Update README.md

Author: kiarashrahmani

README.md

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 # Simple-Search-Engine
  The Simple Search Engine project is a lightweight yet powerful tool designed to provide efficient text search capabilities. this project allows users to search through a corpus of documents and obtain relevant result.
+The Vector Space Model (VSM) is a crucial concept in Natural Language Processing (NLP) used to represent text data numerically in a high-dimensional space. This project implements a search engine using the VSM approach, allowing users to retrieve relevant information from a given corpus.
+
+## Description
+
+This search engine project involves several key steps:
+
+### Step 0: Importing Corpus
+
+The initial step involves reading text corpora from the local machine. The Python script utilizes the NLTK library for further processing.
+
+### Step 1: Preprocessing & Tokenizing
+
+Text preprocessing is carried out to eliminate unnecessary tokens and simplify calculations. The NLTK library is employed for tasks such as tokenization, lemmatization, and stop-word removal.
+
+### Step 2: Creating our Dataset
+
+The preprocessed data is organized into a CSV file, creating a structured dataset for subsequent analysis.
+
+### Step 3: Creating our Matrix
+
+A term-document matrix is generated from the dataset, representing the frequency of terms in each document.
+
+### Step 5: Calculating Cosine Similarity
+
+Cosine similarity is computed to measure the similarity between the input query and the documents in the corpus. The results are ranked based on similarity.
+
+## How to Use
+
+1. Clone the repository to your local machine.
+2. Install the necessary dependencies (NLTK, pandas).
+3. Run the Python script to build the search engine.
+
+## Dependencies
+
+- NLTK
+- Pandas
+
+## Author
+
+[Kiarash Rahmani]
+
+## License
+
+This project is licensed under the [MIT License](LICENSE).
+