By a brief review we can see next assumptions. Annex Correlation matrix results by significance levels. Correlation matrix are shown graphically See annex for full results.
Correlation analysis has shown a high and almost total relationship between orders and sales, this explains that most of orders are not cancelled or affected by a return policy. The brief of the analysis only permitted an autoarima forecasting method.
Gross sales has 2 possible explanations: However I would suggest next possible projects. However also visits are highly explained by — Visits are also explained in a different way; distinct sessions explain a good amount of visitors, that means visitors are generally diverse and different.
White means no relationship. Visits are highly influenced by bounces, this means a high amount of visitors Just visit one page. To ensure this as an assumption, we require to analyze data from other years.
Third, has been identified all users preferences for ISO and Android devices. Previous figure, shows the gross sales for all websites. At this point we have seen sales over time development and a simple regression analysis, next is need to see which variables possible has effects on sales.
Conclusions and possible future analysis. I would recommend a more punctual analysis for each variable that require special treatment, exploratory analysis gives a general and important picture of the scenario of the companies. To ensure this graphic assumption here is a regression analysis.
Results can be understood by comparing both graphics that gross sales are very close related to the amount of sales that has been taken in Graphic review explains that last months bring a possible cycle effect resulting to be the best for selling.
January ofstill this analysis had been reviewed a more punctual forecasting loud be required. Arima forecast modeling shows an stable growth for first 20 days of Blue represents positive and significant correlation, as blue is darker means that this relationship is stronger.
Linear regression model could bring an estimate of how this months has developed. Initially, the general sales overview of has shown that sales exhibit a cycle effect. Analysis has been through different stages.
This could bring business opportunities, stock planning among others factors for years to come. For that next is developed a correlation matrix analysis to explore the possible correlations that exist between variables a possible explanations.
Possible causes of sales development. Previous plot also shows that best sales overcome in the last month of the year. Lighter blue means week relationship.May 28, · Advance Analytics Internship Coding Challenge Sai Charan Thotapalli 01/25/ Data description First, is need to know the amount of information this analysis will involve, in this section a general review of data.
May 15, · INTERNSHIP REPORT ON “HABIB BANK LIMITED” SUBMITTED BY Sadia Siddiqui Reg# Enrollment# SUBMISSION DATE MAY 15, Acknowledgements: All the praises are for the almighty, Allah who bestowed me with the ability and potential to complete this Internship.
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