumulative_customers \n", - "161 0.992333 0.195306 \n", - "160 0.992194 0.390585 \n", - "158 0.720493 0.532390 \n", - "5 0.232973 0.578242 \n", - "165 0.228389 0.623193 \n", - "19 0.152191 0.653146 \n", - "162 0.146171 0.681915 \n", - "169 0.124736 0.706465 \n", - "170 0.104155 0.726964 \n", - "166 0.094788 0.745620 \n", - "164 0.094067 0.764134 \n", - "126 0.093187 0.782474 \n", - "157 0.084249 0.799056 \n", - "216 0.081735 0.815142 \n", - "20 0.077025 0.830302 \n", - "115 0.069364 0.843954 \n", - "214 0.043927 0.852600 \n", - "189 0.039759 0.860425 \n", - "188 0.036266 0.867563 \n", - "163 0.032079 0.873876 \n", - "159 0.029949 0.879771 \n", - "34 0.029836 0.885643 \n", - "36 0.025549 0.890671 \n", - "127 0.024689 0.895531 \n", - "145 0.024577 0.900368 \n", - "61 0.023876 0.905067 \n", - "106 0.015229 0.908064 \n", - "39 0.015017 0.911020 \n", - "110 0.014746 0.913922 \n", - "48 0.012927 0.916466 \n", - "74 0.012887 0.919003 \n", - "72 0.011809 0.921327 \n", - "94 0.011624 0.923614 \n", - "23 0.011141 0.925807 \n", - "186 0.010433 0.927860 \n", - "108 0.010300 0.929888 \n", - "220 0.009937 0.931843 \n", - "105 0.009937 0.933799 \n", - "275 0.009844 0.935736 \n", - "86 0.008554 0.937420 \n", - "128 0.008263 0.939046 \n", - "260 0.008256 0.940671 \n", - "104 0.006900 0.942029 \n", - "32 0.006827 0.943373 \n", - "13 0.006754 0.944702 \n", - "228 0.006212 0.945925 \n", - "117 0.006047 0.947115 \n", - "22 0.005656 0.948228 \n", - "40 0.005464 0.949304 \n", - "240 0.005326 0.950352 \n", - "137 0.005259 0.951387 \n", - "93 0.005253 0.952421 \n", - "135 0.005081 0.953421 \n", - "270 0.004948 0.954395 \n", - "132 0.004783 0.955336 \n", - "116 0.004538 0.956229 \n", - "222 0.004532 0.957121 \n", - "266 0.004492 0.958005 \n", - "102 0.004432 0.958878 \n", - "147 0.004413 0.959746 " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pd.set_option(\"max_colwidth\", None)\n", "print_main_target('1', 60)" ] }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c66a4dc1", - "metadata": {}, - "outputs": [], - "source": [ - "pd.set_option('display.max_rows', None)\n" - ] - }, { "cell_type": "code", "execution_count": null, @@ -1337,6 +900,16 @@ "print_main_target('4', 100)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "52b24d66-92ad-4421-a62b-5cba837f1893", + "metadata": {}, + "outputs": [], + "source": [ + "pd.set_option('display.max_rows', None)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1346,7 +919,7 @@ }, "outputs": [], "source": [ - "pd.set_option('display.max_rows', None)\n", + "\n", "\n", "print_main_target('5', 100)" ] @@ -1379,7 +952,9 @@ "cell_type": "code", "execution_count": null, "id": "fbf2ea42-515a-4cdf-a4c1-50f99c379ed9", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "print_main_target('8', 100)" @@ -1387,3461 +962,72 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "9684045c-4e25-4952-b099-a559baa5d749", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_9/target_information.csv\n", - "Nombre de ciblage : 1399179\n", - "Nombre de client avec étiquette target : 181136\n" - ] - }, - { - "data": { - "text/html": [ - "
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target_namecustomer_idcumulative_customers
184Run Mate0.9999390.129451
233Triathlon0.9999340.258901
