Article Recommendation System 📰

A machine learning project that recommends articles based on content similarity using Natural Language Processing (NLP) techniques. Built as part of my 30 Days of Project Building challenge.

🚀 Overview

This system analyzes the text content of articles to find similarities. It uses TF-IDF Vectorization to convert text into numerical data and Cosine Similarity to calculate the relationship between different articles.

🛠️ Tech Stack

• Python: Core logic
• Pandas: Data manipulation and cleaning
• NumPy: Numerical operations and array handling
• Scikit-learn: Machine Learning (TF-IDF & Cosine Similarity)
• Matplotlib/Seaborn: Data visualization

📊 Features

• Cleans and processes raw article data.
• Automatically generates and saves a Similarity Heatmap (similarity_analysis.png).
• Provides top 3 article recommendations for any given title.

📁 Project Structure

• main.py: The primary script.
• articles.csv: Dataset containing titles and content.
• requirements.txt: List of dependencies.
• similarity_analysis.png: Auto-generated visualization.

⚙️ How to Run

1. Install dependencies:

   pip install -r requirements.txt

2. Run the program:

   python main.py