<ul><li>Feature Selection is the process of choosing the most relevant input variables (features) from your dataset that contribute the most to the output variable.</li><li>Feature selection improves model accuracy by minimizing noise and reducing overfitting.</li><li>Filter methods, such as removing duplicates, using variance thresholds, checking correlations, and applying statistical tests like ANOVA and Chi-Square, are commonly used for feature selection.</li><li>In the next chapter of 100 Days of Machine Learning, the focus will be on Wrapper Methods, where machine learning models guide the selection of features.</li></ul>

Feature Selection Part 1: Choose Wisely, Predict Better

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