diff --git a/Machine Learning - Sentiment Analysis b/Machine Learning - Sentiment Analysis
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+++ b/Machine Learning - Sentiment Analysis	
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+import nltk
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+
+# Download VADER lexicon (if not already downloaded)
+nltk.download('vader_lexicon')
+
+# Initialize the sentiment analyzer
+sid = SentimentIntensityAnalyzer()
+
+def analyze_sentiment(text):
+    # Calculate sentiment scores
+    sentiment_scores = sid.polarity_scores(text)
+
+    # Determine sentiment label based on the compound score
+    compound_score = sentiment_scores['compound']
+    if compound_score >= 0.05:
+        return "Positive"
+    elif compound_score <= -0.05:
+        return "Negative"
+    else:
+        return "Neutral"
+
+# Input text for analysis
+text = "I love this product! It's amazing."
+
+# Perform sentiment analysis
+sentiment = analyze_sentiment(text)
+
+# Display the sentiment result
+print(f"Sentiment: {sentiment}")