In previous projects, I’ve employed various data-cleaning techniques to ensure that the data used for analysis is accurate, complete, and suitable for the intended purpose. Here are some common data-cleaning techniques I’ve used:
1. Handling Missing Values
- Identifying Missing Values: Checking for missing values in datasets using summary statistics and visualizations.
- Imputation: Imputing missing values using techniques such as mean, median, mode imputation, regression imputation, or machine learning-based imputation methods like K-Nearest Neighbors (KNN).
- Deletion: Removing rows or columns with missing data when appropriate, using methods like listwise deletion or pairwise deletion.
2. Dealing with Outliers
- Identifying Outliers: Detecting outliers through visualization (box plots, scatter plots) and statistical methods (z-score, IQR).
- Handling Outliers: Depending on the context, outliers can be treated by transforming data (e.g., log transformation), winsorizing (capping extreme values), or removing outliers that are clearly erroneous or significantly impact analysis.
3. Data Formatting
- Standardization: Standardizing data formats (e.g., date formats, numerical formats) across different sources to ensure consistency.
- Normalization: Scaling numerical data to a standard range (e.g., 0-1) to facilitate comparisons between different variables.
4. Handling Duplicates
- Identifying Duplicates: Identifying and removing duplicate records based on unique identifiers or specific criteria.
- Deduplication: Ensuring data integrity by deduplicating records while retaining necessary information.
5. Handling Inconsistent Data
- Data Transformation: Converting data types (e.g., converting strings to numeric values) to ensure compatibility for analysis.
- Addressing Inconsistencies: Resolving inconsistencies in data entry (e.g., correcting typos, standardizing categorical values) to maintain accuracy.
6. Handling Skewed Data
- Log Transformation: Transforming skewed data distributions to improve model performance, especially for statistical analyses.
- Weighting: Applying weights to adjust for skewed data distributions when necessary.
7. Feature Engineering
- Creating Derived Variables: Generating new features from existing data to enhance predictive power or simplify analysis.
- Dimensionality Reduction: Using techniques like PCA (Principal Component Analysis) to reduce the number of variables while preserving important information.
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