thai_instruction,eng_instruction,table,sql,pandas,real_table
 มีการส่ง Ticket ID จำนวนเท่าใดสำหรับความเข้ากันได้ของอุปกรณ์ต่อพ่วง,How many ticket ID were submitted for 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;
###",,df[df['Ticket_Subject'] == 'Peripheral compatibility'].shape[0],customer
 มีการส่ง Ticket ID จำนวนเท่าใดสำหรับปัญหาเครือข่าย,How many ticket ID were submitted for Network problem?,"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;
###",,df[df['Ticket_Subject'] == 'Network problem'].shape[0],customer
 มีการส่ง Ticket ID จำนวนเท่าใดสำหรับปัญหาในการจัดส่ง,How many ticket ID were submitted for Delivery problem?,"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;
###",,df[df['Ticket_Subject'] == 'Delivery problem'].shape[0],customer
 มีการส่ง Ticket ID จำนวนเท่าใดสำหรับอายุการใช้งานแบตเตอรี่,How many ticket ID were submitted for Battery life?,"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;
###",,df[df['Ticket_Subject'] == 'Battery life'].shape[0],customer
 มีการส่ง Ticket ID จำนวนเท่าใดสำหรับการสนับสนุนการติดตั้ง,How many ticket ID were submitted for Installation support?,"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;
###",,df[df['Ticket_Subject'] == 'Installation support'].shape[0],customer
 มีการส่ง Ticket ID จำนวนเท่าใดสำหรับปัญหาการแสดงผล,How many ticket ID were submitted for Display 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;
###",,df[df['Ticket_Subject'] == 'Display issue'].shape[0],customer
 มีการส่ง Ticket ID จำนวนเท่าใดเพื่อขอคืนเงิน,How many ticket ID were submitted for Refund request?,"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;
###",,df[df['Ticket_Subject'] == 'Refund request'].shape[0],customer
 มีการส่งรหัสตั๋วสำหรับการตั้งค่าผลิตภัณฑ์จำนวนเท่าใด,How many ticket ID were submitted for Product setup?,"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;
###",,df[df['Ticket_Subject'] == 'Product setup'].shape[0],customer
 มีการส่ง Ticket ID สำหรับข้อบกพร่องของซอฟต์แวร์จำนวนเท่าใด,How many ticket ID were submitted for Software bug?,"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;
###",,df[df['Ticket_Subject'] == 'Software bug'].shape[0],customer
 มีการส่ง Ticket ID จำนวนเท่าใดสำหรับปัญหาการชำระเงิน,How many ticket ID were submitted for Payment 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;
###",,df[df['Ticket_Subject'] == 'Payment issue'].shape[0],customer
 มีการส่ง Ticket ID ไปกี่ใบเพื่อขอยกเลิก?,How many ticket ID were submitted for Cancellation request?,"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;
###",,df[df['Ticket_Subject'] == 'Cancellation request'].shape[0],customer
 มีการส่ง Ticket ID จำนวนเท่าใดสำหรับความเข้ากันได้ของผลิตภัณฑ์,How many ticket ID were submitted for Product 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;
###",,df[df['Ticket_Subject'] == 'Product compatibility'].shape[0],customer
 มีการส่ง Ticket ID จำนวนเท่าใดสำหรับคำแนะนำผลิตภัณฑ์,How many ticket ID were submitted for Product recommendation?,"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;
###",,df[df['Ticket_Subject'] == 'Product recommendation'].shape[0],customer
 มีการส่ง Ticket ID จำนวนเท่าใดสำหรับปัญหาฮาร์ดแวร์,How many ticket ID were submitted for Hardware 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;
###",,df[df['Ticket_Subject'] == 'Hardware issue'].shape[0],customer
 มีการส่งรหัสตั๋วสำหรับการเข้าถึงบัญชีจำนวนเท่าใด,How many ticket ID were submitted for 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;
###",,df[df['Ticket_Subject'] == 'Account access'].shape[0],customer
 มีการส่ง Ticket ID จำนวนเท่าใดสำหรับข้อมูลสูญหาย,How many ticket ID were submitted for 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;
###",,df[df['Ticket_Subject'] == 'Data loss'].shape[0],customer
มี Ticket ID จำนวนเท่าใดที่ดำเนินการทางอีเมล,How many ticket ID were conducted by 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;
###",,df[df['Ticket_Channel'] == 'Email'].shape[0],customer
 มี Ticket ID จำนวนเท่าใดที่ดำเนินการโดยโซเชียลมีเดีย,How many ticket ID were conducted by 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;
###",,df[df['Ticket_Channel'] == 'Social media'].shape[0],customer
 โทรศัพท์ดำเนินการ Ticket ID จำนวนเท่าใด,How many ticket ID were conducted by Phone?,"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;
###",,df[df['Ticket_Channel'] == 'Phone'].shape[0],customer
