Supermarket Sales Analysis

The growth of supermarkets in most poulated cities are increasing and market competiitons are also high.This datasets is one of the historical sales of supermarket company which has the records of 3 different branches for 3 months.The aim of this project is to find out how the three branches performed?, which of the cities made more sales?, does the price of a particular item influence the quantities demanded?.All this and more questions will be answered in this project.

The dataset was taken from Kaggle.It contains diverse information ranging from a computed generated slip known as invoivce ID,The three branches which is identified as A,B and c(Branch), the cities where the Supermarkets are located(City),Customer type, Gender of the customer, unit price, 5% tax fee charged to customer for buying(Tax 5%). Total price which include tax(Total), purchase time(time), method of payment used by customent(payment),gross income, gross margin percentage and customer rating on their overall shopping experience(rating). For tthis project i will make use of some important libraries such as Pandas, Matplotlib and Seaborn.

import os
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
%matplotlib inline

Reading Datasets

superstore_df = pd.read_csv('supermarket_sales - Sheet1.csv')
superstore_df