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Live Output and Recommendation Overview

Live Output and Recommendation Overview

Live Output: News Recommendation System

After executing the notebook, the News Recommendation System successfully recommends news articles that are similar to the selected article. The recommendations are generated by comparing the textual content of every news article in the dataset.

Each stage of the project contributes to the final recommendation process.

Dataset Overview

Displays the structure of the HuffPost News Category Dataset, including the available columns and the total number of news articles.

Cleaned Dataset

Shows the processed dataset after removing missing values and duplicate records.

TF-IDF Feature Matrix

Displays the numerical representation of each news article after applying TF-IDF Vectorization.

Similarity Matrix

Generates similarity scores between all news articles using Cosine Similarity.

Recommended News Articles

Displays the top recommended news headlines that are most similar to the selected article.

Together, these outputs demonstrate how Machine Learning techniques can automatically recommend relevant news articles.