Author name: Suditi Seth

Handling Missing Data: MICE, KNN Imputation, and Interpolation

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Handling missing data is a crucial step in data preprocessing. When working with real-world datasets, missing values are common due to various reasons such as data entry errors, sensor malfunctions, or incomplete surveys. If not handled properly, missing data can lead to biased results and reduced accuracy in machine learning models. Ignoring missing data can …

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Handling Imbalanced Datasets with SMOTE, ADASYN & Class Weighing

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Imbalanced datasets are a common challenge in machine learning. This occurs when one class has significantly fewer samples compared to the other, leading to biased models that favor the majority class. Such imbalances can result in misleading accuracy scores, as the model may predict the majority class correctly while failing to identify the minority class …

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Hyperparameter Tuning in Scikit-Learn Using GridSearchCV & RandomizedSearchCV

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Hyperparameter Tuning in Scikit-Learn Using GridSearchCV & RandomizedSearchCV Although machine learning models are effective tools, the selection of hyperparameters has a significant impact on how well they work. One of the most important steps in creating a machine learning model is hyperparameter tuning. To get the greatest results, it entails fine-tuning the model’s hyperparameters. GridSearchCV …

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