Super Store Sales Analytics Final Year Project with Source Code

Project Features in this Final Year Project

• Superstore sales data analysis
• Kaggle Superstore dataset support
• CSV dataset loading
• Automatic sample data generation if dataset is missing
• Exploratory data analysis
• Statistical summary generation
• Missing-value analysis
• Correlation analysis
• Retail sales comparison
• Region-wise sales analysis
• Category-wise sales analysis
• Monthly sales trend analysis
• Sales vs profit analysis
• Customer segment comparison
• Sub-category sales ranking
• Multiple visualization outputs
• Random Forest sales prediction
• Linear Regression sales prediction
• Model evaluation using R²
• Model evaluation using RMSE
• Model evaluation using MAE
• Actual vs predicted sales graph
• Graph export to output folder
• Command-line execution
• Suitable for project report and viva explanation

Data Analytics / EDA Features in this Final Year Project

• Loads Superstore sales CSV dataset

• Reads data from:

  data/

• Prints dataset summary
• Shows statistical description
• Checks missing values
• Performs correlation analysis
• Analyzes sales by region
• Analyzes sales by category
• Analyzes sales trends over time
• Compares sales and profit
• Compares customer segments
• Identifies top sub-categories by sales
• Generates report-ready analytical outputs
• Supports demonstration even when no real dataset is available

Visualization Features in this Final Year Project

The project generates comparison graphs inside:

output/graphs/

1. Sales by Region
   File: 1_sales_by_region.png
   Chart type: Bar chart
   Shows sales comparison across regions.
2. Sales by Category
   File: 2_sales_by_category.png
   Chart type: Pie chart
   Shows sales distribution by product category.
3. Sales Trend Over Time
   File: 3_sales_trend_over_time.png
   Chart type: Line chart
   Shows monthly sales trend.
4. Sales vs Profit
   File: 4_sales_vs_profit.png
   Chart type: Scatter plot
   Shows relationship between sales and profit by category.
5. Segment Comparison
   File: 5_segment_comparison.png
   Chart type: Comparison chart
   Shows sales and profit by customer segment.
6. Top Sub-Categories
   File: 6_top_subcategories.png
   Chart type: Bar chart
   Shows top 10 sub-categories by sales.
7. Correlation Heatmap
   File: 7_correlation_heatmap.png
   Chart type: Heatmap
   Shows correlation between numerical fields.
8. Prediction Sales Graph
   File: 8_prediction_sales_graph.png
   Chart type: Actual vs predicted graph
   Shows comparison between actual sales and predicted sales.

Machine Learning Features in this Final Year Project

• Sales prediction using machine learning
• Uses Random Forest model
• Uses Linear Regression model
• Predicts sales from available retail dataset features
• Performs train-test split
• Trains regression models
• Compares actual and predicted sales
• Evaluates models using:
  • R² score
  • RMSE
  • MAE
• Prints model metrics in console
• Generates prediction sales graph
• Useful for sales forecasting and business analytics demonstration

Dataset Details in this Final Year Project

Dataset:
Kaggle Superstore Sales dataset

Dataset source options:

• Kaggle CLI download
• Manual Kaggle CSV download
• Auto-generated sample data if no dataset is found

Dataset folder:

data/

Common Superstore fields may include:

• Order date
• Ship date
• Region
• Category
• Sub-category
• Segment
• Sales
• Profit
• Discount
• Quantity
• State
• City
• Product details

Files / Modules Included in this Final Year Project

• superstore_sales_analytics.py — main analysis and ML script
• download_dataset.py — Kaggle dataset downloader
• requirements.txt — Python dependency list
• README.md — project documentation
• data/ — dataset folder
• output/graphs/ — generated visualization folder

Output Files in this Final Year Project

The project generates output graphs inside:

output/graphs/

Expected output files:

• 1_sales_by_region.png
• 2_sales_by_category.png
• 3_sales_trend_over_time.png
• 4_sales_vs_profit.png
• 5_segment_comparison.png
• 6_top_subcategories.png
• 7_correlation_heatmap.png
• 8_prediction_sales_graph.png

Console output includes:

• EDA summary
• Missing-value analysis
• Correlation details
• Model R² score
• RMSE value
• MAE value

1. Open terminal in the project folder:

   cd "superstore-sales-prediction-data-analysis"

2. Install dependencies:

   pip install -r requirements.txt

3. Optional: download dataset using Kaggle CLI:

   python download_dataset.py

4. Or manually download a Superstore CSV dataset and place it in:

   data/

5. Run the project:

   python superstore_sales_analytics.py

6. Check generated graphs:

   output/graphs/

This project has no login credentials because it is not a web application.

Credential note:
No admin panel, user login, password, or database authentication is included. The project runs locally as a Python data analysis and machine learning script