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| # Import necessary libraries | |
| import streamlit as st | |
| import eda | |
| import predict | |
| import pandas as pd | |
| # Set up the Streamlit page configuration | |
| st.set_page_config( | |
| page_title="Customer Churn Predictor", | |
| page_icon="🏃", | |
| layout="wide", | |
| initial_sidebar_state="expanded" | |
| ) | |
| def main(): | |
| # Navigation sidebar | |
| st.sidebar.title("🧭 Navigation") | |
| page = st.sidebar.radio("Go to", ["🏠 Home", "📊 EDA", "🔍 Prediction"]) | |
| if page == "🏠 Home": | |
| # Sidebar content for Home page | |
| st.sidebar.markdown("---") | |
| st.sidebar.subheader("📊 About the Models") | |
| # Sentiment Analysis Model | |
| st.sidebar.write("**Sentiment Analysis Model (BERT-based)**") | |
| sentiment_accuracy = 0.89 | |
| st.sidebar.write("🎯 Model Accuracy:") | |
| col1, col2 = st.sidebar.columns(2) | |
| col1.metric("Accuracy", f"{sentiment_accuracy:.2%}") | |
| col2.metric("Error Rate", f"{1-sentiment_accuracy:.2%}") | |
| st.sidebar.write("Analyzes customer feedback to predict sentiment.") | |
| st.sidebar.write("**💡 What does this mean?**") | |
| st.sidebar.write("The model correctly classifies the sentiment of customer feedback 89% of the time. This high accuracy ensures that we can reliably interpret customer opinions and make informed decisions based on their feedback.") | |
| st.sidebar.markdown("---") | |
| # Churn Prediction Model | |
| st.sidebar.write("**Churn Prediction Model (SVC)**") | |
| churn_recall = 0.89 | |
| st.sidebar.write("🎯 Model Recall:") | |
| st.sidebar.progress(churn_recall) | |
| st.sidebar.write(f"{churn_recall:.2%}") | |
| st.sidebar.write("**💡 What does this mean?**") | |
| st.sidebar.write("The model correctly identifies 89% of actual churning customers. This high recall minimizes false negatives, ensuring we catch most at-risk customers and can take proactive retention measures.") | |
| st.sidebar.markdown("---") | |
| st.sidebar.subheader("📚 Fun Fact") | |
| st.sidebar.info("It costs 5-25 times more to acquire a new customer than it does to retain an existing one.") | |
| st.sidebar.markdown("---") | |
| st.sidebar.subheader("🛠️ Tools Utilized") | |
| st.sidebar.write(""" | |
| - `Streamlit` for web app development | |
| - `Pandas` for data manipulation | |
| - `Plotly Express` for interactive visualizations | |
| - `PyTorch` and `Transformers` for sentiment analysis (BERT) | |
| - `Scikit-learn` for machine learning models (SVC) | |
| - `Pickle` for model serialization | |
| """) | |
| # Main content for Home page | |
| st.title("🏃 Welcome to Customer Churn Prediction Tool") | |
| st.write("Empowering businesses with data-driven insights to retain customers and boost growth.") | |
| # Display image | |
| col1, col2, col3 = st.columns([1,2,1]) | |
| with col2: | |
| st.image("predictix.jpg", caption="Predictix: Customer Churn Prediction", use_column_width=True) | |
| st.write(""" | |
| `Predictix` is an **innovative app** designed to help businesses **understand and predict customer churn risk**. | |
| Our application combines **powerful Exploratory Data Analysis (EDA)** with **advanced prediction capabilities**, | |
| utilizing a **sophisticated two-step approach**. First, we employ a **BERT-based model** for **sentiment analysis**, | |
| which accurately predicts sentiment from customer feedback. This sentiment data is then combined with other | |
| customer information and fed into a **Support Vector Classifier (SVC)** to predict the **likelihood of churn**. | |
| This **comprehensive approach** allows businesses to gain **deep insights** into customer behavior and take | |
| **proactive measures** to improve retention. Whether you're looking to **explore your data** or **make predictions**, | |
| Predictix has you covered. Simply use the **navigation pane** on the left to access the different modules and | |
| start leveraging the power of **data-driven decision making** for your business. | |
| """) | |
| st.markdown("---") | |
| # Dataset information | |
| st.write("#### 📊 Dataset") | |
| st.info(""" | |
| The dataset contains customer feedback used to predict sentiment, and then combines this sentiment analysis with customer information to predict customer churn. | |
| This two-step approach allows for a more nuanced understanding of customer behavior and improved churn prediction. | |
| Dataset source: [Florist Customer Churn](https://huggingface.co/datasets/iammkb2002/florist_customer_churn) | |
| """) | |
| # Checkbox to show/hide dataset column description | |
| if st.checkbox("Show dataset column description", value=True): | |
| st.table(pd.DataFrame({ | |
| "Column Name": ["customer_id", "churn", "tenure", "monthly_charges", "total_charges", "contract", "payment_method", "feedback", "sentiment", "topic"], | |
| "Description": [ | |
| "Unique identifier for each customer", | |
| "Indicates whether the customer has left (True/False)", | |
| "Number of months the customer has been with the company", | |
| "Amount charged to the customer monthly (in local currency)", | |
| "Total amount charged to the customer over their tenure", | |
| "Type of contract the customer has (e.g., one year, month-to-month, two year)", | |
| "Payment method used by the customer (e.g., credit card, electronic check)", | |
| "Customer feedback comments regarding the service or product", | |
| "Sentiment of the feedback (positive/negative) - predicted by our BERT model", | |
| "Topic category of the feedback (e.g., bouquet preferences, delivery issues, general feedback)" | |
| ] | |
| })) | |
| # Problem Statement | |
| st.write("#### ⚠️ Problem Statement") | |
| st.warning(""" | |
| In today's competitive market, understanding customer sentiment and predicting churn are crucial for business success. | |
| However, manually analyzing large volumes of customer feedback and identifying potential churners is time-consuming | |
| and prone to human error. Predictix addresses these challenges by automating both the sentiment analysis process | |
| and churn prediction, allowing businesses to respond promptly to customer needs and preferences. | |
| Customer churn is a significant challenge for businesses, leading to revenue loss and increased | |
| acquisition costs. Early identification of customers likely to churn is crucial for implementing | |
| effective retention strategies. As a data scientist, your task is to develop a machine learning | |
| model that can predict customer churn based on historical data, customer behavior patterns, and sentiment analysis. | |
| The goal is to develop a two-step model approach with high accuracy and recall to identify potential churners, | |
| allowing the business to take proactive measures to retain these customers. | |
| """) | |
| # Project Objective | |
| st.write("#### 🎯 Objective") | |
| st.success(""" | |
| This project aims to create a two-step classification model to predict customer churn: | |
| 1. Use a BERT-based model to analyze customer feedback and predict sentiment. | |
| 2. Use an SVC model to predict customer churn based on the predicted sentiment and other customer information. | |
| Model performance will be primarily assessed using Accuracy for the sentiment analysis model and | |
| Recall for the churn prediction model to measure effectiveness in identifying potential churners, | |
| minimizing the risk of missing customers who are likely to leave. | |
| """) | |
| elif page == "📊 EDA": | |
| # Run the EDA module | |
| eda.run() | |
| elif page == "🔍 Prediction": | |
| # Run the Prediction module | |
| predict.run() | |
| if __name__ == "__main__": | |
| main() |