To conduct a comprehensive Exploratory Data Analysis (EDA) on the provided sample Superstore dataset, we will follow these detailed steps: Data Overview and Initial Inspection, Data Cleaning, Descriptive Statistics, Data Visualization and Identifying Business Problems for Weak Areas.
Let's start with each step.
We will begin by loading the data and inspecting its structure, which includes checking for missing values, understanding the types of data in each column, and getting a general sense of the dataset.
Data cleaning involves handling missing values, correcting data types, and addressing any inconsistencies in the data.
This step includes calculating various statistics such as mean, median, mode, standard deviation, etc., to summarize the central tendency, dispersion, and shape of the dataset’s distribution.
We will create visualizations to uncover patterns and relationships in the data. This will include:
- Sales and Profit analysis by different categories such as Segment, Region, and Sub-Category.
- Analysis of Discounts and their impact on Profit.
- Visualization of high-profit and low-profit areas.
Based on the EDA, we will identify key business problems and areas where improvements can be made to increase profitability.
Here are four other recommended analysis:
- Analysis of sales and profit over time to identify seasonal trends and peaks.
- Clustering customers based on their purchasing behavior to target marketing efforts more effectively.
- Investigating the rate and reasons for product returns to reduce losses.
- Analysis of shipping modes and their impact on customer satisfaction and cost.