01 Project Synopsis
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M.E. Project Report
M.E. Sales Trend and Forecasting Using ML Final Year Project Report
Get structured M.E. Sales Trend and Forecasting Using ML Final Year Project Report Final Year Project Report and Documentation with project objectives, methodology, system design, diagrams, implementation details, testing and complete project explanations. Suitable as a learning, documentation and project-presentation resource for students working on related final year projects.
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02 Detailed Project Report
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03 Customized 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
Sales Trend and Forecasting Using ML in MERN Stack is a full-stack sales analytics and machine learning platform designed for businesses to upload sales/customer data, run predictive models, visualize sales trends, forecast future sales, segment customers, optimize marketing campaigns, and manage subscription-based ML access through separate User and Admin portals. The system uses a React frontend, Express backend, MongoDB database, and Python machine learning scripts invoked through python-shell.
The platform includes user authentication, CSV upload, data preview, sample file download, sales forecasting, market basket analysis, RFM segmentation, customer lifetime value prediction, churn analysis, response modeling, uplift modeling, charts, confusion matrices, forecast graphs, result tables, subscription plans, dummy checkout, and admin management for users, plans, ML actions, packages, customers, admins, and revenue.
This project is suitable for B.Tech, M.Tech, BCA, MCA, BE, ME, BSc, and MSc students who need a practical final year project, major project, or minor project based on sales forecasting, machine learning, business analytics, customer segmentation, marketing analytics, MERN stack development, Python ML integration, CSV data processing, MongoDB database design, and admin dashboard functionality. FileMakr can provide this project with source code, project report, documentation, and setup support for academic submission.
Login Credentials
Admin Panel
| Role | Password | |
|---|---|---|
| Super Admin | [email protected] |
Admin@123 |
| Manager | [email protected] |
Manager@123 |
User App
| Plan | Username | Password | Pre-uploaded Sales CSV | |
|---|---|---|---|---|
| Free | free_user |
[email protected] |
User@123 |
No |
| Standard | standard_user |
[email protected] |
User@123 |
Yes |
| Premium | premium_user |
[email protected] |
User@123 |
Yes |
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- Report
- Synopsis
- Diagrams
- Secure admin login
- Admin JWT token stored in cookie
- Admin dashboard
- View customer statistics
- View revenue statistics
- View subscription plans
- Quick navigation links
- Manage subscription plans
- Create subscription plan
- Edit subscription plan
- Delete subscription plan
- Add plan name
- Add plan price in INR
- Manage ML actions
- Create ML feature/action
- Edit ML action
- Delete ML action
- Map each ML action to a subscription plan tier
- Manage packages
- View user package assignments
- Edit user package assignments
- Manage user upgrades
- Manage customers
- List registered users
- Search customers
- View customer details
- Manage registered users
- View payment and revenue records
- Track subscription revenue
- Manage admins
- List all admins
- Update admin records
- Delete admin accounts
- Add new admin accounts
- View admin profile
- Update admin profile details
- Change admin password
- Reset admin password
- Manage users, plans, actions, packages, customers, admins, and revenue from MongoDB-backed admin controls
The admin portal includes dashboard, manage plans, manage ML actions, manage packages, manage customers, revenue, all admins, add admin, admin profile, admin password update, and admin password reset modules
- User registration
- User login
- JWT token stored in cookie
- Password reset
- Landing page
- Product overview
- Login and signup links
- User guide page
- User dashboard
- View account status
- Quick links to predictions
- Quick links to data upload
- User panel as central navigation hub
- Profile management
- View name, email, phone, and username
- Update name
- Update email
- Update phone
- Update username
- Change password
- Upload sales CSV
- Upload
.csvfile only - Upload file up to 100MB
- Preview uploaded sales data
- Paginated CSV preview
- Delete uploaded data where supported
- Download sample file
- View current subscription plan
- Compare plan tiers
- Upgrade plan through dummy checkout
- Access ML modules based on subscription plan
- Run analysis from prediction pages
- View prediction metrics
- View charts and warnings
- Download result files
- Preview tabular output where available
The user portal includes landing page, login, signup, reset password, user guide, dashboard, user panel, profile, change password, upload sales data, preview data, and plans/billing modules.
Machine Learning Modules
1. Sales Forecasting
- Available from Free plan and above
- Uses Facebook Prophet
- Forecasts daily sales for the next 30 days
- Shows top products
- Shows train/test metrics
- Displays interactive forecast charts
2. Market Basket Analysis
- Available from Standard plan and above
- Uses Apriori algorithm through
mlxtend - Finds product association rules
- Shows support, confidence, and lift
- Requires main sales CSV
3. RFM Segmentation
- Available from Standard plan and above
- Uses K-Means clustering
- Works on Recency, Frequency, and Monetary values
- Segments customers into low, mid, and high value groups
4. Customer Lifetime Value Prediction
- Available from Standard plan and above
- Predicts customer lifetime value
- Uses RFM-style features and clustering
5. Churn Analysis
- Available in Premium plan
- Uses XGBoost classifier
- Predicts customer churn
- Requires separate churn CSV
- Shows accuracy, precision, recall, and confusion matrix
6. Response Modeling
- Available in Premium plan
- Uses XGBoost
- Models marketing response by offer type
- Compares actual vs predicted orders and revenue
7. Uplift Modeling
- Available in Premium plan
- Uses XGBoost and K-Means
- Calculates uplift scores
- Supports campaign ROI analysis
- Identifies persuadables and lost causes
The project provides seven ML modules across plan tiers: sales forecasting, market basket analysis, RFM segmentation, LTV prediction, churn analysis, response modeling, and uplift modeling.
