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(Power BI) Superstore Sales Dataset analysis

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 This dataset is accessible to anyone by this link:  Superstore Sales Dataset | Kaggle . Dataset information:  Retail dataset of a global superstore for 4 years. A glimpse of this dataset:  Purpose of this analysis: 1. The general situation of the sales performance from 2015-2018; 2. The sales performance related to different types of customers; 3. The sales performance of different products The dashboard is ready to be used as follows: Data Validation and pre-processing  1. The dataset has been checked for duplicates, null, and error values. 2. Change data type: In this dataset, "Order Date" and "Ship Date" are "text". Change it to "Date" before further exploration.  However, since the date value in these two columns is organized in "dd-mm-yyyy", it seems not possible to change it directly to the "Date" data type. So I first separated these two columns by the delimiter "/" and then merged the separated columns into ...
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(Power BI & Python) Sales performance of different sales method on the same product

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Case background: Six weeks ago company A launched a new line of office stationery. Company A wants to know the sales performance during these 6 weeks and how did the sales methods affect the sales performance. - How many customers were there for each approach?  -   What does the spread of the revenue look like overall? And for each method?  - Was there any difference in revenue over time for each of the methods? (The dashboard created by Power BI is shown here. The detailed research process is after the dashboard.) Data validation Add Comment 1.Data import: import pandas as pd import numpy as np df = pd . read_csv ( "product_sales.csv" ) 2.Let's have a general look of the dataset before we move to the actual validating df . head ( ) week sales_method customer_id nb_sold revenue years_as_customer nb_site_visits state 0 2 Email 2e72d641-95ac-497b-bbf8-4861764a7097 10 null 0 24 Arizona 1 6 Email + Call 3998a98d-70f5-44f7-942e-789bb8ad2fe7 15 225.47 1 28 Kansas 2 5 Call ...
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