diff --git a/NLP/Algorithms/Named_Entity_Recognition/README.md b/NLP/Algorithms/Named_Entity_Recognition/README.md
new file mode 100644
index 0000000..670c4d9
--- /dev/null
+++ b/NLP/Algorithms/Named_Entity_Recognition/README.md
@@ -0,0 +1,87 @@
+# Named Entity Recognition (NER) Project
+
+This project demonstrates a basic Named Entity Recognition (NER) algorithm using Python and the `spacy` library. The goal is to identify named entities in text and classify them into predefined categories.
+
+## Directory Structure
+
+```
+ner_project/
+├── data/
+│   └── ner_data.txt
+├── models/
+│   └── ner_model
+├── preprocess.py
+├── train.py
+└── recognize.py
+└── README.md
+```
+
+- **data/ner_data.txt**: Contains the dataset used for training the NER model.
+- **models/ner_model**: Stores the trained NER model.
+- **preprocess.py**: Contains the code for preprocessing the text data.
+- **train.py**: Script for training the NER model.
+- **recognize.py**: Script for recognizing named entities in new text using the trained model.
+- **README.md**: Project documentation.
+
+## Dataset
+
+The dataset (`ner_data.txt`) contains sentences and their corresponding entity labels in the IOB format. Each line contains a word and its label, separated by a space. Sentences are separated by blank lines.
+
+## Preprocessing
+
+The `preprocess.py` file contains functions to preprocess the text data. It reads the dataset and converts it into a format suitable for training with `spacy`.
+
+## Training the Model
+
+The `train.py` script is used to train the NER model. It performs the following steps:
+
+1. Load a blank English model.
+2. Create the NER pipeline component and add it to the pipeline.
+3. Add labels to the NER component.
+4. Load the training data.
+5. Train the model using the training data.
+6. Save the trained model to `models/ner_model`.
+
+### Running the Training Script
+
+To train the model, run:
+```bash
+python train.py
+```
+
+## Recognizing Named Entities
+
+The `recognize.py` script is used to recognize named entities in new text using the trained model. It performs the following steps:
+
+1. Load the trained model.
+2. Process the input text.
+3. Print the recognized entities and their labels.
+
+### Running the Recognition Script
+
+To recognize named entities in new text, run:
+```bash
+python recognize.py
+```
+
+## Dependencies
+
+The project requires the following Python libraries:
+
+- spacy
+
+You can install the dependencies using:
+```bash
+pip install spacy
+```
+
+## Example Usage
+
+```python
+# Example usage of the recognize.py script
+if __name__ == "__main__":
+    text = "I love programming in Python. Machine learning is fascinating. Spacy is a useful library."
+    recognize_entities(text)
+```
+
+This project provides a basic implementation of Named Entity Recognition using the `spacy` library. You can expand it by using more advanced models or preprocessing techniques based on your requirements.
\ No newline at end of file
diff --git a/NLP/Algorithms/Named_Entity_Recognition/data/ner_data.txt b/NLP/Algorithms/Named_Entity_Recognition/data/ner_data.txt
new file mode 100644
index 0000000..3e60694
--- /dev/null
+++ b/NLP/Algorithms/Named_Entity_Recognition/data/ner_data.txt
@@ -0,0 +1,1015 @@
+I O
+love O
+programming O
+in O
+Python B-LANG
+. O
+
+Machine B-SKILL
+learning I-SKILL
+is O
+fascinating O
+. O
+
+Spacy B-TECH
+is O
+a O
+useful O
+library O
+. O
+
+Python B-LANG
+is O
+a O
+versatile O
+language O
+. O
+
+Named B-TASK
+Entity I-TASK
+Recognition I-TASK
+is O
+important O
+. O
+
+Google B-ORG
+is O
+a O
+leading O
+tech B-INDUSTRY
+company O
+. O
+
+Elon B-PER
+Musk I-PER
+is O
+the O
+CEO O
+of O
+Tesla B-ORG
+. O
+
+The O
+COVID-19 B-EVENT
+pandemic I-EVENT
+has O
+impacted O
+the O
+world O
+significantly O
+. O
+
+Microsoft B-ORG
+was O
+founded O
+by O
+Bill B-PER
+Gates I-PER
+. O
+
+Apple B-ORG
+introduced O
+the O
+iPhone B-PROD
+in O
+2007 B-DATE
+. O
+
+Amazon B-ORG
+is O
+a O
+global O
+e-commerce B-INDUSTRY
+giant O
+. O
+
+Facebook B-ORG
+rebranded O
+to O
+Meta B-ORG
+in O
+2021 B-DATE
+. O
+
+New B-GPE
+York I-GPE
+City I-GPE
+is O
+a O
+major O
+financial B-INDUSTRY
+hub O
+. O
+
+SpaceX B-ORG
