Myntra-Sales-Analysis

Myntra Sales Data Analysis using Python

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Myntra Sales Data Analysis

Project Overview

This project performs exploratory data analysis (EDA) on a Myntra sales dataset using Python. The analysis includes data loading, data quality checks, feature engineering, sales aggregation, and data visualization to uncover insights about monthly sales performance and category-wise revenue distribution.

Features

  • Load data from multiple Excel sheets
  • Inspect dataset structure and summary statistics
  • Detect duplicate records
  • Create new calculated fields
  • Analyze monthly sales trends
  • Analyze revenue contribution by product category
  • Visualize results using bar charts and pie charts

Dataset

The project uses an Excel file named:

Myntra dataset.xlsx

The workbook contains the following sheets:

Sheet Name Description
dim_products Product details
dim_customers Customer information
fact_orders Order transaction data

Technologies Used

  • Python
  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn

Install the required libraries using:

pip install pandas numpy matplotlib seaborn openpyxl

Project Workflow

1. Load Data

The script reads data from the Excel workbook into three DataFrames:

products
customers
orders

2. Data Exploration

The following methods are used to understand the datasets:

.info()
.describe()

These provide information about:

  • Data types
  • Missing values
  • Number of records
  • Statistical summaries

3. Duplicate Detection

The script checks for duplicate records in all tables:

products.duplicated()
customers.duplicated()
orders.duplicated()

It also calculates the total number of duplicate rows:

products.duplicated().sum()
customers.duplicated().sum()
orders.duplicated().sum()

4. Data Cleaning

Duplicate rows are removed from the products table:

products.drop_duplicates()

5. Feature Engineering

Extract Month from Order Date

A new column called Month is created from the order date:

orders["Month"] = orders["Date"].dt.strftime("%B")

Calculate Total Price After Discount

A new column called Total Price is created:

orders["Total Price"] = (
    orders["Original Price"] -
    (orders["Original Price"] * orders["Discount%"])
)

Analysis Performed

Monthly Sales Analysis

Monthly sales are calculated using:

gb = orders.groupby("Month").agg({
    "Original Price": "sum"
})

Visualization

A Seaborn bar chart displays total sales by month.

Insights:

  • Identifies high-performing months
  • Highlights seasonal sales trends

Category-wise Revenue Analysis

Orders and product information are merged using:

df = pd.merge(
    left=orders,
    right=products,
    on="Product ID",
    how="inner"
)

Revenue is then aggregated by product category:

gb1 = df.groupby("Category").agg({
    "Total Price": "sum"
})

Visualization

A pie chart shows the percentage contribution of each category to total revenue.

Insights:

  • Identifies top-performing categories
  • Shows category contribution to overall sales

Output

The script generates:

Reports

  • Dataset information
  • Summary statistics
  • Duplicate counts
  • Monthly sales totals
  • Category-wise revenue totals

Visualizations

  1. Monthly Sales Bar Chart
  2. Category Revenue Pie Chart

Revenue Calculation

Total revenue after discounts:

df["Total Price"].sum()

Project Structure

Myntra-Sales-Analysis/
│
├── Myntra dataset.xlsx
├── analysis.py
└── README.md

How to Run

  1. Place Myntra dataset.xlsx in the project directory.
  2. Install required libraries.
  3. Run the Python script:
python analysis.py
  1. View the generated charts and analysis results.

Business Questions Answered

  • Which months generate the highest sales?
  • How do discounts affect total revenue?
  • Which product categories contribute the most revenue?
  • Are there duplicate records in the dataset?

Future Enhancements

  • Handle missing values and outliers
  • Save cleaned datasets to new files
  • Create interactive dashboards using Power BI or Plotly
  • Perform customer segmentation analysis
  • Build sales forecasting models
  • Analyze discount effectiveness

Author

Myntra Sales Data Analysis Project

A Python-based exploratory data analysis project for understanding sales performance, customer behavior, and product category revenue using Pandas, Matplotlib, and Seaborn.

Built With

  • jupyter-notebook
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