01 Project Synopsis
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MSc Project Report
MSc Myntra E-Commerce Sales Data Analytics Final Year Project Report
Get structured MSc Myntra E-Commerce Sales Data Analytics 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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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
Myntra E-Commerce Sales Data Analytics is a Python-based final year project developed for analyzing synthetic Myntra-style order data for the financial year 2025–26. This major project loads e-commerce sales data from CSV, cleans and prepares order records, performs exploratory data analysis, generates 12 comparative visualization charts, and trains machine learning models for revenue prediction and product category classification. The project analyzes order patterns, monthly revenue trends, revenue by category, top brands, gender distribution, payment modes, state-wise revenue, customer age distribution, discount trends, correlation patterns, product revenue spread, product-type comparison, and MRP vs revenue relationship. It also uses Random Forest Regressor to predict order-line revenue and Random Forest Classifier to predict product category. This Myntra sales data analytics source code is suitable for students who need a final year project, major project, minor project, source code, and project report based on Python, data analytics, EDA, visualization, and machine learning.
Login Credentials
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 script
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Project Features in this Final Year Project
- Myntra-style e-commerce sales data analysis
- Financial year 2025–26 order analytics
- CSV dataset loading
- Data cleaning and preparation
- Order date parsing
- Invalid date row handling
- Discount column renaming
- Numeric column conversion
- Revenue column creation from final price
- Month and year extraction
- YearMonth feature creation
- Exploratory data analysis
- Dataset shape summary
- Date range summary
- Data type inspection
- Missing-value inspection
- Duplicate count checking
- Numeric summary statistics
- Categorical unique count analysis
- Revenue by category analysis
- 12 visualization chart generation
- Machine learning regression model
- Machine learning classification model
- ML metrics report generation
- Feature importance analysis
- PNG chart export
- Text-based ML report export
Data Analytics / EDA Features in this Final Year Project
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Reads:
myntra_style_sales_dataset.csv - Cleans column names
- Parses
Order_Dateas datetime - Drops invalid date rows
- Converts quantity, age, MRP, discount, and final price into usable numeric fields
- Creates
RevenuefromFinal_Price - Creates month-wise trend features
- Prints dataset shape
- Prints date range
- Prints data types
- Prints missing-value summary
- Prints duplicate count
- Prints
describe()summary for key numeric columns - Prints unique counts for categorical fields
- Prints revenue by category
- Supports project report-ready EDA explanation
Visualization Features in this Final Year Project
The project generates 12 PNG charts in the outputs/ folder:
- Monthly revenue trend
File:01_monthly_revenue_trend.png
Chart type: Line chart
Shows total revenue by month. - Revenue by category
File:02_revenue_by_category.png
Chart type: Horizontal bar chart
Shows revenue comparison across Men, Women, and Kids categories. - Top brands by revenue
File:03_top_brands_revenue.png
Chart type: Horizontal bar chart
Shows top 12 brands based on revenue. - Gender distribution
File:04_gender_distribution.png
Chart type: Pie chart
Shows order share by customer gender. - Payment mode orders
File:05_payment_mode_orders.png
Chart type: Vertical bar chart
Shows order count by payment method. - Top states by revenue
File:06_top_states_revenue.png
Chart type: Horizontal bar chart
Shows top 15 states by sales revenue. - Age histogram
File:07_age_histogram.png
Chart type: Histogram
Shows distribution of customer age. - Discount distribution
File:08_discount_distribution.png
Chart type: Histogram
Shows distribution of discount percentage. - Correlation heatmap
File:09_correlation_heatmap.png
Chart type: Heatmap
Shows correlation among Age, Quantity, MRP, Discount, and Revenue. - Revenue boxplot by category
File:10_revenue_boxplot_by_category.png
Chart type: Box plot
Shows revenue spread within each category. - Product type orders vs revenue
File:11_product_type_orders_vs_revenue.png
Chart type: Grouped bar chart
Shows normalized order count and normalized revenue for top product types. - MRP vs revenue scatter
File:12_mrp_vs_revenue_scatter.png
Chart type: Scatter plot
Shows MRP vs revenue by discount band.
Machine Learning Features in this Final Year Project
1. Revenue Prediction
- Uses Random Forest Regressor
- Predicts order-line revenue
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Target variable:
Revenue / Final_Price - Uses customer and basket composition features
- Excludes MRP and discount from revenue regression to avoid overly direct price-rule prediction
- Uses numeric and categorical preprocessing
- Uses train-test split
- Evaluates model using:
- MAE
- RMSE
- R² score
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Saves top feature importances in:
outputs/ml_model_report.txt
Features used for regression:
- Gender
- Age
- Brand
- Category
- Product_Type
- Size
- Quantity
- Payment_Mode
2. Category Classification in this Final Year Project
- Uses Random Forest Classifier
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Predicts product category:
Men / Women / Kids - Uses stratified train-test split
- Uses class balancing
- Evaluates model using:
- Accuracy
- Classification report
- Precision
- Recall
- F1-score
Features used for classification:
- Gender
- Age
- Brand
- Product_Type
- Size
- Quantity
- MRP
- Discount_pct
Dataset Details in this Final Year Project
Dataset file:
myntra_style_sales_dataset.csv
Dataset type:
Synthetic / Myntra-style e-commerce fashion order dataset
Expected columns:
Order_IDOrder_DateCustomer_IDGenderAgeCityStateBrandCategoryProduct_TypeSizeQuantityMRPDiscount (%)Final_PricePayment_Mode
Derived columns created by script:
YearMonthMonthYearRevenueDiscount_pct
Files / Modules Included
myntra_sales_analysis.py— main Python programmyntra_style_sales_dataset.csv— required input datasetrequirements.txt— Python dependency listREADME.md— project documentationoutputs/— generated chart and ML report folderoutputs/ml_model_report.txt— machine learning metrics and classification report
Output Files in this Final Year Project
The project generates all outputs inside:
outputs/
Expected output files:
01_monthly_revenue_trend.png02_revenue_by_category.png03_top_brands_revenue.png04_gender_distribution.png05_payment_mode_orders.png06_top_states_revenue.png07_age_histogram.png08_discount_distribution.png09_correlation_heatmap.png10_revenue_boxplot_by_category.png11_product_type_orders_vs_revenue.png12_mrp_vs_revenue_scatter.pngml_model_report.txt
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Open terminal in the project folder:
cd "Myntra Sales Data Analytics" -
Create virtual environment on Windows:
python -m venv .venv .\.venv\Scripts\Activate.ps1 -
Create virtual environment on macOS/Linux:
python3 -m venv .venv source .venv/bin/activate -
Install dependencies:
pip install -r requirements.txt -
Ensure dataset is available in the same folder as the script:
myntra_style_sales_dataset.csv -
Run the analysis:
python myntra_sales_analysis.py -
Check generated charts and ML report:
outputs/
README Included
Setup Support Available
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 script