Optimum pricing for website meant to rents shops,pop up spaces

machine_learning
predictive_model
data_wrangling
python
optimization-problem

#1

I have a dataset comprising details of people booking shops, pop up spaces on rent basis for different number of days. I have to optimise the profit by maintaining the occupancy and price of shops to be rented.

Data is arranged as per person booking the shops for number of days(mostly 7 days in all and other no of days in few cases).

I have gone through the optimum pricing of products for online vendor article but that has data regarding products where all data is given such increased acquisition and increases reaction and here nothing as such is provided.

I have columns such as .
Variables: 13

$ BRN             <chr> "14212-1", "14212-2", "14212-3", "14212-4", "14212-5", "14212-6", "14212-7", "14212...

$ Creation Date <dttm> 2015-08-07, 2015-08-07, 2015-08-07, 2015-08-07, 2015-08-07, 2015-08-07, 2015-08-07...

$ Venue           <chr> "Norwest Market Town", "Norwest Market Town", "Norwest Market Town", "Norwest Marke...

$ Status          <chr> "Declined", "Declined", "Declined", "Declined", "Declined", "Declined", "Declined",...

$ Site            <chr> "Norwest Market Town - Site 5", "Norwest Market Town - Site 5", "Norwest Market Tow...

$ `Client Name`   <chr> "Client_1", "Client_1", "Client_1", "Client_1", "Client_1", "Client_1", "Client_1",...

$ `Acct-Mgr`      <chr> "ACCT_MGR_1", "ACCT_MGR_1", "ACCT_MGR_1", "ACCT_MGR_1", "ACCT_MGR_1", "ACCT_MGR_1",...

$ From            <dttm> 2016-02-29, 2016-03-07, 2016-03-14, 2016-03-21, 2016-03-28, 2016-04-04, 2016-04-11...

$ To              <dttm> 2016-03-06, 2016-03-13, 2016-03-20, 2016-03-27, 2016-04-03, 2016-04-10, 2016-04-17...
$ Fee             <chr> "$700.00", "$700.00", "$700.00", "$700.00", "$700.00", "$700.00", "$700.00", "$700....

$ Fac. Fee      <chr> "$57.75", "$57.75", "$57.75", "$57.75", "$57.75", "$57.75", "$57.75", "$57.75", "$5...

$ Cost            <chr> "$525.00", "$525.00", "$525.00", "$525.00", "$525.00", "$525.00", "$525.00", "$525....

$ Profit          <chr> "$232.75", "$232.75", "$232.75", "$232.75", "$232.75", "$232.75", "$232.75", "$232....

If anyone could tell how to transform the dataset so we can optimise prices for occupancy and profit.

Profit = FEE ( mentioned on site for renting) + Fac.FEE( charges taken for renting the property paid by the one lists for renting his property) - Cost(price to be paid to lister when shop is renting by the booker