Skip to content

README.md

Latest commit

 

History

History
145 lines (91 loc) · 3.01 KB

README.md

File metadata and controls

145 lines (91 loc) · 3.01 KB
Raw

Sales Data Analysis using Python

Overview

This project performs exploratory data analysis on a product sales dataset using Python. The goal is to extract useful business insights from sales data and visualize patterns such as revenue distribution, profit by region, product performance, and monthly trends.

The analysis uses the Pandas library for data processing and Matplotlib for data visualization.

Features

  • Load and inspect the sales dataset
  • Data cleaning and preprocessing
  • Summary statistics of sales data
  • Revenue and profit analysis
  • Sales performance by category and region
  • Monthly revenue trend visualization
  • Top products by revenue
  • Quantity distribution analysis
  • Identification of top customers
  • Export processed dataset for further use

Technologies Used

  • Python
  • Pandas
  • Matplotlib

Dataset

The dataset used for this project is publicly available on Kaggle.

Download the dataset from:

https://www.kaggle.com/datasets/yashyennewar/product-sales-dataset-2023-2024

After downloading the dataset, place the CSV file in the project directory before running the script.

Expected dataset file name:

product_sales_dataset_final.csv

Project Structure

sales-data-analysis-python/ │ ├── sales_analysis.py ├── README.md └── requirements.txt

Visualizations Generated

The analysis generates several visualizations including:

  • Revenue by Product Category (Bar Chart)
  • Profit by Region (Bar Chart)
  • Monthly Revenue Trend (Line Chart)
  • Top 10 Products by Revenue (Bar Chart)
  • Quantity Distribution (Histogram)
  • Sales Distribution by Region (Pie Chart)

These visualizations help understand overall business performance and trends.

Key Insights

The project helps identify:

  • Categories generating the highest revenue
  • Regions producing the highest profit
  • Top performing products
  • Monthly revenue patterns
  • Distribution of product quantities sold
  • Top customers contributing to revenue

Installation

Install required libraries before running the project.

pip install pandas matplotlib

Or install from requirements file:

pip install -r requirements.txt

How to Run

  1. Download the dataset from Kaggle.
  2. Place the dataset file in the project folder.
  3. Install required dependencies.
  4. Run the Python script.

python sales_analysis.py

The script will:

  • Analyze the dataset
  • Generate visualizations
  • Save a processed dataset file.

Output

After running the script, a processed dataset file will be created:

processed_sales_data.csv

This file contains the cleaned and processed data used in the analysis.

Future Improvements

Possible enhancements for the project include:

  • Interactive dashboards using Plotly or Streamlit
  • Machine learning models for sales prediction
  • Automated reporting
  • Advanced customer segmentation analysis

Author

Deepak B.Tech Computer Science Engineering Student Sharda University

Interested in data analysis, software development, and building technology projects.