Data Cleaning

This repository contains code and resources related to handling data inconsistencies, parsing dates, data transformation techniques (log, sqrt, Box Cox), handling outliers, and imputation techniques.

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Usage

To use the code in this repository, follow these steps:

• Clone the repository: git clone https://github.com/tawfikhammad/Data-cleaning-tutorial.git

• Install the required dependencies. Assuming you have Python and pip installed, run the following command: pip install -r requirements.txt

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Content

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.

Data Transformation:

• 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.

Imputation: Imputation methods aim to estimate the missing values based on the available information in the dataset.

Images: This repository contains images related to the project. You can find them in the images folder.