diff --git a/docs/NLP/projects/next-word-pred.md b/docs/NLP/projects/next-word-pred.md
new file mode 100644
index 00000000..84c68582
--- /dev/null
+++ b/docs/NLP/projects/next-word-pred.md
@@ -0,0 +1,193 @@
+
+
+# Next Word Prediction using LSTM
+
+### AIM 
+To predict the next word using LSTM.
+
+
+### DATASET LINK 
+[Dataset](https://www.kaggle.com/datasets/muhammadbilalhaneef/sherlock-holmes-next-word-prediction-corpus)
+
+### NOTEBOOK LINK 
+[Code](https://colab.research.google.com/drive/1Y1icIR8ZViZzRn6LV-ZSuGvXHde8T7yA)
+
+
+### LIBRARIES NEEDED
+
+??? quote "LIBRARIES USED"
+
+    - pandas
+    - numpy
+    - scikit-learn
+    - matplotlib
+    - seaborn
+    - tensorflow
+    - keras
+
+--- 
+
+### DESCRIPTION 
+
+
+!!! info "What is the requirement of the project?"
+    - To create an intelligent system capable of predicting the next word in a sentence based on its context.
+    - The need for such a system arises in applications like autocomplete, chatbots, and virtual assistants.
+
+??? info "Why is it necessary?"
+    - Enhances user experience in text-based applications by offering accurate suggestions.
+    - Reduces typing effort, especially in mobile applications.
+
+??? info "How is it beneficial and used?"
+    - Improves productivity: By predicting words, users can complete sentences faster.
+    - Supports accessibility: Assists individuals with disabilities in typing.
+    - Boosts efficiency: Helps in real-time text generation in NLP applications like chatbots and email composition.
+
+??? info "How did you start approaching this project? (Initial thoughts and planning)"
+    - Studied LSTM architecture and its suitability for sequential data.
+    - Explored similar projects and research papers to understand data preprocessing techniques.
+    - Experimented with tokenization, padding, and sequence generation for the dataset.
+
+??? info "Mention any additional resources used (blogs, books, chapters, articles, research papers, etc.)."
+    - Blogs on LSTM from Towards Data Science.
+    - TensorFlow and Keras official documentation.
+
+
+--- 
+
+### EXPLANATION 
+
+#### DETAILS OF THE DIFFERENT FEATURES 
+---
+
+#### PROJECT WORKFLOW 
+=== "Step 1"
+
+    Initial data exploration and understanding:
+
+    - Gathered text data from open-source datasets.
+    - Analyzed the structure of the data.
+    - Performed basic text statistics to understand word frequency and distribution.
+
+=== "Step 2"
+    
+    Data cleaning and preprocessing
+
+    - Removed punctuation and convert text to lowercase.
+    - Tokenized text into sequences and pad them to uniform length.
+
+=== "Step 3"
+    Feature engineering and selection
+        
+    - Created input-output pairs for next-word prediction using sliding window techniques on tokenized sequences.
+
+=== "Step 4"
+    Model training and evaluation:
+
+    - Used an embedding layer to represent words in a dense vector space.
+    - Implemented LSTM-based sequential models to learn context and dependencies in text. 
+    - Experimented with hyperparameters like sequence length, LSTM units, learning rate, and batch size.
+
+=== "Step 5"
+    Model optimization and fine-tuning
+        
+    - Adjusted hyperparameters like embedding size, LSTM units, and learning rate.
+
+=== "Step 6"
+    Validation and testing
+
+    - Used metrics like accuracy and perplexity to assess prediction quality.  
+    - Validated the model on unseen data to test generalization. 
+
+--- 
+
+#### PROJECT TRADE-OFFS AND SOLUTIONS 
+
+=== "Trade-Off 1"
+    Accuracy vs Training Time:
+        
+    - **Solution**: Balanced by reducing the model's complexity and using an efficient optimizer.
+
+=== "Trade-Off 2"
+    Model complexity vs. Overfitting:
+        
+    - **Solution**: Implemented dropout layers and monitored validation loss during training.
+
+--- 
+
+### SCREENSHOTS 
+
+
+!!! success "Project workflow"
+
+    ``` mermaid
+      graph LR
+        A[Start] --> B{Data Preprocessed?};
+        B -->|No| C[Clean and Tokenize];
+        C --> D[Create Sequences];
+        D --> B;
+        B -->|Yes| E[Model Designed?];
+        E -->|No| F[Build LSTM/Transformer];
+        F --> E;
+        E -->|Yes| G[Train Model];
+        G --> H{Performant?};
+        H -->|No| I[Optimize Hyperparameters];
+        I --> G;
+        H -->|Yes| J[Deploy Model];
+        J --> K[End];
+    ```
+
+
+--- 
+
+### MODELS USED AND THEIR EVALUATION METRICS 
+
+
+|    Model   | Accuracy |  MSE  | R2 Score |
+|------------|----------|-------|----------|
+| LSTM       |    72%   |   -   |    -     |
+
+--- 
+#### MODELS COMPARISON GRAPHS 
+
+!!! tip "Models Comparison Graphs"
+
+    === "LSTM Loss"
+        ![model perf](https://github.com/user-attachments/assets/db3a6d81-96fa-46d6-84b4-6395d46221d6)
+
+---
+### CONCLUSION 
+
+#### KEY LEARNINGS 
+
+
+!!! tip "Insights gained from the data"
+
+    - The importance of preprocessing for NLP tasks.
+    - How padding and embeddings improve the model’s ability to generalize.
+
+??? tip "Improvements in understanding machine learning concepts"
+
+    - Learned how LSTMs handle sequential dependencies.
+    - Understood the role of softmax activation in predicting word probabilities.
+
+??? tip "Challenges faced and how they were overcome"
+
+    - Challenge: Large vocabulary size causing high memory usage.
+    - Solution: Limited vocabulary to the top frequent words.
+
+--- 
+
+#### USE CASES
+
+=== "Application 1"
+
+    **Text Autocompletion**
+    
+      - Used in applications like Gmail and search engines to enhance typing speed.
+
+=== "Application 2"
+
+    **Virtual Assistants**
+    
+      - Enables better conversational capabilities in chatbots and AI assistants.