Hands-on Support Vector Machines | Data Science Case Study in Python

📊 In this video, we'll cover the Internet Firewall Case Study from Kaggle. Join us as we walk you through the steps to handle categorical and numerical variables in this dataset: Internet Firewall Dataset.
🔍 Step 1: Differentiating Variables
First, we'll identify the categorical and numerical variables. 🚦 Variables like network addresses have no numerical significance and will be treated as categories. 🗂️ This distinction is crucial for the next steps!
🔄 Step 2: Encoding Categorical Variables
Categorical variables need to be converted into numerical form for the algorithm. 🧩 We'll explore different encoding techniques:
🔢 Low Cardinality: Easily converted using dummies or one-hot encoding.
🎯 High Cardinality: Use target encoding with category encoders.
📏 Step 3: Scaling the Data
To prepare our data, we apply a robust scaler. ⚖️ This ensures that outliers, which could represent meaningful activity for a firewall, are not suppressed. 🌐
🧠 Step 4: Fitting the Model
With our data ready, we fit an SVM classifier with a linear kernel. 🧩 This step involves training the model and evaluating its performance. 📈
🔍 Step 5: Evaluating Results
Finally, we analyze the results to see how well our model performs. 📊 We'll discuss the metrics and insights gained from our SVM classifier.
Dataset Link: www.kaggle.com...
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Whether you're new to data science or looking to improve your skills, this hands-on case study is perfect for you! 📚 Don't forget to like, subscribe, and hit the bell icon for more tutorials! 🔔