A comprehensive, structured report detailing the entire data preparation process. It includes: 1. Problem Definition & Objectives – A clear statement of the purpose and objectives of the data preparation process. This section will outline the specific goals of preparing the dataset (e.g., ensuring quality, consistency, or readiness for a specific purpose) and any key challenges addressed during the process. 2. Data Quality Assessment – A thorough evaluation of the raw dataset, including an assessment of completeness, consistency, and accuracy. This section will identify any potential issues such as entry errors, missing values, duplicates, or data inconsistencies. 3. Data Cleaning – Detailed description of the cleaning process applied to the dataset. This will include actions taken to handle missing values (e.g., imputation methods), removal or correction of duplicates, and the handling of inconsistencies or errors in the data. 4. Outlier Detection & Treatment – Explanation of how outliers were identified and treated. This section will describe the techniques used for detecting outliers (e.g., z-scores, IQR method) and the strategy used to handle them (e.g., removal, transformation, imputation). 5. Data Transformation & Normalization – Overview of any transformations or normalization steps applied to the dataset to ensure consistency and compatibility with analysis methods. This could include scaling features, encoding categorical variables, or applying exponential transformations to skewed variables. 6. Feature Engineering – If applicable, a description of any new features or variables created to improve the dataset’s usability for analysis. This section will also outline the rationale behind the creation of new features (e.g., aggregating variables, creating interaction terms, or extracting key patterns). 7. Data Structuring & Format – Details on how the dataset was structured for easy use and further analysis. This will include the organization of variables, standardization of data formats (e.g., date/time, categorical labels), and ensuring that the dataset is ready for visualization, statistical analysis, or machine learning models. 8. Decision Justification – Throughout the report, every decision made during the preparation process will be justified based on best practices in data science and statistics. This section will provide reasoning for the selected techniques to ensure that the data is prepared for the next steps.

If required, a cleaned and pre-processed version of the dataset will be delivered alongside the report. The dataset will be provided in the agreed-upon format (e.g., .csv, .xlsx).