https://github.com/haroontrailblazer/eda
Exploratory Data Analysis
https://github.com/haroontrailblazer/eda
data-analysis data-science data-visualization jupyter-notebooks matplotlib-python numpy pandas python3 scipy
Last synced: about 1 month ago
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Exploratory Data Analysis
- Host: GitHub
- URL: https://github.com/haroontrailblazer/eda
- Owner: haroontrailblazer
- License: mit
- Created: 2024-11-25T07:19:11.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-03-20T16:04:16.000Z (2 months ago)
- Last Synced: 2025-03-25T10:21:21.727Z (2 months ago)
- Topics: data-analysis, data-science, data-visualization, jupyter-notebooks, matplotlib-python, numpy, pandas, python3, scipy
- Language: Jupyter Notebook
- Homepage:
- Size: 76.6 MB
- Stars: 7
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Exploratory Data Analysis (EDA) Public Learning Repository
## Overview
Welcome to the EDA Public Learning Repository! This repository is dedicated to helping learners understand and perform Exploratory Data Analysis (EDA). It contains various Jupyter notebooks, datasets, and resources to guide you through the process of analyzing and visualizing data.
## Contents
- **data/**: Datasets used for analysis.
- **notebooks/**: Jupyter notebooks for EDA.
- **images/**: Visualizations generated during the analysis.
- **README.md**: Project overview and instructions.
- **resources/**: Additional learning materials and references.
## Getting Started
### Prerequisites
To get started, ensure you have the following installed:
- Python 3.x
- Jupyter Notebook
- Necessary libraries (listed in `requirements.txt`)
### Installation
Clone the repository:
```sh
git clone https://github.com/haroontrailblazer/EDA-public-learning.git
cd eda-public-learning
```
Install the required libraries:
```sh
pip install -r requirements.txt
```
### Usage
Navigate to the notebooks/ directory and open the Jupyter notebook:
```sh
jupyter notebook eda_example.ipynb
```
Follow the steps in the notebook to explore the data and generate visualizations.
## Project Structure
- **Data Loading and Cleaning**: Learn how to load and clean datasets.
- **Descriptive Statistics**: Calculate and interpret summary statistics.
- **Data Visualization**: Create and understand various plots and charts.
- **Feature Engineering**: Generate new features from existing data.
- **Correlation Analysis**: Analyze relationships between variables.
## Contributions
Contributions are welcome! If you have any improvements, additional notebooks, or datasets to share, please fork the repository and submit a pull request.
## Community
Join the community of learners and contributors:
- **Discussions**: Participate in discussions here
- **Issues**: Report bugs or request features here
## Contact
For any questions or suggestions, feel free to reach out:
- **Email**: [email protected]
- **GitHub**: haroontrailblazer
## License
This project is licensed under the MIT License. See the LICENSE file for details.