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app.py

@@ -1,21 +1,30 @@
-# -- IMPORT LIBRARIES --
 import streamlit as st
-import time
 
-# -- SET UP CONFIG --
-st.set_page_config(page_title='Predictix', 
-                   layout='wide',
-                   page_icon='👋🏻')
+# Import the other scripts
+import predict
+import eda
 
-def main():
-    # Navigation sidebar
-    st.sidebar.title("🧭 Navigation")
-    page = st.sidebar.selectbox("Go to", ["🏠 Home", "📊 EDA", "🔍 Prediction"])
+# Set the title of the app
+st.title("Customer Churn Analysis App")
 
-    if page == "🏠 Home":
-        # Sidebar content for Home page
-        st.sidebar.markdown("---")
-        st.sidebar.subheader("📊 About the Model")
-        recall_classification = 0.82
-        st.sidebar.write("🎯 Model Sentiment Analysis:")
-        st.sidebar.progress(recall_classification)
+# Create a sidebar for navigation
+st.sidebar.title("Navigation")
+page = st.sidebar.radio("Go to", ["Home", "Predict Churn", "EDA"])
+
+# Define the home page
+if page == "Home":
+    st.write("## Welcome to the Customer Churn Analysis App")
+    st.write("""
+    This application allows you to:
+    - Predict customer churn based on input data.
+    - Perform exploratory data analysis (EDA) on customer churn data.
+    Use the sidebar to navigate between the pages.
+    """)
+
+# Navigate to the Predict Churn page
+elif page == "Predict Churn":
+    predict.run()
+
+# Navigate to the EDA page
+elif page == "EDA":
+    eda.run()

eda.py

@@ -4,160 +4,154 @@ import pandas as pd
 import plotly.express as px  
 import numpy as np 
 
