This repository contains code and resources related to handling data inconsistencies, parsing dates, data transformation techniques (log, sqrt, Box Cox), and handling outliers..
To use the code in this repository, follow these steps:
-
Clone the repository:
git clone https://github.com/tawikhammad/data-cleaning.git -
Install the required dependencies. Assuming you have Python and pip installed, run the following command:
pip install -r requirements.txt
Here are some instructions on how to use the code and resources provided in this repository:
Data Inconsistency: This section provides techniques and code snippets to identify and handle inconsistent data. Refer to Data Inconsistency for detailed information.
Parsing Dates: If you need to parse dates from different formats, check out Parsing Dates for code examples and guidelines.
Log Transformation: Learn how to apply a logarithmic transformation to your data using the techniques outlined in Log Transformation.Square Root Transformation: Explore the benefits of applying a square root transformation to your data. Refer to Square Root Transformation for more information.Box Cox Transformation: Understand how the Box Cox transformation can help normalize your data. Find implementation details in Box Cox Transformation.
Handling Outliers: Discover effective methods for detecting and handling outliers in your dataset. See Handling Outliers for code examples and recommendations.
Images: This repository contains images related to the project. You can find them in the images folder.
This repository is licensed under the MIT License.
Feel free to modify this template according to your specific needs. Make sure to include all the relevant sections and provide clear instructions on how to use the code and resources in your repository.