https://github.com/sajjad425/retail-eda

Perform the Exploratory Data Analysis on dataset sample superstore. As a business manager try to find out the weak areas where you can work to make more profit. What all business problem you can derived by exploring the data?
https://github.com/sajjad425/retail-eda

eda microsoftpowerbi powerbi superstore-data-analysis

Last synced: 5 days ago
JSON representation

Perform the Exploratory Data Analysis on dataset sample superstore. As a business manager try to find out the weak areas where you can work to make more profit. What all business problem you can derived by exploring the data?

• Host: GitHub
• URL: https://github.com/sajjad425/retail-eda
• Owner: sajjad425
• Created: 2024-07-13T05:33:03.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-10-07T12:13:54.000Z (4 months ago)
• Last Synced: 2024-11-17T13:38:20.526Z (2 months ago)
• Topics: eda, microsoftpowerbi, powerbi, superstore-data-analysis
• Homepage:
• Size: 771 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        


  

# Retail EDA




![retailEDA](https://github.com/user-attachments/assets/59c10e0c-cd00-42cc-ae1b-ff8fd9acd0d8)

## Dataset Description

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.

## 1. Data Overview and Initial Inspection

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.

## 2. Data Cleaning

Data cleaning involves handling missing values, correcting data types, and addressing any inconsistencies in the data.

## 3. Descriptive Statistics

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.

## 4. Data Visualization

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.

## 5. Identifying Business Problems and Weak Areas

Based on the EDA, we will identify key business problems and areas where improvements can be made to increase profitability.

## Recommendations for Further Analysis

Here are four other recommended analysis:

### 1. Seasonal Analysis

- Analysis of sales and profit over time to identify seasonal trends and peaks.

### 2. Customer Segmentation

- Clustering customers based on their purchasing behavior to target marketing efforts more effectively.

### 3. Return Analysis

- Investigating the rate and reasons for product returns to reduce losses.

### 4. Operational Efficiency

- Analysis of shipping modes and their impact on customer satisfaction and cost.