01 Synopsis
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B.Tech Project Report
B.Tech Superstore Sales and Profit Prediction Final Year Project Report
Complete B.Tech final-year project report with documentation, diagrams and viva-ready structure. Instant PDF & Word download — plagiarism-free and faculty-aligned.
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02 Pre Defined Project Report
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Abstract
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Table of Content
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01
Introduction
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02
Problem Statement
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03
Existing System
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04
Proposed System
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05
Objectives
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06
System Architecture
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07
Major Functional Modules
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08
Hardware Requirements
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09
Software Requirements
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10
Future Enhancement
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11
Conclusion
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12
References
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Abstract
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Table of Content
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01
Chapter 1 — Introduction
- 1.1 Background of the Project
- 1.2 Problem Statement
- 1.3 Objectives of the System
- 1.4 Scope of the Project
- 1.5 Existing System Overview
- 1.6 Proposed System Overview
- 1.7 Technologies Used
- 1.8 Overview
- 1.9 Limitations
- 1.10 Advantages
- 1.11 Disadvantages
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02
Chapter 2 — Literature Review / System Study
- 2.1 Introduction
- 2.2 Review of Similar Systems
- 2.3 Comparative Analysis
- 2.4 Software Development Models
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03
Chapter 3 — System Analysis
- 3.1 Introduction
- 3.2 Functional Requirements
- 3.3 Non-Functional Requirements
- 3.4 User Requirements
- 3.5 Feasibility Study
- 3.5.1 Technical Feasibility
- 3.5.2 Economic Feasibility
- 3.5.3 Operational Feasibility
- 3.6 System Architecture
- 3.7 Data Flow Diagram (Level 0)
- 3.8 Data Flow Diagram (Level 1)
- 3.9 Data Flow Diagram (Level 2)
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04
Chapter 4 — System Design
- 4.1 Introduction
- 4.2 Use Case Diagram
- 4.3 Class Diagram
- 4.4 Sequence Diagram
- 4.5 Activity Diagram
- 4.6 ER Diagram
- 4.7 Database Schema
- 4.8 Table Structures
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05
Chapter 5 — System Implementation
- 5.1 Introduction
- 5.2 Development Environment
- 5.3 Tools and Technologies Used
- 5.4 Hardware Requirements
- 5.5 Software Requirements
- 5.6 Module-wise Implementation
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06
Chapter 6 — Testing
- 6.1 Introduction
- 6.2 Testing Strategy
- 6.3 Unit Testing
- 6.4 Integration Testing
- 6.5 System Testing
- 6.6 Test Cases
- 6.7 Bug Reports
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07
Chapter 7 — Results and Discussion
- All screenshots
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08
Chapter 8 — Conclusion and Future Enhancements
- 8.1 Conclusion
- 8.2 Future Enhancements
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09
Chapter 9 — References
Project's Overview
Superstore Sales and Profit Prediction is a full-stack Python Flask based final year project developed for predicting sales and profit using the Superstore dataset. This major project includes user management, dataset management, CSV upload, dataset preview, data cleaning, filters, machine learning model training, prediction forms, bulk CSV prediction, business insights, training history, prediction history, and admin management. Users can sign up, log in, manage profile, recover passwords through email and security question, upload datasets, select dataset versions, preview data with search/sort, clean missing values and duplicates, apply filters, train Random Forest models, predict sales and profit, and view prediction history. Admin users can manage users and clean datasets. This Superstore Sales and Profit Prediction source code is suitable for students who need a final year project, major project, minor project, source code, and project report based on Flask, pandas, scikit-learn, SQLite, and machine learning.
Login Credentials
Admin Account
- Email:
[email protected] - Password:
Admin@123
Demo User Account
- Email:
[email protected] - Password:
User@123
Other dummy users:
Any seeded dummy user can be used with the default user password if available in the seed data.
