Advance Analytics Internship Coding Challenge Sai Charan...

Advance Analytics Internship Coding Challenge Sai Charan Thotapalli 01/25/2015 Data description First, is need to know the amount of information this analysis will involve, in this section a general review of data. Number of rows, this mean the number of observations to be analysed. 21,061 observations are found. ## [1] 21061 Number of columns, this mean the number of variables to be analysed. ## [1] 12 The original names of the variables. ## [1] ## [4] ## [7] ## [10] day platform orders add_to_cart site visits gross_sales product_page_views new_customer distinct_sessions bounces search_page_views Data dictionary. Data dictionary is a set of information to explain the variables that are up to be analysed.†¦show more content†¦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. For that next is developed a correlation matrix analysis to explore the possible correlations that exist between variables a possible explanations. Correlation matrix are shown graphically (See annex for full results). Blue represents positive and significant correlation, as blue is darker means that this relationship is stronger. Lighter blue means week relationship. White means no relationship. By a brief review we can see next assumptions. - Gross sales are highly influenced by orders and add cart (regression analysis confirmed it). - Visits are highly influenced by bounces, this means a high amount of visitors just visit one page. 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. Conclusions and possible future analysis. Analysis has been through different stages. Initially, the general sales overview of 2013 has shown that sales exhibit a cycle effect. To ensure this as an assumption, we require to analyze data from other years. 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. An ARIMA forecast has demonstrate a possible stable