11 Jun The provided dataset Franchises Dataset? contains data collected from different 100 franchises. The data contains the net profit (million $) for each franchise,
The provided dataset “Franchises Dataset” contains data collected from different 100 franchises. The data contains the net profit (million $) for each franchise, the counter sales (million $), the drive-through sales (million $), and the type of the franchise.
Address the following questions:
- a) Develop a Neural Network (NN) prediction model for the net profit (Number of layers = 3, dense layers, activation function of Tan, number of nodes in each layer = 20).
- b) What is the forecast of the net profit, if the counter sales are 500,000 $, drive-through sales are 700,000$, and the franchise is a pizza store. Comment on the forecasted value.
- c) What are the limitations of the model?
Submit a PDF file for your answers as well as the excel sheet. Include the Python code in the PDF.
Franchise_data
| Net Profit | Counter Sales | Drive-through Sales | Business Type | |||
| 2 | 8.4 | 7.7 | 1 | Café | 1 | |
| 1.3 | 3.3 | 4.5 | 1 | Pizza Store | 2 | |
| 1.2 | 5.8 | 8.4 | 2 | Burger store | 3 | |
| 2.4 | 10 | 7.8 | 3 | |||
| 0.7 | 4.7 | 2.4 | 1 | |||
| 1.3 | 7.7 | 4.8 | 1 | |||
| 1.1 | 4.5 | 2.5 | 1 | |||
| 2.3 | 8.6 | 3.4 | 3 | |||
| 0.9 | 5.9 | 2 | 1 | |||
| 1.6 | 6.3 | 4.1 | 3 | |||
| 2 | 8.4 | 7.7 | 1 | |||
| 1.3 | 3.3 | 4.5 | 1 | |||
| 1.7 | 5.8 | 8.4 | 1 | |||
| 1.4 | 10 | 7.8 | 2 | |||
| 1.2 | 4.7 | 2.4 | 3 | |||
| 1.8 | 7.7 | 4.8 | 3 | |||
| 1.6 | 4.5 | 2.5 | 3 | |||
| 1.8 | 8.6 | 3.4 | 1 | |||
| 1.4 | 5.9 | 2 | 3 | |||
| 1.1 | 6.3 | 4.1 | 1 | |||
| 1.5 | 8.4 | 7.7 | 2 | |||
| 1.3 | 3.3 | 4.5 | 1 | |||
| 1.2 | 5.8 | 8.4 | 2 | |||
| 2.4 | 10 | 7.8 | 3 | |||
| 1.2 | 4.7 | 2.4 | 3 | |||
| 1.3 | 7.7 | 4.8 | 1 | |||
| 1.1 | 4.5 | 2.5 | 1 | |||
| 1.3 | 8.6 | 3.4 | 2 | |||
| 0.9 | 5.9 | 2 | 1 | |||
| 1.6 | 6.3 | 4.1 | 3 | |||
| 2 | 8.4 | 7.7 | 1 | |||
| 0.8 | 3.3 | 4.5 | 2 | |||
| 2.2 | 5.8 | 8.4 | 3 | |||
| 2.4 | 10 | 7.8 | 3 | |||
| 1.2 | 4.7 | 2.4 | 3 | |||
| 0.8 | 7.7 | 4.8 | 2 | |||
| 1.6 | 4.5 | 2.5 | 3 | |||
| 1.8 | 8.6 | 3.4 | 1 | |||
| 0.9 | 5.9 | 2 | 1 | |||
| 1.6 | 6.3 | 4.1 | 3 | |||
| 2 | 8.4 | 7.7 | 1 | |||
| 0.8 | 3.3 | 4.5 | 2 | |||
| 1.2 | 5.8 | 8.4 | 2 | |||
| 1.9 | 10 | 7.8 | 1 | |||
| 0.7 | 4.7 | 2.4 | 1 | |||
| 0.8 | 7.7 | 4.8 | 2 | |||
| 0.6 | 4.5 | 2.5 | 2 | |||
| 2.3 | 8.6 | 3.4 | 3 | |||
| 0.9 | 5.9 | 2 | 1 | |||
| 1.6 | 6.3 | 4.1 | 3 | |||
| 2 | 8.4 | 7.7 | 1 | |||
| 1.3 | 3.3 | 4.5 | 1 | |||
| 1.2 | 5.8 | 8.4 | 2 | |||
| 2.4 | 10 | 7.8 | 3 | |||
| 0.7 | 4.7 | 2.4 | 1 | |||
| 1.8 | 7.7 | 4.8 | 3 | |||
| 0.6 | 4.5 | 2.5 | 2 | |||
| 2.3 | 8.6 | 3.4 | 3 | |||
| 0.4 | 5.9 | 2 | 2 | |||
| 1.1 | 6.3 | 4.1 | 1 | |||
| 2.5 | 8.4 | 7.7 | 3 | |||
| 1.8 | 3.3 | 4.5 | 3 | |||
| 2.2 | 5.8 | 8.4 | 3 | |||
| 2.4 | 10 | 7.8 | 3 | |||
| 1.2 | 4.7 | 2.4 | 3 | |||
| 0.8 | 7.7 | 4.8 | 2 | |||
| 1.1 | 4.5 | 2.5 | 1 | |||
| 1.3 | 8.6 | 3.4 | 2 | |||
| 0.9 | 5.9 | 2 | 1 | |||
| 0.6 | 6.3 | 4.1 | 2 | |||
| 2.5 | 8.4 | 7.7 | 3 | |||
| 1.3 | 3.3 | 4.5 | 1 | |||
| 2.2 | 5.8 | 8.4 | 3 | |||
| 1.9 | 10 | 7.8 | 1 | |||
| 0.2 | 4.7 | 2.4 | 2 | |||
| 1.8 | 7.7 | 4.8 | 3 | |||
| 1.6 | 4.5 | 2.5 | 3 | |||
| 2.3 | 8.6 | 3.4 | 3 | |||
| 1.4 | 5.9 | 2 | 3 | |||
| 0.6 | 6.3 | 4.1 | 2 | |||
| 1.7 | 8.4 | 7.7 | 2 | |||
| 1.8 | 3.3 | 4.5 | 3 | |||
| 2.6 | 5.8 | 8.4 | 3 | |||
| 1.4 | 10 | 7.8 | 2 | |||
| 0.2 | 4.7 | 2.4 | 2 | |||
| 1 | 7.7 | 4.8 | 2 | |||
| 0.6 | 4.5 | 2.5 | 2 | |||
| 2.3 | 8.6 | 3.4 | 3 | |||
| 1.6 | 5.9 | 2 | 1 | |||
| 0.6 | 6.3 | 4.1 | 2 | |||
| 2.5 | 8.4 | 7.7 | 3 | |||
| 1.6 | 3.3 | 4.5 | 3 | |||
| 1.2 | 5.8 | 8.3 | 2 | |||
| 1.9 | 9.8 | 7.8 | 1 | |||
| 1.2 | 4.7 | 2.4 | 3 | |||
| 1.8 | 7.7 | 4.8 | 3 | |||
| 0.6 | 4.5 | 2.5 | 2 | |||
| 1.3 | 9.1 | 3.4 | 2 | |||
| 1 | 5.9 | 2.2 | 1 | |||
| 1.8 | 6.3 | 4.2 | 3 |