Subscription Plans
| Plan | Price | ML Modules Included |
|---|---|---|
| Free | ₹0 | Sales Forecasting |
| Standard | ₹999/month | Sales Forecasting, Market Basket Analysis, RFM Segmentation, Customer LTV |
| Premium | ₹1,999/month | All Standard modules plus Churn Analysis, Response Modeling, Uplift Modeling |
Plans and ML action mappings are seeded into MongoDB and can be modified from the Admin Panel.
Other Features
- Full-stack MERN architecture
- React frontend
- Express backend
- MongoDB database
- Python ML scripts
- Node-to-Python integration using
python-shell - CSV upload
- Sales data preview
- Sample CSV download
- Result file generation
- Excel file output storage
- Charts and visualizations
- Confusion matrices
- Forecast graphs
- Data tables
- Subscription-based ML access
- Dummy payment checkout
- Admin revenue management
- User and admin portals
- JWT authentication
- Password reset
- MongoDB-backed user, admin, plan, action, and revenue data
- Static output files served from backend
- Development setup using concurrently
- Demo seed data
- Sample users with Free, Standard, and Premium plans
- Pre-uploaded sales CSV for selected demo accounts
- Python path resolver support
The system architecture shows React client, Express API, Python ML scripts, and MongoDB working together, with uploaded CSV files stored under public/ and ML output files saved under excel_files/{userId}/.
Technology Used
| Layer | Technology |
|---|---|
| Frontend | React 16, React Router 5 |
| UI / Styling | Bootstrap 4, Material-UI 4, Custom CSS, CSS Modules |
| Charts | Chart.js, React-Chartjs-2 |
| Alerts / Cookies | SweetAlert, js-cookie |
| API Client | Axios |
| Backend | Node.js, Express 4 |
| Authentication | JWT |
| File Upload | Multer |
| Data Parsing | csv-parse, xlsx |
| Database | MongoDB, Mongoose 7 |
| Machine Learning | Python 3.12+, pandas, numpy, scikit-learn, Prophet, mlxtend, xgboost |
| Visualization / Output | matplotlib, plotly, openpyxl |
| Integration | python-shell |
| Development | concurrently |
The uploaded documentation lists React, Express, JWT, Multer, MongoDB, Mongoose, Python, pandas, numpy, scikit-learn, Prophet, mlxtend, XGBoost, matplotlib, plotly, python-shell, and concurrently as part of the technology stack.
CSV Requirements
Main Sales CSV
Used by Sales Forecasting, Market Basket Analysis, RFM Segmentation, and LTV Prediction.
| Column | Description |
|---|---|
InvoiceNo |
Invoice / transaction ID |
Description |
Product description |
Quantity |
Units sold |
InvoiceDate |
Transaction date |
CustomerID |
Customer identifier |
UnitPrice |
Price per unit |
Country |
Country |
Stored as:
public/{userId}.csv
Churn CSV
Required columns include:
customerID, gender, SeniorCitizen, Partner, Dependents, tenure, PhoneService, PaperlessBilling, MonthlyCharges, TotalCharges, Churn
Stored as:
public/{userId}churn.csv
Uplift CSV
Required columns:
history, offer, conversion
Stored as:
public/{userId}uplift.csv
Market Response CSV
Required columns:
offer, conversion
The project documentation defines separate CSV requirements for main sales data, churn analysis, uplift modeling, and market response modeling
Python Scripts Reference
| Script | Algorithm | Output |
|---|---|---|
Sales_Forecasting.py |
Prophet time-series | Forecast xlsx/csv |
Apriori.py |
Apriori association rules | Market_Basket.csv |
RFM_Value.py |
K-Means RFM | RFM.csv |
LTValue.py |
K-Means + LTV scoring | LTV.csv |
Churn.py |
XGBoost classification | churn.csv |
Market.py |
XGBoost response modeling | Console metrics |
Uplift.py |
XGBoost + K-Means uplift | uplift.csv, uplift1000.csv |
Step 1: Navigate to application folder
cd "Code - Frontend + Backend"
Step 2: Install backend dependencies
npm install
Step 3: Install frontend dependencies
cd client
npm install
cd ..
Step 4: Install Python ML dependencies
pip install prophet pandas numpy scikit-learn mlxtend xgboost openpyxl matplotlib plotly
Step 5: Seed the database
npm run seed
Step 6: Start development servers
npm run dev
This starts both backend and frontend together.
Alternative: Run separately
npm start
npm run client
The setup instructions include installing backend dependencies, frontend dependencies, Python ML packages, seeding the database, and running development servers.
README Included
Setup Support Available
Admin Panel
| Role | Password | |
|---|---|---|
| Super Admin | [email protected] |
Admin@123 |
| Manager | [email protected] |
Manager@123 |
User App
| Plan | Username | Password | Pre-uploaded Sales CSV | |
|---|---|---|---|---|
| Free | free_user |
[email protected] |
User@123 |
No |
| Standard | standard_user |
[email protected] |
User@123 |
Yes |
| Premium | premium_user |
[email protected] |
User@123 |
Yes |