Simple search engine for articles:

In this project, you will build a simple search engine that can index a set of text documents, and then search through that indexing

Based on user queries.

Project steps:

Data collection:

Gather a collection of articles or text documents that you will use as a source of information. These articles can be from

News websites, blogs, or any other source.

Word processing:

Remove special characters, and convert all text to lowercase to standardize formatting.

Use the "Tokenization" process to break text into words or sentences.

Remove "Words Stop" $($common words like "and", $"$in$",$ etc.$)$ to leave only the important words.

Use the process of $$Stemming$$ or $$Lemmatization$$ to convert words to their root roots.

Index construction:

Create a data structure $($such as "Dictionary" or "Index Inverted"$)$ to index words and link them to the documents in which they appear.

Count the frequency of words in each document to determine the relative importance of each word $($such as using $$Frequency Term$$ or $-$TF$).$

$.)"$IDF

Search and retrieval:

Create a simple interface that allows users to enter queries.

Analyze the query the same way you analyzed the texts $($tokenization$,$ stemming, etc.$).$

Use the index to find documents that contain the words in the query.

You can use algorithms such as $$Similarity Cosine$$ to rank results based on how similar documents are to the query.

Show results:

Display a list of documents that match the user's query, sorted by how closely they match.

Add additional features such as AutoComplete for queries or Suggestions.

Technology used: write codes necessary and screenshots for all processes and outputs

You can use a programming language like Python with libraries like NLTK or spaCy to process text.

To use a web interface, you can rely on Flask or Django.

To maintain the index, you can use simple data structures or databases such as SQLite or MongoDB