HackerEarth | Predict the minimum support price in Python | Solution now


Problem statement:

Note: : This is a time-series-based problem.

A commodity is a basic good used in commerce. It is interchangeable with other similar goods. Examples of commodities include grains, gold, meat, oil, and natural gas.

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For investors, commodities are an important way to diversify their portfolios beyond traditional securities. However, commodity prices are volatile as are commodity exchanges which are also dynamic. For many financial institutions, worldwide commodity trading has become an important means of making a profit.

One of the main reasons for the price changes in commodities is the inflation rate. The inflation rate is affected by the price of production costs that mainly depend on the final price of the goods and services in a market. Therefore, price changes of the most important commodities in the world’s commodity exchange markets influence the price of local producers or imported production.


You are given datasets that are related to each other.

Your task is to use these datasets to predict the MSP (minimum support price) of various commodities mentioned across some months in a year that are provided in the test data.

Note In test data, you are required to predict the target value for A Month-year of a specific commodity only

Evaluation metric minimum support price

score = max(0, 100*metrics.r2_score(actual, predicted))

Solution will be posted on the Telegram channel:

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