+launched O
+the O
+Falcon B-PROD
+Heavy I-PROD
+rocket I-PROD
+. O
+
+The O
+Olympics B-EVENT
+are O
+held O
+every O
+four O
+years O
+. O
+
+Python B-LANG
+and O
+Java B-LANG
+are O
+popular O
+programming O
+languages O
+. O
+
+NASA B-ORG
+is O
+planning O
+a O
+mission O
+to O
+Mars B-LOC
+. O
+
+The O
+Great B-LOC
+Wall I-LOC
+of I-LOC
+China I-LOC
+is O
+a O
+historic O
+landmark O
+. O
+
+Tesla B-ORG
+produces O
+electric B-PROD
+vehicles I-PROD
+. O
+
+The O
+Eiffel B-LOC
+Tower I-LOC
+is O
+in O
+Paris B-GPE
+. O
+
+Google B-ORG
+Maps I-PROD
+is O
+a O
+widely O
+used O
+navigation O
+app O
+. O
+
+The O
+Amazon B-LOC
+Rainforest I-LOC
+is O
+the O
+largest O
+tropical O
+rainforest O
+. O
+
+Lionel B-PER
+Messi I-PER
+is O
+a O
+famous O
+football B-SPORT
+player O
+. O
+
+The O
+United B-ORG
+Nations I-ORG
+is O
+an O
+international O
+organization O
+. O
+
+The O
+stock B-INDUSTRY
+market O
+can O
+be O
+volatile O
+. O
+
+The O
+Academy B-EVENT
+Awards I-EVENT
+celebrate O
+cinematic B-INDUSTRY
+achievements O
+. O
+
+Mount B-LOC
+Everest I-LOC
+is O
+the O
+highest O
+mountain O
+in O
+the O
+world O
+. O
+
+Jeff B-PER
+Bezos I-PER
+is O
+the O
+founder O
+of O
+Amazon B-ORG
+. O
+
+The O
+Pacific B-LOC
+Ocean I-LOC
+is O
+the O
+largest O
+ocean O
+. O
+
+The O
+Sahara B-LOC
+Desert I-LOC
+is O
+in O
+Africa B-LOC
+. O
+
+Harvard B-ORG
+University I-ORG
+is O
+an O
+Ivy B-INDUSTRY
+League I-INDUSTRY
+school O
+. O
+
+The O
+World B-ORG
+Health I-ORG
+Organization I-ORG
+monitors O
+global O
+health O
+trends O
+. O
+
+The O
+Wright B-PER
+Brothers I-PER
+invented O
+the O
+airplane B-PROD
+. O
+
+Python B-LANG
+is O
+used O
+in O
+data B-TASK
+science I-TASK
+. O
+
+The O
+Taj B-LOC
+Mahal I-LOC
+is O
+in O
+India B-GPE
+. O
+
+Tesla B-ORG
+is O
+known O
+for O
+its O
+electric B-PROD
+cars I-PROD
+. O
+
+The O
+Google B-ORG
+search B-TASK
+engine I-TASK
+is O
+widely O
+used O
+. O
+
+NASA B-ORG
+is O
+exploring O
+space B-LOC
+. O
+
+The O
+Great B-LOC
+Barrier I-LOC
+Reef I-LOC
+is O
+in O
+Australia B-GPE
+. O
+
+The O
+United B-GPE
+States I-GPE
+of I-GPE
+America I-GPE
+is O
+a O
+large O
+country O
+. O
+
+The O
+Silicon B-LOC
+Valley I-LOC
+is O
+a O
+tech B-INDUSTRY
+hub O
+. O
+
+The O
+European B-ORG
+Union I-ORG
+is O
+a O
+political B-INDUSTRY
+and O
+economic B-INDUSTRY
+union O
+. O
+
+Python B-LANG
+is O
+great O
+for O
+machine B-TASK
+learning I-TASK
+. O
+
+The O
+Sphinx B-LOC
+is O
+a O
+historic O
+monument O
+in O
+Egypt B-GPE
+. O
+
+New B-GPE
+York I-GPE
+is O
+a O
+busy O
+city O
+. O
+
+The O
+Grand B-LOC
+Canyon I-LOC
+is O
+a O
+natural O
+wonder O
+. O
+
+Python B-LANG
+supports O
+multiple O
+programming O
+paradigms O
+. O
+
+Amazon B-ORG
+Prime I-PROD
+offers O
+fast O
+shipping O
+. O
+
+The O
+Mona B-PROD
+Lisa I-PROD
+is O
+a O
+famous O
+painting O
+. O
+
+The O
+Berlin B-LOC
+Wall I-LOC
+fell O
+in O
+1989 B-DATE
+. O
+
+Elon B-PER
+Musk I-PER
+founded O
+SpaceX B-ORG
+. O
+
+The O
+Great B-LOC
+Lakes I-LOC
+are O
+a O
+group O
+of O
+five O
+large O
+lakes O
+. O
+
+Python B-LANG
+is O
+used O
+for O
+web B-TASK
+development I-TASK
+. O
+
+The O
+Rocky B-LOC
+Mountains I-LOC
+are O
+in O
+North B-LOC
+America I-LOC
+. O
+
+Microsoft B-ORG
+Office I-PROD
+is O
+a O
+suite O
+of O
+productivity O
+tools O
+. O
+
+Python B-LANG
+is O
+popular O
+in O
+data B-TASK
+analysis I-TASK
+. O
+
+The O
+Golden B-LOC
+Gate I-LOC
+Bridge I-LOC
+is O
+in O
+San B-GPE
+Francisco I-GPE
+. O
+
+The O
+Big B-LOC
+Ben I-LOC
+is O
+in O
+London B-GPE
+. O
+
+Python B-LANG
+is O
+known O
+for O
+its O
+simplicity O
+. O
+
+The O
+Statue B-LOC
+of I-LOC
+Liberty I-LOC
+is O
+in O
+New B-GPE
+York I-GPE
+. O
+
+The O
+Great B-LOC
+Pyramids I-LOC
+are O
+in O
+Giza B-LOC
+. O
+
+The O
+Amazon B-LOC
+River I-LOC
+is O
+the O
+second O
+longest O
+river O
+. O
+
+Python B-LANG
+is O
+widely O
+used O
+in O
+scientific B-TASK
+computing I-TASK
+. O
+
+The O
+Great B-LOC
+Barrier I-LOC
+Reef I-LOC
+is O
+in O
+Australia B-GPE
+. O
+
+The O
+Sydney B-LOC
+Opera I-LOC
+House I-LOC
+is O
+a O
+famous O
+landmark O
+. O
+
+Amazon B-ORG
+Web I-PROD
+Services I-PROD
+is O
+a O
+cloud B-INDUSTRY
+computing I-INDUSTRY
+platform O
