thai_instruction
stringlengths 22
82
| eng_instruction
stringlengths 33
93
| table
stringclasses 1
value | sql
float64 | pandas
stringlengths 13
130
| real_table
stringclasses 1
value |
|---|---|---|---|---|---|
วันเดียวกันมีการเปิดและปิดตั๋วกี่ใบ? | How many tickets were opened and closed on the same day? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[(data['Opened'] == data['Closed']) & (data['Closed'].notna())].shape[0] | customer |
ลูกค้าที่ไม่ซ้ำทั้งหมดมีจำนวนเท่าใด | What is the total number of unique customers? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Customer ID'].nunique() | customer |
มีตั๋วกี่ใบที่เลื่อนระดับไปสู่ลำดับความสำคัญที่สูงกว่า | How many tickets escalated to a higher priority? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Priority Escalation'] == 'Yes'].shape[0] | customer |
เวลาตอบสนองที่บันทึกไว้นานที่สุดคือเท่าไร? | What is the longest response time recorded? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Response Time (hrs)'].max() | customer |
ตั๋วกี่ใบที่มีการนับการโต้ตอบเป็นศูนย์? | How many tickets have an interaction count of zero? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Number of Interactions'] == 0].shape[0] | customer |
เวลาแก้ไขโดยเฉลี่ยสำหรับตั๋วที่มีลำดับความสำคัญ 'ต่ำ' คือเท่าใด | What is the average resolution time for 'Low' priority tickets? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Priority'] == 'Low']['Resolution Time (days)'].mean() | customer |
มีตั๋วกี่ใบที่ได้รับคะแนนความพึงพอใจของลูกค้าที่ 5? | How many tickets received a customer satisfaction rating of 5? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Customer Satisfaction'] == 5].shape[0] | customer |
กี่เปอร์เซ็นต์ของตั๋วที่ได้รับการแก้ไขภายในหนึ่งวัน? | What percentage of tickets are resolved within a day? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (data[data['Resolution Time (days)'] <= 1].shape[0] / data.shape[0]) * 100 | customer |
มีตัวแทนมากกว่าหนึ่งคนจัดการตั๋วกี่ใบ? | How many tickets were handled by more than one agent? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Agents Involved'] > 1].shape[0] | customer |
จำนวนการโต้ตอบเฉลี่ยของตั๋วทั้งหมดคือเท่าใด | What is the median number of interactions for all tickets? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Number of Interactions'].median() | customer |
ตั๋วกี่ใบที่ไม่มีตัวแทนที่ได้รับมอบหมาย? | How many tickets have no assigned agent? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Assigned Agent'].isna()].shape[0] | customer |
จำนวนตั๋วทั้งหมดที่มีปัญหาร้ายแรงคือเท่าใด | What is the total number of tickets with a critical issue? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Issue Type'] == 'Critical'].shape[0] | customer |
มีตั๋วกี่ใบที่ได้รับการแก้ไขหลังจากการโต้ตอบมากกว่า 5 ครั้ง | How many tickets were resolved after more than 5 interactions? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Number of Interactions'] > 5].shape[0] | customer |
เวลาแก้ไขที่สั้นที่สุดที่บันทึกไว้คือเท่าใด | What is the shortest resolution time recorded? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Resolution Time (days)'].min() | customer |
มีตั๋วกี่ใบที่มีโน้ตเกิน 100 ตัวอักษร? | How many tickets have notes exceeding 100 characters? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Ticket Notes'].str.len() > 100].shape[0] | customer |
เวลาเฉลี่ยในการแก้ปัญหาสำหรับตั๋วที่มีลำดับความสำคัญ 'ด่วน' คือเท่าใด | What is the average resolution time for tickets with priority 'Urgent'? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Priority'] == 'Urgent']['Resolution Time (days)'].mean() | customer |
มีตั๋วกี่ใบที่ได้รับคะแนนความพึงพอใจของลูกค้าต่ำกว่า 3 | How many tickets received a customer satisfaction rating below 3? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Customer Satisfaction'] < 3].shape[0] | customer |
ตั๋วกี่เปอร์เซ็นต์ที่ต้องการความช่วยเหลือด้านเทคนิค? | What percentage of tickets required technical assistance? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (data[data['Technical Assistance'] == 'Yes'].shape[0] / data.shape[0]) * 100 | customer |
ในเดือนที่แล้วมีการเปิดตั๋วกี่ใบ? | How many tickets were opened in the last month? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Opened'] >= (pd.Timestamp.now() - pd.DateOffset(days=30))].shape[0] | customer |
เวลาตอบกลับเฉลี่ยสำหรับตั๋วทั้งหมดคือเท่าไร? | What is the median response time for all tickets? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data['Response Time (hrs)'].median() | customer |
มีตั๋วกี่ใบที่มีอายุมากกว่าหนึ่งปี? | How many tickets are older than one year? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Opened'] <= (pd.Timestamp.now() - pd.DateOffset(days=365))].shape[0] | customer |
จำนวนการโต้ตอบโดยเฉลี่ยสำหรับตั๋วประเด็นสำคัญคือเท่าใด | What is the average number of interactions for critical issue tickets? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Issue Type'] == 'Critical']['Number of Interactions'].mean() | customer |
มีตั๋วกี่ใบที่ถูกยกระดับและปิดในวันเดียวกัน | How many tickets were escalated and closed on the same day? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[(data['Priority Escalation'] == 'Yes') & (data['Opened'] == data['Closed'])].shape[0] | customer |
ตั๋วเปิดนานที่สุดเมื่อใด? | What is the longest time a ticket has been open? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (pd.Timestamp.now() - data['Opened'].min()).days | customer |
ตั๋วมีเอกสารแนบกี่ใบ? | How many tickets have attachments? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Attachments'] == 'Yes'].shape[0] | customer |