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- Report
- Synopsis
- Diagrams
- Admin login in this final year project
- Admin dashboard
- User management
- View registered users
- Manage user records
- Dataset cleanup
- Clean uploaded datasets
- Manage dataset records
- Support platform maintenance
- Monitor user and dataset activity
- User signup in this final year project
- User login
- User profile management
- Forgot password through email
- Forgot password through security question
- Update profile details
- Upload Superstore CSV dataset
- Select dataset versions
- Preview uploaded dataset
- Search dataset preview
- Sort dataset preview
- Clean missing values
- Remove duplicate records
- Format dataset columns
- Apply date range filter
- Apply region filter
- Apply state filter
- Apply city filter
- Apply category filter
- Use preset filters
- Train machine learning model
- Train Random Forest for sales prediction
- Train Random Forest for profit prediction
- Make single form-based prediction
- Upload CSV for bulk prediction
- View prediction result
- View top category insights
- View region summary
- View trend view
- View training history
- View prediction history
- Use sample Superstore dataset for testing
Dataset Management Features in this Final Year Project
- Upload CSV dataset
- Select dataset version
- Preview dataset records
- Search dataset records
- Sort dataset records
- Clean missing values
- Remove duplicate records
- Format column names
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Use sample dataset:
sample_superstore.csv - Upload sample dataset through Dataset section
- Prepare dataset for model training
- Store dataset version for later use
Data Cleaning Features in this Final Year Project
- Missing-value handling
- Duplicate record cleanup
- Column formatting
- Dataset validation
- Cleaned data preparation
- Dataset preview after cleanup
- User-controlled dataset selection
- Admin dataset cleanup support
Filter Features in this Final Year Project
The system supports business filters such as:
- Date range
- Region
- State
- City
- Category
- Preset filters
- Dataset-level filtered view
- Analysis-ready filtered records
- Prediction-ready filtered records
Machine Learning Features in this Final Year Project
- Sales prediction using machine learning
- Profit prediction using machine learning
- Random Forest model training
- Trains model from selected dataset
- Uses Superstore dataset features
- Supports single prediction
- Supports bulk prediction using CSV upload
- Stores model training history
- Stores prediction history
- Useful for business forecasting
- Suitable for sales analytics explanation
- Suitable for profit analytics explanation
Prediction Features in this Final Year Project
- Single form-based sales prediction
- Single form-based profit prediction
- Bulk CSV prediction upload
- View prediction output
- Store prediction result
- Review prediction history
- Compare prediction records
- Use trained model for future predictions
Insights Features in this Final Year Project
- Top categories analysis
- Region summary
- Trend view
- Sales insight generation
- Profit insight generation
- Dataset-based business analytics
- Report-ready insight output
- Useful for project report charts and explanation
Other Features in this Final Year Project
- Complete Superstore Sales and Profit Prediction source code
- Suitable for final year project, major project, and minor project
- Project report content can be prepared from included modules
- Flask web application
- User authentication
- Admin management
- Dataset upload
- Dataset preview
- Dataset cleaning
- Sales prediction
- Profit prediction
- Random Forest machine learning
- pandas data processing
- scikit-learn model training
- SQLite database
- Flask-Mail support
- Gmail forgot-password support
- Sample Superstore dataset support
- Training and prediction history
- Business insights module
- Useful for viva, source code review, project report writing, and project demonstration
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Open terminal in the project folder:
cd "superstore-sales-and-profit-prediction" -
Create virtual environment:
python -m venv venv -
Activate virtual environment on Windows:
venv\Scripts\activate -
Install dependencies:
pip install -r requirements.txt - Optional: configure forgot-password email using Gmail App Password.
-
Create
.envfile by copying:.env.example -
Add mail credentials:
[email protected] MAIL_PASSWORD=your-app-password -
Initialize database and seed dummy data:
python seed_data.py -
Run the application:
python app.py - Open project in browser:
http://localhost:5000
- Upload sample dataset from Dataset section:
sample_superstore.csv
README Included
Setup Support Available
Admin Account
- Email:
[email protected] - Password:
Admin@123
Demo User Account
- Email:
[email protected] - Password:
User@123
Other dummy users:
Any seeded dummy user can be used with the default user password if available in the seed data.