Advanced Excel - Class Project Conduct a typical big data analytics research project, evaluate the results, and write an academic research paper. Decide on two things: 1) A topic in which you would...

Abstract:
Demand for analysing data among the superstore owner is increasing to get insights of product , segment wise sales in order to strength their existence in the market. This project is to gain insights about the super store data by employing analysis of pivot table, regression analysis. It is found that both East and West region contributes more than 50% of the profit .In order to strength the super store existence in central home office product needs some changes. Regression model indicates that there is more space to add more feature variable should be included to get proper prediction on profit and sales
Problem statement:
With rising market demands and fierce competition, a Superstore Giant seeks expertise in determining what works best for them. They want to know which items, geographies, categories, and consumer groups to target or avoid.
Methodology:
About data set: Superstore data set was obtained from the website Kaggle(https://www.kaggle.com/datasets/vivek468/superstore-dataset-final). Total number of observation in this dataset is 994 obs.
Description of variables :
Row ID = Unique ID for each row.
Order ID = Unique Order ID for each Customer.
Order Date = Order Date of the product.
Ship Date = Shipping Date of the Product.
Ship Mode= Shipping Mode specified by the Customer.
Customer ID = Unique ID to identify each Customer.
Customer Name = Name of the Customer.
Segment = The segment where the Customer belongs.
Country = Country of residence of the Customer.
City = City of residence of of the Customer.
State = State of residence of the Customer.
Postal Code = Postal Code of every Customer.
Region = Region where the Customer belong.
Product ID = Unique ID of the Product.
Category = Category of the product ordered.
Sub-Category = Sub-Category of the product ordered.
Product Name = Name of the Product
Sales = Sales of the Product.
Quantity = Quantity of the Product.
Discount =Discount provided.
Profit = Profit/Loss incurred.
Process of data wrangling:
This process involves the cleaning of dataset before conducting any analysis.
Formatted the columns sales and profit from general to number format. Then deleted the columns Row ID, order ID, order date, ship date, customer ID, Customer Name, Country, city, state, postal code, region, sub category of the product .Next, I checked for blank or empty cells of each column using Count if blank function. The dataset has no blank cells. So I proceeded to Exploratory Data analysis
Pivot table creation:
Created Pivot table for Region wise total sales and profit, Bar chart was created for analysing which region has highest percentage contribution in profit
Doughnut chart was created for each region to analysis which product segment has highest contribution towards profit.
Correlation matrix
This matrix was used to find the strength of relationship between the variables sales, profit and discount.
Regression Models
Logistic Linear...