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+# Name Entity Recognition (NER) Project
+
+## AIM
+To develop a system that identifies and classifies named entities (such as persons, organizations, locations, dates, etc.) in text using Named Entity Recognition (NER) with SpaCy.
+
+## DATASET LINK
+N/A (This project uses text input for NER analysis, not a specific dataset)
+- It uses real time data as input .
+
+## NOTEBOOK LINK
+[Note book link ]
+(https://colab.research.google.com/drive/1pBIEFA4a9LzyZKUFQMCypQ22M6bDbXM3?usp=sharing)
+
+## LIBRARIES NEEDED
+- SpaCy
+
+
+## DESCRIPTION
+
+!!! info "What is the requirement of the project?"
+- Named Entity Recognition (NER) is essential to automatically extract and classify key entities from text, such as persons, organizations, locations, and more.
+- This helps in analyzing and organizing data efficiently, enabling various NLP applications like document analysis and information retrieval.
+
+??? info "Why is it necessary?"
+- NER is used for understanding and structuring unstructured text, which is widely applied in industries such as healthcare, finance, and e-commerce.
+- It allows users to extract actionable insights from large volumes of text data
+
+??? info "How is it beneficial and used?"
+- NER plays a key role in tasks such as document summarization, information retrieval.
+- It automates the extraction of relevant entities, which reduces manual effort and improves efficiency.
+
+??? info "How did you start approaching this project? (Initial thoughts and planning)"
+- The project leverages SpaCy's pre-trained NER models, enabling easy text analysis without the need for training custom models.
+
+### Mention any additional resources used (blogs, books, chapters, articles, research papers, etc.)
+- SpaCy Documentation: [SpaCy NER](https://spacy.io/usage/linguistic-features#named-entities)
+- NLP in Python by Steven Bird et al.
+
+## EXPLANATION
+
+### DETAILS OF THE DIFFERENT ENTITY TYPES
+
+The system extracts the following entity types:
+
+| Entity Type | Description |
+|-------------|-------------|
+| PERSON      | Names of people (e.g., "Anuska") |
+| ORG         | Organizations (e.g., "Google", "Tesla") |
+| LOC         | Locations (e.g., "New York", "Mount Everest") |
+| DATE        | Dates (e.g., "January 1st, 2025") |
+| GPE         | Geopolitical entities (e.g., "India", "California") |
+
+## WHAT I HAVE DONE
+
+### Step 1: Data collection and preparation
+- Gathered sample text for analysis (provided by users in the app).
+- Explored the text structure and identified entity types.
+
+### Step 2: NER model implementation
+- Integrated SpaCy's pre-trained NER model (`en_core_web_sm`).
+- Extracted named entities and visualized them with labels and color coding.
+
+### Step 3: Testing and validation
+- Validated results with multiple test cases to ensure entity accuracy.
+- Allowed users to input custom text for NER analysis in real-time.
+
+## PROJECT TRADE-OFFS AND SOLUTIONS
+
+### Trade Off 1: Pre-trained model vs. custom model
+- **Pre-trained models** provide quick results but may lack accuracy for domain-specific entities.
+- **Custom models** can improve accuracy but require additional data and training time.
+
+### Trade Off 2: Real-time analysis vs. batch processing
+- **Real-time analysis** in a web app enhances user interaction but might slow down with large text inputs.
+- **Batch processing** could be more efficient for larger datasets.
+
+## SCREENSHOTS
+
+### NER Example
+  ``` mermaid
+graph LR
+    A[Start] --> B[Text Input];
+    B --> C[NER Analysis];
+    C --> D{Entities Extracted};
+    D -->|Person| E[Anuska];
+    D -->|Location| F[New York];
+    D -->|Organization| G[Google];
+    D -->|Date| H[January 1st, 2025];