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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.
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
Reads:
myntra_style_sales_dataset.csv
Order_Date as datetimeRevenue from Final_Pricedescribe() summary for key numeric columnsThe project generates 12 PNG charts in the outputs/ folder:
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.pngTarget variable:
Revenue / Final_Price
Saves top feature importances in:
outputs/ml_model_report.txt
Features used for regression:
Predicts product category:
Men / Women / Kids
Features used for classification:
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_ModeDerived columns created by script:
YearMonthMonthYearRevenueDiscount_pctmyntra_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 reportThe 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.txtOpen 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/
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