Update eda

app.py

@@ -2,11 +2,11 @@
 import streamlit as st
 
 # Import the created Streamlit Page
-# import eda
+import eda
 import prediction
 
 # Navigation button
-navigasi = st.sidebar.selectbox(label='Select Page:', options=['Home Page', 'Prediction']) # 'Exploratory Data Analysis', 
+navigasi = st.sidebar.selectbox(label='Select Page:', options=['Home Page', 'Exploratory Data Analysis', 'Prediction']) 
 
 # Looping for navigation
 if navigasi == 'Home Page':
@@ -18,9 +18,9 @@ if navigasi == 'Home Page':
     st.write('**Classification Model for Predicting Customer Churn**') 
     st.caption('Please select another menu in the Select Box on the left of your screen to start!')
 
-# # Displays the EDA page
-# elif navigasi == 'Exploratory Data Analysis':
-#     eda.run()
+# Displays the EDA page
+elif navigasi == 'Exploratory Data Analysis':
+    eda.run()
 
 # Displays the Predict page
 elif navigasi == 'Prediction':

eda.py

@@ -0,0 +1,152 @@
+
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Function to run the EDA
+def run():
+    # Load dataset
+    df = pd.read_csv('Churn_Modelling.csv') 
+
+    # Set the title in the Streamlit app
+    st.title('Exploratory Data Analysis of Customer Churn')
+    st.write('---')
+
+    # 1. Distribution of Customer Churn
+    st.subheader('1. Distribution of Customer Churn')
+    value_counts = df['Exited'].value_counts()
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))
+    colors_pie = ['pink', 'grey']
+    ax1.pie(value_counts, labels=['Not Churn', 'Churn'], autopct='%1.1f%%', colors=colors_pie)
+    ax1.set_title('Distribution of Customer Churn', fontsize=12)
+    colors_bar = ['pink', 'grey']
+    value_counts.index = ['Not Churn', 'Churn']
+    value_counts.plot(kind='bar', ax=ax2, color=colors_bar)
+    ax2.set_title('Count of Customer Churn', fontsize=12)
+    ax2.set_xlabel('Churn Status', fontsize=10)
+    ax2.set_ylabel('Count', fontsize=10)
+    ax2.tick_params(axis='x', rotation=0)
+    for i, v in enumerate(value_counts):
+        ax2.text(i, v + 10, str(v), ha='center', va='bottom')
+    st.pyplot(fig)
+    st.write('---')
+
+    # 2. Customer Churn by Gender
+    st.subheader('2. Customer Churn by Gender')
+    gender_group = df.groupby(['Exited', 'Gender']).size().unstack()
+    fig, ax = plt.subplots(figsize=(8, 5))
+    bar_chart = gender_group.plot(kind='bar', color=['pink', 'grey'], ax=ax)
+    for container in bar_chart.containers:
+        bar_chart.bar_label(container)
+    bar_chart.set_xticklabels(['Not Churn', 'Churn'], rotation=0)
+    bar_chart.set_xlabel('Churn Status')
+    bar_chart.set_ylabel('Count')
+    bar_chart.set_title('Customer Churn by Gender')
+    st.pyplot(fig)
+    st.write('**Churn by Gender Statistics**')
+    st.write('Percentage of Male customers who churned: 16.5%')
+    st.write('Percentage of Female customers who churned: 25.1%')
+    st.write('---')
+
+
+    # 3 Customer Churn by Age
+    st.subheader('3. Customer Churn by Age')
+    churn_customers = df[df['Exited'] == 1]
+    not_churn_customers = df[df['Exited'] == 0]
+    plt.figure(figsize=(10, 6))
+    plt.hist(not_churn_customers['Age'], bins=30, alpha=0.5, label='Not Churn', color='pink')
+    plt.hist(churn_customers['Age'], bins=30, alpha=0.5, label='Churn', color='grey')
+    plt.title('Customer Churn by Age', fontsize=14)
+    plt.xlabel('Age')
+    plt.ylabel('Frequency')
+    plt.legend(loc='upper right')
+    st.pyplot()
+    st.write('---')
+
+    # 4. Customer Churn by Geography
+    st.subheader('4. Customer Churn by Geography')
+    geo_group = df.groupby(['Exited', 'Geography']).size().unstack()
+    fig, ax = plt.subplots(figsize=(8, 5))
+    bar_char = geo_group.plot(kind='bar', color=['pink', 'grey', 'lightblue'], ax=ax)
+    for container in bar_char.containers:
+        bar_char.bar_label(container)
+    bar_char.set_xticklabels(['Not Churn', 'Churn'], rotation=0)
