Lovish123456

NumPy and Pandas Data Manipulation for E-commerce Analysis

Welcome to the NumPy and Pandas Data Manipulation repository. This project showcases advanced techniques for data analysis, cleaning, and business intelligence using e-commerce sales data. You can download the latest version of this project from the Releases section.

Table of Contents

1. Project Overview
2. Installation
3. Usage
4. Features
5. Technologies Used
6. Data Sources
7. Contributing
8. License
9. Contact

Project Overview

This repository contains a comprehensive set of scripts and notebooks designed for manipulating and analyzing e-commerce sales data. It emphasizes practical applications of NumPy and Pandas for data cleaning, transformation, and analysis. The project is aimed at data scientists, analysts, and anyone interested in gaining insights from sales data.

Key Objectives

• Perform data cleaning to prepare raw data for analysis.
• Use NumPy for numerical operations and data manipulation.
• Leverage Pandas for data analysis and visualization.
• Implement business intelligence techniques to derive actionable insights.

Installation

To get started with this project, you need to have Python installed on your machine. Follow these steps to set up the environment:

1. Clone the repository:

   git clone https://github.com/Lovish123456/numpy-pandas-data-manipulation.git
   

2. Navigate to the project directory:

   cd numpy-pandas-data-manipulation
   

3. Install the required packages:

   pip install -r requirements.txt
   

4. (Optional) Set up a virtual environment for better package management:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   

Usage

After installation, you can start using the scripts and notebooks in the project. Here’s how to run the main script:

1. Navigate to the src directory:

   cd src
   

2. Run the main script:

   python main.py
   

This will execute the data manipulation tasks and generate output files.

Example Notebooks

The project includes Jupyter notebooks that demonstrate various data manipulation techniques. You can open these notebooks using Jupyter Notebook or JupyterLab:

jupyter notebook

Then, navigate to the notebooks directory and open the desired notebook.

Features

• Data Cleaning: Handle missing values, duplicates, and outliers.
• Data Transformation: Reshape data, pivot tables, and merge datasets.
• Data Analysis: Perform exploratory data analysis (EDA) and generate insights.
• Visualization: Create plots and charts using Matplotlib and Seaborn.
• Business Intelligence: Generate reports and dashboards for decision-making.

Technologies Used

• Python: The main programming language for this project.
• NumPy: For numerical operations and array manipulations.
• Pandas: For data analysis and manipulation.
• Matplotlib: For data visualization.
• Seaborn: For enhanced visualizations.
• Jupyter Notebook: For interactive data analysis.

Data Sources

The project uses synthetic e-commerce sales data, which can be found in the data directory. You can modify the data or replace it with your own datasets for testing and analysis.

Contributing

Contributions are welcome! If you have suggestions or improvements, please create a pull request. For major changes, please open an issue first to discuss what you would like to change.

Steps to Contribute

1. Fork the repository.

2. Create your feature branch:

   git checkout -b feature/YourFeature
   

3. Commit your changes:

   git commit -m "Add some feature"
   

4. Push to the branch:

   git push origin feature/YourFeature
   

5. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or feedback, feel free to reach out:

• Author: Lovish
• GitHub: Lovish123456
• Email: lovish@example.com

For the latest updates, visit the Releases section and download the latest version.