Sem Spirit

Mind the Semantics

Aller au contenu
  • Accueil
  • Use Cases
    • Cinema & Movie Investment
    • Trading Bot — Real Time
    • Toxic Spotter
  • Artificial Intelligence
    • Symbolic Reasoning
      • Semantic Networks
      • Resource Description Format (RDF/S)
      • Ontology Web Language (OWL)
    • Machine Learning
      • Installing the tools
        • Installing Python and Spyder IDE
        • Installing R and RStudio IDE
      • Preparing the data
        • Preparing the data in Python
          • Loading the data
          • How to handle blank cells
          • Numerical relabeling of textual data
          • Correcting irrelevent orders
          • Separating source and target variables
          • Feature scaling
          • Splitting the dataset into training and test sets
        • Preparing the data in R
          • Loading the data
          • Numerical relabeling
          • Filling the blanks
          • Feature scaling
          • Splitting the dataset into training and test sets
      • Regression
        • Simple Linear Regression
          • Simple Linear Regression in Python
          • Simple Linear Regression in R
        • Multilinear Regression
          • Multilinear Regression Model in Python
          • Multilinear Regression Model in R
        • Polynomial Regression
          • Polynomial Regression in Python
          • Polynomial Regression in R
        • Support Vector Regression
          • Support Vector Regression in Python
          • Support Vector Regression in R
        • Decision Tree Regression
          • Decision Tree Regression in Python
          • Decision Tree Regression in R
        • Random Forest Regression
          • Random Forest Regression in Python
          • Random Forest Regression in R
      • Classification
        • Logistic Regression
          • Logistic Regression in Python
          • Logistic Regression in R
        • k-Nearest-Neighbors Classification
          • k-Nearest-Neighbors Classification in Python
          • k-Nearest-Neighbors Classification in R
        • Support Vector Machine
          • Support Vector Machine classification in Python
          • Support Vector Machine classification in R
        • Naive Bayes Classification
          • Naive Bayes Classification in Python
          • Naive Bayes Classification in R
        • Decision Tree Classification
          • Decision Tree Classification in Python
          • Decision Tree Classification in R
        • Random Forest Classification
          • Random Forest Classification in Python
          • Random Forest Classification in R
        • Classifier Evaluation
          • Confusion Matrix
          • Precision and Recall
          • Sensitivity and specificity
          • Receiver Operating Characteristic (ROC) Curves
          • Classifier evaluation with CAP curve in Python
      • Clustering
        • K-Means Clustering
          • K-Means Clustering in Python
          • K-Means Clustering in R
        • Hierarchical Clustering
          • Hierarchical Clustering in Python
          • Hierarchical Clustering in R
      • Association Rule Learning
        • Apriori
          • Apriori in Python
    • Knowledge Engineering
    • Formal Semantics
  • Data
  • Networks
    • The Web
    • The Semantic Web
  • Contact
    • Operating Hours
    • About Me
Sem Spirit

Preparing the data in R

Once R is installed, the steps to prepare the data are basically
– understanding the dataset
– loading the data
– encoding labels
– handling missing data
– feature scaling
– splitting the dataset into training set and test set

Next step : installing R and RStudio.

Fièrement propulsé par WordPress

Fièrement hébergé par WordPress Hébergement