diff --git a/Domains/AI-ML/MiniProjects/Emotion_classifier/README.md b/Domains/AI-ML/MiniProjects/Emotion_classifier/README.md
new file mode 100644
index 00000000..9098c08d
--- /dev/null
+++ b/Domains/AI-ML/MiniProjects/Emotion_classifier/README.md
@@ -0,0 +1,49 @@
+**Contributor:** Ansh-1019
+
+# 🎭 Real-Time Emotion Detection using DeepFace and OpenCV
+
+This project detects human emotions in real-time using a webcam feed.  
+It utilizes the **DeepFace** library for emotion analysis and **OpenCV** for face detection and visualization.
+
+---
+
+## 🚀 Features
+
+- Detects emotions such as **happy**, **sad**, **angry**, **surprise**, **neutral**, and more.  
+- Works in **real-time** using your webcam.  
+- Displays bounding boxes and emotion labels on detected faces.  
+- Gracefully handles frames without detectable faces.
+
+---
+
+## 🧠 Technologies Used
+
+- **Python 3.8+**
+- **OpenCV** – for capturing webcam feed and drawing on frames.
+- **DeepFace** – for facial emotion analysis.
+- **Haar Cascade Classifier** – for basic face detection.
+
+---
+
+## 📦 Installation
+
+### 1️⃣ Clone or Download the Repository
+```bash
+git clone https://github.com/yourusername/emotion-detection.git
+cd emotion-detection
+```
+
+---
+
+## ▶️ How to Run
+
+1. Save the provided code as **`emotion.py`** in your project folder.  
+2. Open **Command Prompt** or **Terminal** in that folder.  
+3. Run the script:
+
+   ```bash
+   python emotion.py
+    ```
+4. Your webcam will open automatically.  
+5. A bounding box with your detected emotion will appear on screen.  
+6. Press **`q`** to quit the program.
diff --git a/Domains/AI-ML/MiniProjects/Emotion_classifier/emotion.py b/Domains/AI-ML/MiniProjects/Emotion_classifier/emotion.py
new file mode 100644
index 00000000..87221883
--- /dev/null
+++ b/Domains/AI-ML/MiniProjects/Emotion_classifier/emotion.py
@@ -0,0 +1,45 @@
+import cv2
+from deepface import DeepFace
+
+
+cap = cv2.VideoCapture(0)
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    try:
+        
+        result = DeepFace.analyze(frame, actions=['emotion'], enforce_detection=False)
+        
+        
+        if isinstance(result, list):
+            emotion = result[0]['dominant_emotion']
+        else:
+            emotion = result['dominant_emotion']
+
+        
+        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+        faces = face_cascade.detectMultiScale(frame, 1.3, 5)
+
+        for (x, y, w, h) in faces:
+            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
+            cv2.putText(frame, emotion, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+
+    except Exception as e:
+        
+        print("No face detected or error:", e)
+        
+        cv2.putText(frame, "No face detected", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+
+    
+    cv2.imshow('Emotion Detection', frame)
+
+    
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+
+cap.release()
+cv2.destroyAllWindows()
\ No newline at end of file