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| import streamlit as st | |
| import pandas as pd | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| import plotly.express as px | |
| from PIL import Image | |
| st.set_page_config( | |
| page_title = 'Churn Condition', | |
| layout = 'wide', | |
| initial_sidebar_state='expanded' | |
| ) | |
| def run(): | |
| # title | |
| st.title( 'Churn Customer') | |
| # sub header | |
| st.subheader('Churn or Not Churn') | |
| # insert image | |
| image = Image.open('2.jpg') | |
| st.image(image, caption='image from project pro, education purpose only') | |
| # Deskripsi | |
| st.write('Exploratory Data from Churn dataset') | |
| # show data frame | |
| st.write('The first 10 Data') | |
| df = pd.read_csv('https://raw.githubusercontent.com/mukhlishr/rasyidi/main/churn.csv') | |
| st.dataframe(df.head(10)) | |
| # Barplot target columns | |
| st.write('###### Churn condition ') | |
| st.write('###### Churn = 1 ; Not Churn = 0 ') | |
| fig=plt.figure(figsize=(15,5)) | |
| sns.countplot(x='churn_risk_score', data = df) | |
| st.pyplot(fig) | |
| # Barplot avg transaction value | |
| st.write('###### Avg Transaction Value by Customer churn') | |
| a=df[df['churn_risk_score']==1]['avg_transaction_value'] | |
| fig=plt.figure(figsize=(15,5)) | |
| sns.barplot(x=a.index, y=a) | |
| st.pyplot(fig) | |
| # Barplot frequency login | |
| st.write('###### Avg Frequency login (1 = 1-10, 2 = 11-20, ... 7 >= 51)') | |
| bins = [-1, 10,20,30,40,50,100] | |
| labels =[1,2,3,4,5,6,7] | |
| df['binned_frequency_login'] = pd.cut(df['avg_frequency_login_days'], bins,labels=labels).astype(float) | |
| fig=plt.figure(figsize=(15,5)) | |
| sns.countplot(x='binned_frequency_login', data = df) | |
| st.pyplot(fig) | |
| # Pieplot membership | |
| st.write('###### Membership') | |
| data = df['membership_category'].value_counts() | |
| keys = df['membership_category'].value_counts().index | |
| palette_color = sns.color_palette('bright') | |
| fig=plt.figure(figsize=(15,5)) | |
| plt.pie(data, labels=keys, colors=palette_color, autopct='%.0f%%') | |
| plt.title('Pieplot') | |
| st.pyplot(fig) | |
| # Pieplot joined through referral | |
| st.write('###### joined through referral') | |
| data = df['joined_through_referral'].value_counts() | |
| keys = df['joined_through_referral'].value_counts().index | |
| palette_color = sns.color_palette('bright') | |
| fig=plt.figure(figsize=(15,5)) | |
| plt.pie(data, labels=keys, colors=palette_color, autopct='%.0f%%') | |
| plt.title('Pieplot') | |
| st.pyplot(fig) | |
| # Pieplot preferred_offer_types | |
| st.write('###### preferred offer types') | |
| data = df['preferred_offer_types'].value_counts() | |
| keys = df['preferred_offer_types'].value_counts().index | |
| palette_color = sns.color_palette('bright') | |
| fig=plt.figure(figsize=(15,5)) | |
| plt.pie(data, labels=keys, colors=palette_color, autopct='%.0f%%') | |
| plt.title('Pieplot') | |
| st.pyplot(fig) | |
| # Pieplot past_complaint | |
| st.write('###### past complaint') | |
| data = df['past_complaint'].value_counts() | |
| keys = df['past_complaint'].value_counts().index | |
| palette_color = sns.color_palette('bright') | |
| fig=plt.figure(figsize=(15,5)) | |
| plt.pie(data, labels=keys, colors=palette_color, autopct='%.0f%%') | |
| plt.title('Pieplot') | |
| st.pyplot(fig) | |
| # Pieplot feedback | |
| st.write('###### feedback ') | |
| data = df['feedback'].value_counts() | |
| keys = df['feedback'].value_counts().index | |
| palette_color = sns.color_palette('bright') | |
| fig=plt.figure(figsize=(15,5)) | |
| plt.pie(data, labels=keys, colors=palette_color, autopct='%.0f%%') | |
| plt.title('Pieplot') | |
| st.pyplot(fig) | |
| if __name__ == '__main__': | |
| run() |