77HGM0.9999340.388351
2420km Genève0.9999170.517799
75GM230.9965610.646813
68GGM - inscrits 2015 - 2023 - FR0.2614390.680658
85HGM - Inscrits 2005-2019 - FR0.2431100.712131
187RunMate - 500km autour montreux - FR0.2366120.742763
188RunMate - 500km autour montreux - FR v20.2041620.769193
46BDD - Semi & Marathon_FR0.1695360.791141
83HGM - GM Public - FR0.1544200.811132
84HGM - Inscrits 2005-2019 - ENG0.1134010.825813
67GGM - inscrits 2015 - 2023 - ENG0.1074050.839717
73GGM23 - inscrits - FR0.0667340.848357
45BDD - Semi & Marathon_ENG0.0632400.856544
82HGM - GM Public - ENG0.0618930.864556
95LTGT - Inscrits 2008-2019 - FR0.0503710.871077
186RunMate - 500km autour montreux - ENG0.0423160.876555
210TMB - Opt-in0.0397380.881700
118LTGT23-Amateur-FR0.0360670.886369
88HGM désinscriptions0.0318930.890498
213TMB - anciens participants 2015 - 2020 - FR+ENG0.0281450.894141
1920km - Inscrits 2017-2019 - FR0.0272670.897671
100LTGT - inscrits 2015 - 2020 - FR0.0261850.901061
1020KM23_Inscrit NL 25.10_FR0.0230490.904045
185Run Mate désinscriptions0.0217460.906860
303test listes0.0213210.909621
72GGM23 - inscrits - ENG0.0213040.912379
920KM23_FINISHER_FR20.0203160.915009
820KM23_FINISHER_FR0.0203160.917639
3820km23_nl finisher fr0.0203160.920269
107LTGT21 - Inscrits FR+ENG0.0190580.922736
3320km23_inscrits fr 05.10_test0.0174450.924995
2520km Genève désinscriptions0.0173900.927246
113LTGT22 - inscris FR+ENG0.0166010.929395
47Bénévoles Mass Events0.0165010.931531
3220km23_inscrits fr 02.100.0164790.933665
64GGM - Contact PT OK0.0160650.935744
112LTGT22 - Anciens participants Half, Half relais, standard, standard relais - FR0.0154250.937741
225TMB23-Amateur-FR0.0133490.939470
110LTGT21 - inscrits FR0.0126420.941106
219TMB22 - inscrits FR0.0107320.942496
94LTGT - Inscrits 2008-2019 - ENG0.0106880.943879
115LTGT22 - inscrits FR0.0103680.945221
203TIMB22 - incritS FR+ENG0.0100640.946524
2920km22 - inscrits - FR0.0100090.947820
2720km20 - Inscrits finaux - FR0.0095180.949052
420KM21 - inscrits FR0.0095070.950283
220KM - Contact PT OKb0.0094510.951507
175RM21- Inscrits FR+ENG0.0090980.952684
222TMB23 - inscrits FR + ENG0.0085180.953787
223TMB23 - inscrits FR + ENG -0.0085180.954890
304tmb - pro - fr - VF0.0082980.955964
221TMB23 - inscrits FR0.0078340.956978
105LTGT20 - Inscrits - FR0.0077460.957981
234Triathlon désinscriptions0.0077400.958983
238ggm23 - inscrits fr + eng0.0072760.959925
117LTGT23-Amateur-ENG- vf0.0071660.960853
116LTGT23-Amateur-ENG0.0071660.961780
173RM21 - inscrits FR (reportés + new)0.0071600.962707
197TE23_Inscrits_FR0.0071160.963629
153RM - Contact PT OK0.0069670.964531
177RM22 - inscrits FR0.0068790.965421
3520km23_inscrits nl 1 fr - 17.080.0068790.966312
1220KM23_Inscrits NL 1 FR - 17.080.0068790.967202
97LTGT - Inscrits NL - FR0.0066690.968066
166RM20 - inscrits 2020 (Total avec reports) - FR0.0065920.968919
297rm23_nl finisher fr0.0061170.969711
180RM23_NL Finisher FR0.0061170.970503
162RM19 - Inscrits - FR0.0061060.971293
292rm23_inscrits nl 19.09 fr0.0060890.972081
66GGM - Inscrits NL - FR0.0060230.972861
93LTGT - Contact PT OK0.0059730.973635
174RM21 - new inscrits - FR0.0058080.974386
1820km - Inscrits 2017-2019 - ENG0.0055430.975104
4220km23_no show fr0.0054430.975809
