Start by defining the problem statement and target variable clearly..Remove duplicate records and correct inconsistent data entries carefully..Handle missing values using imputation or domain informed removal..Normalize numerical features to improve model convergence stability..Encode categorical variables using one hot or target encoding..Detect and treat outliers using statistical or model based methods..Split data into training, validation, and test sets properly..Address class imbalance using resampling or weighted loss techniques..Validate dataset bias to ensure fairness and generalization performance..Read more stories.Join our WhatsApp Channel to get the latest news, exclusives and videos on WhatsApp