An effective way to deal with missing data in a dataset is through imputation techniques such as mean, median, or mode replacement, which can help preserve the statistical properties of the dataset Alternatively, use predictive modeling such as random forests or regression are used to estimate missing values based on other variables If unavailability is randomized and does not bias the analysis. DATAFOREST’s Data Science Solution https://dataforest.ai/services/data-scienceincorporates these techniques to ensure proper handling of missing data.
An effective way to deal with missing data in a dataset is through imputation techniques such as mean, median, or mode replacement, which can help preserve the statistical properties of the dataset Alternatively, use predictive modeling such as random forests or regression are used to estimate missing values based on other variables If unavailability is randomized and does not bias the analysis. DATAFOREST’s Data Science Solution https://dataforest.ai/services/data-science incorporates these techniques to ensure proper handling of missing data.