AIAT/Machima-openthai-llama-pretrained-7b-table-trained
Text Generation
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Updated
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12
Datasets:
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Ticket ID สำหรับบันทึกแรกคือ 1 หรือไม่ | Is the Ticket ID for the first record 1? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Ticket_ID'].iloc[0] == 1 | customer |
ลูกค้าที่มีอายุสูงสุดมากกว่า 70 ปีใช่หรือไม่ | Is the highest customer age greater than 70? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Age'].max() > 70 | customer |
คะแนนความพึงพอใจของลูกค้าต่ำสุดน้อยกว่า 2 หรือไม่ | Is the lowest customer satisfaction rating less than 2? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Satisfaction_Rating'].min() < 2 | customer |
ลูกค้าท่านใดมีสถานะตั๋ว 'ปิด' แล้วบ้าง | Does any customer have 'Closed' ticket status? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Ticket_Status'].str.contains('Closed').any() | customer |
จำนวนตั๋วทั้งหมดน้อยกว่า 20 ใบใช่หรือไม่ | Is the total number of tickets less than 20? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | len(data) < 20 | customer |
มีลูกค้าชื่อ 'เจสสิก้า ริออส' มั้ย | Is there a customer named 'Jessica Rios'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Name'].str.contains('Jessica Rios').any() | customer |
'การสอบถามการเรียกเก็บเงิน' เป็นตั๋วประเภทหนึ่งหรือไม่ | Is 'Billing inquiry' a type of ticket? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Ticket_Type'].str.contains('Billing inquiry').any() | customer |
'อีเมล' เป็นช่องทางตั๋วสำหรับบันทึกที่ห้าหรือไม่ | Is 'Email' a ticket channel for the fifth record? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Ticket_Channel'].iloc[4] == 'Email' | customer |
เป็นเวลาตอบกลับครั้งแรกสำหรับบันทึกครั้งแรกในปี 2023 หรือไม่ | Is the first response time for the first record in 2023? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['First_Response_Time'].iloc[0].year == 2023 | customer |
ตั๋วใด ๆ ที่ถูกทำเครื่องหมายว่า 'สำคัญ' หรือไม่ | Is any ticket marked as 'Critical'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Ticket_Priority'].str.contains('Critical').any() | customer |
มีลูกค้าอายุเกิน 50 บ้างไหม | Are there any customers older than 50? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Age'] > 50 | customer |
มีตั๋วจากลูกค้าผู้หญิงหรือเปล่า | Is any ticket from a female customer? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Gender'].str.contains('Female').any() | customer |
มีการซื้อใดๆ ในปี 2021 หรือไม่ | Has any purchase been made in 2021? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Date_of_Purchase'].dt.year == 2021 | customer |
มีสินค้าที่ซื้อชื่อ 'GoPro Hero' หรือไม่ | Is there any product purchased named 'GoPro Hero'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Product_Purchased'].str.contains('GoPro Hero').any() | customer |
บันทึกล่าสุดมีคะแนนความพึงพอใจของลูกค้าสูงกว่า 4 หรือไม่ | Does the last record have a customer satisfaction rating above 4? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Satisfaction_Rating'].iloc[-1] > 4 | customer |
ตั๋วใดๆ เกี่ยวข้องกับปัญหาทางเทคนิคหรือไม่ | Does any ticket involve a technical issue? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Ticket_Type'].str.contains('Technical issue').any() | customer |
อายุเฉลี่ยของลูกค้าต่ำกว่า 30 ปีหรือไม่ | Is the average customer age below 30? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Age'].mean() < 30 | customer |
มีลูกค้าที่ได้รับอีเมลจาก 'example.com' หรือไม่ | Is there any customer with an email from 'example.com'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Email'].str.contains('example.com').any() | customer |
ตั๋วทั้งหมดเปิดหรือปิดแล้ว | Are all tickets either open or closed? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (data['Ticket_Status'] == 'Open') | (data['Ticket_Status'] == 'Closed') | customer |
ลำดับความสำคัญของตั๋วใบที่สามต่ำหรือไม่ | Is the third ticket's priority low? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Ticket_Priority'].iloc[2] == 'Low' | customer |
มีตั๋วที่ส่งทางอีเมลหรือไม่ | Are there any tickets submitted via email? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Ticket_Channel'].str.contains('Email').any() | customer |
คะแนนความพึงพอใจของลูกค้าสำหรับตั๋วใบแรกน้อยกว่า 5 หรือไม่ | Is the customer satisfaction rating for the first ticket less than 5? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Satisfaction_Rating'].iloc[0] < 5 | customer |
