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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 = 'Customer Churn Predictor'
)
def run():
# Membuat Title
st.title('Customer Churn Predictor')
#Sub header
st.subheader('Description for Customer Churn Predicto')
# Insert Gambar
image = Image.open('music.jpg')
st.image(image, caption ='Dengar')
#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.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')
#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)
#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)
#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() |