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Analytics_Vidya-datahack

BLACK FRIDAY SALES PREDICTION

Objective:

Building a model around the data which will help us predict the Sales forecasting of the data and derive insights from the previous data. The model was validated by RMSE Score.

Challenges Faced:

  1. There were quite a few ambeguious data viz the "Occupation Data" data was masked.
  2. The "Age" data was highly categorised.
  3. Missing data was present in "Product_Category_2" and "Product_Category_3".

Challenges Solved:

  1. There was no way of getting around the "Occupation Data" hence I had to analyze the Value counts of the "Occupation" data
  2. The "Age" data's categorization was further segmented into.
  • (0-17) == kids
  • (18-25) == youth
  • (26-35) == adults
  • (36-45) == midage
  • (46-50) == old age
  • (51-55) == elderly
  • (55+) == senior
  1. The "Product_Category_2" and "Product_Category_3" has 31.56% and 69.67% missing values (NaN) in them respectively. Since Product_Category_2" has fewer NAN values relatively, we will impute it using mean.
  2. "Product_Category_3" which has higher(69.67%) was imputed by '0', since we dont want to disturd the pattern of the distribution.

Observations:

  1. Age group 2 [Adults] perticularly males seem to contribute higher number of transactions of successful purchases.
  2. Customers Residing in the current city fro about 1 year have higher transaction count.
  3. City Category '2' has highest transaction count. 4."Occupation" 4 0 7 have higher transaction counts respectively.