+. O
+
+Python B-LANG
+is O
+used O
+for O
+AI B-TASK
+research I-TASK
+. O
+
+The O
+Empire B-LOC
+State I-LOC
+Building I-LOC
+is O
+in O
+New B-GPE
+York I-GPE
+. O
+
+The O
+Great B-LOC
+Wall I-LOC
+of I-LOC
+China I-LOC
+is O
+a O
+historic O
+landmark O
+. O
+
+Google B-ORG
+Drive I-PROD
+is O
+a O
+cloud B-INDUSTRY
+storage I-INDUSTRY
+service O
+. O
+
+The O
+Amazon B-LOC
+Rainforest I-LOC
+is O
+the O
+largest O
+tropical O
+rainforest O
+. O
+
+Python B-LANG
+is O
+known O
+for O
+its O
+extensive O
+libraries O
+. O
+
+The O
+Colosseum B-LOC
+is O
+in O
+Rome B-GPE
+. O
+
+Tesla B-ORG
+produces O
+electric B-PROD
+cars I-PROD
+. O
+
+The O
+United B-ORG
+Nations I-ORG
+was O
+founded O
+in O
+1945 B-DATE
+. O
+
+Python B-LANG
+is O
+great O
+for O
+data B-TASK
+visualization I-TASK
+. O
+
+The O
+Sphinx B-LOC
+is O
+a O
+historic O
+monument O
+in O
+Egypt B-GPE
+. O
+
+The O
+Rocky B-LOC
+Mountains I-LOC
+are O
+in O
+North B-LOC
+America I-LOC
+. O
+
+The O
+World B-ORG
+Health I-ORG
+Organization I-ORG
+monitors O
+global O
+health O
+trends O
+. O
+
+Python B-LANG
+is O
+used O
+in O
+natural B-TASK
+language I-TASK
+processing I-TASK
+. O
+
+The O
+Grand B-LOC
+Canyon I-LOC
+is O
+a O
+natural O
+wonder O
+. O
+
+The O
+Pacific B-LOC
+Ocean I-LOC
+is O
+the O
+largest O
+ocean O
+. O
+
+The O
+Wright B-PER
+Brothers I-PER
+invented O
+the O
+airplane B-PROD
+. O
+
+The O
+Great B-LOC
+Lakes I-LOC
+are O
+a O
+group O
+of O
+five O
+large O
+lakes O
+. O
+
+Python B-LANG
+is O
+used O
+for O
+web B-TASK
+development I-TASK
+. O
+
+Amazon B-ORG
+Prime I-PROD
+offers O
+fast O
+shipping O
+. O
+
+The O
+Eiffel B-LOC
+Tower I-LOC
+is O
+in O
+Paris B-GPE
+. O
+
+The O
+United B-GPE
+Kingdom I-GPE
+is O
+a O
+country O
+in O
+Europe B-LOC
+. O
+
+Python B-LANG
+is O
+known O
+for O
+its O
+simplicity O
+. O
+
+The O
+Great B-LOC
+Pyramids I-LOC
+are O
+in O
+Giza B-LOC
+. O
+
+Google B-ORG
+Maps I-PROD
+is O
+a O
+widely O
+used O
+navigation O
+app O
+. O
+
+The O
+Amazon B-LOC
+River I-LOC
+is O
+the O
+second O
+longest O
+river O
+. O
+
+Python B-LANG
+is O
+widely O
+used O
+in O
+scientific B-TASK
+computing I-TASK
+. O
+
+The O
+Berlin B-LOC
+Wall I-LOC
+fell O
+in O
+1989 B-DATE
+. O
+
+The O
+Olympics B-EVENT
+are O
+held O
+every O
+four O
+years O
+. O
+
+Python B-LANG
+and O
+Java B-LANG
+are O
+popular O
+programming O
+languages O
+. O
+
+The O
+Mona B-PROD
+Lisa I-PROD
+is O
+a O
+famous O
+painting O
+. O
+
+The O
+Great B-LOC
+Barrier I-LOC
+Reef I-LOC
+is O
+in O
+Australia B-GPE
+. O
+
+The O
+Sydney B-LOC
+Opera I-LOC
+House I-LOC
+is O
+a O
+famous O
+landmark O
+. O
+
+Elon B-PER
+Musk I-PER
+is O
+the O
+CEO O
+of O
+Tesla B-ORG
+. O
+
+Microsoft B-ORG
+was O
+founded O
+by O
+Bill B-PER
+Gates I-PER
+. O
+
+The O
+Academy B-EVENT
+Awards I-EVENT
+celebrate O
+cinematic B-INDUSTRY
+achievements O
+. O
+
+The O
+Great B-LOC
+Wall I-LOC
+of I-LOC
+China I-LOC
+is O
+a O
+historic O
+landmark O
+. O
+
+Jeff B-PER
+Bezos I-PER
+is O
+the O
+founder O
+of O
+Amazon B-ORG
+. O
+
+The O
+Pacific B-LOC
+Ocean I-LOC
+is O
+the O
+largest O
+ocean O
+. O
+
+Lionel B-PER
+Messi I-PER
+is O
+a O
+famous O
+football B-SPORT
+player O
+. O
+
+The O
+United B-GPE
+States I-GPE
+of I-GPE
+America I-GPE
+is O
+a O
+large O
+country O
+. O
+
+The O
+Silicon B-LOC
+Valley I-LOC
+is O
+a O
+tech B-INDUSTRY
+hub O
+. O
+
+The O
+World B-ORG
+Health I-ORG
+Organization I-ORG
+monitors O
+global O
+health O
+trends O
+. O
+
+The O
+Golden B-LOC
+Gate I-LOC
+Bridge I-LOC
+is O
+in O
+San B-GPE
+Francisco I-GPE
+. O
+
+The O
+Empire B-LOC
+State I-LOC
+Building I-LOC
+is O
+in O
+New B-GPE
+York I-GPE
+. O
+
+Python B-LANG
+is O
+great O
+for O
+machine B-TASK
+learning I-TASK
+. O
+
+NASA B-ORG
+is O
+planning O
+a O
+mission O
+to O
+Mars B-LOC
+. O
+
+The O
+Olympics B-EVENT
+are O
+held O
+every O
+four O
+years O
+. O
+
+The O
+COVID-19 B-EVENT
+pandemic I-EVENT
+has O
+impacted O
+the O
+world O
+significantly O
+. O
\ No newline at end of file
diff --git a/NLP/Algorithms/Named_Entity_Recognition/preprocess.py b/NLP/Algorithms/Named_Entity_Recognition/preprocess.py
new file mode 100644
index 0000000..fec41dd
--- /dev/null