1620KM23_NO SHOW_FR0.0054430.976513
101LTGT - inscrits 2015-2020 - ENG0.0054050.977213
2120km - Inscrits NL - FR0.0053770.977909
109LTGT21 - Reportés - FR0.0053660.978604
214TMB - anciens participants Standard et HALF0.0049240.979241
242ltgt - pro - fr vf0.0047980.979862
3620km23_nl 3 26.10 eng0.0047200.980474
2820km22 - inscrits - ANG0.0045990.981069
74GGM24_INSCRIS FR&ENG0.0042450.981619
239ggm24_inscrits 05.100.0042450.982168
62Entreprises - FR0.0040910.982698
290rm23_inscrits fr nl2 21.070.0040030.983216
208TMB - Contact PT OK0.0037710.983704
3120km23_inscrits eng 02.100.0036990.984183
3720km23_nl finisher eng0.0035880.984647
720KM23_FINISHER_ENG0.0035880.985112
111LTGT22 - Anciens participants Half, Half relais, standard, standard relais - EN0.0035550.985572
207TIMB23 - liste d'attente - FR0.0033070.986000
205TIMB22 - reports FR+EN - VF0.0030580.986396
165RM20 - Inscrits sur liste d'attente - FR0.0030580.986792
106LTGT21 - Inscrits ENG0.0030310.987185
216TMB21 - Liste d'attente - FR0.0029920.987572
202TIMB21 - liste d'attente - FR0.0029920.987959
245ltgt23 - non half eng0.0028710.988331
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2Blackliste0.1886690.705348
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37import opt-out fév 230.0550160.800731
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35fichierspectateurs_recreatrales0.0160520.932715
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33fichier2 news quartier grand t0.0073830.944353
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30fichier op les dodos 21 nov global0.0030200.973101
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13 M - spec du samedi (toutes rep)0.0025540.977071
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13Presse 20210.0022000.984190
20Spectateurs-rices SpaceSongs0.0020690.985792
15Presse Tous Terriens0.0020690.987395
46nouveaux acheteurs 23-24 - relance Mazut0.0020320.988969
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11P6S0.0012860.995567
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34fichiercontactstous_enssup_relance étudiants0.0008200.996925
42mails intervenants mdla fev230.0005220.997329
48spec Jessica0.0005220.997733
7Invit conf presse EUH : artistes + DRAC0.0004660.998094
10P2S0.0003920.998397
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12Personnel du Grand T0.0003540.998975
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38liste billetterie0.0000560.999870
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45CAT/buit all0.7553870.130116
274lista bbdd opt-in0.5105590.218061
157Obren mails0.3984660.286697
165Participantes por primera vez0.3760120.351465
267buit all0.3559070.412771
158Obren mails CAT0.3027730.464924
94ES all0.2189340.502636
105Festivals 19-21-22-23 CAT0.2133480.539385
51Comptes cashless - tots0.1768830.569853
265allcomptespersonalsambnom0.1768830.600321
264allcomptespersonals0.1768780.630789
268compradores habituales0.1553270.657544
49Compradors CE20230.1354780.680881
16All CE2023 CAT0.1163290.700918
160Obren mails ES0.0892240.716287
263all unsubscribed 09.03.230.0877660.731405
27All unsubscribed 09.03.230.0877660.746523
106Festivals 19-21-22-23 ES0.0775940.759888
273lista bbdd opt out0.0762140.773016
171Primer control de acceso 07/07/230.0747100.785885
