Remember to approach your dataset with curiosity and creativity, and don't hesitate to seek help from online resources or data professionals. With the right mindset and tools, you can turn your dataset into a goldmine of insights and opportunities.
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Overfitting occurs when a model performs well on the training data but fails to generalize to new, unseen data. This can lead to inaccurate predictions and unreliable results. Avoid overfitting by using techniques such as cross-validation and regularization.
Using Biased Datasets
Bias in datasets can lead to biased results, which can have serious consequences for decision-making. Be mindful of potential biases in your datasets, such as sampling bias or selection bias, and take steps to mitigate them to ensure unbiased analysis.