Turning around model performance starts with a rigorous assessment of the data quality issues at hand. I implement a robust data cleaning protocol, identifying and correcting errors, inconsistencies, and outliers in the dataset. By incorporating automated data quality checks into the workflow, we can continually monitor and maintain high data standards. Additionally, retraining the model with cleaner, more relevant data ensures that it better reflects the underlying patterns and improves accuracy. This approach not only revives model performance but also strengthens the overall data handling strategy.

To address poor model performance due to data quality, start by analyzing the dataset for issues like missing values, outliers, or incorrect labels. Clean and preprocess the data, ensuring it aligns with the model's requirements. Enrich the dataset with additional relevant and high-quality data, if possible. Revisit feature engineering to create more meaningful inputs. Finally, retrain and validate the model, using performance metrics to confirm improvements.

Improving data quality is essential for reliable model performance. One effective strategy is thorough data cleaning, where you identify and fix or remove incorrect, duplicate, or missing entries. Implementing feature engineering helps by creating relevant features that better represent the data's underlying patterns. Regular audits are also crucial; by frequently reviewing your data processes, you can catch and address issues early before they affect your model. Additionally, establishing clear data governance policies and using automated tools for data validation can maintain high standards. Engaging the team in maintaining data quality ensures everyone is committed to accuracy and consistency.

A great model is useless if trained on bad data, while a bad model can perform well with high-quality data. Improving data quality is crucial and can be achieved through: 1. Firstly ensure your data is relevant in predicting the target variable, eg: number of customers can't predict factory output 2. Preprocessing: Cleaning data, removing outliers, scaling, encoding categorical variables, and imputing missing values. 3. Understanding how your model is analyzing the data: Sometimes models are bad at understanding categories so rather than label encoding one-hot encoding is required even if it takes more computing. Similarly model's interaction with missing data may also vary

interpolation if the frequency of data is lacking and you need more data points for better training your deep learning model considering different factors you can apply a suitable interpolation method in your cleaned data in order to provide more training data for your model. of course this method should be used very carefully in order to not reduce the reliability of the model.