+++ b/NLP/Algorithms/Named_Entity_Recognition/preprocess.py
@@ -0,0 +1,41 @@
+import spacy
+from spacy.tokens import DocBin
+import os
+
+def create_training_data(file_path):
+    nlp = spacy.blank("en")
+    doc_bin = DocBin()
+    
+    with open(file_path, 'r') as file:
+        lines = file.read().split('\n')
+    
+    words = []
+    tags = []
+    for line in lines:
+        if line:
+            parts = line.split()
+            words.append(parts[0])
+            tags.append(parts[1])
+        else:
+            if words:
+                doc = nlp.make_doc(' '.join(words))
+                ents = []
+                start = 0
+                for i, word in enumerate(words):
+                    end = start + len(word)
+                    tag = tags[i]
+                    if tag != 'O':
+                        label = tag.split('-')[1]
+                        span = doc.char_span(start, end, label=label)
+                        if span:
+                            ents.append(span)
+                    start = end + 1
+                doc.ents = ents
+                doc_bin.add(doc)
+                words = []
+                tags = []
+    
+    doc_bin.to_disk("data/training_data.spacy")
+
+if __name__ == "__main__":
+    create_training_data("data/ner_data.txt")
\ No newline at end of file
diff --git a/NLP/Algorithms/Named_Entity_Recognition/recognize.py b/NLP/Algorithms/Named_Entity_Recognition/recognize.py
new file mode 100644
index 0000000..6bb9cfe
--- /dev/null
+++ b/NLP/Algorithms/Named_Entity_Recognition/recognize.py
@@ -0,0 +1,17 @@
+import spacy
+
+def recognize_entities(text):
+    # Load the trained model
+    nlp = spacy.load("models/ner_model")
+    
+    # Process the text
+    doc = nlp(text)
+    
+    # Print the entities
+    for ent in doc.ents:
+        print(f"Text: {ent.text}, Label: {ent.label_}")
+
+# Example usage
+if __name__ == "__main__":
+    text = "I love programming in Python. Machine learning is fascinating. Spacy is a useful library."
+    recognize_entities(text)
diff --git a/NLP/Algorithms/Named_Entity_Recognition/train.py b/NLP/Algorithms/Named_Entity_Recognition/train.py
new file mode 100644
index 0000000..d82007a
--- /dev/null
+++ b/NLP/Algorithms/Named_Entity_Recognition/train.py
@@ -0,0 +1,42 @@
+import spacy
+from spacy.util import minibatch, compounding
+import random
+
+def train_ner_model():
+    # Load blank English model
+    nlp = spacy.blank("en")
+    
+    # Create the NER pipeline component and add it to the pipeline
+    if "ner" not in nlp.pipe_names:
+        ner = nlp.create_pipe("ner")
+        nlp.add_pipe(ner, last=True)
+    
+    # Add labels to the NER component
+    labels = ["LANG", "SKILL", "TECH", "TASK"]
+    for label in labels:
+        ner.add_label(label)
+    
+    # Load training data
+    doc_bin = DocBin().from_disk("data/training_data.spacy")
+    training_data = list(doc_bin.get_docs(nlp.vocab))
+    
+    # Train the model
+    optimizer = nlp.begin_training()
+    for itn in range(20):
+        random.shuffle(training_data)
+        losses = {}
+        batches = minibatch(training_data, size=compounding(4.0, 32.0, 1.5))
+        for batch in batches:
+            texts, annotations = zip(*[(doc.text, {"entities": [(ent.start_char, ent.end_char, ent.label_) for ent in doc.ents]}) for doc in batch])
+            nlp.update(texts, annotations, drop=0.5, losses=losses)
+        print(f"Iteration {itn}, Losses: {losses}")
+    
+    # Save the trained model
+    output_dir = "models/ner_model"
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    nlp.to_disk(output_dir)
+    print(f"Model saved to {output_dir}")
+
+if __name__ == "__main__":
+    train_ner_model()
\ No newline at end of file
diff --git a/NLP/Algorithms/Text_Classification/README.md b/NLP/Algorithms/Text_Classification/README.md
new file mode 100644
index 0000000..0108124
--- /dev/null
+++ b/NLP/Algorithms/Text_Classification/README.md
@@ -0,0 +1,90 @@
+# Text Classification Project
+
+This project demonstrates a basic text classification algorithm using Python and common NLP libraries. The goal is to classify text into different categories, specifically positive and negative sentiments.
