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Prepare a classification model using Naive Bayes for salary data.

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categorical-naive-bayes classification gaussian-naive-bayes label-encoder machine-learning multinomial-naive-bayes naive-bayes sklearn-metrics assignment-12

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Assignment12-Naive-Bayes

ExcelR Data Science Assignment No 12

Naive Bayes Algorithm :

  • Naive Bayes algorithm is a supervised learning algorithm, which is based on Bayes theorem and used for solving classification problems.
  • It is mainly used in text classification that includes a high-dimensional training dataset.
  • Naive Bayes Classifier is one of the simple and most effective Classification algorithms which helps in building the fast machine learning models that can make quick predictions.
  • It is a probabilistic classifier, which means it predicts on the basis of the probability of an object.
  • Some popular examples of Naïve Bayes Algorithm are spam filtration, Sentimental analysis, and classifying articles.

Each algorithm of Naive Bayes expects different types of data.

  • GaussianNB → When you have continuous features.
  • CategoricalNB → When you have categorical data.
  • MultinomialNB → Applied to text data.

This assignment will study following Question :

  1. Prepare a classification model using Naive Bayes for salary data

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