Most projects focus solely on the model and its accuracy score. That’s not enough. A good project should explain everything from the beginning. What problem was chosen, where the data came from, what problems were faced while cleaning, which model was used, and why?
Even if the model did not give high accuracy, it’s okay. What matters is how the whole thing was handled. Even if someone has created a dashboard or a simple prediction tool, explaining it makes a significant difference.
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