Please consider the data file marketing.csv above that contains data about a marketing campaign conducted by a bank. Following is the description of the columns$/$features$/$attributes:

age: Age of the participant

job: Type of job

marital: Marital status

education: Level of Education

default: If the participant has has credit in default

balance: Credit balance

housing: Has housing loan

loan: Has personal loan

contact: Contact communication type$/$mediam

day: Last contact day of the week

month: Last contact month of Year

duration: Last contact duration, in seconds

campaign: Number of contacts performed during this campaign and for this client

pdays: Number of days that passed by after the client was last contacted from a previous campaign

previous: Number of contacts performed before this campaign and for this client

poutcome: Outcome of the previous marketing campaign

deposit: Has the client subscribed a term deposit. This column also tells how successful was the campaign and if the client subscribed to the service.

Please use the data to answer the following questions:

Read the file into a DataFrame.

Find the following statistical measures for the columns that contains numerical values: $1)$ Total row count, $2)$ Mean, $3)$ Standard Deviation, $4)$ Min $5)25$ percentile, $6)50$ Percentile, $7)75$ Percentile, and $8)$ Max.

Remove any column deemed unnecessary. For the columns contains text values, please change the case to lower.

What are the different ways the company used to contact the clients?

What is the age group of the people who took the service of term deposit from the bank? Visualize the outcome using plots.

How often a client with no education and housing loan deposited with the bank?

Perform a groupby operation on the marketing data based on 'deposit', 'education', 'age', 'marital' columns and report the result.