A leading global leader of e-commerce has over 150 million paid subscription users. One of the many perks of the subscription is the privilege of buying products at lower prices. For an upcoming sale, the organization has decided to promote local artisans and their products, to help them through these tough times. However, slashed prices may impact local artists.

To not let discounts affect local artists, the company has decided to determine the lowest price at which a particular good can be sold. Your task is to build a predictive model using Machine Learning that helps them set up a lowest-pricing model for these products.

You have to predict the Low_Cap_Price column.

Data

Data description

Column	Description
Item_Id	Unique item ID
Date	Date
State_of_Country	State no. of the country
Market_Category	Category of the market to which the product belongs to
Product_Category	Category of the product
Grade	Quality of the product
Demand	Demand rate of the product in the market
Low_Cap_Price [Target]	Lowest price that can be offered
High_Cap_Price	Original maximum price in the current market

Data files

The dataset folder consists of the following files:

• Train.csv: Contains training data [9798 x 9] that must be used to build the model
• Test.csv: Contains test data [5763 x 8] to be predicted on
• sample_submission.csv: Contains sample submission format with dummy values filled for test data

Submission format

Please refer to sample_submission.csv for more details.

Evaluation metric

\(score = max(0, (100- mean\_squared\_log\_error(actual\_values, predicted\_values)))\)