+    bar_char.set_xlabel('Churn Status', fontsize=9)
+    bar_char.set_ylabel('Count', fontsize=9)
+    bar_char.set_title('Customer Churn by Geography', fontsize=10)
+    st.pyplot()
+    st.write('**Churn by Geography Statistics**')
+    st.write('Percentage of customers who churned in France: 16.2 %')
+    st.write('Percentage of customers who churned in Germany: 32.5 %')
+    st.write('Percentage of customers who churned in Spain: 16.7 %')
+    st.write('---')
+
+    # 5. Customer Churn by Tenure
+    st.subheader('5. Customer Churn by Tenure')
+    churn_by_tenure = df.groupby(['Tenure', 'Exited']).size().unstack()
+    fig, ax = plt.subplots(figsize=(8, 5))
+    churn_by_tenure.plot(kind='bar', stacked=True, color=['pink', 'grey'], ax=ax)
+    plt.title('Customer Churn by Tenure', fontsize=14)
+    plt.xticks(rotation=0)
+    plt.xlabel('Tenure (years)')
+    plt.ylabel('Number of Customers')
+    plt.legend(['Not Churn', 'Churn'])
+    for container in ax.containers:
+        ax.bar_label(container, label_type='center')
+    st.pyplot(fig)
+    st.write('---')
+
+    # 6. Customer Churn by Number of Products
+    st.subheader('6. Customer Churn by Number of Products')
+    plt.figure(figsize=(8, 5))
+    ax = sns.countplot(data=df, x='NumOfProducts', hue='Exited', palette=['grey', 'pink'])
+    plt.title('Customer Churn by Number of Products', fontsize=14)
+    plt.xlabel('Number of Products', fontsize=10)
+    plt.ylabel('Number of Customers', fontsize=10)
+    for container in ax.containers:
+        ax.bar_label(container)
+    st.pyplot()
+    st.write('**Churn by Number of Products Statistics**')
+    st.write('Percentage of customers who churned with 1 products: 27.7 %')
+    st.write('Percentage of customers who churned with 2 products: 7.6 %')
+    st.write('Percentage of customers who churned with 3 products: 82.7 %')
+    st.write('Percentage of customers who churned with 4 products: 100 %')
+    st.write('---')
+
+    # 7. Customer Churn by Has Credit Card or Not
+    st.subheader('7. Customer Churn by Has Credit Card or Not')
+    churn_by_HasCrCard = df.groupby(['HasCrCard', 'Exited']).size().unstack()
+    fig, ax = plt.subplots(figsize=(8, 4))
+    churn_by_HasCrCard.plot(kind='bar', stacked=True, color=['pink', 'grey'], ax=ax)
+    plt.title('Customer Churn by HasCrCard', fontsize=11)
+    plt.xticks(rotation=0)
+    plt.xlabel('HasCrCard', fontsize=10)
+    plt.ylabel('Number of Customers', fontsize=10)
+    plt.legend(['Not Churn', 'Churn'])
+    ax.set_xticklabels(['Has Credit Card', 'No Credit Card'])
+    for container in ax.containers:
+        ax.bar_label(container, label_type='center')
+    st.pyplot(fig)
+    st.write('**Churn by Has Credit Card or Not Statistics**')
+    st.write('Percentage of customers who churned with No Credit Card: 20.8 %')
+    st.write('Percentage of customers who churned with Has Credit Card: 20.2 %')
+    st.write('---')
+
+    # 8. Customer Churn by Is Active Member or Not
+    st.subheader('8. Customer Churn by Is Active Member or Not')
+    churn_by_IsActiveMember = df.groupby(['IsActiveMember', 'Exited']).size().unstack()
+    fig, ax = plt.subplots(figsize=(8, 4))
+    churn_by_IsActiveMember.plot(kind='bar', stacked=True, color=['pink', 'grey'], ax=ax)
+    plt.title('Customer Churn by IsActiveMember', fontsize=11)
+    plt.xticks(rotation=0)
+    plt.xlabel('IsActiveMember', fontsize=10)
+    plt.ylabel('Number of Customers', fontsize=10)
+    plt.legend(['Not Churn', 'Churn'],loc='lower center')
+    ax.set_xticklabels(['Inactive Member', 'Active Member'])
+    for container in ax.containers:
+        ax.bar_label(container, label_type='center')
+    st.pyplot(fig)
+    st.write('**Churn by Is Active Member or Not Statistics**')
+    st.write('Percentage of customers who churned with Inactive Member: 26.9 %')
+    st.write('Percentage of customers who churned with Active Member: 14.3 %')