A diamond distributor has put up a set of 3,000 diamonds for auction.

Seeing this as an opportunity to expand its inventory, a jewellery company has shown interest in making a bid. To decide how much to bid, I will use a database of diamond prices to build a regression model to predict the price of each diamond based on attributes.

The results are used to make a recommendation for how much the company should bid.

Last year a company that manufactures and sells high-end home goods sent out its first print catalogue and is preparing to send out another edition.

The company has 250 new customers that they would like to add to their mailing list.

Determine how much profit the company can expect from sending a catalogue to these new customers. Management does not want to send the catalogue to these new customers unless the expected profit contribution exceeds $10,000.

A school district wants to predict the per pupil costs of a school based on high level summary data. This way they’ll have a good estimation of how well a school is managing its costs relative to what a model would predict.

The objective is to prepare the data for modelling.

A store chain in Texas has 13 stores throughout the state. The chain would like to expand and open the 14th store this year.

The task is to perform an analysis to recommend the City for the newest store, based on predicted yearly sales.

For this project, I will use a clean dataset to train a linear regression model to predict sales for the prospective new store.

An accountant is working on a profit and loss statement (P&L) for the next financial year. The P&L statement requires input on forecasted wages from different business units (BU).

While the files can be split manually, if there are 100 BU the process could quickly become time intensive. Once split the file needs to be sent to the respective BU for completion. Once filled in, all the files will need to be merged into one file to calculate the total employment cost across the organisation.