18All CE2023 ES0.0636730.796853
181Push Joan Miquel Oliver CAT0.0561740.806529
200Segments Joan Miquel Oliver AX0.0499780.815138
204Segments jajas0.0399960.822027
199Segments Fatoumata PowerBI0.0370710.828412
278segments fatoumata powerbi0.0370710.834798
88Divendres CE20230.0365470.841093
205Segments jajas CAT0.0323490.846665
73Dijous CE20230.0284480.851565
83Dissabte CE20230.0281680.856417
201Segments Joan Miquel Oliver PowerBI0.0274260.861142
279segments joan miquel oliver powerbi0.0274260.865866
178Push Fatoumata CAT0.0272530.870560
93EN all0.0256750.874983
203Segments Pinpilincinos PowerBI0.0208510.878574
280segments pinpilincinos powerbi0.0208510.882166
78Dimecres CE20230.0207020.885732
89Divendres CE2023 CAT0.0204630.889257
19All CE2023 buit0.0200020.892702
276regalentradesrondes0.0194580.896054
41Assistents concerts last year0.0194580.899405
183Push Pinpilincinos CAT0.0187080.902628
266barres comedy festival0.0180660.905740
44Barres Comedy festival0.0180660.908851
270in risus0.0180330.911958
50Compradors In Risus0.0180330.915064
182Push Joan Miquel Oliver ES0.0172050.918027
22All abo wkd-2dies CE20230.0170970.920972
84Dissabte CE2023 CAT0.0160800.923742
42Assistents concerts last year CAT0.0158780.926477
9Abonament Weekend ST0.0154000.929130
176Push Al·lèrgiques CAT0.0146420.931652
74Dijous CE2023 CAT0.0141390.934087
196Segments Al·lèrgiques AX0.0140410.936506
180Push Fatoumata ES0.0121580.938600
79Dimecres CE2023 CAT0.0102870.940372
23All abo wkd-2dies CE2023 CAT0.0098880.942075
91Divendres CE2023 ES0.0095000.943712
112Funzo & Baby Loud CT20230.0090640.945273
17All CE2023 EN0.0082320.946691
76Dijous CE2023 ES0.0080010.948069
20All abo full-4dies CE20230.0079800.949444
184Push Pinpilincinos ES0.0075350.950742
206Segments jajas ES0.0074120.952018
115Funzo & Baby Loud CT2023 buit0.0073210.953279
236The Tyets CT2024 16.110.0067980.954450
86Dissabte CE2023 ES0.0066910.955603
7Abonament Full ST0.0066740.956752
159Obren mails EN0.0064680.957867
031 FAM CT20230.0064680.958981
202Segments Pinpilincinos AX0.0062790.960062
211Sen Senra CH20240.0060070.961097
81Dimecres CE2023 ES0.0057350.962085
21All abo full-4dies CE2023 CAT0.0054510.963024
239The Tyets CT2024 16.11 buit0.0054140.963956
77Dijous CE2023 buit0.0052570.964862
193SF 7 anys o més0.0052030.965758
281sf7anysomes0.0052030.966654
25All abo wkd-2dies CE2023 ES0.0050340.967521
197Segments Al·lèrgiques ST0.0048660.968359
277segments al·lèrgiques st0.0048660.969198
260XXS Comedy0.0048370.970031
92Divendres CE2023 buit0.0048280.970862
87Dissabte CE2023 buit0.0046140.971657
215Sen Senra CH2024 buit0.0045110.972434
82Dimecres CE2023 buit0.0044210.973196
5Abo 3 dies CE20230.0042720.973932
198Segments Fatoumata AX0.0042190.974658
66Convidats pro0.0039800.975344
269identified_contacts0.0039760.976029
177Push Al·lèrgiques ES0.0038850.976698
254Viva Suecia CH20240.0037700.977347
220Sidonie CH20240.0036830.977982
194SF 7 anys o més CAT0.0035310.978590
43Assistents concerts last year ES0.0035180.979196
232The Tyets CT2024 15.110.0035020.979799
283usuaris cruïlla green day0.0032420.980358
240Usuaris Cruïlla Green Day0.0032420.980916
131 FAM CT2023 CAT0.0031760.981463
68Cupido CH20240.0031480.982005
