Course Highlights
  • Prepare data for analysis by handling missing values, formatting and normalizing data, binning, and turning categorical values into numeric values.
  • Conduct exploratory data analysis using descriptive statistics, data grouping, analysis of variance (ANOVA), and correlation statistics.
  • Develop a predictive model using various regression methods.
  • Evaluate a model for overfitting and underfitting conditions and tune its performance using regularization and grid search.
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IBM: Analyzing Data with R

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