Exploratory Data Analysis Project

Target

I Phase - exploration and factors analysis

• Check chosen factors on customers quantity and cart value
  • Store type
  • School-free days
  • Special Offer
  • Work days and week days
  • Month splitted by 3 parts
  • Month splitted by weeks
  • Weather
  • Seasons
  • Months
  • States
  • States position
  • Others
• Use regression from ols model and A/B test.
• You can use unsupervised learning for searching focus groups
• Try to define what are types of shops (a, b, c, d)

II Phase - prediction

• check factors influence on sales
  • regression
  • A/B tests
• Try to propose a method of analysis that will allow us to determine what store types a, b, c, d might mean.

Part I

• build model for every day of week decoupled from season and global factors
• find anomalies and their causes

Part II

• estimate mean sales for next 8 weeks (June and July) based on data to May 2015.
• designate model to data matching error

What was made?

This is only summary. Full description in notebook

Phase I

• Functions for generic operations
• Heatmap of greater part of data was created
• Influence on sales, customers quantity and cart value was checked on factors like:
  • store type
  • assortment
  • special offer
  • school-free days
  • days of the week
  • month phase
  • weather
    • storm
    • rain
    • thunderstorm
    • fog
    • hail
  • months
  • states
• also was checked dependence between:
  • store types and assortment
  • hail and assortment
  • states and store types
  • states and assortment
  • states and school-free days

Phase II

• Depending on sales, factors such as store type, assortment, promotion, phase of the month, month, hail, day of the week were used.
• Sales prediction with 0.769 R-Squared mapping compared to real data on whole time range.
• Sales prediction with 0.999 R-Squared mapping compared to real data on next 6 month.
• Adding the State/StateName factor significantly reduces the result value.