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"18 All CE2023 ES 0.063673 0.796853\n", - "181 Push Joan Miquel Oliver CAT 0.056174 0.806529\n", - "200 Segments Joan Miquel Oliver AX 0.049978 0.815138\n", - "204 Segments jajas 0.039996 0.822027\n", - "199 Segments Fatoumata PowerBI 0.037071 0.828412\n", - "278 segments fatoumata powerbi 0.037071 0.834798\n", - "88 Divendres CE2023 0.036547 0.841093\n", - "205 Segments jajas CAT 0.032349 0.846665\n", - "73 Dijous CE2023 0.028448 0.851565\n", - "83 Dissabte CE2023 0.028168 0.856417\n", - "201 Segments Joan Miquel Oliver PowerBI 0.027426 0.861142\n", - "279 segments joan miquel oliver powerbi 0.027426 0.865866\n", - "178 Push Fatoumata CAT 0.027253 0.870560\n", - "93 EN all 0.025675 0.874983\n", - "203 Segments Pinpilincinos PowerBI 0.020851 0.878574\n", - "280 segments pinpilincinos powerbi 0.020851 0.882166\n", - "78 Dimecres CE2023 0.020702 0.885732\n", - "89 Divendres CE2023 CAT 0.020463 0.889257\n", - "19 All CE2023 buit 0.020002 0.892702\n", - "276 regalentradesrondes 0.019458 0.896054\n", - "41 Assistents concerts last year 0.019458 0.899405\n", - "183 Push Pinpilincinos CAT 0.018708 0.902628\n", - "266 barres comedy festival 0.018066 0.905740\n", - "44 Barres Comedy festival 0.018066 0.908851\n", - "270 in risus 0.018033 0.911958\n", - "50 Compradors In Risus 0.018033 0.915064\n", - "182 Push Joan Miquel Oliver ES 0.017205 0.918027\n", - "22 All abo wkd-2dies CE2023 0.017097 0.920972\n", - "84 Dissabte CE2023 CAT 0.016080 0.923742\n", - "42 Assistents concerts last year CAT 0.015878 0.926477\n", - "9 Abonament Weekend ST 0.015400 0.929130\n", - "176 Push Al·lèrgiques CAT 0.014642 0.931652\n", - "74 Dijous CE2023 CAT 0.014139 0.934087\n", - "196 Segments Al·lèrgiques AX 0.014041 0.936506\n", - "180 Push Fatoumata ES 0.012158 0.938600\n", - "79 Dimecres CE2023 CAT 0.010287 0.940372\n", - "23 All abo wkd-2dies CE2023 CAT 0.009888 0.942075\n", - "91 Divendres CE2023 ES 0.009500 0.943712\n", - "112 Funzo & Baby Loud CT2023 0.009064 0.945273\n", - "17 All CE2023 EN 0.008232 0.946691\n", - 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"260 XXS Comedy 0.004837 0.970031\n", - "92 Divendres CE2023 buit 0.004828 0.970862\n", - "87 Dissabte CE2023 buit 0.004614 0.971657\n", - "215 Sen Senra CH2024 buit 0.004511 0.972434\n", - "82 Dimecres CE2023 buit 0.004421 0.973196\n", - "5 Abo 3 dies CE2023 0.004272 0.973932\n", - "198 Segments Fatoumata AX 0.004219 0.974658\n", - "66 Convidats pro 0.003980 0.975344\n", - "269 identified_contacts 0.003976 0.976029\n", - "177 Push Al·lèrgiques ES 0.003885 0.976698\n", - "254 Viva Suecia CH2024 0.003770 0.977347\n", - "220 Sidonie CH2024 0.003683 0.977982\n", - "194 SF 7 anys o més CAT 0.003531 0.978590\n", - "43 Assistents concerts last year ES 0.003518 0.979196\n", - "232 The Tyets CT2024 15.11 0.003502 0.979799\n", - "283 usuaris cruïlla green day 0.003242 0.980358\n", - "240 Usuaris Cruïlla Green Day 0.003242 0.980916\n", - "1 31 FAM CT2023 CAT 0.003176 0.981463\n", - "68 Cupido CH2024 0.003148 0.982005" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "print_main_target('12', 100)\n" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, + "id": "fa93aecd-d117-481e-8507-15e49937ce14", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "print_main_target('13', 100)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "a115ebcf-4488-47f3-9d7e-75a1fca52f0f", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_14/target_information.csv\n", - "Nombre de ciblage : 779658\n", - "Nombre de client avec étiquette target : 240541\n" - ] - }, - { - "data": { - "text/html": [ - "