มีการลงมติสำหรับตั๋วใบแรกหรือไม่ | Has the resolution for the first ticket been provided? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | pd.notna(data['Resolution'].iloc[0]) | customer |
มีลูกค้าคนไหนอายุต่ำกว่า 25 ปี บ้างมั้ยคะ | Is there any customer under 25 years old? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Age'] < 25 | customer |
ชุดข้อมูลมีตั๋วมากกว่า 10 ใบหรือไม่ | Are there more than 10 tickets in the dataset? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | len(data) > 10 | customer |
ตั๋วใด ๆ มีคะแนนความพึงพอใจอยู่ที่ 5 หรือไม่ | Is any ticket rated with a satisfaction rating of 5? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Satisfaction_Rating'] == 5 | customer |
มีตั๋วที่มีปัญหาที่ยังไม่ได้รับการแก้ไขหรือไม่ | Are there any tickets with unresolved issues? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Ticket_Status'].str.contains('Pending').any() | customer |
มีตั๋วใดบ้างที่ได้รับการแก้ไขภายในหนึ่งวันนับจากการตอบกลับครั้งแรก | Has any ticket been resolved within a day of its first response? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (data['Time_to_Resolution'] - data['First_Response_Time']).dt.days < 1 | customer |
ตั๋วใดๆ มีสถานะลำดับความสำคัญสูงหรือไม่ | Do any tickets have a high priority status? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Ticket_Priority'].str.contains('High').any() | customer |
มีผลิตภัณฑ์ที่ซื้อเรียกว่า 'Microsoft Office' หรือไม่ | Is there a product purchased called 'Microsoft Office'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Product_Purchased'].str.contains('Microsoft Office').any() | customer |
เวลาสูงสุดในการแก้ไขคือมากกว่า 10 วันหรือไม่ | Is the maximum time to resolution over 10 days? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (data['Time_to_Resolution'] - data['First_Response_Time']).dt.days.max() > 10 | customer |
ลูกค้าท่านใดให้คะแนนความพึงพอใจ 1 บ้าง | Has any customer given a satisfaction rating of 1? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Satisfaction_Rating'] == 1 | customer |
มีตั๋วเกี่ยวกับ 'ข้อมูลสูญหาย' หรือไม่ | Is there a ticket concerning 'Data loss'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Ticket_Subject'].str.contains('Data loss').any() | customer |
ชุดข้อมูลมีตั๋วจากปี 2022 หรือไม่ | Does the dataset contain any tickets from 2022? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Date_of_Purchase'].dt.year == 2022 | customer |
มีบันทึกของลูกค้าชื่อ 'อเล็กซานเดอร์ คาร์โรลล์' หรือไม่ | Is there a record of a customer named 'Alexander Carroll'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Name'].str.contains('Alexander Carroll').any() | customer |
ตั๋วใบแรกส่งผ่านโซเชียลมีเดียหรือไม่ | Is the first ticket submitted through social media? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Ticket_Channel'].iloc[0] == 'Social media' | customer |
มีตั๋วที่มีลำดับความสำคัญที่มีป้ายกำกับว่า 'ปานกลาง' หรือไม่ | Is there a ticket with a priority labeled as 'Moderate'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Ticket_Priority'].str.contains('Moderate').any() | customer |
คะแนนความพึงพอใจของลูกค้าเฉลี่ยมากกว่า 3 หรือไม่ | Does the customer satisfaction rating average more than 3? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Satisfaction_Rating'].mean() > 3 | customer |
ตั๋วทั้งหมดเป็นของลูกค้าผู้ชายหรือเปล่า | Are all tickets from male customers? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Gender'].unique() == ['Male'] | customer |
มีตั๋วใดบ้างที่ได้รับการกำหนดสถานะลำดับความสำคัญเป็น 'ด่วน' หรือไม่ | Has any ticket been assigned a priority status of 'Urgent'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Ticket_Priority'].str.contains('Urgent').any() | customer |
มีการส่งตั๋วในเดือนมิถุนายน 2023 หรือไม่ | Is there a ticket submitted in June 2023? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['First_Response_Time'].dt.month == 6 and data['First_Response_Time'].dt.year == 2023 | customer |
ตั๋วใดบ้างที่มีเวลาในการแก้ไขปัญหาเกิน 48 ชั่วโมง | Do any tickets have a resolution time over 48 hours? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (data['Time_to_Resolution'] - data['First_Response_Time']).dt.total_seconds() / 3600 > 48 | customer |
Ticket ID 10 เชื่อมโยงกับลูกค้าที่มีอายุต่ำกว่า 30 ปีหรือไม่ | Is the Ticket ID 10 associated with a customer under 30? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data.loc[data['Ticket_ID'] == 10, 'Customer_Age'].iloc[0] < 30 | customer |
