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.

• 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.

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.

"stock exchange pakistan"

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

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

• Python 3
• pandas
• matplotlib