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target_namecustomer_idcumulative_customers
11BDS 17/18/190.3714830.114611
28Cible non-acheteurs franciliens0.2088710.179052
34FHU 2018 - Acheteurs bds0.1848170.236071
14BDS FHU180.1845050.292995
12BDS 20210.1832290.349525
79importer_huma0.1832290.406055
15BDS FHU210.1832240.462584
5BCOM 17/18/190.1622470.512640
16BDS FHU23 VDéf0.1522980.559627
13BDS 20220.1390950.602541
29Cible non-acheteurs provinciaux0.1369910.644806
7BCOM 2023 - PASS 3 JOURS0.1069460.677801
55PROSPECT CONTACT -35ANS0.1034830.709728
73billets bds au 11/090.0776040.733670
64Pass 3J0.0703370.755371
9BCOM FHU180.0642140.775182
6BCOM 20180.0594700.793530
72bds 2022 +50 ans0.0525190.809733
10BCOM FHU190.0455930.823800
36Festivaliers WEB 2020 - plein tarif0.0454100.837810
19CAMPING0.0411780.850514
41Inscriptions newsletters (depuis 2019)0.0395980.862731
56PROSPECT FHU NORMANDIE 2021 #10.0389250.874740
25Camping FHU220.0337160.885142
3ACTIVATION BDS 24/080.0318620.894972
75data bcom 2023 - 15.06.230.0265940.903177
20CAMPING 2021 #10.0221540.910012
21CAMPING 24/080.0221500.916846
58PROSPECT FHU NORMANDIE 2021 #30.0205790.923195
54PROSPECT ACHETEURS FHU23 POUR FHU NORMANDIE 20230.0179010.928717
85prospect acheteurs fhu23_pour_fhu rouen 20230.0179010.934240
63PROSPECT NORMANDIE0.0141180.938596
60PROSPECT FHU NORMANDIE 2022 BDS0.0137770.942847
87prospect bds fhu normandie0.0137770.947097
44PARKING 2021 #10.0124840.950949
45PARKING 24/080.0120810.954676
42PARKING - PORTE B0.0118480.958332
43PARKING - PORTE J0.0117360.961953
86prospect bcom fhu normandie0.0107090.965257
59PROSPECT FHU NORMANDIE 2022 BCOM0.0107090.968561
50PASS 3J - ENFANTS0.0097160.971558
49PASS 3J - ADOS0.0086430.974225
18BILLETS NUIT0.0073330.976487
51PASS Culture 2023 au 11/090.0070920.978675
84pass culture 11/090.0070920.980863
83pass culture - pass 3j0.0070760.983046
4Ados FHU220.0067180.985119
33Enfants FHU220.0066310.987165
46PARKING CAMPEURS0.0047810.988640
39INSCRIPTION NL VOYAGES HUMA0.0036250.989758
17BILLET CAMPING-CAR0.0025940.990559
61PROSPECT FORUM LOGEMENT 20220.0020620.991195
27Camping-car FHU220.0019960.991811
26Camping Zen FHU220.0015960.992303
66RESP. STANDS POUR FHUA20200.0015590.992784
52PRESSE 20210.0015590.993265
70accreditations presse_fhu230.0014340.993707
0ACCREDITES - FHU 230.0014340.994150
74billets enfants 2023_06_050.0012390.994532
2ACHETEURS TIPIS0.0010230.994848
8BCOM FHU NORMANDIE 20210.0010230.995163
88prospect forum logement 20230.0010140.995476
62PROSPECT FORUM LOGEMENT 20230.0010140.995789
38INSCRIPTION FORUM LOGEMENT - 14/020.0008400.996048
57PROSPECT FHU NORMANDIE 2021 #20.0008020.996296
22CN - 38e CONGRÈS / 20210.0007900.996540
65RESP. STANDS PCF 20210.0007650.996776
40INSCRITS VISIO 14/01/220.0006980.996991
24CROISIÉRISTES 2021 / RIVAGES DU MONDE0.0006860.997203
31DINER DE PRESSE 20200.0006690.997409
71acheteurs bcom fhu normandie 20220.0006570.997612