-
-# def eda():
-# Set the title of the Streamlit app
-st.title('📊 Exploratory Data Analysis')
-st.write('---')
-
-# Load the dataset from a CSV filez
-df = pd.read_csv('florist_customer_churn_raw_fix_cleaned.csv')
-
-# Display the first few rows of the dataset
-st.subheader('📂 Dataset Overview: ')
-st.dataframe(df.head(10))
-
-# -- CONTAINER --
-# Creating container for home-page description
-ins_total_churn = st.container(border=True)
-ins_total_churn.markdown('<h1 style="font-size: 30px;">🧠 Quick to Know about Dataset: </h1>', unsafe_allow_html=True)
-ins_total_churn.write("The dataset contains various customer behavior indicators that may be associated with **customer churn**. From this data, our team will provide a classification based on the sentiment from the `feedback` to predict whether a customer will churn or not.")
-st.write('---')
-
-# -- DATA EXPLORATION --
-st.subheader('🗺️ Data Exploration')
-
-# Display the number of rows and columns in the dataset
-
-# --1. CHURN PIE CHART VIZ ---
-# Count the number of True/False in 'churn' column
-churn_count = df['churn'].value_counts()
-
-# Create a pie chart using Plotly with a purple color palette
-fig = px.pie(values=churn_count.values, 
-             names=churn_count.index, 
-             title="Total Churn Pie Chart Distribution",
-             color_discrete_sequence=px.colors.sequential.Purples_r) 
-
-# Show the chart in Streamlit
-st.subheader("🏃 Total Churn")
-st.plotly_chart(fig)
-
-# -- INSIGHT TOTAL CHURN --
-ins_total_churn = st.container(border=True)
-ins_total_churn.markdown('<h1 style="font-size: 30px;">💭 Insight: </h1>', unsafe_allow_html=True)
-ins_total_churn.write("""
-                    - A nearly equal split of `true` and `false` churn indicates that about half of customers remain `loyal` and `the other half churn`.
-                    - Churn ratio approaching `50-50` indicates that there is a significant risk of losing customers.
-                    - This indicates that we should focus on customer retention strategies and service improvements to `reduce true churn and maintain customer loyalty`."""
-                      )
-
-# -- 2. POSITIF FEEDBACK --
-# Membuat chart menggunakan Plotly Express
-# Filter for rows where sentiment is 'positive'
-positive_df = df[df['sentiment'] == 'positive']
-
-# Group the data by 'topic' and count the occurrences
-positive_topic_counts = positive_df['topic'].value_counts().reset_index()
-positive_topic_counts.columns = ['Topic', 'Count of Sentiment']
-
-# Create the bar chart using Plotly
-fig = px.bar(positive_topic_counts, 
-             x='Count of Sentiment', 
-             y='Topic', 
-             orientation='h', 
-             color_discrete_sequence=['#8a2be2'],  # Purple color
-             title="Positive Sentiment by Topic")
-
-# Display the plot in Streamlit
-st.subheader("🗣️💬 Positive Sentiment by Topic")
-st.plotly_chart(fig)
-
-# -- INSIGHT POSITIF FEEDBACK BY TOPIC --
-ins_positive_feedback = st.container(border=True)
-ins_positive_feedback.markdown('<h1 style="font-size: 30px;">💭 Insight: </h1>', unsafe_allow_html=True)
-ins_positive_feedback.write("""
-                            - `Product Quality` (Kualitas Produk) receives the most attention in positive sentiment, with more than 100 people expressing satisfaction with the product.
-                            - `General Feedback` is also quite high, indicating that many customers provide good general feedback regarding the service or product.
-                            - `Bouquet Preferences` also has a substantial amount of positive sentiment, indicating that customers are quite satisfied with the available flower arrangement options.
-                            - `Customer Service` receives positive sentiment, although not as high as some other topics, but it shows that customer service is still fairly appreciated.
-                            - `Price Appreciation` (Apresiasi Harga) shows that some customers feel the offered prices are quite reasonable.
-                            - `Delivery Quality` (Kualitas Pengiriman) and `Delivery Issues` (Masalah Pengiriman) are relatively low in positive sentiment, meaning the delivery aspect is not a major strength.
-                            """)
-st.write('---')
-
-
-# -- 3. NEGATIF FEEDBACK --
-# Filter for rows where sentiment is 'negative'
-negative_df = df[df['sentiment'] == 'negative']
-
-# Group the data by 'topic' and count the occurrences
-negative_topic_counts = negative_df['topic'].value_counts().reset_index()
-negative_topic_counts.columns = ['Topic', 'Count of Sentiment']
-
-# Create the bar chart using Plotly
-fig = px.bar(negative_topic_counts, 
-             x='Count of Sentiment', 
-             y='Topic', 
-             orientation='h', 
-             color_discrete_sequence=['#8a2be2'],  # Purple color
-             title="Negative Feedback by Topic")
-
-# Display the plot in Streamlit
-st.subheader("🗣️💬 Negative Sentiment by Topic")
-st.plotly_chart(fig)
-
-# -- INSIGHT NEGATIF FEEDBACK BY TOPIC --
-ins_negative_feedback = st.container(border=True)
-ins_negative_feedback.markdown('<h1 style="font-size: 30px;">💭 Insight: </h1>', unsafe_allow_html=True)
-ins_negative_feedback.write("""
-                            - `Product Quality` is also a major topic in negative sentiment, with more than 140 negative comments. This indicates that while there is a lot of praise, there are also significant complaints about product quality.