+
+## Directory Structure
+
+```
+text_classification/
+├── data/
+│   └── text_data.csv
+├── models/
+│   └── text_classifier.pkl
+├── preprocess.py
+├── train.py
+└── classify.py
+```
+
+- **data/text_data.csv**: Contains the dataset used for training and testing the model.
+- **models/text_classifier.pkl**: Stores the trained text classification model.
+- **preprocess.py**: Contains the code for preprocessing the text data.
+- **train.py**: Script for training the text classification model.
+- **classify.py**: Script for classifying new text using the trained model.
+
+## Dataset
+
+The dataset (`text_data.csv`) contains 200 entries with two columns: `text` and `label`. The `text` column contains the text to be classified, and the `label` column contains the corresponding category (positive or negative).
+
+## Preprocessing
+
+The `preprocess.py` file contains functions to preprocess the text data. It includes removing non-alphabetic characters, converting text to lowercase, removing stopwords, and lemmatizing the words.
+
+## Training the Model
+
+The `train.py` script is used to train the text classification model. It performs the following steps:
+
+1. Load the dataset.
+2. Preprocess the text data.
+3. Split the dataset into training and testing sets.
+4. Create a pipeline with `TfidfVectorizer` and `MultinomialNB`.
+5. Train the model.
+6. Evaluate the model and print the accuracy and classification report.
+7. Save the trained model to `models/text_classifier.pkl`.
+
+### Running the Training Script
+
+To train the model, run:
+```bash
+python train.py
+```
+
+## Classifying New Text
+
+The `classify.py` script is used to classify new text using the trained model. It performs the following steps:
+
+1. Load the trained model.
+2. Preprocess the input text.
+3. Predict the class of the input text.
+
+### Running the Classification Script
+
+To classify new text, run:
+```bash
+python classify.py
+```
+
+## Dependencies
+
+The project requires the following Python libraries:
+
+- pandas
+- scikit-learn
+- nltk
+- joblib
+
+You can install the dependencies using:
+```bash
+pip install pandas scikit-learn nltk joblib
+```
+
+## Example Usage
+
+```python
+# Example usage of the classify.py script
+if __name__ == "__main__":
+    new_text = "I enjoy working on machine learning projects."
+    print(f'Text: "{new_text}"')
+    print(f'Predicted Class: {classify_text(new_text)}')
+```
+
+This project provides a simple text classification model using Naive Bayes with TF-IDF vectorization. You can expand it by using more advanced models or preprocessing techniques based on your requirements.
\ No newline at end of file
diff --git a/NLP/Algorithms/Text_Classification/classify.py b/NLP/Algorithms/Text_Classification/classify.py
new file mode 100644
index 0000000..d10bae6
--- /dev/null
+++ b/NLP/Algorithms/Text_Classification/classify.py
@@ -0,0 +1,19 @@
+import joblib
+from preprocess import preprocess_text
+
+# Load the model
+model = joblib.load('models/text_classifier.pkl')
+
+def classify_text(text):
+    # Preprocess the text
+    processed_text = preprocess_text(text)
+    
+    # Predict the class
+    prediction = model.predict([processed_text])
+    return prediction[0]
+
+# Example usage
+if __name__ == "__main__":
+    new_text = "I enjoy working on machine learning projects."
