Question_Answering_explanation
Project Overview
Question Answering (QA) is a critical task in NLP focused on extracting precise answers from a given context based on user queries. While early methods relied on keyword matching and regex, modern QA leverages transformer-based models like BERT or RoBERTa for contextual understanding. In this project, we implement a QA pipeline using tools like scikit-learn, and vector databases for semantic retrieval. You’ll explore real-world coding practices to build, evaluate, and optionally deploy a QA model that powers intelligent search, AI chatbots, or document assistants—demonstrating the practical impact of NLP in action.
Dataset Links
Link to dataset: [Download from Kaggle]
Columns used : ‘Reviews’, ‘ID’, ‘Rating
Tools & Technologies Used
Methodology / Process
1. Data Cleaning (null values, punctuation, casing).
2. Text Preprocessing, Tokenizing and Lemmatizing.
3. Using TfiDf For Vectorizer (Text to Numeric)
4. Evaluation using accuracy.
Data Loading and Cleaning (null values, punctuation, casing)
Data Processing, Joining Question, article column and Cleaning Datasets
Text Cleaning again, Tokenizing and Lemmatize
Using Tfidf For Vectorizer(Text to Numeric)
Saving Model
Displaying The Result
Users Prediction
Conclusion
This project highlights the power of modern Natural Language Processing in solving real-world problems like intelligent question answering. By integrating transformer models with semantic search and vector embeddings, we’ve moved far beyond simple keyword matching into a realm where machines can truly understand context. Through hands-on coding, we’ve not only built a functional QA system but also demonstrated how such models can be deployed in chatbots, search tools, or business applications. This project stands as a solid example of applied NLP—bridging data science theory with real-world functionality.
🔐 Full Project Files – Locked Content
This download contains:
✔️ Full Jupyter Notebook
✔️ Cleaned Review Dataset (.csv)
✔️ Trained Naive Bayes Model (.pkl)
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