diff --git a/README.md b/README.md
index 06cd396..7a9d57b 100644
--- a/README.md
+++ b/README.md
@@ -2,11 +2,7 @@
 
 [![Downloads](https://pepy.tech/badge/deepface)](https://pepy.tech/project/deepface)
 
-**deepface** is a lightweight python based facial analysis framework including face recognition and demography. You can use the framework with a just few lines of codes.
-
-# Face Recognition
-
-Verify function under the DeepFace interface is used for face recognition.
+**deepface** is a lightweight python based face recognition framework. You can verify faces with just a few lines of codes.
 
 ```python
 from deepface import DeepFace
@@ -56,54 +52,9 @@ Instead of using pre-tuned threshold values, you can alternatively check the dis
 distance = result[1] #the less the better
 threshold = 0.30 #threshold for VGG-Face and Cosine Similarity
 if distance < threshold:
-   return True
+	return True
 else:
-   return False
-```
-
-# Facial Attribute Analysis
-
-Deepface also offers facial attribute analysis including [`age`](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [`gender`](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [`emotion`](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) and [`race`](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/) predictions. Analysis function under the DeepFace interface is used to find demography of a face.
-
-```python
-from deepface import DeepFace
-demography = DeepFace.analyze("img.zip") #passing nothing as 2nd argument will find everything
-#demography = DeepFace.analyze("img.zip", ['age', 'gender', 'race', 'emotion']) #identical to above line
-```
-
-Analysis function returns a json object.
-
-```
-{
-   "age": 31.940666721338523
-   , "gender": "Woman"
-   , "race": {
-      "asian": 11.314528435468674,
-      "indian": 17.498773336410522,
-      "black": 3.541698679327965,
-      "white": 21.96589708328247,
-      "middle eastern": 19.87851709127426,
-      "latino hispanic": 25.800585746765137
-   }
-   , "dominant_race": "latino hispanic"
-   , "emotion": {
-      "angry": 6.004959843039945e-16,
-      "disgust": 4.9082449499136944e-34,
-      "fear": 4.7907148065142067e-23,
-      "happy": 100.0,
-      "sad": 4.8685008000541987e-14,
-      "surprise": 5.66862615875019e-10,
-      "neutral": 3.754812086254056e-09
-   }
-   , "dominant_emotion": "happy"
-}
-```
-
-Then, you can retrieve the fields of the response object easily in Python.
-
-```python
-import json
-print("Age: ",demography["age"])
+	return False
 ```
 
 # Installation
@@ -126,13 +77,11 @@ Initial tests are run for Python 3.5.5 on Windows 10 but this is an OS-independe
 
 ```
 pip install numpy==1.14.0
-pip install pandas==0.23.4
 pip install matplotlib==2.2.2
 pip install gdown==3.10.1
 pip install opencv-python==3.4.4
 pip install tensorflow==1.9.0
 pip install keras==2.2.0
-pip install tqdm==4.30.0
 ```
 
 # Disclaimer