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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() |