1pani/social_cops_task

Data Science Internship Task_final

social_cops_task

Data Science Internship Task_final

The problem statement for the given task is as follows:

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:

1. Test and filter outliers.
2. 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

3. Compare prices in APMC/Mandi with MSP(Minimum Support Price)- raw and deseasonalised
4. Flag set of APMC/mandis and commodities with highest price fluctuation across different commodities in each relevant season, and year.

My approach/steps involved-

1. Simple EDA

• Checking distribution plots
• Checking scatter plots, boxplots of modal_price with date, year along with APMC, commodities.

2. Outlier removal

• Used IQR approach and removed rows beyond (Q1 - 1.5.IQR) and (Q3+1.5.IQR) in modal_price and msprice

3. Again visualised the outlier_removed data

4. Used seasonal_decompose to get trend, seasonal and residual plots

• Time series model is made up of trend, seasonal and residual portions. It can be termed as either additive or multiplicative type.
• y(t) = Trend + Seasonal + Residual - Additive seasonality
• y(t) = Trend Seasonal Residual - Multiplicative seasonality

5. Rolling mean approach to calculate seasonality,trend

• Trend = calculated via rolling mean of modal_price
• Detrended_add = modal_price - trend
• Detrended_mult = modal_price / trend
• Seasonality_a = divided date into 4 quarters and calculated mean of detrended_add for each quarter
• Seasonality_m = divided date into 4 quarters and calculated mean of detrended_mult for each quarter
• Residual_a = detrended_add - seasonal_a
• Residual_m = detrended_mul / seasonal_m
• To check the seasonality type, I used the autocorrelation of residual which gives the correlation coefficient with the time lag.
• Additive seasonality - It does not depend on the time lag.
• Multiplicative seasonality - It does depend on the time lag.
• Therefore compared the autocorr values of residual_a and residual_m
• Conclusion - The given modal_price variation is a multiplicative model

6. Performed multiplicative seasonality decomposition

• deseasonalised = modal_price / seasonal_m

7. Comparing raw/deseasonalised prices with msprice

• Plots were plotted for each commodity and their modal_price(raw price) and for each commodity and their deseasonalised_modal_price(deseasonalised) and were compared with the corresponding msprice.
• Conclusion - The plots were very similar(raw and deseasonalised) but there were variations compared to msprice.

8. Checking for maximum fluctuations

• Approach is to check the difference of deseasonalised max_prices and deseasonalised min_prices
• fluctuation = deseasonalised_max - deseasonalised_min