เวลาตอบกลับโดยเฉลี่ยสำหรับตั๋วที่มีลำดับความสำคัญ 'ปานกลาง' คือเท่าใด | What is the average response time for 'Moderate' priority tickets? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Priority'] == 'Moderate']['Response Time (hrs)'].mean() | customer |
ตั๋วถูกเปิดใหม่กี่ใบ? | How many tickets have been reopened? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Status'] == 'Reopened'].shape[0] | customer |
มีการจัดการตั๋วกี่เปอร์เซ็นต์โดยไม่มีการติดตามผลจากลูกค้า | What percentage of tickets were handled without any customer follow-up? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (data[data['Customer Follow-Up'] == 'No'].shape[0] / data.shape[0]) * 100 | customer |
วันหยุดสุดสัปดาห์มีการเปิดตั๋วกี่ใบ? | How many tickets were opened on weekends? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Opened'].dt.dayofweek >= 5].shape[0] | customer |
เวลาเฉลี่ยในการแก้ไขสำหรับตั๋วที่มีลำดับความสำคัญ 'สูง' คือเท่าใด | What is the median resolution time for 'High' priority tickets? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Priority'] == 'High']['Resolution Time (days)'].median() | customer |
มีตั๋วกี่ใบที่มีทั้งปัญหาที่มีลำดับความสำคัญสูงและวิกฤติ | How many tickets have both high priority and critical issues? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[(data['Priority'] == 'High') & (data['Issue Type'] == 'Critical')].shape[0] | customer |
ตั๋วยังคงเปิดอยู่โดยเฉลี่ยกี่วัน? | What is the average number of days tickets remain open? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (pd.Timestamp.now() - data['Opened']).dt.days.mean() | customer |
มีตั๋วกี่ใบที่ได้รับการติดตามลูกค้ามากกว่า 3 ครั้ง? | How many tickets have received more than 3 customer follow-ups? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Customer Follow-Ups'] > 3].shape[0] | customer |
ระยะเวลาที่สั้นที่สุดที่ตั๋วยังคงเปิดอยู่คือเท่าไร? | What is the shortest time a ticket has remained open? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (data['Closed'] - data['Opened']).dt.days.min() | customer |
มีตั๋วกี่ใบที่เพิ่มขึ้นเนื่องจากปัญหาทางเทคนิค | How many tickets have escalated due to technical issues? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Escalation Reason'] == 'Technical Issue'].shape[0] | customer |
เวลาตอบกลับโดยเฉลี่ยสำหรับตั๋วที่ปิดในวันเดียวกับที่เปิดคือเท่าไร? | What is the average response time for tickets closed on the same day they were opened? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Opened'] == data['Closed']]['Response Time (hrs)'].mean() | customer |
มีตั๋วกี่ใบที่ได้รับการแก้ไขโดยไม่มีการยกระดับ? | How many tickets were resolved without escalation? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[(data['Escalated'] == 'No') & (data['Status'] == 'Resolved')].shape[0] | customer |
ตั๋วมีเวลาตอบกลับน้อยกว่า 1 ชั่วโมงกี่เปอร์เซ็นต์ | What percentage of tickets have a response time under 1 hour? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | (data[data['Response Time (hrs)'] < 1].shape[0] / data.shape[0]) * 100 | customer |
วันหยุดมีการเปิดตั๋วกี่ใบ? | How many tickets were opened on holidays? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Opened'].dt.date.isin(holidays)].shape[0] | customer |
จำนวนวันเฉลี่ยในการปิดตั๋วพร้อมเอกสารแนบคือเท่าใด | What is the median number of days tickets with attachments take to close? | this is a detail of this database it have 3 suffix
1.start with ###, This is a name of column
2.start with Description:, This is a Description of column
3.start with Data Type:, This is a Data Type of column """
###Ticket_ID
Description: A unique identifier for each ticket.
Data Type: numerical;
##Customer_Email
Description: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
Data Type: Text;
###Customer_Age
Description: The age of the customer.
Data Type: numeric;
###Customer_Gender
Description: The gender of the customer.
Data Type: Categorical;
###Product_Purchased
Description: The tech product purchased by the customer.
Data Type: Text;
###Date_of_Purchase
Description: The date when the product was purchased.
Data Type: Date;
###Ticket_Type
Description: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
Data Type: Categorical;
###Ticket_Subject
Description: The subject/topic of the ticket.
Data Type: Categorical;
###Ticket_Description
Description: The description of the customer's issue or inquiry.
Ticket_Status: The status of the ticket (e.g., open, closed, pending customer response).
Data Type: Text;
###Resolution
Description: The resolution or solution provided for closed tickets.
Data Type: Text;
###Ticket_Priority
Description: The priority level assigned to the ticket (e.g., low, medium, high, critical).
Data Type: Categorical;
###Ticket_Channel
Description: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
Data Type: Categorical;
###First_Response_Time
Description:The time taken to provide the first response to the customer.
Data Type: Date;
###Time_to_Resolution
Description: The time taken to resolve the ticket.
Data Type: Date;
###Customer_Satisfaction_Rating
Description: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).
Data Type: Numeric;
### | null | data[data['Attachments'] == 'Yes']['Resolution Time (days)'].median() | customer |
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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | 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;
### | null | df[df['Product_Purchased'] == 'Google Pixel'].shape[0] | customer |