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+title = "📰 Classifying BBC News Articles with Machine Learning"
+description = "Using machine learning to automatically classify BBC news articles by topic—comparing unsupervised and supervised approaches to see which performs best."
+date = 2025-10-31
+[taxonomies]
+tags = ["machine_learning"]
+[extra]
+styles = ["notebooks.css", ]
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+
+In this project, I explored how machine learning can help categorize BBC news
+articles into topics like **business**, **politics**, **sport**,
+**entertainment**, and **tech**. The idea was to see how well different models
+could understand the content of an article and assign it to the right
+category—without any human input.
+
+## Getting Started
+
+The dataset comes from a Kaggle competition and includes a mix of labeled and
+unlabeled articles. Before diving into modeling, I spent some time cleaning the
+data—removing duplicates, checking for balance across categories, and making
+sure everything was in the right format.
+
+## Preprocessing the Text
+
+To prepare the articles for analysis, I used a technique called TF-IDF, which
+helps highlight the most meaningful words in each article. I also tried a few
+tweaks, like replacing numbers with a placeholder, to see if that would improve
+performance. Turns out, it didn’t help much—some numbers (like years) actually
+carry useful context.
+
+## Unsupervised Learning: Finding Patterns Without Labels
+
+I started with an unsupervised approach using Non-negative Matrix Factorization
+(NMF). This method doesn’t rely on labeled data—it just looks for patterns in
+the text. Surprisingly, it did quite well, reaching around **91% accuracy**
+after some tuning.
+
+## Supervised Learning: Training with Labels
+
+Next, I tried supervised models, which learn from labeled examples. I used
+Logistic Regression and LinearSVC, and both performed even better than NMF.
+With enough training data, they reached up to **97% accuracy**.
+
+What stood out was how efficient LinearSVC was—it managed to get solid results
+even with a smaller portion of the training data.
+
+## Final Thoughts
+
+This project was a great way to compare different approaches to text
+classification. It showed that while unsupervised models can be useful,
+supervised learning tends to be more accurate when labels are available. It
+also highlighted how preprocessing choices can impact performance in subtle
+ways.
+
+If you're curious about the details, the full notebook is embedded below 👇
+
+<!-- markdownlint-disable MD033 -->
+<iframe title="Spam Email Classification notebook" class="notebook-embed" src="notebook.html"></iframe>
+
+You can also view the notebook in [a separate page](notebook.html), or check it
+on [GitHub](https://github.com/Farzat07/BBC-News-Classification-Kaggle-Mini-Project).