Update README.md

Author: huseyincenik

README.md

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 | Sentiment Analysis: Predicting Product Recommendations via Text Analysis | This project focuses on sentiment analysis to predict product recommendations through advanced text analysis techniques. Explore various machine learning algorithms and deep learning models for accurate predictions. | [Kaggle](https://www.kaggle.com/huseyincenik/sentiment-analysis-predicting-product-recommends) | [GitHub](https://github.com/huseyincenik/nlp_natural_language_processing/tree/main/sentiment_analysis_predicting_product_recommendations_via_text_analysis) |
 | Sentiment Analysis on Movie Reviews | This project explores various pre-trained models for sentiment analysis on movie reviews. By leveraging Kaggle's TPU, I trained BERT, RoBERTa, DistilBERT, XLNet, GPT, BART, MobileBERT, ALBERT, and ELECTRA models, each for a maximum of 2 epochs to prevent overfitting. | [Kaggle Notebook 1](https://www.kaggle.com/code/huseyincenik/transformers-for-sentiment-classification-1) [Kaggle Notebook 2](https://www.kaggle.com/code/huseyincenik/transformers-for-sentiment-classification-2) | [GitHub](https://github.com/huseyincenik/nlp_natural_language_processing/tree/main/projects/transformers_for_sentiment_classification) |
-| **Medical RAG QA App** | A question-answering application for medical queries using Meditron 7B LLM, Qdrant Vector Database, and PubMedBERT Embedding Model. | [GitHub](https://github.com/huseyincenik/nlp_natural_language_processing/tree/main/projects/rag/medical_rag_qa_app) | - |
+| **Medical RAG QA App** | A question-answering application for medical queries using Meditron 7B LLM, Qdrant Vector Database, and PubMedBERT Embedding Model. | - | [GitHub](https://github.com/huseyincenik/nlp_natural_language_processing/tree/main/projects/rag/medical_rag_qa_app)  |
 
 
 ## Libraries and Tools