Data classification with Naive Bayes
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Master data classification using Naive Bayes, a simple yet powerful probabilistic algorithm. Learn how to apply it to various datasets for tasks like spam detection, sentiment analysis, and classification of categorical data.
At a Glance
Learn to classify data with Naive Bayes. Dive into this supervised machine learning algorithm that’s widely used in text classification scenarios, and learn with a practical application. Navigate through a step-by-step tutorial, emphasizing its performance in spam filtering. Using Pandas and sklearn, master the art of understanding and implementing Naive Bayes for precision and efficiency in data classification.
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A look at the project ahead
- Gain a solid understanding of Naive Bayes concepts in the context of data classification
- Learn to load and manipulate data sets using essential libraries such as NumPy and Pandas
- Learn to preprocess the data with natural language processing tasks
- Get hands-on experience with evaluating your model using sklearn
What you’ll need
- No installation required: Everything is available in the JupyterLab, including any Python libraries and data sets.
- Some understanding of Python: Having some understanding of Python is required for some preprocessing text tasks using NLTK.
- Some understanding of statistical concepts: It’s helpful to have some understanding of statistic concepts, particularly Naive Bayes.
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