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.
- 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.
- msprice- Minimum Support Price
- arrivals_in_qtl- Quantity arrival in market (in quintal)
- min_price- Minimum price charged per quintal
- max_price- Maximum price charged per quintal
- modal_price- Mode (Average) price charged per quintal
- Final cleaned file(s). (Bonus: if the files are shared using GitHub with well-versioned log)
- Documentation around the methodology, analysis, and final results that you want to share with the Government of Maharashtra. Do use graphs and charts to substantiate your analysis. (Bonus- if you use GitHub pages / RPubs / etc. to share your documentation)
- Script(s) and their documentation. (Bonus - using Jupyter Notebook or GitHub ReadMe.)
- Visualisations, if any. (Bonus - if you use interactive dashboards)