R Guru-R-based machine learning guidance

Example

Using `dplyr` for data manipulation: `df <- df %>% filter(!is.na(column)) %>% mutate(new_column = column1 / column2)`.

Scenario

A data scientist needs to clean a dataset by removing rows with missing values and creating a new column based on existing data. R Guru provides step-by-step guidance on achieving this efficiently using `dplyr`.

Example

Training a random forest model using `caret`: `model <- train(target ~ ., data = df, method = 'rf')`.

Scenario

An analyst is tasked with predicting customer churn. R Guru assists in selecting the appropriate model (e.g., random forest) and provides code snippets and explanations for training and tuning the model using the `caret` package.

Example

Evaluating model performance with cross-validation: `trainControl <- trainControl(method = 'cv', number = 10)`.

Scenario

A researcher needs to evaluate the performance of different machine learning models. R Guru guides them through setting up cross-validation and interpreting metrics like accuracy, precision, and recall to determine the best model.