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diff --git a/content/blog/csca5632-w4/index.md b/content/blog/csca5632-w4/index.md new file mode 100644 index 0000000..3fa102e --- /dev/null +++ b/content/blog/csca5632-w4/index.md @@ -0,0 +1,62 @@ ++++ +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", ] ++++ + +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). |