มีลูกค้ารายใดที่ซื้อผลิตภัณฑ์มากกว่าหนึ่งรายการหรือไม่ | Has any customer purchased more than one product? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Email'].duplicated().any() | customer |
มีตั๋วที่ยังไม่ได้รับการแก้ไขจากปี 2020 หรือไม่ | Are there any unresolved tickets from 2020? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (data['Date_of_Purchase'].dt.year == 2020) & (data['Ticket_Status'].str.contains('Pending').any()) | customer |
ตั๋วประเภท 'ปัญหาทางเทคนิค' ที่พบบ่อยที่สุดใช่หรือไม่ | Is the most common ticket type 'Technical issue'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Ticket_Type'].mode()[0] == 'Technical issue' | customer |
มีตั๋วจากลูกค้าอายุ 40 พอดีหรือไม่ | Is there a ticket from a customer aged exactly 40? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Age'] == 40 | customer |
ลูกค้ารายใดมีคะแนนความพึงพอใจต่ำกว่า 3 หรือไม่ | Does any customer have a satisfaction rating below 3? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Satisfaction_Rating'] < 3 | customer |
มีตั๋วใดบ้างที่มีการแก้ไขเกี่ยวกับ 'การเข้าถึงบัญชี' หรือไม่ | Are there any tickets with a resolution involving 'account access'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Resolution'].str.contains('account access', case=False, na=False).any() | customer |
มีลูกค้าชื่อ 'Christina Dillon' ที่มีตั๋วสอบถามเรื่องการเรียกเก็บเงินหรือไม่ | Is there a customer named 'Christina Dillon' with a billing inquiry ticket? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (data['Customer_Name'] == 'Christina Dillon') & (data['Ticket_Type'] == 'Billing inquiry') | customer |
มีตั๋วจากลูกค้าที่มีอายุมากกว่า 80 ปีหรือไม่ | Is there any ticket from a customer over 80 years old? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Age'] > 80 | customer |
ลูกค้ารายแรกชื่อ 'มาริสา โอเบรียน' หรือเปล่าคะ | Is the first customer named 'Marisa Obrien'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Name'].iloc[0] == 'Marisa Obrien' | customer |
ตั๋วใดบ้างที่เกี่ยวข้องกับ 'ความเข้ากันได้ของอุปกรณ์ต่อพ่วง' หรือไม่ | Are any tickets related to 'Peripheral compatibility'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Ticket_Subject'].str.contains('Peripheral compatibility').any() | customer |
มีผลิตภัณฑ์ใดบ้างที่ซื้อชื่อ 'Dell XPS' | Is any product purchased named 'Dell XPS'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Product_Purchased'].str.contains('Dell XPS').any() | customer |
ตั๋วใด ๆ ได้รับการแก้ไขภายใน 5 ชั่วโมงหลังจากการตอบกลับครั้งแรกหรือไม่ | Has any ticket been resolved within 5 hours of its first response? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (data['Time_to_Resolution'] - data['First_Response_Time']).dt.total_seconds() / 3600 < 5 | customer |
มีตั๋วจากลูกค้าที่มีโดเมนอีเมล '@example.org' หรือไม่ | Is there a ticket from a customer with the email domain '@example.org'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Email'].str.contains('@example.org').any() | customer |
มีตั๋ว 15 ใบในชุดข้อมูลหรือไม่ | Are there exactly 15 tickets in the dataset? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | len(data) == 15 | customer |
มีลูกค้ารายใดจากชุดข้อมูลที่ได้รับคะแนนความพึงพอใจตั้งแต่ 4 ขึ้นไป | Has any customer from the dataset given a satisfaction rating of 4 or higher? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Satisfaction_Rating'] >= 4 | customer |
มีตั๋วที่มีลำดับความสำคัญ 'ต่ำ' ที่ถูกปิดแล้วหรือไม่ | Is there a ticket with a 'Low' priority that has been closed? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | ((data['Ticket_Priority'] == 'Low') & (data['Ticket_Status'] == 'Closed')).any() | customer |
ชุดข้อมูลรวมตั๋วใดๆ ที่เพิ่มขึ้นในเดือนมกราคมหรือไม่ | Does the dataset include any tickets raised in January? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Date_of_Purchase'].dt.month == 1 | customer |
อายุของลูกค้าทุกคนในชุดข้อมูลมากกว่า 20 ปีหรือไม่ | Are all customer ages in the dataset above 20? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Age'].min() > 20 | customer |
มีการยื่นตั๋วใด ๆ ภายใต้ 'การเข้าถึงบัญชี' หรือไม่ | Has any ticket been filed under 'Account access'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Ticket_Subject'].str.contains('Account access').any() | customer |
