Steps to Perform Data Cleanliness
Performing data cleaning involves a systematic process to identify and rectify errors, inconsistencies, and inaccuracies in a dataset. The following are essential steps to perform data cleaning.
Data Cleaning
- Removal of Unwanted Observations: Identify and eliminate irrelevant or redundant observations from the dataset. The step involves scrutinizing data entries for duplicate records, irrelevant information, or data points that do not contribute meaningfully to the analysis. Removing unwanted observations streamlines the dataset, reducing noise and improving the overall quality.
- Fixing Structure errors: Address structural issues in the dataset, such as inconsistencies in data formats, naming conventions, or variable types. Standardize formats, correct naming discrepancies, and ensure uniformity in data representation. Fixing structure errors enhances data consistency and facilitates accurate analysis and interpretation.
- Managing Unwanted outliers: Identify and manage outliers, which are data points significantly deviating from the norm. Depending on the context, decide whether to remove outliers or transform them to minimize their impact on analysis. Managing outliers is crucial for obtaining more accurate and reliable insights from the data.
- Handling Missing Data: Devise strategies to handle missing data effectively. This may involve imputing missing values based on statistical methods, removing records with missing values, or employing advanced imputation techniques. Handling missing data ensures a more complete dataset, preventing biases and maintaining the integrity of analyses.
ML | Overview of Data Cleaning
Data cleaning is one of the important parts of machine learning. It plays a significant part in building a model. In this article, we’ll understand Data cleaning, its significance and Python implementation.