+    print(f'Text: "{new_text}"')
+    print(f'Predicted Class: {classify_text(new_text)}')
diff --git a/NLP/Algorithms/Text_Classification/data/text_data.csv b/NLP/Algorithms/Text_Classification/data/text_data.csv
new file mode 100644
index 0000000..699ba2e
--- /dev/null
+++ b/NLP/Algorithms/Text_Classification/data/text_data.csv
@@ -0,0 +1,175 @@
+text,label
+"I love programming in Python.",positive
+"I hate bugs in my code.",negative
+"Machine learning is fascinating.",positive
+"This is frustrating.",negative
+"I enjoy learning new algorithms.",positive
+"Debugging can be very annoying.",negative
+"Data science is an interesting field.",positive
+"I don't like syntax errors.",negative
+"Natural Language Processing is a complex field.",positive
+"Software crashes are irritating.",negative
+"Python is a versatile language.",positive
+"I dislike runtime errors.",negative
+"Artificial Intelligence is the future.",positive
+"Dealing with legacy code is a pain.",negative
+"Deep learning opens new possibilities.",positive
+"Compilation errors are troublesome.",negative
+"I find joy in coding challenges.",positive
+"Unoptimized code is slow.",negative
+"Exploring new tech is exciting.",positive
+"Unexpected bugs can ruin the day.",negative
+"Cloud computing is a game changer.",positive
+"I get frustrated with merge conflicts.",negative
+"Building models is rewarding.",positive
+"Manual testing is tedious.",negative
+"AI can solve many problems.",positive
+"I dislike waiting for tests to run.",negative
+"Analyzing data is intriguing.",positive
+"Code reviews can be stressful.",negative
+"Learning new frameworks is fun.",positive
+"I hate network issues.",negative
+"Data visualization brings insights.",positive
+"Memory leaks are a nightmare.",negative
+"Training models is satisfying.",positive
+"Race conditions are tricky to debug.",negative
+"Big data is powerful.",positive
+"Resolving dependencies is a hassle.",negative
+"Predictive analytics is impactful.",positive
+"Performance bottlenecks are annoying.",negative
+"AI research is fascinating.",positive
+"Segmentation faults are frustrating.",negative
+"Reinforcement learning is innovative.",positive
+"Version control is essential.",positive
+"Data preprocessing is crucial.",positive
+"Invalid inputs cause errors.",negative
+"Optimization is challenging but fun.",positive
+"Cross-browser compatibility is a pain.",negative
+"Learning SQL is beneficial.",positive
+"Cryptic error messages are frustrating.",negative
+"Automation increases efficiency.",positive
+"Legacy systems are difficult to manage.",negative
+"Model deployment is exciting.",positive
+"Kernel panics are troublesome.",negative
+"Feature engineering is creative.",positive
+"System crashes are annoying.",negative
+"Understanding algorithms is rewarding.",positive
+"Dependency hell is frustrating.",negative
+"Blockchain is revolutionary.",positive
+"Server downtimes are a nuisance.",negative
+"Quantum computing is the next frontier.",positive
+"Troubleshooting can be time-consuming.",negative
+"Big data analytics is powerful.",positive
+"Compiler warnings should be addressed.",negative
+"Graph theory is fascinating.",positive
+"Interrupted connections are frustrating.",negative
+"Text mining reveals hidden patterns.",positive
+"Deadlocks are problematic.",negative
+"APIs make development easier.",positive
+"Stale documentation is unhelpful.",negative
+"Predictive modeling is insightful.",positive
+"Slow internet is frustrating.",negative
+"AI ethics is important.",positive
+"Memory corruption is a serious issue.",negative
+"Text classification is useful.",positive
+"Server misconfigurations cause issues.",negative
+"Computer vision is fascinating.",positive
+"Outdated libraries can cause problems.",negative
+"Speech recognition is impressive.",positive
+"Hotfixes can be risky.",negative
+"Recommender systems are powerful.",positive
+"Intermittent bugs are hard to fix.",negative
+"AI in healthcare is promising.",positive
+"Security vulnerabilities are concerning.",negative
+"Natural language understanding is impressive.",positive
+"Hard disk failures are disruptive.",negative
+"Image recognition is advanced.",positive
+"Permission issues are annoying.",negative
+"AI in finance is transformative.",positive
+"Unintended behaviors are problematic.",negative
+"Robotics combines AI and engineering.",positive
+"Network congestion is troublesome.",negative
+"Machine learning pipelines are essential.",positive
+"File corruption is a serious issue.",negative
+"AI in marketing is effective.",positive
+"Out-of-memory errors are problematic.",negative
+"Ethical AI is necessary.",positive
+"Server overloads are concerning.",negative
+"AI in manufacturing boosts efficiency.",positive
+"Software glitches are frustrating.",negative
+"Text summarization is useful.",positive
+"Packet loss causes issues.",negative
+"AI in transportation is innovative.",positive
+"Application freezes are annoying.",negative
+"Data augmentation improves models.",positive
+"Compatibility issues are troublesome.",negative
+"AI in education is transformative.",positive
+"Blue screen errors are problematic.",negative
+"AI in retail enhances customer experience.",positive
+"Invalid configurations cause problems.",negative
+"Time series forecasting is powerful.",positive
+"Network latency is annoying.",negative
+"AI in agriculture is revolutionary.",positive
+"Disk space issues are frustrating.",negative
+"AI in cybersecurity is critical.",positive
+"Software conflicts cause problems.",negative
+"Sentiment analysis provides insights.",positive
+"Permission denied errors are annoying.",negative
+"AI in entertainment is exciting.",positive
+"Server timeouts are problematic.",negative
+"AI in energy management is impactful.",positive