1ACHETEURS BCOM FHU NORMANDIE 20220.0006570.997814
47PARKING PMR - PORTE B0.0006360.998011
53PROSPECT / TOURISTRA0.0006280.998204
48PARKING PMR - PORTE J0.0005900.998386
78fichier presse - journalistes20.0005780.998565
35FHU22 - DIFFUSION CP0.0005700.998740
32DINER DE PRESSE 20210.0005490.998910
81liste médias cp0.0005160.999069
67SECRETAIRES FÉDÉRAUX 20210.0004370.999203
68Soirée solidarité Ukraine0.0004370.999338
89tipi 20230.0004120.999465
69TIPI 20230.0004120.999592
77fhu22 - pass 3j shotgun0.0003530.999701
30DEMANDES PRESSE 20220.0002830.999788
76demandes accréditation 20230.0002540.999867
23COIN DES MÔMES0.0001700.999919
82orders_302493 (1)0.0001080.999953
37Humacumba Shotgun FHU220.0001080.999986
80liste agences cp0.0000461.000000
\n", - "
" - ], - "text/plain": [ - " target_name customer_id \\\n", - "11 BDS 17/18/19 0.371483 \n", - "28 Cible non-acheteurs franciliens 0.208871 \n", - "34 FHU 2018 - Acheteurs bds 0.184817 \n", - "14 BDS FHU18 0.184505 \n", - "12 BDS 2021 0.183229 \n", - "79 importer_huma 0.183229 \n", - "15 BDS FHU21 0.183224 \n", - "5 BCOM 17/18/19 0.162247 \n", - "16 BDS FHU23 VDéf 0.152298 \n", - "13 BDS 2022 0.139095 \n", - "29 Cible non-acheteurs provinciaux 0.136991 \n", - "7 BCOM 2023 - PASS 3 JOURS 0.106946 \n", - "55 PROSPECT CONTACT -35ANS 0.103483 \n", - "73 billets bds au 11/09 0.077604 \n", - "64 Pass 3J 0.070337 \n", - "9 BCOM FHU18 0.064214 \n", - "6 BCOM 2018 0.059470 \n", - "72 bds 2022 +50 ans 0.052519 \n", - "10 BCOM FHU19 0.045593 \n", - "36 Festivaliers WEB 2020 - plein tarif 0.045410 \n", - "19 CAMPING 0.041178 \n", - "41 Inscriptions newsletters (depuis 2019) 0.039598 \n", - "56 PROSPECT FHU NORMANDIE 2021 #1 0.038925 \n", - "25 Camping FHU22 0.033716 \n", - "3 ACTIVATION BDS 24/08 0.031862 \n", - "75 data bcom 2023 - 15.06.23 0.026594 \n", - "20 CAMPING 2021 #1 0.022154 \n", - "21 CAMPING 24/08 0.022150 \n", - "58 PROSPECT FHU NORMANDIE 2021 #3 0.020579 \n", - "54 PROSPECT ACHETEURS FHU23 POUR FHU NORMANDIE 2023 0.017901 \n", - "85 prospect acheteurs fhu23_pour_fhu rouen 2023 0.017901 \n", - "63 PROSPECT NORMANDIE 0.014118 \n", - "60 PROSPECT FHU NORMANDIE 2022 BDS 0.013777 \n", - "87 prospect bds fhu normandie 0.013777 \n", - "44 PARKING 2021 #1 0.012484 \n", - "45 PARKING 24/08 0.012081 \n", - "42 PARKING - PORTE B 0.011848 \n", - "43 PARKING - PORTE J 0.011736 \n", - "86 prospect bcom fhu normandie 0.010709 \n", - "59 PROSPECT FHU NORMANDIE 2022 BCOM 0.010709 \n", - "50 PASS 3J - ENFANTS 0.009716 \n", - "49 PASS 3J - ADOS 0.008643 \n", - "18 BILLETS NUIT 0.007333 \n", - "51 PASS Culture 2023 au 11/09 0.007092 \n", - "84 pass culture 11/09 0.007092 \n", - "83 pass culture - pass 3j 0.007076 \n", - "4 Ados FHU22 0.006718 \n", - "33 Enfants FHU22 0.006631 \n", - "46 PARKING CAMPEURS 0.004781 \n", - "39 INSCRIPTION NL VOYAGES HUMA 0.003625 \n", - "17 BILLET CAMPING-CAR 0.002594 \n", - "61 PROSPECT FORUM LOGEMENT 2022 0.002062 \n", - "27 Camping-car FHU22 0.001996 \n", - "26 Camping Zen FHU22 0.001596 \n", - "66 RESP. STANDS POUR FHUA2020 0.001559 \n", - "52 PRESSE 2021 0.001559 \n", - "70 accreditations presse_fhu23 0.001434 \n", - "0 ACCREDITES - FHU 23 0.001434 \n", - "74 billets enfants 2023_06_05 0.001239 \n", - "2 ACHETEURS