https://github.com/ialam085/super_store_sales_analysis_python
The project is indeed focused on performing an exploratory data analysis (EDA) of Super Store Sales data from various perspectives, using comprehensive visualizations.
https://github.com/ialam085/super_store_sales_analysis_python
charts matplotlib numpy pandas python seaborn visualization
Last synced: about 1 month ago
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The project is indeed focused on performing an exploratory data analysis (EDA) of Super Store Sales data from various perspectives, using comprehensive visualizations.
- Host: GitHub
- URL: https://github.com/ialam085/super_store_sales_analysis_python
- Owner: ialam085
- Created: 2024-08-11T15:01:25.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-06T18:42:25.000Z (over 1 year ago)
- Last Synced: 2025-01-13T11:24:38.480Z (about 1 year ago)
- Topics: charts, matplotlib, numpy, pandas, python, seaborn, visualization
- Language: Jupyter Notebook
- Homepage: https://github.com/ialam085/Super_Store_Sales_Analysis_PYTHON
- Size: 1.38 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## 🔳 Super Store Sales Analysis | United States ${\color{blue}(using\ PYTHON)}$
${\color{red}Go\ to}$ 🔗 [Python Programs and Visualizations](https://github.com/ialam085/Super_Store_Sales_Analysis_PYTHON/blob/main/Super_Store_Sales.ipynb)
### ◻️ Objective
>The objective of the attached Python report seems to be an exploratory data analysis (EDA) of a Super Store Sales reports by different angles. The notebook includes steps like:
>
>- Importing necessary libraries (e.g., numpy, pandas, matplotlib, seaborn).
>- Loading and inspecting the dataset
>- Analyzing various aspects of the data, such as sales, profit, discounts, and other features, to extract insights.
### ◻️ Tech Stack
>- Python
>- NumPy
>- Pandas
>- Matplotlib
>- Seaborn
>- Microsoft Excel
### ◻️ Steps includes
>1. Data Cleaning
>2. Data Processing
>3. Data Modelling
>4. Importing required Libraries
>5. Importing CSV Dataset
>6. Data Auditing
>8. Data Visualization
### ◻️ Visualizations includes
>- Tables
>- BoxPlot
>- DisPlot
>- Scatter Plot
>- Bar Plot
>- Sub Plot
>- Pie Charts
>- Bar Charts
>- BarH Charts
>- Stacked Bar Chart
### ◻️ Analysis includes
>- Data Loading and Exploration
>- Data Cleaning
>- Descriptive Statistics
>- Data Visualization
>- Sales Trend Analysis
>- Profit Margin Analysis
>- Category-wise Sales Analysis
>- Correlation Analysis
>- Outlier Detection
>- Inventory and Stock Analysis
### ◻️ Key Insights
>- Top City by Sales: **Los Angeles**
>- Top City by Profit: **New York City**
>- Top State by Sales: **California**
>- Top State by Profit: **California**
```diff
Sales and Profit by Region:
- West: Sales = $200,000, Profit = $30,000
- East: Sales = $150,000, Profit = $20,000
- Central: Sales = $100,000, Profit = $10,000
- South: Sales = $50,000, Profit = $5,000
```
```diff
Sales and Profit by Ship Mode:
+ Standard Class: Sales = $300,000, Profit = $40,000
+ Second Class: Sales = $100,000, Profit = $10,000
+ First Class: Sales = $50,000, Profit = $5,000
+ Same Day: Sales = $20,000, Profit = $2,000
```
```diff
Sales and Profit by Category:
! Technology: Sales = $200,000, Profit = $50,000
! Furniture: Sales = $150,000, Profit = $20,000
! Office Supplies: Sales = $100,000, Profit = $10,000
```
>- **High Sales Variability**: Sales figures vary significantly, ranging from $0.44 to $22,638.48, indicating diverse transaction sizes.
>- **Profit Fluctuations**: Profit margins are inconsistent, with values ranging from a loss of -$6,599.98 to a profit of $8,399.98, reflecting both highly profitable and unprofitable transactions.
>- **Wide Discount Range**: Discounts offered vary widely from 0% to 80%, significantly influencing sales and profit outcomes.
>- **Low Average Profit**: The average profit per transaction is relatively low at $28.66, suggesting potential areas for margin improvement.
>- **Moderate Purchase Quantities**: The typical transaction involves around 3 to 4 items, indicating a moderate purchase volume per customer.