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Data Manipulation: Using R for common data manipulation tasks like sorting, filtering, transubstantiating, adding up, etc.
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Linear regression: fitting a direct model on sample data, making prognostications, and assessing model performance.
Logistic Retrogression: enforcing double logistic retrogression, interpreting portions, and model evaluation.
Time Series Analysis: assaying and vaticinating time series data using methods like ARIMA, Holt-Winters, etc.
Clustering Analysis: Applying clustering algorithms like K-means and hierarchical clustering to a dataset.
Text Mining Text processing, creating document-term matrices, and sentiment analysis.
soothsaying: demand/deal soothsaying using exponential smoothing and SARIMA models.
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