📬 Spam Classifier Mini Project

Learn how to build a simple machine learning model to detect SPAM messages! 🚀

In this mini project, we will build a simple spam detector that can tell if a message is SPAM or HAM (not spam).

This is a real-world problem solved using Machine Learning! Let's dive step-by-step. 🌟

📥 Step 1: Collect Data

We need a dataset of labeled messages.
Example:

• HAM: "Hey, are we still meeting today?"
• SPAM: "Congratulations! You won a free ticket!"

You can use popular datasets like SMS Spam Collection Dataset.

🧹 Step 2: Preprocessing the Data

Before training, we clean the text:

• Lowercase all messages.
• Remove special characters (like @, #, $, %, etc.).
• Remove stopwords (like "the", "is", "at", "on").

Cleaning helps the model learn better patterns without noise! 🧹

🛠 Step 3: Feature Extraction

We cannot feed raw text into machine learning models.
We must convert messages into numbers!

📊 Use techniques like:

• Bag of Words: Counts how many times each word appears.
• TF-IDF: Weighs important words more than common ones.

Result: Every message becomes a vector of numbers!

⚡ Step 4: Train the Model

Now, we feed the numeric data into a simple classifier.

• Naive Bayes algorithm is perfect for text classification.
• It is fast, simple, and works well with small data!

After training, the model can predict if a new message is SPAM or not. 🧠

✅ Step 5: Test and Evaluate

Test the model on unseen messages.

• Use accuracy, precision, recall to measure performance.
• High precision means fewer false SPAM warnings!

Always test your model before using it in real life! 📈

🔎 Try It Yourself!

Type your own message below and check the prediction!

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By Darchums Technologies Inc - April 26, 2025