Project overview
In this project, you’ll take on the role of a data scientist tasked with building a spam filter for SMS messages. You’ll leverage your skills in conditional probability and apply the Naive Bayes algorithm using R to create a classifier that can effectively distinguish between spam and legitimate messages.
This hands-on project allows you to put probability theory into practice in a real-world application. You’ll preprocess text data, build a vocabulary from the training set, calculate probabilities, and tune your model to optimize predictive performance. The end result will be a functional spam filter you can add to your data science portfolio.
Objective: Use conditional probability concepts and the Naive Bayes algorithm to build an SMS spam filter in R, developing an end-to-end data science solution to a practical problem.
Key skill required
To complete this project, it's recommended to build these foundational skills in R
- Understanding conditional probability concepts and notation
- Calculating conditional probabilities of words appearing in spam vs ham emails
- Applying Bayes' Theorem to update spam probabilities based on word frequencies
- Understanding how Naive Bayes classifies emails as spam or ham
Projects steps
Step 1: Exploring the Dataset
Step 2: Training, Cross-validation and Test Sets
Step 3: Data Cleaning
Step 4: Creating the Vocabulary
Step 5: Calculating Constants First
Step 6: Calculating Probability Parameters
Step 7: Classifying A New Message
Step 8: Calculating Accuracy
Step 9: Hyperparameter Tuning and Cross-validation
Step 10: Test Set Performance
Step 11: Next Steps
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