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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() |