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identify target customer

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This commit is contained in:
Alexis REVELLE 2024-03-14 21:00:14 +00:00
parent 6d0f67bd31
commit 54fbad0344
2 changed files with 20 additions and 12 deletions
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20
Descriptive_statistics/generate_stat_desc.py
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@ -16,7 +16,7 @@ S3_ENDPOINT_URL = "https://" + os.environ["AWS_S3_ENDPOINT"]
fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL}) fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})
companies = {'musee' : ['1', '2', '3', '4'], # , '101' companies = {'musee' : ['1', '2', '3', '4'], # , '101'
'sport': ['5'], 'sport': ['5', '6'],
'musique' : ['10', '11', '12', '13', '14']} 'musique' : ['10', '11', '12', '13', '14']}
@ -30,13 +30,17 @@ customer, campaigns_kpi, campaigns_brut, tickets, products = load_files(list_of_
outlier_list = outlier_detection(tickets, list_of_comp) outlier_list = outlier_detection(tickets, list_of_comp)
# Identify valid customer (customer who bought tickets after starting date or received mails after starting date) # Identify valid customer (customer who bought tickets after starting date or received mails after starting date)
customer_valid_list = valid_customer_detection(products) customer_valid_list = valid_customer_detection(products, campaigns_brut)
# Identify customer who bought during the period of y
consumer_target_period = identify_purchase_during_target_periode(products)
databases = [customer, campaigns_kpi, campaigns_brut, tickets, products] databases = [customer, campaigns_kpi, campaigns_brut, tickets, products]
for dataset in databases: for dataset in databases:
dataset['customer_id'] = dataset['customer_id'].apply(lambda x: remove_elements(x, outlier_list))# remove outlier dataset['customer_id'] = dataset['customer_id'].apply(lambda x: remove_elements(x, outlier_list))# remove outlier
dataset['customer_id'] = dataset['customer_id'].isin(customer_valid_list) # keep only valid customer dataset['customer_id'] = dataset['customer_id'].isin(customer_valid_list) # keep only valid customer
dataset['has_purchased_target_period'] = np.where(dataset['customer_id'].isin(customer_valid_list), 1, 0)
#print(f'shape of {dataset} : ', dataset.shape) #print(f'shape of {dataset} : ', dataset.shape)
# Generate graph and automatically saved them in the bucket # Generate graph and automatically saved them in the bucket
@ -46,16 +50,16 @@ maximum_price_paid(customer, type_of_activity)
mailing_consent(customer, type_of_activity) mailing_consent(customer, type_of_activity)
gender_bar(customer, type_of_activity) #gender_bar(customer, type_of_activity)
country_bar(customer, type_of_activity) #country_bar(customer, type_of_activity)
lazy_customer_plot(campaigns_kpi, type_of_activity) #lazy_customer_plot(campaigns_kpi, type_of_activity)
#campaigns_effectiveness(customer, type_of_activity) #campaigns_effectiveness(customer, type_of_activity)
sale_dynamics(products, campaigns_brut, type_of_activity) #sale_dynamics(products, campaigns_brut, type_of_activity)
tickets_internet(tickets, type_of_activity) #tickets_internet(tickets, type_of_activity)
box_plot_price_tickets(tickets, type_of_activity) #box_plot_price_tickets(tickets, type_of_activity)

10
Descriptive_statistics/plot.py
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@ -99,12 +99,16 @@ def valid_customer_detection(products, campaigns_brut):
consumer_valid = consumer_valid_product + consumer_valid_campaigns consumer_valid = consumer_valid_product + consumer_valid_campaigns
return consumer_valid return consumer_valid
def identify_purchase_during_target_periode(products):
products_target_period = products[products['purchase_date']>="2022-11-01" & products['purchase_date']<="2023-11-01"]
consumer_target_period = products_target_period['customer_id'].to_list()
return consumer_target_period
def remove_elements(lst, elements_to_remove): def remove_elements(lst, elements_to_remove):
return ''.join([x for x in lst if x not in elements_to_remove]) return ''.join([x for x in lst if x not in elements_to_remove])
def keep_elements(lst, elements_to_remove):
return ''.join([x for x in lst if x in elements_to_remove])
def compute_nb_clients(customer, type_of_activity): def compute_nb_clients(customer, type_of_activity):
company_nb_clients = customer[customer["purchase_count"]>0].groupby("number_company")["customer_id"].count().reset_index() company_nb_clients = customer[customer["purchase_count"]>0].groupby("number_company")["customer_id"].count().reset_index()