 Chat มีรหัสตั๋วจำนวนเท่าใด,How many ticket ID were conducted by Chat?,"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;
###",,df[df['Ticket_Channel'] == 'Chat'].shape[0],customer
 ผู้ที่ซื้อ MacBook Pro เป็นผู้ดำเนินการ Ticket ID  กี่ใบ,How many ticket ID were conducted by the person who purchased MacBook Pro?,"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;
###",,df[df['Product_Purchased'] == 'MacBook Pro'].shape[0],customer
 บุคคลที่ซื้อ Microsoft Xbox Controller ดำเนินการ Ticket ID  จำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Microsoft Xbox Controller?,"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;
###",,df[df['Product_Purchased'] == 'Microsoft Xbox Controller'].shape[0],customer
 ผู้ที่ซื้อ Fitbit Charge เป็นผู้ดำเนินการตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Fitbit Charge?,"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;
###",,df[df['Product_Purchased'] == 'Fitbit Charge'].shape[0],customer
 ผู้ที่ซื้อ Amazon Echo ดำเนินการ Ticket ID  กี่รหัส,How many ticket ID were conducted by the person who purchased Amazon Echo?,"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;
###",,df[df['Product_Purchased'] == 'Amazon Echo'].shape[0],customer
 บุคคลที่ซื้อ Amazon Kindle มีรหัสตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Amazon Kindle?,"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;
###",,df[df['Product_Purchased'] == 'Amazon Kindle'].shape[0],customer
 บุคคลที่ซื้อเครื่องดูดฝุ่น Dyson เป็นผู้ดำเนินการตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Dyson Vacuum Cleaner?,"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;
###",,df[df['Product_Purchased'] == 'Dyson Vacuum Cleaner'].shape[0],customer
 บุคคลที่ซื้อ Autodesk AutoCAD ดำเนินการ Ticket ID  กี่รหัส,How many ticket ID were conducted by the person who purchased Autodesk AutoCAD?,"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;
###",,df[df['Product_Purchased'] == 'Autodesk AutoCAD'].shape[0],customer
 ผู้ที่ซื้อ Nest Thermostat เป็นผู้ดำเนินการตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Nest Thermostat?,"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;
###",,df[df['Product_Purchased'] == 'Nest Thermostat'].shape[0],customer
 ผู้ที่ซื้อ Sony PlayStation ดำเนินการ Ticket ID จำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Sony PlayStation?,"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;
###",,df[df['Product_Purchased'] == 'Sony PlayStation'].shape[0],customer
ผู้ที่ซื้อ Roomba Robot Vacuum เป็นผู้ดำเนินการตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Roomba Robot Vacuum?,"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;
###",,df[df['Product_Purchased'] == 'Roomba Robot Vacuum'].shape[0],customer
 ผู้ที่ซื้อ Samsung Soundbar เป็นผู้ดำเนินการตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Samsung Soundbar?,"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;
###",,df[df['Product_Purchased'] == 'Samsung Soundbar'].shape[0],customer
 ผู้ที่ซื้อลำโพง Bose SoundLink ดำเนินการ Ticket ID จำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Bose SoundLink Speaker?,"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;
###",,df[df['Product_Purchased'] == 'Bose SoundLink Speaker'].shape[0],customer
 ผู้ที่ซื้อ Nintendo Switch ดำเนินการ Ticket ID  กี่ใบ,How many ticket ID were conducted by the person who purchased Nintendo Switch?,"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;
###",,df[df['Product_Purchased'] == 'Nintendo Switch'].shape[0],customer
 ผู้ที่ซื้อ PlayStation มี Ticket ID จำนวนเท่าใด,How many ticket ID were conducted by the person who purchased PlayStation?,"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;
###",,df[df['Product_Purchased'] == 'PlayStation'].shape[0],customer
 ผู้ที่ซื้อ Samsung Galaxy ดำเนินการ Ticket ID  กี่รหัส,How many ticket ID were conducted by the person who purchased Samsung Galaxy?,"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;
###",,df[df['Product_Purchased'] == 'Samsung Galaxy'].shape[0],customer
 ผู้ที่ซื้อ Asus ROG มี Ticket ID  กี่ใบ?,How many ticket ID were conducted by the person who purchased Asus ROG?,"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;
###",,df[df['Product_Purchased'] == 'Asus ROG'].shape[0],customer
