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Python 3
News Recommendation System Input and Output Explained

News Recommendation System Input and Output Explained Guide

Input
Output

Input

The input to this project is the HuffPost News Category Dataset stored in JSON format.

Example:

News_Category_Dataset_v3.json

The dataset contains structured news records where:

Each row represents one news article.
Each column stores a specific attribute of the article.

After loading the dataset into a Pandas DataFrame, we can preprocess the textual information and generate recommendations.

Output

The project generates several useful outputs based on the news dataset.

These include:

Dataset Summary

Displays the structure and information of the dataset.

Text Preprocessing

Cleans and prepares the textual data.

Feature Extraction

Converts news articles into numerical feature vectors using TF-IDF.

Similarity Matrix

Calculates similarity scores between news articles.

Personalized News Recommendations

Suggests articles similar to the selected news headline.

Recommendation Results

Displays the top recommended news articles based on textual similarity.

These outputs demonstrate how machine learning techniques can be used to personalize news recommendations.

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News Recommendation System Input and Output Explained Guide

Contents

Input

Output

Dataset Summary

Text Preprocessing

Feature Extraction

Similarity Matrix

Personalized News Recommendations

Recommendation Results

News Recommendation System Using Machine Learning

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