Common Pitfalls to Avoid on Your Machine Learning Journey: Top Mistakes in Model Training The world of machine learning (ML) offers immense potential, but the journey to building effective models is fraught with challenges. Even experienced practitioners can fall prey to common mistakes. By understanding these pitfalls, you can avoid them and increase your chances of building successful models. Here are some of the top mistakes to steer clear of while training your model: 1. Neglecting Data Quality: Garbage in, garbage out: This adage holds true for ML. Training a model on inaccurate, incomplete, or biased data will lead to unreliable and potentially harmful results. Clean and organize your data: Ensure consistency in formatting and address missing values before feeding it into your model. Be mindful of bias: Check for and mitigate biases present in your data, as they can lead to discriminatory or unfair outcomes. 2. Ignoring Feature Engineering: Raw data might not be en...