 ผู้ที่ซื้อ Google Nest เป็นผู้ดำเนินการตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Google Nest?,"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;
###",,df[df['Product_Purchased'] == 'Google Nest'].shape[0],customer
 ผู้ที่ซื้อ Lenovo ThinkPad ดำเนินการ Ticket ID  กี่ใบ,How many ticket ID were conducted by the person who purchased Lenovo ThinkPad?,"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;
###",,df[df['Product_Purchased'] == 'Lenovo ThinkPad'].shape[0],customer
 ผู้ที่ซื้อ iPhone ดำเนินการ Ticket ID  กี่รหัส,How many ticket ID were conducted by the person who purchased iPhone?,"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;
###",,df[df['Product_Purchased'] == 'iPhone'].shape[0],customer
 ผู้ที่ซื้อเครื่องซักผ้า LG เป็นผู้ดำเนินการตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased LG Washing Machine?,"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;
###",,df[df['Product_Purchased'] == 'LG Washing Machine'].shape[0],customer
 ผู้ที่ซื้อ Adobe Photoshop ดำเนินการ Ticket ID  กี่รหัส,How many ticket ID were conducted by the person who purchased Adobe Photoshop?,"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;
###",,df[df['Product_Purchased'] == 'Adobe Photoshop'].shape[0],customer
ผู้ที่ซื้อ LG OLED ดำเนินการ Ticket ID  กี่ใบ,How many ticket ID were conducted by the person who purchased LG OLED?,"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;
###",,df[df['Product_Purchased'] == 'LG OLED'].shape[0],customer
 ผู้ที่ซื้อ Sony Xperia เป็นผู้ดำเนินการตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Sony Xperia?,"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;
###",,df[df['Product_Purchased'] == 'Sony Xperia'].shape[0],customer
 ผู้ที่ซื้อ Garmin Forerunner ดำเนินการ Ticket ID  กี่ใบ,How many ticket ID were conducted by the person who purchased Garmin Forerunner?,"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;
###",,df[df['Product_Purchased'] == 'Garmin Forerunner'].shape[0],customer
 ผู้ที่ซื้อ LG Smart TV มีรหัสตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased LG Smart TV?,"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;
###",,df[df['Product_Purchased'] == 'LG Smart TV'].shape[0],customer
 ผู้ที่ซื้อ Nintendo Switch Pro Controller ดำเนินการ Ticket ID  กี่ใบ,How many ticket ID were conducted by the person who purchased Nintendo Switch Pro Controller?,"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;
###",,df[df['Product_Purchased'] == 'Nintendo Switch Pro Controller'].shape[0],customer
 ผู้ที่ซื้อ GoPro Action Camera เป็นผู้ดำเนินการตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased GoPro Action Camera?,"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;
###",,df[df['Product_Purchased'] == 'GoPro Action Camera'].shape[0],customer
 บุคคลที่ซื้อ Xbox มี Ticket ID จำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Xbox?,"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;
###",,df[df['Product_Purchased'] == 'Xbox'].shape[0],customer
 ผู้ที่ซื้อ Microsoft Surface ดำเนินการ Ticket ID  กี่รหัส,How many ticket ID were conducted by the person who purchased Microsoft Surface?,"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;
###",,df[df['Product_Purchased'] == 'Microsoft Surface'].shape[0],customer
 ผู้ที่ซื้อ Bose QuietComfort มี Ticket ID จำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Bose QuietComfort?,"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;
###",,df[df['Product_Purchased'] == 'Bose QuietComfort'].shape[0],customer
 บุคคลที่ซื้อ Nikon D เป็นผู้ดำเนินการตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Nikon D?,"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;
###",,df[df['Product_Purchased'] == 'Nikon D'].shape[0],customer
 บุคคลที่ซื้อ Apple AirPods เป็นผู้ดำเนินการตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Apple AirPods?,"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;
###",,df[df['Product_Purchased'] == 'Apple AirPods'].shape[0],customer
 ผู้ที่ซื้อ Fitbit Versa Smartwatch ดำเนินการตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Fitbit Versa Smartwatch?,"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;
###",,df[df['Product_Purchased'] == 'Fitbit Versa Smartwatch'].shape[0],customer
ผู้ที่ซื้อทีวี Sony 4K HDR มีรหัสตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Sony 4K HDR TV?,"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;
