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Challenge 1: Agriculture Commodities, Prices & Seasons Aim: Your team is working on building a variety of insight packs to measure key trends in the Agriculture sector in India. You are presented with a data set around Agriculture and your aim is to understand trends in APMC (Agricultural produce market committee)/mandi price & quantity arrival data for different commodities in Maharashtra. Objective: Test and filter outliers. Understand price fluctuations accounting the seasonal effect Detect seasonality type (multiplicative or additive) for each cluster of APMC and commodities De-seasonalise prices for each commodity and APMC according to the detected seasonality type Compare prices in APMC/Mandi with MSP(Minimum Support Price)- raw and deseasonalised Flag set of APMC/mandis and commodities with highest price fluctuation across different commodities in each relevant season, and year.

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time-series-analysis python pandas numpy onehot-encoder dbscan matplotlib

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