TIPIS 0.001023 \n", - "8 BCOM FHU NORMANDIE 2021 0.001023 \n", - "88 prospect forum logement 2023 0.001014 \n", - "62 PROSPECT FORUM LOGEMENT 2023 0.001014 \n", - "38 INSCRIPTION FORUM LOGEMENT - 14/02 0.000840 \n", - "57 PROSPECT FHU NORMANDIE 2021 #2 0.000802 \n", - "22 CN - 38e CONGRÈS / 2021 0.000790 \n", - "65 RESP. STANDS PCF 2021 0.000765 \n", - "40 INSCRITS VISIO 14/01/22 0.000698 \n", - "24 CROISIÉRISTES 2021 / RIVAGES DU MONDE 0.000686 \n", - "31 DINER DE PRESSE 2020 0.000669 \n", - "71 acheteurs bcom fhu normandie 2022 0.000657 \n", - "1 ACHETEURS BCOM FHU NORMANDIE 2022 0.000657 \n", - "47 PARKING PMR - PORTE B 0.000636 \n", - "53 PROSPECT / TOURISTRA 0.000628 \n", - "48 PARKING PMR - PORTE J 0.000590 \n", - "78 fichier presse - journalistes2 0.000578 \n", - "35 FHU22 - DIFFUSION CP 0.000570 \n", - "32 DINER DE PRESSE 2021 0.000549 \n", - "81 liste médias cp 0.000516 \n", - "67 SECRETAIRES FÉDÉRAUX 2021 0.000437 \n", - "68 Soirée solidarité Ukraine 0.000437 \n", - "89 tipi 2023 0.000412 \n", - "69 TIPI 2023 0.000412 \n", - "77 fhu22 - pass 3j shotgun 0.000353 \n", - "30 DEMANDES PRESSE 2022 0.000283 \n", - "76 demandes accréditation 2023 0.000254 \n", - "23 COIN DES MÔMES 0.000170 \n", - "82 orders_302493 (1) 0.000108 \n", - "37 Humacumba Shotgun FHU22 0.000108 \n", - "80 liste agences cp 0.000046 \n", - "\n", - " cumulative_customers \n", - "11 0.114611 \n", - "28 0.179052 \n", - "34 0.236071 \n", - "14 0.292995 \n", - "12 0.349525 \n", - "79 0.406055 \n", - "15 0.462584 \n", - "5 0.512640 \n", - "16 0.559627 \n", - "13 0.602541 \n", - "29 0.644806 \n", - "7 0.677801 \n", - "55 0.709728 \n", - "73 0.733670 \n", - "64 0.755371 \n", - "9 0.775182 \n", - "6 0.793530 \n", - "72 0.809733 \n", - "10 0.823800 \n", - "36 0.837810 \n", - "19 0.850514 \n", - "41 0.862731 \n", - "56 0.874740 \n", - "25 0.885142 \n", - "3 0.894972 \n", - "75 0.903177 \n", - "20 0.910012 \n", - "21 0.916846 \n", - "58 0.923195 \n", - "54 0.928717 \n", - "85 0.934240 \n", - "63 0.938596 \n", - "60 0.942847 \n", - "87 0.947097 \n", - "44 0.950949 \n", - "45 0.954676 \n", - "42 0.958332 \n", - "43 0.961953 \n", - "86 0.965257 \n", - "59 0.968561 \n", - "50 0.971558 \n", - "49 0.974225 \n", - "18 0.976487 \n", - "51 0.978675 \n", - "84 0.980863 \n", - "83 0.983046 \n", - "4 0.985119 \n", - "33 0.987165 \n", - "46 0.988640 \n", - "39 0.989758 \n", - "17 0.990559 \n", - "61 0.991195 \n", - "27 0.991811 \n", - "26 0.992303 \n", - "66 0.992784 \n", - "52 0.993265 \n", - "70 0.993707 \n", - "0 0.994150 \n", - "74 0.994532 \n", - "2 0.994848 \n", - "8 0.995163 \n", - "88 0.995476 \n", - "62 0.995789 \n", - "38 0.996048 \n", - "57 0.996296 \n", - "22 0.996540 \n", - "65 0.996776 \n", - "40 0.996991 \n", - "24 0.997203 \n", - "31 0.997409 \n", - "71 0.997612 \n", - "1 0.997814 \n", - "47 0.998011 \n", - "53 0.998204 \n", - "48 0.998386 \n", - "78 0.998565 \n", - "35 0.998740 \n", - "32 0.998910 \n", - "81 0.999069 \n", - "67 0.999203 \n", - "68 0.999338 \n", - "89 0.999465 \n", - "69 0.999592 \n", - "77 0.999701 \n", - "30 0.999788 \n", - "76 0.999867 \n", - "23 0.999919 \n", - "82 0.999953 \n", - "37 0.999986 \n", - "80 1.000000 " - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "print_main_target('14', 100)\n" ]