-                            - `Price Complaints` is a major negative topic, meaning many customers feel that the prices offered are too high or not meeting their expectations.
-                            - `Delivery Issues` is also a major problem in negative sentiment, showing that delivery is a primary source of complaints.
-                            - `Bouquet Preferences` also has a fair amount of negative sentiment, indicating that while many are satisfied, there are also those who feel the flower arrangements do not meet their expectations.
-                            - `Customer Service` has received some negative sentiment, though it is not as prominent as other topics.
-                            - `Delivery Quality` has very minimal negative sentiment, indicating that the quality of delivery is less frequently complained about compared to delivery issues overall.
-                            """)
-st.write('---')
-
-# -- 3. CHURN RATE --
-# Group the data by 'churn' and 'contract' and count the occurrences
-# Map the churn column to categorical values: False -> 'Not Churned', True -> 'Churned'
-df['churn_category'] = df['churn'].map({False: 'Not Churned', True: 'Churned'})
-
-# Group the data by 'churn_category' and 'contract' and count the occurrences
-churn_contract_counts = df.groupby(['churn_category', 'contract']).size().reset_index(name='Count of Churn')
-
-# Create the bar chart using Plotly
-fig = px.bar(churn_contract_counts, 
-             x='Count of Churn', 
-             y='contract', 
-             color='churn_category',  # Use the new categorical churn column
-             barmode='group', 
-             orientation='h',
-             color_discrete_sequence=['#8a2be2', '#c8a2c8'],  # Purple color shades
-             title="Churn Rate by Contract Type")
-
-# Display the plot in Streamlit
-st.subheader("🏃  or  🙆  by Contract Type")
-st.plotly_chart(fig)
-
-# -- INSIGHT CHURN RATE BY CONTRACT TYPE --
-ins_churn_rate = st.container(border=True)
-ins_churn_rate.markdown('<h1 style="font-size: 30px;">💭 Insight: </h1>', unsafe_allow_html=True)
-ins_churn_rate.write("""
-                    - `Short-term (monthly)` contracts have a very high churn rate, indicating that customers tend to leave the service more easily if they are not tied to a long-term contract
-                    - `Long-term contracts` (one and two years) are more effective in retaining customers than short-term contracts.
-                     """)
-st.write('---')
-
-st.write(
-    '<p style="font-size: 15px; text-align: center;">All Rights Reserved | Made by ❤️</p>', 
-    unsafe_allow_html=True
-)
-
-
-
-
-
-
+def run():
+    # Set the title of the Streamlit app
+    st.title('📊 Exploratory Data Analysis')
+    st.write('---')
+
+    # Load the dataset from a CSV file
+    df = pd.read_csv('florist_customer_churn_raw_fix_cleaned.csv')
+
+    # Display the first few rows of the dataset
+    st.subheader('📂 Dataset Overview: ')
+    st.dataframe(df.head(10))
+
+    # -- CONTAINER --
+    # Creating container for home-page description
+    ins_total_churn = st.container()
+    ins_total_churn.markdown('<h1 style="font-size: 30px;">🧠 Quick to Know about Dataset: </h1>', unsafe_allow_html=True)
+    ins_total_churn.write("The dataset contains various customer behavior indicators that may be associated with **customer churn**. From this data, our team will provide a classification based on the sentiment from the `feedback` to predict whether a customer will churn or not.")
+    st.write('---')
+
+    # -- DATA EXPLORATION --
+    st.subheader('🗺️ Data Exploration')
+
+    # --1. CHURN PIE CHART VIZ ---
+    # Count the number of True/False in 'churn' column
+    churn_count = df['churn'].value_counts()
+
+    # Create a pie chart using Plotly with a purple color palette
+    fig = px.pie(values=churn_count.values, 
+                 names=churn_count.index, 
+                 title="Total Churn Pie Chart Distribution",
+                 color_discrete_sequence=px.colors.sequential.Purples_r) 
+
+    # Show the chart in Streamlit
+    st.subheader("🏃 Total Churn")
+    st.plotly_chart(fig)
+
+    # -- INSIGHT TOTAL CHURN --
+    ins_total_churn = st.container()
+    ins_total_churn.markdown('<h1 style="font-size: 30px;">💭 Insight: </h1>', unsafe_allow_html=True)
+    ins_total_churn.write("""
+                        - A nearly equal split of `true` and `false` churn indicates that about half of customers remain `loyal` and `the other half churn`.
+                        - Churn ratio approaching `50-50` indicates that there is a significant risk of losing customers.
+                        - This indicates that we should focus on customer retention strategies and service improvements to `reduce true churn and maintain customer loyalty`."""
+                          )
+
+    # -- 2. POSITIF FEEDBACK --
+    # Membuat chart menggunakan Plotly Express
+    # Filter for rows where sentiment is 'positive'
+    positive_df = df[df['sentiment'] == 'positive']
+
+    # Group the data by 'topic' and count the occurrences
+    positive_topic_counts = positive_df['topic'].value_counts().reset_index()
+    positive_topic_counts.columns = ['Topic', 'Count of Sentiment']
+
+    # Create the bar chart using Plotly
+    fig = px.bar(positive_topic_counts, 
+                 x='Count of Sentiment', 
+                 y='Topic', 
+                 orientation='h', 
+                 color_discrete_sequence=['#8a2be2'],  # Purple color
+                 title="Positive Sentiment by Topic")
+
+    # Display the plot in Streamlit
