To clean and prepare a dataset with missing values, inconsistent formats, and duplicate records using Python and Pandas, I would start by loading the dataset into a Pandas DataFrame. First, I would identify missing values using the isnull function and decide on an appropriate method to handle them, such as filling with mean or median values using the fillna method or dropping them if they are not significant. For inconsistent formats, I would standardize data types using the astype function to ensure consistency across the dataset. I would also address any inconsistencies in data values by applying transformations or using string functions to clean text data. To handle duplicate records, I would use the drop_duplicates method to remove them, ensuring the dataset is unique and accurate. Throughout this process, I would leverage my experience with Power BI and SQL to ensure the data is well-prepared for analysis and visualization.