This is a solution to a hackathon conducted on HackerEarth.
It can be difficult to navigate the logistics when it comes to buying art. These include, but are not limited to, the following:
Effective collection management Shipping the paintings, antiques, sculptures, and other collectibles to their respective destinations after purchase Though many companies have made shipping consumer goods a relatively quick and painless procedure, the same rules do not always apply while shipping paintings or transporting antiques and collectibles.
You work for a company that sells sculptures that are acquired from various artists around the world. Your task is to predict the cost required to ship these sculptures to customers based on the information provided in the dataset.
The dataset folder contains the following files:
train.csv: 6500 x 20
test.csv: 3500 x 19
sample_submission.csv: 5 x 2
The columns provided in the dataset are as follows:
| Column name | Description |
|---|---|
| Customer Id | Represents the unique identification number of the customers |
| Artist Name | Represents the name of the artist |
| Artist Reputation | Represents the reputation of an artist in the market (the greater the reputation value, the higher the reputation of the artist in the market) |
| Height | Represents the height of the sculpture |
| Width | Represents the width of the sculpture |
| Weight | Represents the weight of the sculpture |
| Material | Represents the material that the sculpture is made of |
| Price Of Sculpture | Represents the price of the sculpture |
| Base Shipping Price | Represents the base price for shipping a sculpture |
| International | Represents whether the shipping is international |
| Express Shipment | Represents whether the shipping was in the express (fast) mode |
| Installation Included | Represents whether the order had installation included in the purchase of the sculpture |
| Transport | Represents the mode of transport of the order |
| Fragile | Represents whether the order is fragile |
| Customer Information | Represents details about a customer |
| Remote Location | Represents whether the customer resides in a remote location |
| Scheduled Date | Represents the date when the order was placed |
| Delivery Date | Represents the date of delivery of the order |
| Customer Location | Represents the location of the customer |
| Cost | Represents the cost of the order |
score = 100*max(0, 1-metrics.mean_squared_log_error(actual, predic