커서AI로 네이버 부동산 매물 자동 수집하기 | cursor ai

Summary

The video script demonstrates how to create a highly efficient real estate crawling program using AI and advanced techniques. The presenter explains a simple and easy method for collecting property data from Naver Real Estate, highlighting the use of network tools and AI assistance. With minimal coding knowledge required, users can automate the crawling process, extract property listings, and save them in various formats like JSON. The presenter also showcases how to build a user-friendly interface using Python, offering insights into how even non-coders can harness AI to streamline real estate data collection and make it accessible to others.

Takeaways

• 😀 The speaker demonstrates how to automate real estate data collection using Python, simplifying the process with AI tools.
• 😀 Instead of using traditional web scraping methods with Selenium, the speaker uses network requests for a more efficient approach.
• 😀 The process begins by analyzing network traffic in the browser's developer tools to extract data without opening the entire page.
• 😀 By copying the network request and converting it into Python code using an online tool, the process becomes accessible even for those with no coding experience.
• 😀 The speaker shows how to collect real estate listings by simply copying and converting a network request into Python code that pulls data directly.
• 😀 AI tools like 'Cursor AI' assist in auto-completing Python code, making it easier to handle data requests and automate the scraping process.
• 😀 The script demonstrates converting scraped data into JSON format and storing it, which helps in organizing and later using the data efficiently.
• 😀 Once the data is collected, the speaker showcases how to convert it into a user-friendly graphical interface (GUI) using the PySide6 library.
• 😀 By using AI tools, the speaker is able to quickly troubleshoot and fix errors in the code, streamlining the process without needing deep technical knowledge.
• 😀 The speaker highlights that, even though they offer a paid version of their real estate data collection program, the open-source methods demonstrated can be just as effective.
• 😀 The script concludes by discussing how automation through AI allows users to create and sell their own real estate collection programs without extensive coding skills.

Q & A

What is the main purpose of the script in the transcript?

The main purpose of the script is to demonstrate how to automate the process of crawling real estate listings from Naver, specifically focusing on using advanced techniques like network requests instead of traditional browser automation. The script walks through creating a program to collect real estate data easily and efficiently.

What is the method used to collect real estate data in the video?

The method used is crawling real estate data from Naver by analyzing network traffic in the browser's developer tools. Instead of using a traditional web scraping method like Selenium, the script utilizes network requests to directly retrieve the data without opening a browser.

What technology does the speaker mention for automating the browser in the script?

The speaker mentions Selenium as a tool for automating the browser, but clarifies that for this particular task, they are using network requests instead, which is a more advanced and efficient technique.

How does the speaker suggest handling multiple pages of real estate listings?

The speaker explains that Naver displays a maximum of 20 listings per page, and as the user scrolls, additional data for the next pages is loaded. By monitoring the network traffic using the browser's developer tools, the speaker can track the network requests that bring in the data for subsequent pages.

What is the role of the 'curl' command in the process described in the script?

The 'curl' command is used to capture the network request from the browser's developer tools. Once the data is captured using 'curl', it is then converted into Python code using a special website. This Python code can then be used to automate the data retrieval process.

How does the speaker automate the conversion of 'curl' commands into Python code?

The speaker uses a website that allows 'curl' commands to be converted into Python code. By pasting the 'curl' command into the site, the site generates the corresponding Python code that can be used with the 'requests' library to collect the data programmatically.

What feature does the speaker add to the program after crawling the data?

After crawling the real estate data, the speaker demonstrates how to convert the script into an executable (EXE) program. This allows others to run the program on their computers without needing to have Python installed, making it easier to share the program.

What does the speaker show as the final result of the automation process?

The final result is a program that can automatically search for real estate listings by location (e.g., Apgujeong), display them, and even allow the user to filter the results. The program can then download the data in a user-friendly format, such as Excel or JSON.

How does the program handle errors during execution?

When an error occurs during execution, the speaker shows how the AI can automatically analyze the traceback and suggest fixes. For example, if there is a data type issue (like a string being converted to an integer), the AI can identify and suggest corrections.

What does the speaker say about the accessibility of the program?

The speaker notes that while some people may not have the skills to create such a program themselves, the program can be sold or shared with others. They also mention that similar programs are being sold for prices ranging from 4,000 to 25,000 KRW, indicating that people can use the same techniques to build and sell their own versions.