https://github.com/parthds02/e-commerce-data-analysis-with-python

This project focuses on analyzing an e-commerce dataset using Python. The goal is to derive meaningful insights through exploratory data analysis (EDA) and uncover trends and patterns that can drive business decisions.
https://github.com/parthds02/e-commerce-data-analysis-with-python

data-analysis ecommerce exploratory-data-analysis jupyter-notebook pytho sales-analysis visualization

Last synced: about 1 year ago
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This project focuses on analyzing an e-commerce dataset using Python. The goal is to derive meaningful insights through exploratory data analysis (EDA) and uncover trends and patterns that can drive business decisions.

• Host: GitHub
• URL: https://github.com/parthds02/e-commerce-data-analysis-with-python
• Owner: ParthDS02
• License: mit
• Created: 2024-12-17T13:53:58.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-17T13:59:04.000Z (over 1 year ago)
• Last Synced: 2025-05-22T10:56:44.012Z (about 1 year ago)
• Topics: data-analysis, ecommerce, exploratory-data-analysis, jupyter-notebook, pytho, sales-analysis, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 2.33 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# E-Commerce Data Analysis with Python

![Python](https://img.shields.io/badge/Python-3.8%2B-blue) ![Jupyter Notebook](https://img.shields.io/badge/Tool-Jupyter_Notebook-orange) ![EDA](https://img.shields.io/badge/EDA-Exploratory%20Data%20Analysis-yellowgreen)

## 📜 Project Overview

This project focuses on analyzing an **e-commerce dataset** using Python. The goal is to derive meaningful insights through exploratory data analysis (EDA) and uncover trends and patterns that can drive business decisions.

## 🔍 Purpose

- Understand customer purchasing behaviors.

- Analyze sales trends over time.

- Identify missing data and handle data cleaning.

- Create visualizations to represent findings effectively.

## 🛠️ Tools and Libraries

This project is implemented in a **Jupyter Notebook** and utilizes the following libraries:

- **NumPy**: For numerical computations.

- **Pandas**: For data manipulation and analysis.

- **Matplotlib & Seaborn**: For static visualizations.

- **Plotly**: For interactive visualizations.

- **Scipy**: For statistical analysis.

- **Missingno**: To visualize missing data patterns.

- **Warnings**: To handle warnings.

- **IPython**: For advanced Jupyter Notebook features.

## 🧩 Methods

1. **Data Cleaning**:

   - Identified and handled missing values using `Missingno`.

   - Converted date columns into proper datetime formats for time-series analysis.

   

2. **Exploratory Data Analysis (EDA)**:

   - Analyzed sales trends by day, month, and year.

   - Explored customer behavior through metrics like average order value and product popularity.

   - Visualized correlations and distributions of key features.

3. **Visualization**:

   - Created static visualizations with Matplotlib and Seaborn.

   - Developed interactive dashboards using Plotly to enhance data exploration.

## 📊 Results

- **Sales Trends**: Identified peak sales periods and seasonal patterns.

- **Customer Insights**: Uncovered the most popular product categories and purchasing patterns.

- **Correlations**: Found significant relationships between key features such as sales and time.

## 📝 Outcome

This analysis provides actionable insights into customer behaviors and sales trends, empowering businesses to make data-driven decisions and optimize their e-commerce strategies.