+"System hangs are troublesome.",negative
+"Reinforcement learning is promising.",positive
+"Invalid pointers cause crashes.",negative
+"Graph neural networks are innovative.",positive
+"Network partitions are concerning.",negative
+"Text generation is creative.",positive
+"Hardware malfunctions are problematic.",negative
+"AI in sports analytics is exciting.",positive
+"Unexpected reboots are annoying.",negative
+"AI in urban planning is beneficial.",positive
+"Corrupt databases are frustrating.",negative
+"Transfer learning is effective.",positive
+"Incorrect settings cause issues.",negative
+"Federated learning is emerging.",positive
+"Browser crashes are annoying.",negative
+"AI in environmental monitoring is crucial.",positive
+"Unresponsive applications are troublesome.",negative
+"AI in logistics optimizes operations.",positive
+"Disk read errors are problematic.",negative
+"AI-driven diagnostics are accurate.",positive
+"Permission errors are frustrating.",negative
+"Collaborative filtering is powerful.",positive
+"DNS issues cause connectivity problems.",negative
+"AI in customer service is efficient.",positive
+"Unexpected shutdowns are annoying.",negative
+"Self-supervised learning is innovative.",positive
+"Invalid arguments cause errors.",negative
+"AI in gaming enhances experience.",positive
+"Configuration mismatches cause problems.",negative
+"AI in language translation is impressive.",positive
+"System instability is concerning.",negative
+"Explainable AI improves transparency.",positive
+"Corrupted installations cause issues.",negative
+"AI in social media analysis is insightful.",positive
+"Browser compatibility issues are annoying.",negative
+"AI in supply chain management is impactful.",positive
+"Software updates can cause disruptions.",negative
+"Graph embeddings are useful.",positive
+"Invalid states cause crashes.",negative
+"Generative models are creative.",positive
+"Network security breaches are concerning.",negative
+"AI in fraud detection is crucial.",positive
+"System errors are frustrating.",negative
+"Zero-shot learning is impressive.",positive
+"Device driver issues are problematic.",negative
+"AI in personalized recommendations is effective.",positive
+"Login failures are annoying.",negative
+"AI in autonomous vehicles is groundbreaking.",positive
+"Application bugs are concerning.",negative
+"Few-shot learning is promising.",positive
+"Memory allocation errors cause issues.",negative
+"AI in medical imaging is revolutionary.",positive
+"Database deadlocks are troublesome.",negative
+"Unsupervised learning reveals patterns.",positive
+"Network drops cause problems.",negative
\ No newline at end of file
diff --git a/NLP/Algorithms/Text_Classification/preprocess.py b/NLP/Algorithms/Text_Classification/preprocess.py
new file mode 100644
index 0000000..e42c8b6
--- /dev/null
+++ b/NLP/Algorithms/Text_Classification/preprocess.py
@@ -0,0 +1,24 @@
+import nltk
+import re
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+
+# Download necessary NLTK data
+nltk.download('stopwords')
+nltk.download('wordnet')
+
+# Initialize lemmatizer and stop words
+lemmatizer = WordNetLemmatizer()
+stop_words = set(stopwords.words('english'))
+
+def preprocess_text(text):
+    # Remove non-alphabetic characters
+    text = re.sub(r'[^a-zA-Z\s]', '', text, re.I|re.A)
+    text = text.lower()
+    text = text.strip()
+    
+    # Tokenize and lemmatize
+    tokens = text.split()
+    tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]
+    
+    return ' '.join(tokens)
\ No newline at end of file
diff --git a/NLP/Algorithms/Text_Classification/train.py b/NLP/Algorithms/Text_Classification/train.py
new file mode 100644
index 0000000..e629a9e
--- /dev/null
+++ b/NLP/Algorithms/Text_Classification/train.py
@@ -0,0 +1,34 @@
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import Pipeline
+from sklearn.metrics import accuracy_score, classification_report
+import joblib
+from preprocess import preprocess_text
+
+# Load dataset
+data = pd.read_csv('data/text_data.csv')
+
+# Preprocess text data
+data['text'] = data['text'].apply(preprocess_text)
+
+# Split dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(data['text'], data['label'], test_size=0.2, random_state=42)
+
+# Create a pipeline with TfidfVectorizer and MultinomialNB
+model = Pipeline([
+    ('tfidf', TfidfVectorizer()),
+    ('nb', MultinomialNB())
+])
+
+# Train the model
+model.fit(X_train, y_train)
+
+# Evaluate the model
+y_pred = model.predict(X_test)
+print(f'Accuracy: {accuracy_score(y_test, y_pred)}')
+print(f'Classification Report:\n{classification_report(y_test, y_pred)}')
+
+# Save the model
+joblib.dump(model, 'models/text_classifier.pkl')
diff --git a/NLP/Documentation/Transformers.md b/NLP/Documentation/Transformers.md
new file mode 100644
index 0000000..489f793
--- /dev/null
+++ b/NLP/Documentation/Transformers.md
@@ -0,0 +1,95 @@
+# 📚 Transformers Library Overview
+
+Welcome to the official documentation for the **Transformers** library! 🚀 This library, developed by Hugging Face, is designed to provide state-of-the-art natural language processing (NLP) models and tools. It's widely used for a variety of NLP tasks, including text classification, translation, summarization, and more.
+
+## 📑 Table of Contents
+
+1. [Overview](#-overview)
+2. [Installation](#-installation)
+3. [Quick Start](#-quick-start)
+4. [Documentation](#-documentation)
+5. [Community and Support](#-community-and-support)
+7. [Additional Resources](#-additional-resources)
+8. [FAQ](#-faq)
+
+## 🔍 Overview
+
+Transformers are a type of deep learning model that excel in handling sequential data, like text. They rely on mechanisms such as attention to process and generate text in a way that captures long-range dependencies and contextual information.
