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How to Clean Data Using AI

Analytics Vidhya

Cleaning data used to be a time-consuming and repetitive process, which took up much of the data scientist’s time. But now with AI, the data cleaning process has become quicker, wiser, and more efficient.

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Build a Data Cleaning & Validation Pipeline in Under 50 Lines of Python

KDnuggets

The key is having a reliable, reusable system that handles the mundane tasks so you can focus on extracting insights from clean data. Happy data cleaning! She likes working at the intersection of math, programming, data science, and content creation. 🔗 You can find the complete script on GitHub.

Python 257
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Innovations in Analytics: Elevating Data Quality with GenAI

Towards AI

Hype Cycle for Emerging Technologies 2023 (source: Gartner) Despite AI’s potential, the quality of input data remains crucial. Inaccurate or incomplete data can distort results and undermine AI-driven initiatives, emphasizing the need for clean data. Clean data through GenAI!

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Build ETL Pipelines for Data Science Workflows in About 30 Lines of Python

KDnuggets

Here, were loading our clean data into a proper SQLite database. def load_data_to_sqlite(df, db_name=ecommerce_data.db, table_name=transactions): print(f"Loading data to SQLite database {db_name}.") Now instead of just having transaction amounts, we have meaningful business segments. conn = sqlite3.connect(db_name)

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Build Your Own Simple Data Pipeline with Python and Docker

KDnuggets

return df_cleaned def load_data(df, output_path): df.to_csv(output_path, index=False) print("Data Loading completed.") def run_pipeline(): df_raw = extract_data(input_path) df_cleaned = transform_data(df_raw) load_data(df_cleaned, output_path) print("Data pipeline completed successfully.")

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How Dataiku and Snowflake Strengthen the Modern Data Stack

phData

This accessible approach to data transformation ensures that teams can work cohesively on data prep tasks without needing extensive programming skills. With our cleaned data from step one, we can now join our vehicle sensor measurements with warranty claim data to explore any correlations using data science.

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Data preprocessing

Dataconomy

By improving data quality, preprocessing facilitates better decision-making and enhances the effectiveness of data mining techniques, ultimately leading to more valuable outcomes. Key techniques in data preprocessing To transform and clean data effectively, several key techniques are employed.