How to Use AI for Data Cleaning

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. AI models such as ChatGPT, Claude, Gemini, etc, can be used to automate anything from correcting format issues to handling missing data and outliers. Platforms such as Google Colab, Google Sheets, Windsurf, and Cursor have incorporated AI models into them, making it easier even for non-coders to automate their data cleaning process. In this blog, we’ll explore how AI is changing the data cleaning process for the better.

Why Data Cleaning Matters

It is crucial to understand why data cleaning is key to accurate analysis and machine learning. Raw datasets are not perfect and often come from multiple sources. They frequently consist of missing values, duplicates, inconsistent formatting, anomalies, and outliers. These issues can affect the results, reduce the accuracy of models, and even lead to incorrect business decisions. A well-cleaned dataset helps algorithms learn more effectively, reduces bias, and improves generalization to new data. It is a critical component of the entire data science workflow, directly influencing the success of data-driven solutions.

How To Speed Up Your Data Cleaning Process

There are several ways to clean your data such as . In this article, we’ll be covering how to enhance the data cleaning process using some AI tools and AI-powered assistants. These AI-powered data cleaning solutions will enhance your efficiency, reduce manual effort, and improve accuracy.

There are several ways to clean your data, such as using Excel functions, SQL queries, Python scripts (like with pandas), etc. You could also use the data cleaning features in BI tools like Power BI or Tableau to do it. But most of these

Let’s dive into how each of these solutions can streamline your data cleaning process.

1. Using Generative AI Assistants (ChatGPT, Claude, Gemini, etc.)

These assistants can help you clean your data in two main ways:

1. Direct cleaning: Upload your file and ask AI to clean it. It removes null values, formats columns, and more. Explain your intent in the form of prompts and tools like ChatGPT, Claude, etc, can provide a cleaned version according to your needs.
2. Code Generation: If you’re not sure how to clean data on your own, but are not sure how to do it. Just describe your problem, and AI can generate the exact code.

Sample Prompt: “Perform data cleaning on this CSV and provide a cleaned dataset, also show the file before and after cleaning.”

2. Using AI-Integrated Platforms

Modern data workflows are integrating AI into their platforms. For instance, Google Colab and Google Sheets have embraced this trend by incorporating Gemini, Google’s advanced AI assistant. This integration empowers users to streamline data cleaning, analysis, and visualization tasks efficiently. Similarly, tools like Windsurf and Cursor assist with real-time suggestions, intelligent data handling, and code generation. Making it easier than ever to clean, transform, and understand data within your workflow.

This hybrid approach keeps you in control while giving you the productivity boost of AI.

Let’s see how they work.

1. Google Colab

Google Colab has introduced a built-in Data Science Agent, powered by Gemini 2.0, designed to simplify data analysis. It includes:

• Automated Setup: The agent handles tasks like importing libraries, loading data, and writing boilerplate code.
• Natural Language Interaction: You can describe your goal in English, and Gemini will generate the code for it. Example: Visualize the trends in the dataset.
• EDA and Data Cleaning: Assist in data preprocessing, handle missing values, and perform exploratory data analysis.

How to clean data on Google Colab

1. Upload your file.
2. Write a prompt describing what you want.
3. Chill, sit back, and relax while AI does it for you.

2. Google Sheets

Users can transform their spreadsheets into intelligent, interactive documents with the integration of Gemini. Here’s what it can do:

• Data Cleaning: Finds and removes duplicate entries, handles formatting, and fills missing or null values, enhancing overall data quality.
• Insight Generation: Gemini-powered sheets analyze trends, create pivot tables, or build charts or graphs. It also provides summaries and visualizations to aid decision-making.

3. Windsurf and Cursor

If you feel that uploading your file is too tedious a task and is ruining your vibe coding, then welcome to Windsurf and Cursor. Platforms like Windsurf and Cursor offer a step up by supporting multiple AI models like ChatGPT, Claude, etc, not just Gemini. This flexibility allows users to have more control over the tools they use.

Here are some other advantages of using these platforms for data cleaning:

• Contextual understanding: The AI can analyze your existing code, data structures, and variable names to provide better cleaning suggestions.
• Faster Debugging: The AI can reference your project’s context to suggest or even implement fixes. Saving time compared to starting from scratch.
• File-Level Intelligence: By accessing the local datasets (CSV, Excel, JSON, etc.), the AI can provide more accurate transformations and offer previews of how the data will look post-cleaning.

How to clean your data with Windsurf or Cursor

1. Open the folder containing your file.
2. Write the prompt and watch AI do its job.

Which Approach Is Better?

AI-generated code is ideal if you want to understand the cleaning process. Additionally, direct cleaning through AI assistants and integrated tools like Google Sheets and Google Colab is fast and user-friendly.

For complex projects and professional workflows, multi-model platforms like Windsurf and Cursor provide the best flexibility, deeper context awareness, and debugging support. I recommend using Windsurf. That’s what I use for my workflows.

Fast, but Flawed: The Limitations of Using AI for Data Cleaning

While AI for data cleaning offers incredible efficiency, it’s not without limitations. One major concern is data privacy; sensitive or proprietary data can’t always be shared with AI models, especially those hosted on external servers. Even when data can be shared, these AI models tend to hallucinate sometimes, generating plausible but incorrect values. This can lead to inaccurate cleaning and wrong decisions based on it, while AI can drastically speed up the process, it’s crucial to use it with caution.

Conclusion

As AI evolved, what used to take hours or days can now be done in minutes. By integrating AI, you can accelerate your data cleaning process without sacrificing quality. However, always balance speed with oversight. Use AI as a collaborator, not a replacement for your domain expertise. Human judgment is still essential to validate results, understand nuances in data, and ensure the cleaning aligns with your specific goal.

Data Scientist | AWS Certified Solutions Architect | AI & ML Innovator

As a Data Scientist at Analytics Vidhya, I specialize in Machine Learning, Deep Learning, and AI-driven solutions, leveraging NLP, computer vision, and cloud technologies to build scalable applications.

With a B.Tech in Computer Science (Data Science) from VIT and certifications like AWS Certified Solutions Architect and TensorFlow, my work spans Generative AI, Anomaly Detection, Fake News Detection, and Emotion Recognition. Passionate about innovation, I strive to develop intelligent systems that shape the future of AI.