+
+### Key Features
+
+- **State-of-the-art Models**: Access pre-trained models like BERT, GPT, T5, and many more. 🏆
+- **Easy-to-use Interface**: Simplify the process of using and fine-tuning models with a user-friendly API. 🎯
+- **Tokenization Tools**: Tokenize and preprocess text efficiently for model input. 🧩
+- **Multi-Framework Support**: Compatible with PyTorch and TensorFlow, giving you flexibility in your deep learning environment. ⚙️
+- **Extensive Documentation**: Detailed guides and tutorials to help you get started and master the library. 📖
+
+## 🔧 Installation
+
+To get started with the Transformers library, you need to install it via pip:
+
+```bash
+pip install transformers
+```
+
+### System Requirements
+
+- **Python**: Version 3.6 or later.
+- **PyTorch** or **TensorFlow**: Depending on your preferred framework. Visit the [official documentation](https://huggingface.co/transformers/installation.html) for compatibility details.
+
+## 🚀 Quick Start
+
+Here's a basic example to demonstrate how to use the library for sentiment classification:
+
+```python
+from transformers import pipeline
+
+# Initialize the pipeline for sentiment analysis
+classifier = pipeline('sentiment-analysis')
+
+# Analyze sentiment of a sample text
+result = classifier("Transformers are amazing for NLP tasks! 🌟")
+
+print(result)
+```
+
+### Common Pipelines
+
+- **Text Classification**: Classify text into predefined categories.
+- **Named Entity Recognition (NER)**: Identify entities like names, dates, and locations.
+- **Text Generation**: Generate text based on a prompt.
+- **Question Answering**: Answer questions based on a given context.
+- **Translation**: Translate text between different languages.
+
+## 📚 Documentation
+
+For comprehensive guides, tutorials, and API references, check out the following resources:
+
+- **[Transformers Documentation](https://huggingface.co/transformers/)**: The official site with detailed information on using and customizing the library.
+- **[Model Hub](https://huggingface.co/models)**: Explore a wide range of pre-trained models available for different NLP tasks.
+- **[API Reference](https://huggingface.co/transformers/main_classes/pipelines.html)**: Detailed descriptions of classes and functions in the library.
+
+## 🛠️ Community and Support
+
+Join the vibrant community of Transformers users and contributors to get support, share your work, and stay updated:
+
+- **[Hugging Face Forums](https://discuss.huggingface.co/)**: Engage with other users and experts. Ask questions, share your projects, and participate in discussions.
+- **[GitHub Repository](https://github.com/huggingface/transformers)**: Browse the source code, report issues, and contribute to the project. Check out the [issues](https://github.com/huggingface/transformers/issues) for ongoing conversations.
+
+## 🔗 Additional Resources
+
+- **[Research Papers](https://huggingface.co/papers)**: Read the research papers behind the models and techniques used in the library.
+- **[Blog Posts](https://huggingface.co/blog/)**: Discover insights, tutorials, and updates from the Hugging Face team.
+- **[Webinars and Talks](https://huggingface.co/events/)**: Watch recorded talks and webinars on the latest developments and applications of Transformers.
+
+## ❓ FAQ
+
+**Q: What are the main differences between BERT and GPT?**
+
+A: BERT (Bidirectional Encoder Representations from Transformers) is designed for understanding the context of words in both directions (left and right). GPT (Generative Pre-trained Transformer), on the other hand, is designed for generating text and understanding context in a left-to-right manner.
+
+**Q: Can I fine-tune a model on my own data?**
+
+A: Yes, the Transformers library provides tools for fine-tuning pre-trained models on your custom datasets. Check out the [fine-tuning guide](https://huggingface.co/transformers/training.html) for more details.
+
+Happy Transforming! 🌟
\ No newline at end of file
diff --git a/NLP/README.md b/NLP/README.md
index 57eeec5..d0f71dc 100644
--- a/NLP/README.md
+++ b/NLP/README.md
@@ -7,7 +7,8 @@
 | S.No  | Algorithm | S.No. | Algorithm | S.No. | Algorithm |
 |-------|-----------|-------|-----------|-------|-----------|
 |   1   | [Bag of Words](./Algorithms/BagOfWords) |   2   | [TF-IDF](./Algorithms/TF-IDF)          |   3   | [Word2Vec](./Algorithms/Word2Vec)           |
-|   4   | [GloVe](./Algorithms/GloVe) |   5   | [FastText](./Algorithms/FastText)          |   6   |          |
+|   4   | [GloVe](./Algorithms/GloVe) |   5   | [FastText](./Algorithms/FastText)          |   6   |  [Text Classification](./Algorithms/Text_Classification)        |
+|   7   | [Named Entity Recognition](./Algorithms/Named_Entity_Recognition) |   8   |           |   9   |          |
 
 ## Available Documentations
 
@@ -16,7 +17,7 @@
 | S.No  | Documentation | S.No  | Documentation | S.No | Documentation |
 |-------|---------------|-------|---------------|------|---------------|
 |   1   | [NLP Introduction](./Documentation/NLP_Introduction.md) |   2   | [NLTK Setup](./Documentation/NLTK-Setup.md) |   3  | [Text Preprocessing Techniques](./Documentation/Text_Preprocessing_Techniques.md) |
-|   4   | [Word Embeddings](./Documentation/Word_Embeddings.md) |   5   |               |   6  |               |
+|   4   | [Word Embeddings](./Documentation/Word_Embeddings.md) |   5   | [Transformers Overview](./Documentation/Transformers.md) |   6  |               |
 
 ## Available Projects