owing information: New Customer Revenue e.g., new customers not present in previous year only Lost Customers New Customers
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A data set has 4 columns--index, CUSTOMER_EMAIL, Net Revenue, and Year.
For each year we need the following information:
- New Customer Revenue e.g., new customers not present in previous year only
- Lost Customers
- New Customers
- Total Customers Previous Year
- Total Customers Current Year
Please implement it using python.
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- Given is a Python program that connects to a sqlite database and has one table called writers with two columnns: name - the name of a writer num - the number of works the writer has written The writers table originally has the following data: name, num Jane Austen,6 Charles Dickens,20 Ernest Hemingway,9 Jack Kerouac,22 F. Scott Fitzgerald,8 Mary Shelley,7 Charlotte Bronte,5 Mark Twain,11 Agatha Christie,73 Ian Flemming,14 J.K. Rowling,14 Stephen King,54 Oscar Wilde,1Given is a Python program that connects to a sqlite database and has one table called writers with two columnns: name - the name of a writer num - the number of works the writer has written The writers table originally has the following data: name, num Jane Austen,6 Charles Dickens,20 Ernest Hemingway,9 Jack Kerouac,22 F. Scott Fitzgerald,8 Mary Shelley,7 Charlotte Bronte,5 Mark Twain,11 Agatha Christie,73 Ian Flemming,14 J.K. Rowling,14 Stephen King,54 Oscar Wilde,1 Update the Python program to ask the user if they want to update entries or add new entries. If the name entered already exists in the writers table then the database record is updated, overwriting the original contents. If the name does not exist in the writers table, then add a new record with the writer's name and number of works. The following TODO sections must be completed. Check if a writer exists in the writers table If the writer exists in the table, locate an entry to be updated by writer's name and update…Given is a Python program that connects to a sqlite database and has one table called writers with two columnns: name - the name of a writer num - the number of works the writer has written The writers table originally has the following data: name, num Jane Austen,6 Charles Dickens,20 Ernest Hemingway,9 Jack Kerouac,22 F. Scott Fitzgerald,8 Mary Shelley,7 Charlotte Bronte,5 Mark Twain,11 Agatha Christie,73 Ian Flemming,14 J.K. Rowling,14 Stephen King,54 Oscar Wilde,1 Update the Python program to ask the user if they want to update entries or add new entries. If the name entered already exists in the writers table then the database record is updated, overwriting the original contents. If the name does not exist in the writers table, then add a new record with the writer's name and number of works. The following TODO sections must be completed. Check if a writer exists in the writers table If the writer exists in the table, locate an entry to be updated by writer's name and update the…
- What happens to the existing rows of a table if the DEFAULT value of a column is changed?There's a "friend_requests" table that has a column "action_taken" that displays if the friend request was rejected, requested, or accepted.Implement a SQL query to return the number of "action_taken" that were "accepted".Which implementation of the size and isEmpty methods would you choose if you had the choice of having a million-element list? (Think about the problem with the University of Michigan's alumni database.) How would you select between making a million different lists? Consider the difficulty of monitoring dependents for a million income tax returns.)
- If you had the option of having a million-element list, which implementation of the size and isEmpty functions would you use? (Consider the issue with the University of Michigan's alumni database.) If you had the option of making a million little lists, how would you choose? Think about the challenge of tracking dependents for a million income tax filings.)There's a "friend_requests" table that has a column "action_taken" that displays if the friend request was rejected, requested, or accepted.Implement a SQL query to return the percentage of "action_taken" that were "accepted".Write a simple Python Program where we can store information of 3 employees in a dictionary so that their information can be accessedusing their id. Show the use of nested dictionary and lists. Print some data from thedictionary. Note: Use python Only
- Write a program in lua to design a table and fill elements and you to perform these operations Add elements to table Return max index Remove the value from tableA database has four transactions. Let min sup = 60% and min conf = 80%.cust ID TID items bought (in the form of brand-item category)01 T100 {King’s-Crab, Sunset-Milk, Dairyland-Cheese, Best-Bread}02 T200 {Best-Cheese, Dairyland-Milk, Goldenfarm-Apple, Tasty-Pie, Wonder-Bread}01 T300 {Westcoast-Apple, Dairyland-Milk, Wonder-Bread, Tasty-Pie}03 T400 {Wonder-Bread, Sunset-Milk, Dairyland-Cheese}(a) At the granularity of item category (e.g., itemi could be “Milk”), for the ruletemplate,∀X ∈ transaction, buys(X,item1) ∧ buys(X,item2) ⇒ buys(X,item3) [s,c],list the frequent k-itemset for the largest k, and all the strong association rules(with their support s and confidence c) containing the frequent k-itemset for thelargest k.(b) At the granularity of brand-item category (e.g., itemi could be “Sunset-Milk”),for the rule template,∀X ∈ customer, buys(X,item1) ∧ buys(X,item2) ⇒ buys(X,item3),list the frequent k-itemset for the largest k (but do not print any rules).Use python machine learning. A group of data scientists want to analyze some data. They already cleaned up the data, with the result being a Dataframe called X_train. The Dataframe X_train has 1058 rows and 13 columns. It has the below columns (picture attached on how it looks like): 'LotFrontage','LotArea','BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF','TotalBsmtSF','1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea','TotRmsAbvGrd','GarageArea','OpenPorchSF' Answer the following questions: 1. Transform X_train using PCA. Assign the output to a variable X_train_pca. 2. What is wrong with above approach? Scale the data, then repeat the above.
