This repository contains two essential data preparation tasks often required in the data analysis pipeline: Data Cleaning and Standardizing Data. These steps are vital for ensuring high-quality, consistent, and reliable datasets for subsequent analysis.
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DataCleaning Project:
Data cleaning is the process of identifying and rectifying issues in a dataset to improve its quality. This includes handling missing values, correcting data inconsistencies, removing duplicates, and ensuring proper formatting.
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Handling Missing Values: Identifies and fills or removes missing data using various techniques (e.g., imputation, deletion).
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Fixing Inconsistencies: Standardizes values (e.g., correcting typos, harmonizing categories).
Data standardization ensures that the data follows a consistent format across all variables, making it easier for analysis, reporting, and further processing.
Features:
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Standardizing Date Formats: Converts all date fields to a consistent format (e.g., YYYY-MM-DD).
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Scaling Numerical Data: Normalizes numerical columns using standard techniques like Min-Max scaling or Z-score normalization.
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Text Normalization: Standardizes text fields by converting to lowercase, removing special characters, and trimming unnecessary spaces.
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Categorical Encoding: Converts categorical variables into a standard encoding format (e.g., one-hot encoding or label encoding).
If you want to contribute to this project, feel free to fork the repository, make changes, and submit a pull request. Please ensure that your code adheres to the existing style and includes relevant tests where necessary.
This project is open-source and available under the MIT License>