Cosine Similarity Algorithm

This repository contains a Python implementation of the Cosine Similarity algorithm without using any machine learning libraries. The implementation ranks documents based on their similarity to a given query using TF-IDF (Term Frequency - Inverse Document Frequency) and cosine similarity.

Features

• Splits documents into tokens and calculates term frequencies.
• Computes inverse document frequency (IDF) for each term.
• Calculates TF-IDF weights for documents and the query.
• Computes cosine similarity scores to rank documents based on relevance.
• Visualizes the similarity scores using a bar chart.

Usage

1. Clone the repository:

   git clone https://github.com/shaheerAlam1/Cosine-Similarity-Algorithm-with-python.git
   cd Cosine-Similarity-Algorithm-with-python

2. Install dependencies (if not already installed):

   pip install pandas matplotlib

3. Run the notebook or script to see the ranking results.

Example

Query:

"stock exchange pakistan"

Documents:

1. "Market of stock exchange is affected by brokers."
2. "Pakistan stock market is very popular."
3. "Stock exchange Pakistan is in loss nowadays."

Output:

The algorithm ranks the documents based on their relevance to the query using cosine similarity, with the highest-ranking document being the most relevant.

Dependencies

• Python 3
• pandas
• matplotlib