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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 | |
| # Melebarkan visualisasi untuk memaksimalkan browser | |
| st.set_page_config( | |
| page_title='Churn Customer', | |
| layout='wide', | |
| initial_sidebar_state='expanded' | |
| ) | |
| def run(): | |
| # Membuat title | |
| st.title('Churn Customer Prediction') | |
| st.write('### by Fadya Ulya Salsabila') | |
| # Menambahkan Gambar | |
| image = Image.open('churn.png') | |
| st.image(image, caption='Illustration of Churn Customer') | |
| # Menambahkan Deskripsi | |
| st.write('## Background') | |
| st.write(""" | |
| A make-up company "Sister" wants to minimize the risk of a customer stopping using their product. | |
| The company then analyzes the history of its customers in making purchases based on time and frequency. Then, this company also looks at the feedback that customers have given it. | |
| This is intended to determine customer predictions whether to stop using their product or not. | |
| Because if many customers stop, the company will evaluate product sales and marketing to customers. In addition, the company will also provide discounts and special offers to loyal customers. | |
| The objectives from this analysis and modeling in this dataset are: | |
| 1. Find out the customer prediction, whether customer churn or not. | |
| 2. Find out the best model prediction using Artificial Neural Network (ANN).""") | |
| st.write('## Dataset') | |
| st.write(""" | |
| The dataset is from Github Milestones 1 Hacktiv8 `churn.csv` that contains 22 columns. | |
| 1. `user_id`: ID of a customer | |
| 2. `age`: Age of a customer | |
| 3. `gender`: Gender of a customer | |
| 4. `region_category`: Region that a customer belongs to | |
| 5. `membership_category`: Category of the membership that a customer is using | |
| 6. `joining_date`: Date when a customer became a member | |
| 7. `joined_through_referral`: Whether a customer joined using any referral code or ID | |
| 8. `preferred_offer_types`: Type of offer that a customer prefers | |
| 9. `medium_of_operation`: Medium of operation that a customer uses for transactions | |
| 10. `internet_option`: Type of internet service a customer uses | |
| 11. `last_visit_time`: The last time a customer visited the website | |
| 12. `days_since_last_login`: Number of days since a customer last logged into the website | |
| 13. `avg_time_spent`: Average time spent by a customer on the website | |
| 14. `avg_transaction_value`: Average transaction value of a customer | |
| 15. `avg_frequency_login_days`: Number of times a customer has logged in to the website | |
| 16. `points_in_wallet`: Points awarded to a customer on each transaction | |
| 17. `used_special_discount`: Whether a customer uses special discounts offered | |
| 18. `offer_application_preference`: Whether a customer prefers offers | |
| 19. `past_complaint`: Whether a customer has raised any complaints | |
| 20. `complaint_status`: Whether the complaints raised by a customer was resolved | |
| 21. `feedback`: Feedback provided by a customer | |
| 22. `churn_risk_score`: Churn score (0 : Not churn, 1 : Churn)""") | |
| # Membuat Garis Lurus | |
| st.markdown('---') | |
| # Membuat Sub Headrer | |
| st.subheader('EDA for Churn Customer') | |
| # Magic Syntax | |
| st.write( | |
| ' On this page, the author will do a simple exploration.' | |
| ' The dataset used is the Churn Customer dataset.' | |
| ' This dataset comes from Github Project Hacktiv8.') | |
| # Show DataFrame | |
| df1 = pd.read_csv('churn.csv') | |
| st.dataframe(df1) | |
| # Membuat Barplot | |
| st.write('#### Churn Risk Plot') | |
| fig = plt.figure(figsize=(10,7)) | |
| sns.countplot(x='churn_risk_score', data=df1, palette="PuRd") | |
| st.pyplot(fig) | |
| st.write('The target data is balanced.') | |
