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import streamlit as st
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from PIL import Image
st.set_page_config(
page_title = 'Customer Churn Predictor'
)
def run():
# Membuat Title
st.title('Customer Churn Predictor')
#Sub header
st.subheader('Description for Customer Churn Predictor')
# Insert Gambar
image = Image.open('music.jpg')
st.image(image, caption ='Dengar')
#description
st.write('The goals of this churn estimator')
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')
st.markdown('---')
#show dataframe
st.write('Dataset')
dup = pd.read_csv('https://raw.githubusercontent.com/FerdiErs/SQL/main/churn.csv')
st.dataframe(dup)
#visualization Function
def plot_hist(data, title, x_label):
#create hist plot
fig = plt.figure(figsize=(7, 5))
sns.histplot(data, kde=True, bins=20, edgecolor='black')
#Title and Labels
st.title(title)
st.pyplot(fig)
def plot_countplot_with_numbers(data, x, hue, title, palette, figsize=(7, 5)):
# Create CountPlot
fig = plt.figure(figsize=figsize)
g = sns.countplot(x=x, hue=hue, data=data, palette=palette)
# Rotate x labels and move legend outside of the plot
g.set_xticklabels(g.get_xticklabels(), rotation=45, ha="right")
sns.move_legend(g, "upper left", bbox_to_anchor=(1, 1))
# Number in visualization
for p in g.patches:
height = p.get_height()
g.annotate(f'{height}', (p.get_x() + p.get_width() / 2., height), ha='center', va='bottom', fontsize=10)
# Title and labels
st.title(title)
st.pyplot(fig)
#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')
#pie chart customer region
st.write('### Customer Region Distribution')
reg = dup.region_category.value_counts()
def make_autopct(values):
def my_autopct(pct):
total = sum(values)
val = int(round(pct*total/100.0))
return '{p:.2f}% ({v:d})'.format(p=pct,v=val)
return my_autopct
# Define a custom color palette
colors = plt.cm.tab20c.colors
fig = plt.figure(figsize=(5,5))
reg.plot.pie(autopct=make_autopct(reg), startangle=90, colors=colors)
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))
#membuat pie chart churn risk
#count churn
ch = dup.churn_risk_score.value_counts()
# Define a custom color palette
colors = plt.cm.Set3.colors
# plot the data
fig = plt.figure(figsize=(5,5))
ch.plot.pie(autopct=make_autopct(ch), startangle=90, colors=colors)
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
plot_countplot_with_numbers(data=dup, x='gender', hue='churn_risk_score', title='Churn Risk based on gender', palette='crest', figsize=(7, 5))
#churn risk based on membership
plot_countplot_with_numbers(data=dup, x='membership_category', hue='churn_risk_score', title='Churn Risk based on Membership', palette='flare', figsize=(7, 5))
if __name__== '__main__':
run() |