Data Cleaning: The Foundation of Reliable Analysis

Data cleaning is an essential step in any data analysis process, ensuring that the data used for modelling is accurate, consistent, and free of errors. In other words, the practice of repairing or removing inaccurate, incorrect, improperly formatted, duplicated data or missing data space ("NA") data from a dataset is known as data cleaning. Clean data leads to better model performance and more reliable insights. Without proper data cleaning, models can produce misleading results, leading to poor decisions. Below are some key tasks involved in data cleaning:

Handling Missing Values

Missing data is common in real-world datasets and can arise from various reasons, such as incomplete records or data collection errors. To handle missing values, several strategies can be used:

• Removing rows/columns: If the amount of missing data is small, you can remove the affected rows or columns.
• Imputation: For larger datasets, missing values can be filled with statistical estimates like the mean, median, or mode.
• Advanced methods: Techniques like K-nearest neighbours (KNN) or regression-based imputation can predict missing values based on other data.

Dealing with Outliers

Outliers are extreme values that deviate significantly from the rest of the data. They can skew the analysis and distort model results. To manage outliers:

• Remove them: If they are due to data entry errors or irrelevant factors.
• Transform them: Applying transformations (e.g., Log, Differences, Percentage change etc) can reduce their impact.
• Analyze separately: In some cases, outliers may offer valuable insights and can be analyzed on their own. 

NOTE: In some cases like financial data, most probably outliers won't be removed, the professionals prefer to include the outliers in the analysis by adding some special event dummy.

Identifying and Addressing Inliers

Inliers are data points that appear to be normal but could still mislead the model. They may fall within the expected range but might not follow the typical trend of the rest of the data. Detecting and handling inliers often involves closer scrutiny of patterns and relationships between variables.

Removing Duplicate Entries

Duplicate entries can arise during data collection or merging datasets. Removing duplicates ensures that each data point is unique and contributes to accurate analysis. This step is straightforward but essential to avoid inflated data counts and skewed results.

Additional Data Cleaning Steps

• Standardizing data: Ensuring that all data follows the same format (e.g., dates in the same format, consistent measurement units).
• Correcting data types: Ensuring that numerical values are stored as numbers, categorical values as factors, etc.
• Handling inconsistent data: Resolving discrepancies in names, categories, or labelling conventions.

By performing these tasks, you can ensure that your data is clean, well-organized, and ready for analysis.

For those working in R, here's a crazy 😜 guide that covers everything you need to know about the data cleaning process is explained in the R language - Data cleaning in R