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| Service Type | Price Range |
|---|---|
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| Advanced Transformation | $80 - $150 |
| Full Assignment Solutions | $120 - $250 |
| Customized Projects | $150 and above |
| Rush Orders | Prices may vary |
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- Python with Pandas and NumPy: Python is a versatile programming language widely used in data science, and libraries like Pandas and NumPy offer powerful tools for data manipulation and analysis. Our experts leverage these libraries to perform various data cleaning and transformation tasks efficiently.
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Michael Davis
Masters in Statistics
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Jason McLaughlin
Masters in Statistics
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Masters in Statistics
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Masters in Statistics
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Feature engineering involves creating new features from existing ones to improve the performance of machine learning models. Common techniques include binning, one-hot encoding for categorical variables, polynomial transformations, and interaction terms creation to capture complex relationships between features.
Data normalization and standardization are crucial for ensuring that features have a consistent scale, which is essential for many machine learning algorithms. Normalization typically involves scaling features to a range, such as [0, 1] or [-1, 1], while standardization involves transforming features to have a mean of 0 and a standard deviation of 1.
Outliers can be identified using statistical methods like Z-score, interquartile range (IQR), or visualization techniques such as box plots. For treatment, outliers may be removed, transformed (e.g., log transformation), or winsorized (replaced with a value within a certain range) depending on the specific context of the analysis.
Common techniques for handling missing data include mean imputation, median imputation, forward or backward fill, and predictive modeling approaches such as K-nearest neighbors (KNN) imputation or multiple imputation techniques.