# Introduction Data cleaning is the first step in data preprocessing and an important step in ensuring the correctness of subsequent results. If data accuracy cannot be guaranteed, the machine learning model will learn incorrect results during training. In this challenge, we will focus on data cleaning, which is the process of identifying and correcting errors or inconsistencies in a dataset. Specifically, we will be working with a CSV file called `raw_data.csv` that contains some dirty data. The goal is to delete all rows containing dirty data and generate a new file called `clean_data.csv` that only includes clean and valid data. This will help ensure the accuracy and reliability of the dataset for further analysis or machine learning tasks.
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