Update visualization explanation

eda.py

@@ -14,7 +14,7 @@ def run():
     st.title('Customer Churn Predictor')
 
     #Sub header 
-    st.subheader('Description for Customer Churn Predicto') 
+    st.subheader('Description for Customer Churn Predictor') 
 
     # Insert Gambar 
     image = Image.open('music.jpg')
@@ -22,8 +22,8 @@ def run():
 
     #description 
     st.write('The goals of this churn estimator')
-    st.write('Dengar adalah sebuah platform streaming music yang ingin memprediksi customer yang akan churn sehingga meminta bantuan data scientist untuk membuat model dalam melakukan prediksi.')
-    st.write('Dengan model ini diharapkan dengar dapat memprediksi customer churn atau tidak sehingga bisa lebih berfokus pada tujuannya.')
+    st.write('Dengar is a music streaming platform that ask data scientist to predict will the customer churn')
+    st.write('With this model we hope Dengar will be more focused with their goals')
     st.markdown('---')
     
     st.write('This page is created to show the visualization of the dataset')
@@ -71,6 +71,7 @@ def run():
 
     #Age Distribution
     plot_hist(data=dup['age'], title='Age distribution', x_label='age')
+    st.write('We can see that dengar had a distribution of age from 10-60')
 
     #Time Spent
     plot_hist(data=dup['avg_time_spent'], title='Time Spent', x_label='avg_time_spent')
@@ -91,6 +92,7 @@ def run():
     plt.title('Customer Region')
     plt.axis('equal')
     st.pyplot(fig)
+    st.write('We can see that dengar had 3 region with the most users from town')
 
      #Memberhsip based on Region
     plot_countplot_with_numbers(x='membership_category',hue='region_category', title='Memberhsip based on Region', data=dup, palette='flare', figsize=(7, 5))
@@ -108,7 +110,7 @@ def run():
     plt.title('Churn Risk')
     plt.axis('equal')
     st.pyplot(fig)
-    
+    st.write('We can see from the data that most users in Dengar will churn')
     
 
     #churn risk based on gender