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https://github.com/abhishekyadav915/e-commerce-sales-analysis
E-Commerce Sales Analysis is a data analysis project that explores sales data from an e-commerce platform to uncover insights and trends. The analysis includes visualizing sales performance, customer behavior, and product trends to help optimize business strategies and improve customer satisfaction.
https://github.com/abhishekyadav915/e-commerce-sales-analysis
matplotlib-pyplot numpy pandas-library plotly python3
Last synced: 30 days ago
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E-Commerce Sales Analysis is a data analysis project that explores sales data from an e-commerce platform to uncover insights and trends. The analysis includes visualizing sales performance, customer behavior, and product trends to help optimize business strategies and improve customer satisfaction.
- Host: GitHub
- URL: https://github.com/abhishekyadav915/e-commerce-sales-analysis
- Owner: AbhishekYadav915
- Created: 2024-12-02T12:15:28.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-12-03T14:12:02.000Z (about 1 month ago)
- Last Synced: 2024-12-03T15:18:56.301Z (about 1 month ago)
- Topics: matplotlib-pyplot, numpy, pandas-library, plotly, python3
- Language: Jupyter Notebook
- Homepage:
- Size: 1.38 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# E-Commerce Sales Analysis
This project focuses on analyzing the sales data of an e-commerce platform to uncover key insights and trends that can drive business decisions. The analysis includes exploring the performance of various products, sales patterns, and customer behavior.
## Table of Contents
Project Overview
Features
Data Sources
Technologies Used
Setup
Usage
Contributing
License
## Project Overview
The goal of this project is to analyze sales data from an e-commerce platform to identify trends and patterns, helping to optimize product sales and improve customer satisfaction. The analysis includes visualizations and insights into various aspects of the data, such as sales by product, region, time, and customer demographics.
## Features
Sales Performance Analysis: Insights into overall sales, sales by product, region, and time.
Customer Insights: Analysis of customer behavior, purchasing patterns, and demographic insights.
Product Trends: Identifying best-performing products and sales trends over time.
Data Visualizations: Interactive charts and graphs for easy interpretation of data.
## Data Sources
The dataset used for this analysis is sourced from e-commerce sales transactions, including product sales, customer information, and transaction details. The data may include:
**Product ID**: Identification of products sold.
**Quantity Sold**: Number of units sold.
**Total Sales**: Total value of products sold.
**Region**: Geographic information about the customer.
**Customer** Demographics: Age, gender, and other details of customers.
**Time**: Timestamp of each transaction.
The data is in CSV format, which is processed and analyzed for meaningful insights.
## Technologies Used
**Python**: For data analysis and manipulation.
**Pandas**: For data handling and analysis.
**Matplotlib**: For data visualization and chart generation.
**Seaborn**: For statistical data visualization.
**Jupyter Notebooks**: For interactive development and documentation.
## Setup
To set up the project locally, follow these steps:
1.Clone the repository:
bash
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git clone https://github.com/AbhishekYadav915/E-Commerce-Sales-Analysis.git
cd E-Commerce-Sales-Analysis
2.Install the required dependencies:
bash
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pip install -r requirements.txt
3.Make sure you have the necessary dataset available (e.g., CSV files).
## Usage
Once the setup is complete, you can run the analysis in Jupyter Notebook. Open the notebook using:
bash
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jupyter notebook
Navigate to the notebook that contains the analysis and start exploring the data.
## Key Steps in the Analysis
**Data Loading**: Load the sales dataset.
**Data Cleaning**: Clean and preprocess the data to handle missing values, duplicates, and outliers.
**Data Exploration**: Perform exploratory data analysis (EDA) to identify key patterns.
**Data Visualization**: Create visualizations such as bar charts, line graphs, and heatmaps to present the insights.
Contributing
Contributions are welcome! If you find any bugs or want to improve the project, feel free to submit a pull request. Please ensure your changes are well-documented and tested.
Fork the repository.
Create a new branch for your feature/fix.
Make your changes and commit them with a clear message.
Push to your fork and create a pull request.
License
**This project is licensed under the MIT License - see the LICENSE file for details.**