diff --git a/Readme.md b/Readme.md
index 79bfdee..8516c5a 100644
--- a/Readme.md
+++ b/Readme.md
@@ -1,42 +1,44 @@
 # 📰 QuickFactChecker – Fake News Detection
 
 ## 📌 Project Overview
-QuickFactChecker is a machine learning–based web app that helps detect whether a news article is **real** or **fake**.  
-It uses different models (e.g., Naive Bayes, LSTM) trained on the **LIAR dataset** to evaluate credibility and assist users in identifying potentially misleading information.  
+QuickFactChecker is a **machine learning–based web app** that helps detect whether a news article is **real** or **fake**.
+It uses different models (e.g., Naive Bayes, LSTM) trained on the **LIAR dataset** to evaluate credibility and assist users in identifying potentially misleading information.
 
 ---
 
 ## ✨ Features
-- ✅ Fake news classification using ML models (Naive Bayes, LSTM).  
-- ✅ Interactive web app built with **Flask** and **HTML templates**.  
-- ✅ Preprocessed dataset included (`train.tsv`, `test.tsv`, `valid.tsv`).  
-- ✅ Notebooks for **data analysis & experimentation** (`liar-data-analysis.ipynb`, `dataset.ipynb`).  
-- ✅ Easy setup with `requirements.txt`.  
+- ✅ Fake news classification using ML models (**Naive Bayes**, **Logistic Regression**, **Random Forest**, and **LSTM**).
+- ✅ Interactive web app built with **Flask** and **HTML templates**.
+- ✅ **Automated NLTK Setup** to prevent missing resource errors.
+- ✅ Preprocessed dataset included (`train.tsv`, `test.tsv`, `valid.tsv`).
+- ✅ Notebooks for **data analysis & experimentation** (`liar-data-analysis.ipynb`, `dataset.ipynb`).
+- ✅ Easy setup with `requirements.txt`.
 
 ---
 
 ## 📂 Project Structure
 ```bash
 dataset/liar
-   ├── README.md   ##Dataset description
-   ├── train.tsv   ##Training data
-   ├── test.tsv    ##Testing data
-   ├── valid.tsv   ##Validation data
+   ├── README.md   ##Dataset description
+   ├── train.tsv   ##Training data
+   ├── test.tsv    ##Testing data
+   ├── valid.tsv   ##Validation data
 
 module/
-   ├── dataset.ipynb
-   ├── fake-news-detection-using-lstm.ipynb
-   ├── fake-news-detection-using-nb.ipynb
-   ├── liar-data-analysis.ipynb
+   ├── dataset.ipynb
+   ├── fake-news-detection-using-lstm.ipynb
+   ├── fake-news-detection-using-nb.ipynb
+   ├── liar-data-analysis.ipynb
 
 templates/
-   ├── index.html
+   ├── index.html
 
 scripts/
-   └── fake_news_logreg_rf.py     ## Train & evaluate Naive Bayes, Logistic Regression, Random Forest
+   ├── setup_nltk.py          
+   └── fake_news_logreg_rf.py     ## Train & evaluate Naive Bayes, Logistic Regression, Random Forest
 results/
-   ├── model_comparison.md        ## Generated baseline comparison table (markdown)
-   └── comparison.png             ## Generated accuracy bar chart
+   ├── model_comparison.md        ## Generated baseline comparison table (markdown)
+   └── comparison.png             ## Generated accuracy bar chart
 
 .gitattributes
 app.py
@@ -67,6 +69,11 @@ requirements.txt
    pip install -r requirements.txt
    ```
 
+5. Download NLTK Corpora:
+```bash
+python scripts/setup_nltk.py
+```
+
 ## 📊 Baseline Model Comparison
 
 We evaluated three models on the LIAR dataset using TF-IDF features. Example results (accuracy & precision):
diff --git a/scripts/setup_nltk.py b/scripts/setup_nltk.py
new file mode 100644
index 0000000..af91752
--- /dev/null
+++ b/scripts/setup_nltk.py
@@ -0,0 +1,49 @@
+import nltk
+import ssl
+
+def setup_nltk_resources():
+    """
+    Downloads required NLTK resources, handling potential SSL and download errors.
+    """
+
+    try:
+        _create_unverified_https_context = ssl._create_unverified_context
+    except AttributeError:
+        pass
+    else:
+        ssl._create_default_https_context = _create_unverified_https_context
+
+    resource_paths = {
+        "punkt": "tokenizers/punkt",
+        "stopwords": "corpora/stopwords",
+        "wordnet": "corpora/wordnet",
+        "omw-1.4": "corpora/omw-1.4",
+    }
+
+    print("🚀 Starting NLTK resource setup...\n")
+
+    for package, path in resource_paths.items():
+        print(f"Processing: {package}")
+
+        try:
+            nltk.data.find(path)
+            print(f"  ✅ {package} is already installed.")
+        except LookupError:
+            try:
+                print(f"  ⬇️ Downloading {package}...")
+                nltk.download(package, quiet=False) 
+                print(f"  ✅ Successfully downloaded: {package}")
+            except Exception as e:
+                print(f"--- 🛑 ERROR downloading {package} ---")
+                print("  -> Check your network connection or run with administrator permissions.")
+                print(f"  -> Details: {type(e).__name__}: {e}")
+        except Exception as e:
+            print(f"⚠️ Unexpected error while checking {package}: {e}")
+
+        print()
+
+    print("🎉 NLTK resource setup complete.")
+    print("If you still encounter 'LookupError', ensure NLTK is installed correctly and your Python environment is active.")
+
+if __name__ == "__main__":
+    setup_nltk_resources()