Spam Classifier in Python | Part 2 – Data Preprocessing Explained

Hey everyone! Welcome back to LernoSync

In this video, we continue building our Machine Learning project - Spam Email Classifier.
In Part 2, we focus on one of the most important steps in ML: Data Preprocessing.

👉 In this video, you will learn:
• How to clean and organize your dataset
• How to select and rename important columns
• How to convert labels (spam/ham → 1/0)
• Why machine learning works with numbers, not text
• How to convert text into numbers using CountVectorizer

This step is very important because a machine learning model can only work properly when the data is clean and structured.

📌 Project Series:
Part 1 – Setup & Dataset
Part 2 – Data Preprocessing (this video)
Part 3 – Model Training (Naive Bayes)
Part 4 – Prediction & Final Output

Dataset Used:
SMS Spam Collection Dataset
Download: https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset

💻 Tools Used:
Python
Pandas
Scikit-learn
VS Code

If you found this helpful:
Like 👍
Subscribe 🔔
Comment 💬

#MachineLearning #PythonProjects #DataPreprocessing #SpamClassifier #BeginnerML #LernoSync

Видео Spam Classifier in Python | Part 2 – Data Preprocessing Explained канала LernoSync