+    st.subheader("🗣️💬 Positive Sentiment by Topic")
+    st.plotly_chart(fig)
+
+    # -- INSIGHT POSITIF FEEDBACK BY TOPIC --
+    ins_positive_feedback = st.container()
+    ins_positive_feedback.markdown('<h1 style="font-size: 30px;">💭 Insight: </h1>', unsafe_allow_html=True)
+    ins_positive_feedback.write("""
+                                - `Product Quality` (Kualitas Produk) receives the most attention in positive sentiment, with more than 100 people expressing satisfaction with the product.
+                                - `General Feedback` is also quite high, indicating that many customers provide good general feedback regarding the service or product.
+                                - `Bouquet Preferences` also has a substantial amount of positive sentiment, indicating that customers are quite satisfied with the available flower arrangement options.
+                                - `Customer Service` receives positive sentiment, although not as high as some other topics, but it shows that customer service is still fairly appreciated.
+                                - `Price Appreciation` (Apresiasi Harga) shows that some customers feel the offered prices are quite reasonable.
+                                - `Delivery Quality` (Kualitas Pengiriman) and `Delivery Issues` (Masalah Pengiriman) are relatively low in positive sentiment, meaning the delivery aspect is not a major strength.
+                                """)
+    st.write('---')
+
+
+    # -- 3. NEGATIF FEEDBACK --
+    # Filter for rows where sentiment is 'negative'
+    negative_df = df[df['sentiment'] == 'negative']
+
+    # Group the data by 'topic' and count the occurrences
+    negative_topic_counts = negative_df['topic'].value_counts().reset_index()
+    negative_topic_counts.columns = ['Topic', 'Count of Sentiment']
+
+    # Create the bar chart using Plotly
+    fig = px.bar(negative_topic_counts, 
+                 x='Count of Sentiment', 
+                 y='Topic', 
+                 orientation='h', 
+                 color_discrete_sequence=['#8a2be2'],  # Purple color
+                 title="Negative Sentiment by Topic")
+
+    # Display the plot in Streamlit
+    st.subheader("🗣️💬 Negative Sentiment by Topic")
+    st.plotly_chart(fig)
+
+    # -- INSIGHT NEGATIF FEEDBACK BY TOPIC --
+    ins_negative_feedback = st.container()
+    ins_negative_feedback.markdown('<h1 style="font-size: 30px;">💭 Insight: </h1>', unsafe_allow_html=True)
+    ins_negative_feedback.write("""
+                                - `Product Quality` is also a major topic in negative sentiment, with more than 140 negative comments. This indicates that while there is a lot of praise, there are also significant complaints about product quality.
+                                - `Price Complaints` is a major negative topic, meaning many customers feel that the prices offered are too high or not meeting their expectations.
+                                - `Delivery Issues` is also a major problem in negative sentiment, showing that delivery is a primary source of complaints.
+                                - `Bouquet Preferences` also has a fair amount of negative sentiment, indicating that while many are satisfied, there are also those who feel the flower arrangements do not meet their expectations.
+                                - `Customer Service` has received some negative sentiment, though it is not as prominent as other topics.
+                                - `Delivery Quality` has very minimal negative sentiment, indicating that the quality of delivery is less frequently complained about compared to delivery issues overall.
+                                """)
+    st.write('---')
+
+    # -- 4. CHURN RATE --
+    # Group the data by 'churn' and 'contract' and count the occurrences
+    # Map the churn column to categorical values: False -> 'Not Churned', True -> 'Churned'
+    df['churn_category'] = df['churn'].map({False: 'Not Churned', True: 'Churned'})
+
+    # Group the data by 'churn_category' and 'contract' and count the occurrences
+    churn_contract_counts = df.groupby(['churn_category', 'contract']).size().reset_index(name='Count of Churn')
+
+    # Create the bar chart using Plotly
+    fig = px.bar(churn_contract_counts, 
+                 x='Count of Churn', 
+                 y='contract', 
+                 color='churn_category',  # Use the new categorical churn column
+                 barmode='group', 
+                 orientation='h',
+                 color_discrete_sequence=['#8a2be2', '#c8a2c8'],  # Purple color shades
+                 title="Churn Rate by Contract Type")
+
+    # Display the plot in Streamlit
+    st.subheader("🏃  or  🙆  by Contract Type")
+    st.plotly_chart(fig)
+
+    # -- INSIGHT CHURN RATE BY CONTRACT TYPE --
+    ins_churn_rate = st.container()
+    ins_churn_rate.markdown('<h1 style="font-size: 30px;">💭 Insight: </h1>', unsafe_allow_html=True)
+    ins_churn_rate.write("""
+                        - `Short-term (monthly)` contracts have a very high churn rate, indicating that customers tend to leave the service more easily if they are not tied to a long-term contract
+                        - `Long-term contracts` (one and two years) are more effective in retaining customers than short-term contracts.
+                         """)
+    st.write('---')
+
+    st.write(
+        '<p style="font-size: 15px; text-align: center;">All Rights Reserved | Made by ❤️</p>', 
+        unsafe_allow_html=True
+    )
+
+if __name__ == "__main__":
+    run()