###",,df[df['Product_Purchased'] == 'Sony 4K HDR TV'].shape[0],customer
 บุคคลที่ซื้อ Microsoft Office ดำเนินการ Ticket ID  กี่รหัส,How many ticket ID were conducted by the person who purchased 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;
###",,df[df['Product_Purchased'] == 'Microsoft Office'].shape[0],customer
 ผู้ที่ซื้อ GoPro Hero ดำเนินการ Ticket ID จำนวนเท่าใด,How many ticket ID were conducted by the person who purchased 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;
###",,df[df['Product_Purchased'] == 'GoPro Hero'].shape[0],customer
 บุคคลที่ซื้อ Dell XPS ดำเนินการ Ticket ID  จำนวนเท่าใด,How many ticket ID were conducted by the person who purchased 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;
###",,df[df['Product_Purchased'] == 'Dell XPS'].shape[0],customer
 ผู้ที่ซื้อ Philips Hue Lights ดำเนินการตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Philips Hue Lights?,"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;
###",,df[df['Product_Purchased'] == 'Philips Hue Lights'].shape[0],customer
 ผู้ที่ซื้อกล้อง Canon DSLR มีบัตรประจำตัวจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Canon DSLR Camera?,"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;
###",,df[df['Product_Purchased'] == 'Canon DSLR Camera'].shape[0],customer
 ผู้ที่ซื้อ Google Pixel ดำเนินการ Ticket ID  กี่ใบ,How many ticket ID were conducted by the person who purchased Google Pixel?,"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;
###",,df[df['Product_Purchased'] == 'Google Pixel'].shape[0],customer
 ผู้ที่ซื้อ Canon EOS เป็นผู้ดำเนินการตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased Canon EOS?,"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;
###",,df[df['Product_Purchased'] == 'Canon EOS'].shape[0],customer
 บุคคลที่ซื้อ HP Pavilion เป็นผู้ดำเนินการตั๋วจำนวนเท่าใด,How many ticket ID were conducted by the person who purchased HP Pavilion?,"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;
###",,df[df['Product_Purchased'] == 'HP Pavilion'].shape[0],customer
 รหัสตั๋วมีลำดับความสำคัญปานกลางกี่รหัส,How many ticket ID were in Medium 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;
###",,df[df['Ticket_Priority'] == 'Medium'].shape[0],customer
 มี Ticket ID กี่ใบที่อยู่ในลำดับความสำคัญวิกฤต,How many ticket ID were in Critical 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;
###",,df[df['Ticket_Priority'] == 'Critical'].shape[0],customer
 รหัสตั๋วมีลำดับความสำคัญสูงจำนวนเท่าใด,How many ticket ID were in 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;
###",,df[df['Ticket_Priority'] == 'High'].shape[0],customer
 รหัสตั๋วที่มีลำดับความสำคัญต่ำมีกี่รหัส,How many ticket ID were in Low 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;
###",,df[df['Ticket_Priority'] == 'Low'].shape[0],customer
 Ticket ID กี่ใบที่เป็นปัญหาทางเทคนิค,How many ticket ID that is 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;
###",,df[df['Ticket_Type'] == 'Technical issue'].shape[0],customer
รหัสตั๋วกี่ใบที่สามารถขอคืนเงินได้?,How many ticket ID that is Refund request?,"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;
###",,df[df['Ticket_Type'] == 'Refund request'].shape[0],customer
 รหัสตั๋วกี่ใบที่เป็นคำขอยกเลิก?,How many ticket ID that is Cancellation request?,"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;
###",,df[df['Ticket_Type'] == 'Cancellation request'].shape[0],customer
 มี Ticket ID กี่ใบที่สอบถาม Billing?,How many ticket ID that is Billing inquiry?,"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;
###",,df[df['Ticket_Type'] == 'Billing inquiry'].shape[0],customer
 รหัสตั๋วกี่ใบที่สอบถามผลิตภัณฑ์?,How many ticket ID that is Product inquiry?,"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;
###",,df[df['Ticket_Type'] == 'Product inquiry'].shape[0],customer
 รหัสตั๋วที่มาจากลูกค้ารายอื่นมีกี่รหัส?,How many ticket ID that come from Other 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;
###",,df[df['Customer_Gender'] == 'Other'].shape[0],customer
 รหัสตั๋วที่มาจากลูกค้าชายมีกี่ใบ?,How many ticket ID that come from Male 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;
###",,df[df['Customer_Gender'] == 'Male'].shape[0],customer
 Ticket ID ที่มาจากลูกค้าผู้หญิงมีกี่ใบ?,How many ticket ID that come from 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;
###",,df[df['Customer_Gender'] == 'Female'].shape[0],customer