Project overview
In this project, you’ll take on the role of a data scientist tasked with building an SMS spam filter using the multinomial Naive Bayes algorithm. You’ll work with a real-world dataset to clean and prepare text data, calculate probabilities, and train the algorithm to classify messages as spam or ham.
This hands-on project allows you to apply your knowledge of conditional probability and Naive Bayes to solve a practical problem. You’ll strengthen your skills in data preparation, probability calculations, and implementing machine learning algorithms in Python. The project will be a valuable addition to your portfolio, demonstrating your ability to build a functional spam filter.
**Objective:** Use the multinomial Naive Bayes algorithm to create an SMS spam filter in Python, developing your machine learning skills with a practical application.
Key skill required
To complete this project, it's recommended to build these foundational skills in Python
- Assigning probabilities based on specific conditions
- Updating probabilities using prior knowledge
- Employing Naive Bayes for spam filter classification
- Applying the multinomial Naive Bayes algorithm
Projects steps
Step 1: Exploring the Dataset
Step 2: Training and Test Set
Step 3: Letter Case and Punctuation
Step 4: Creating the Vocabulary
Step 5: The Final Training Set
Step 6: Calculating Constants First
Step 7: Calculating Parameters
Step 8: Classifying A New Message
Step 9: Measuring the Spam Filter's Accuracy
Step 10: Next Steps
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