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| 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 Predictor', | |
| layout='wide', | |
| initial_sidebar_state='expanded' | |
| ) | |
| def run(): | |
| # judul | |
| st.title('**Churn Exploration**') | |
| st.subheader('Explore The Churn Dataset') | |
| # tambah gambar | |
| image = Image.open('churn.jpg') | |
| st.image(image) | |
| st.markdown('---') | |
| markdown_text = ''' | |
| ## Background | |
| In today's competitive business landscape, customer churn has become a | |
| significant concern for many companies. Customer churn refers to the | |
| phenomenon where customers discontinue using a company's products or | |
| services. Churn can have a negative impact on a company's revenue, | |
| growth, and overall success. Therefore, companies are increasingly | |
| focused on identifying customers who are likely to churn so that they | |
| can take proactive measures to retain them. | |
| ## Objective | |
| The objective of this project is to develop a deep learning model for | |
| churn prediction. The company wants to minimize the risk of customer | |
| churn by accurately predicting which customers are likely to stop using | |
| their products or services. By identifying potential churners in advance, | |
| the company can take targeted actions and implement retention strategies | |
| to reduce churn rates and maximize customer loyalty. | |
| ## About Dataset | |
| | Variable | Description | | |
| |-------------------------|-----------------------------------------------------------------------------------------------| | |
| | user_id | ID of a customer | | |
| | age | Age of a customer | | |
| | gender | Gender of a customer | | |
| | region category | Region that a customer belongs to | | |
| | membership category | Category of the membership that a customer is using | | |
| | joining date | Date when a customer became a member | | |
| | joined through referal | Whether a customer joined using any referral code or ID | | |
| | preferred_offer types | Type of offer that a customer prefers | | |
| | medium_of operation | Medium of operation that a customer uses for transactions | | |
| | internet option | Type of internet service a customer uses | | |
| | last visit time | The last time a customer visited the website | | |
| | days since last login | Number of days since a customer last logged into the website | | |
| | average time spent | Average time spent by a customer on the website | | |
| | average transaction | Average transaction value of a customer | | |
| | average freq login days | Number of times a customer has logged in to the website | | |
| | point in wallet | Points awarded to a customer on each transaction | | |
| | used spesial discount | Whether a customer uses special discounts offered | | |
| | offer app preference | Whether a customer prefers offers | | |
| | past complaint | Whether a customer has raised any complaints | | |
| | complaint status | Whether the complaints raised by a customer was resolved | | |
| | feedback | Feedback provided by a customer | | |
| | churn risk score | Churn Score | | |
| ''' | |
| st.markdown(markdown_text) | |
| st.markdown('---') | |
| st.subheader('**Data Exploratory**') | |
| st.markdown('---') | |
| st.write('### Customer Information') | |
| # show dataset | |
| data = pd.read_csv('churn.csv') | |
| st.dataframe(data) | |
| st.markdown('---') | |
| st.write("### Today's Condition") | |
| st.markdown('---') | |
| # show distribusi customer churn | |
| fig, ax = plt.subplots() | |
| plt.pie(data['churn_risk_score'].value_counts(), | |
| labels=['Churn', 'Not-Churn'], | |
| autopct='%1.1f%%', | |
| colors=['Grey', 'Orange'], | |
| startangle=40, | |
| explode=[0.05, 0]) | |
| plt.title('Customer Churn Percentage') | |
| plt.axis('equal') | |
| st.pyplot(fig) | |
| ''' | |
| Based on the above graph, it can be observed that the distribution of Churn Risk | |
| Score ***tends to be evenly divided*** among its values, indicating that the feature | |
| follows a normal distribution | |
| ''' | |
| # visual numerical | |
| st.subheader('Chart Based on Metrics') | |
| st.markdown('---') | |
| choice = st.selectbox('Pick Numeric Columns: ', ('age', 'days_since_last_login', 'avg_time_spent', | |
| 'avg_transaction_value', 'avg_frequency_login_days', 'points_in_wallet')) | |
| fig, ax = plt.subplots(figsize=(8, 6)) | |
| sns.kdeplot(data=data, x=choice, fill=True, | |
| hue='churn_risk_score', palette='inferno') | |
| ax.set_title(choice.capitalize()+' Ratio') | |
| st.pyplot(fig) | |
| st.markdown('---') | |
| # visual categorical | |
| choice_2 = st.selectbox('Pick Category Column : ', ('gender', 'region_category', | |
| 'membership_category', 'joined_through_referral', 'preferred_offer_types', | |
| 'medium_of_operation', 'internet_option', 'used_special_discount', | |
| 'offer_application_preference', 'past_complaint', 'complaint_status', 'feedback')) | |
| fig = plt.figure(figsize=(15, 10)) | |
| sns.countplot(data=data, x=choice_2, | |
| hue='churn_risk_score', palette='viridis') | |
| plt.xlabel(choice_2.capitalize()) | |
| plt.ylabel('Count') | |
| plt.title(choice_2.capitalize()+' Ratio') | |
| plt.legend(title='Churn Risk Score') | |
| st.pyplot(fig) | |
| if __name__ == '__main__': | |
| run() | |