https://github.com/abderrahmane-stack/data-cleaning-mysql
This project covers essential data cleaning techniques using MySQL, including identifying and handling missing data, correcting data inconsistencies, normalizing data, and ensuring data integrity. It demonstrates practical methods for transforming raw data into a clean, usable dataset.
https://github.com/abderrahmane-stack/data-cleaning-mysql
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This project covers essential data cleaning techniques using MySQL, including identifying and handling missing data, correcting data inconsistencies, normalizing data, and ensuring data integrity. It demonstrates practical methods for transforming raw data into a clean, usable dataset.
- Host: GitHub
- URL: https://github.com/abderrahmane-stack/data-cleaning-mysql
- Owner: abderrahmane-stack
- Created: 2024-07-31T11:44:54.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-31T12:04:03.000Z (almost 2 years ago)
- Last Synced: 2025-01-22T04:09:19.494Z (over 1 year ago)
- Topics: mysql
- Homepage:
- Size: 54.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Data Cleaning in MySQL
## Description
This project involves comprehensive data cleaning techniques using MySQL. The key tasks include:
- Identifying and handling missing data
- Correcting data inconsistencies
- Normalizing data
- Ensuring data integrity
Practical examples and methods are demonstrated to transform raw data into a clean, usable dataset. This project is essential for data analysts and database managers aiming to maintain high-quality data.
## Data Source
The data used in this project is sourced from the Kaggle dataset [Layoffs 2022](https://www.kaggle.com/datasets/swaptr/layoffs-2022) by Swaptr.
## Project Structure
- `scripts/` - SQL scripts for data cleaning operations.
- `data/` - Sample datasets used in the project.
- `docs/` - Documentation and notes related to the project.
## Getting Started
### Prerequisites
- MySQL Server installed
- MySQL Workbench or any MySQL client for running scripts
### Setup
1. **Clone the Repository:**
```sh
git clone https://github.com/yourusername/Data-Cleaning-MySQL.git
2. Navigate to the Project Directory:
```sh
cd Data-Cleaning-MySQL
3. Load Sample Data:
Import the sample data files from the data/ directory into your MySQL database.
4. Run SQL Scripts:
Execute the SQL scripts found in the scripts/ directory to perform various data cleaning operations.