Data Cleaning
I am studying data science and working with a small dataset for class. The machine learning part looks interesting, but I am having trouble cleaning the data before using a model. While searching for simple explanations, I found Machine learning assignment help, but I want to understand the steps myself. How do you find missing values or wrong entries? Should I clean the data before splitting it into training and test sets? I would really appreciate some beginner tips because I want to understand why each step matters.
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The Data Cleaning discussion makes a strong point about understanding the data before trusting a model. I once started working with a dataset without checking missing values and later discovered that several entries needed attention. I considered do my exam for me during a busy study period, but checking the data carefully helped me understand why each cleaning step mattered. I also learned that data preparation should be planned carefully around the train-test split.