Business Context

Newsify, a digital news aggregator, wants to improve its article recommendation system by classifying articles into predefined categories based on their content. Currently, articles are manually tagged, which is time-consuming and inconsistent. The goal is to automate this process using TF-IDF to analyze the textual content of 10,000 articles and classify them into categories like Sports, Politics, Technology, and Health.

Data Characteristics

• Volume: 10,000 articles
• Text length: Average 300 words per article
• Language: English
• Label distribution: Approximately 25% in each category

Success Criteria

1. Achieve at least 80% accuracy in classifying articles into their respective categories.
2. Ensure the model can process and classify new articles in under 2 seconds.
3. Maintain a clear and interpretable model to allow for easy updates as new articles come in.

Constraints

• Must handle varying article lengths and ensure preprocessing is robust against noise in the text.
• The model must be scalable to accommodate an increasing volume of articles over time.

Requirements

1. Implement TF-IDF to vectorize articles.
2. Train a classifier (e.g., Logistic Regression) on the TF-IDF features.
3. Evaluate the model using accuracy and confusion matrix.
4. Provide detailed documentation of preprocessing steps and model performance.