eda.py

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 Prediction')

    # 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, ... 6 >= 51)')
    bins = [-1, 10,20,30,40,50,100]
    labels =[1,2,3,4,5,6]
    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()