ตั๋วมีเวลาตอบกลับครั้งแรกในช่วงสุดสัปดาห์หรือไม่ | Is any ticket's first response time on a weekend? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['First_Response_Time'].dt.weekday >= 5 | customer |
มีตั๋วที่มีสถานะลำดับความสำคัญสูงที่ยังคงเปิดอยู่หรือไม่ | Is there a ticket with a high priority status that is still open? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | ((data['Ticket_Priority'] == 'High') & (data['Ticket_Status'].str.contains('Open'))).any() | customer |
มีตั๋วใดบ้างที่ถูกทำเครื่องหมายว่า 'ด่วน' โดยมีคะแนนความพึงพอใจต่ำกว่า 2 หรือไม่ | Has any ticket been marked as 'Urgent' with a satisfaction rating below 2? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | ((data['Ticket_Priority'] == 'Urgent') & (data['Customer_Satisfaction_Rating'] < 2)).any() | customer |
มีตั๋วใดบ้างที่มีเวลาแก้ไขเป็นหนึ่งวันพอดี | Are there any tickets with a resolution time of exactly one day? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (data['Time_to_Resolution'] - data['First_Response_Time']).dt.days == 1 | customer |
มีบันทึกเกี่ยวกับผลิตภัณฑ์ที่ลูกค้ารายอื่นซื้อมากกว่า 10 ครั้งหรือไม่ | Is there any record of a product purchased more than 10 times by different customers? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Product_Purchased'].value_counts().max() > 10 | customer |
เวลาเฉลี่ยในการแก้ปัญหาสำหรับตั๋วเกิน 12 ชั่วโมงหรือไม่ | Is the average resolution time for tickets over 12 hours? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (data['Time_to_Resolution'] - data['First_Response_Time']).dt.total_seconds().mean() / 3600 > 12 | customer |
หัวข้อตั๋ว 'การตั้งค่าผลิตภัณฑ์' เกี่ยวข้องกับลำดับความสำคัญสูงหรือไม่ | Is the ticket subject 'Product setup' associated with a high priority? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (data[data['Ticket_Subject'] == 'Product setup']['Ticket_Priority'] == 'High').any() | customer |
ตั๋วใด ๆ จาก 'โซเชียลมีเดีย' ถูกทำเครื่องหมายว่าได้รับการแก้ไขแล้วหรือไม่ | Has any ticket from 'Social media' been marked as resolved? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | ((data['Ticket_Channel'] == 'Social media') & (data['Ticket_Status'] == 'Closed')).any() | customer |
มีลูกค้าชื่อ 'John Doe' ในชุดข้อมูลหรือไม่ | Is there a customer named 'John Doe' in the dataset? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Name'].str.contains('John Doe').any() | customer |
มีการออกตั๋วในวันคริสต์มาส (25 ธ.ค. ) หรือไม่ | Has any ticket been issued on Christmas Day (Dec 25)? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Date_of_Purchase'].dt.month == 12 and data['Date_of_Purchase'].dt.day == 25 | customer |
มีตั๋วที่เปิดเกิน 30 วันไหม | Is there any ticket that has been open for more than 30 days? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (data['Ticket_Status'] == 'Open') & ((pd.Timestamp.now() - data['Date_of_Purchase']).dt.days > 30) | customer |
ตั๋วทั้งหมดมาจากลูกค้าที่มีอายุ 30 ปีขึ้นไปหรือไม่ | Do all tickets come from customers aged 30 or older? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Age'].min() >= 30 | customer |
มีตั๋วใดบ้างที่ถูกปิดภายใน 24 ชั่วโมงหลังจากเปิดหรือไม่ | Has any ticket been closed within 24 hours of its opening? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (data['Ticket_Status'] == 'Closed') & ((data['Time_to_Resolution'] - data['First_Response_Time']).dt.total_seconds() / 3600 <= 24) | customer |
วิธีแก้ปัญหาที่พบบ่อยที่สุดเกี่ยวข้องกับ 'ปัญหาบัญชี' หรือไม่ | Is the most common resolution related to 'account issues'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Resolution'].str.contains('account').value_counts().idxmax() | customer |
มีตั๋วจากลูกค้านามสกุล 'Smith' หรือไม่ | Are there any tickets from a customer with the last name 'Smith'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Name'].str.contains('Smith').any() | customer |
ลูกค้ารายใดให้คะแนนความพึงพอใจเป็นศูนย์หรือไม่ | Has any customer given a satisfaction rating of zero? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Satisfaction_Rating'] == 0 | customer |
มีตั๋วจากลูกค้าที่มีอายุต่ำกว่า 18 ปีหรือไม่ | Are there any tickets from customers under the age of 18? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer_Age'] < 18 | customer |
ชุดข้อมูลมีตั๋วจากทุกเดือนของปีหรือไม่ | Does the dataset contain tickets from every month of the year? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Date_of_Purchase'].dt.month.nunique() == 12 | customer |
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