| st.write('#### Gender Based on Churn Risk') | |
| fig1, ax1 = plt.subplots(figsize=(15, 8)) | |
| sns.countplot(x='gender', hue='churn_risk_score', data=df1, ax=ax1) | |
| st.pyplot(fig1) | |
| st.write('Gender distribution is normal between men and women.') | |
| # Mengelompokkan Usia | |
| bins = [8, 20, 30, 40, 50, 60, 120] | |
| labels = ['10-19', '20-29', '30-39', '40-49', '50-59', '60-69'] | |
| df1['agerange'] = pd.cut(df1.age, bins, labels = labels,include_lowest = True) | |
| # Menampilkan visualisasi usia berdasarkan churn risk | |
| st.write('#### Age Based on Churn Risk') | |
| fig2, ax2 = plt.subplots(figsize=(10,7)) | |
| sns.countplot(x='agerange', data=df1, hue="churn_risk_score", ax=ax2) | |
| st.pyplot(fig2) | |
| st.write('Customers in this company varies greatly, ranging from 10-64 years old. ') | |
| # Membuat heatmap correlation | |
| st.write('#### Heatmap Correlation') | |
| fig = plt.figure(figsize = (15,8)) | |
| sns.heatmap(df1.corr(), annot = True, square = True) | |
| st.pyplot(fig) | |
| st.write(""" | |
| The heatmap correlation above shows that the column that has a very high relationship with churn risk is the `avg_freqeuncy_login_days` column with score `0.11`. This column shows how many customers log in in a day. | |
| It means they are still interested in the product in this company. Meanwhile, `avg_transaction_value` have a strong negative correlation with churn risk witn score `-0.22`. | |
| This shows that the number of purchase transactions on this product has no significant effect on customer churn.""") | |
| # Membuat internet option berdasarkan churn risk | |
| st.write('#### Internet Option Based on Churn Risk') | |
| fig3, ax3 = plt.subplots(figsize=(10,7)) | |
| sns.countplot(x='internet_option', data=df1, hue="churn_risk_score", ax=ax3, palette="Blues") | |
| st.pyplot(fig3) | |
| st.write(""" | |
| Bar plot visualization above, shows that the `internet option` of customers doesn't have a strong correlation with churn risk. | |
| Distribution of internet option data almost have the same number of values and there is no significant difference. | |
| Customers who use the internet with Wi-Fi, Fiber Optic, and Mobile Data are almost the same.""") | |
| # Membuat region category berdasarkan churn risk | |
| st.write('#### Region Category Based on Churn Risk') | |
| fig4, ax4 = plt.subplots(figsize=(10,7)) | |
| sns.countplot(x='region_category', data=df1, hue="churn_risk_score", ax=ax4, palette="Blues") | |
| st.pyplot(fig4) | |
| st.write(""" | |
| Based on customer region, there is no significant correlation with churn risk. | |
| It's just that many customers of this product live in town areas compared to villages and cities.""") | |
| # Membuat membership category berdasarkan churn risk | |
| st.write('#### Membership Category on Churn Risk') | |
| fig5, ax5 = plt.subplots(figsize=(10,7)) | |
| sns.countplot(y='membership_category', data=df1, hue="churn_risk_score", ax=ax5, palette="Blues") | |
| st.pyplot(fig5) | |
| st.write(""" | |
| In `membership_category` column, customers which include in `No Membership` dan `Basic Membership` are customers with the highest churn risk. | |
| This can happen because the customer is deemed not a loyal customer so the risk of stopping the transaction is high. | |
| In contrast to silver, premium, gold, and platinum members where customers are considered loyal to product transactions. | |
| """) | |
| # Membuat Histogram Berdasarkan Input User | |
| st.write('#### Histogram Based On User Input') | |
| pilihan = st.selectbox('Choose Column : ', ('age', 'gender', 'days_since_last_login', 'avg_time_spent', | |
| 'avg_transaction_value', 'avg_frequency_login_days', | |
| 'points_in_wallet')) | |
| fig = plt.figure(figsize=(15,5)) | |
| sns.histplot(df1[pilihan], bins=30, kde=True) | |
| st.pyplot(fig) | |
| if __name__ == '__main__': | |
| run() |