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import streamlit as st |
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import pandas as pd |
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import seaborn as sns |
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import matplotlib.pyplot as plt |
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import plotly.express as px |
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from PIL import Image |
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def run() : |
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st.markdown("<h1 style='text-align: center;'>Exploratory Data Analysis</h1>", unsafe_allow_html=True) |
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st.write('Berikut adalah EDA dari setiap feature') |
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df_eda = pd.read_csv('eda_churn.csv') |
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st.subheader('**EDA Feature Churn**') |
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st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :') |
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st.markdown('- *Customer* yang *churn* lebih banyak dari pada *customer* yang tidak *churn*') |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(x='churn_risk_score', data=df_eda, palette="winter", ax=ax[0]) |
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ax[0].set_xlabel("churn_risk_score", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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fig.suptitle('Customer Churn Distribution', fontsize=18, fontweight='bold') |
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ax[0].set_ylim(0,23000) |
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plt.xlabel("churn_risk_score", fontsize= 12) |
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plt.ylabel("# of Customer", fontsize= 12) |
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ax[0].set_xticks([0,1], ['Not Churn', 'Churn']) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+405), ha='center', va='center',fontsize = 11) |
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df_eda['churn_risk_score'].value_counts().plot(kind='pie', labels = ['Churn', 'Not Churn'],autopct='%1.1f%%', textprops = {"fontsize":12}) |
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ax[1].set_ylabel("% of Customer", fontsize= 12) |
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st.pyplot(fig) |
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st.subheader('**EDA Feature Age**') |
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st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :') |
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st.markdown('- *Customer* paling banyak adalah *customer* yang memiliki *range* umur 40-50 tahun') |
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st.markdown('- *Customer* yang paling banyak *churn* adalah *customer* dengan *range* umur 50-60 tahun') |
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st.markdown('- Akan tetapi jika dilihat dari persentase *churn* pada setiap kelas *range* umur, maka tidak ada perbedaan signifikan') |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(x='AgeBin', data=df_eda, palette='winter', ax=ax[0]) |
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ax[0].set_xlabel("Range Customer Age", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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fig.suptitle('Range Customer Age Distribution', fontsize=18, fontweight='bold') |
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ax[0].set_ylim(0,7600) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+105), ha='center', va='center',fontsize = 10) |
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df_eda['AgeBin'].value_counts().plot(kind='pie', autopct='%1.1f%%', textprops = {"fontsize":12}) |
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ax[1].set_ylabel("% of Customer", fontsize= 10) |
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st.pyplot(fig) |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(data = df_eda, x = 'AgeBin', hue="churn_risk_score", palette = 'winter', order = ['(10, 20]', '(20, 30]', '(30, 40]', '(40, 50]', '(50, 60]', '(60, 70]'], ax=ax[0]) |
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ax[0].set_title('Range Age Distribution', fontsize=14, fontweight='bold',) |
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ax[0].set_xlabel("Range Age", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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ax[0].tick_params(axis="x", labelsize= 9.5) |
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ax[0].legend(fontsize=10,title='Churn Classification', loc='upper right', labels=['Not Churn', 'Churn']) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+75), ha='center', va='center',fontsize = 10) |
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ax[0].set_ylim(0,4700) |
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sns.barplot(x = 'AgeBin', y = 'churn_risk_score', data = df_eda, palette = 'winter', order = ['(10, 20]', '(20, 30]', '(30, 40]', '(40, 50]', '(50, 60]', '(60, 70]'],ax=ax[1]) |
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ax[1].set_xlabel("Range Age", fontsize= 12) |
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ax[1].set_ylabel("% Churn", fontsize= 12) |
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ax[1].set_title('% Churn based on Age', fontsize=14, fontweight='bold') |
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ax[1].set_ylim(0,0.7) |
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for p in ax[1].patches: |
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ax[1].annotate("%.2f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+0.03), ha='center', va='center',fontsize = 11) |
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st.pyplot(fig) |
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st.subheader('**EDA Feature Time Spent**') |
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st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :') |
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st.markdown('- Jika dilihat pada visualisasi diatas maka `avg_time_spent` antara *customer* yang *churn* dan *customer* yang tidak *churn* tidak berbeda secara signifikan') |
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fig =plt.figure(figsize=(15,6)) |
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plt.rcParams['figure.figsize'] = (10, 5) |
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sns.boxenplot(y=df_eda['avg_time_spent'], x= df_eda['churn_risk_score'], palette = 'Blues') |
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plt.title('Average Time Spent vs Churn', fontsize = 20) |
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st.pyplot(fig) |
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st.subheader('**EDA Feature Transaction Value**') |
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st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :') |
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st.markdown('- *Customer* yang tidak *churn* memiliki *average transaction value* yang lebih tinggi (terpusat di 18.000-40.000) dari pada *customer* yang *churn* (terpusat di 16.000-36.000)') |
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fig =plt.figure(figsize=(15,6)) |
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plt.rcParams['figure.figsize'] = (10, 5) |
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sns.boxenplot(y=df_eda['avg_transaction_value'], x= df_eda['churn_risk_score'], palette = 'Blues') |
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plt.title('Average Transaction Value vs Churn', fontsize = 20) |
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st.pyplot(fig) |
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st.subheader('**EDA Feature Avg Frequency Login Days**') |
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st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :') |
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st.markdown('- *Customer* yang tidak *churn* memiliki *average frequency login days* yang lebih rendah (terpusat di 8-20x) dari pada *customer* yang *churn* (terpusat di 10-25x)') |
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fig =plt.figure(figsize=(15,6)) |
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plt.rcParams['figure.figsize'] = (10, 5) |
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sns.boxenplot(y=df_eda['avg_frequency_login_days'], x= df_eda['churn_risk_score'], palette = 'Blues') |
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plt.title('Average Frequency Login Days vs Churn', fontsize = 20) |
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st.pyplot(fig) |
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st.subheader('**EDA Feature Point Wallet**') |
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st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :') |
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st.markdown('- *Customer* yang tidak *churn* memiliki *points in wallet* yang lebih tinggi (terpusat di 700-800) dari pada *customer* yang *churn* (terpusat di 600-700)') |
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fig =plt.figure(figsize=(15,6)) |
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plt.rcParams['figure.figsize'] = (10, 5) |
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sns.boxenplot(y=df_eda['points_in_wallet'], x= df_eda['churn_risk_score'], palette = 'Blues') |
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plt.title('Points in Wallet vs Churn', fontsize = 20) |
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st.pyplot(fig) |
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st.subheader('**EDA Feature Gender**') |
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st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :') |
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st.markdown('- *Customer* paling banyak adalah *customer* wanita (50.1%). Akan tetapi tidak berbeda signifikan, hanya berbeda 0.2% dari *customer* pria') |
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st.markdown('- *Customer* yang paling banyak *churn* adalah *customer* wanita') |
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st.markdown('- Akan tetapi jika dilihat dari persentase *churn* pada setiap kelas *gender*, maka tidak ada perbedaan signifikan') |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(x='gender', data=df_eda, palette='winter', ax=ax[0]) |
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ax[0].set_xlabel("Gender", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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fig.suptitle('Gender Distribution', fontsize=18, fontweight='bold') |
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ax[0].set_ylim(0,21000) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+305), ha='center', va='center',fontsize = 10) |
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df_eda['gender'].value_counts().plot(kind='pie', autopct='%1.1f%%', textprops = {"fontsize":12}) |
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ax[1].set_ylabel("% of Customer", fontsize= 10) |
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st.pyplot(fig) |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(data = df_eda, x = 'gender', hue="churn_risk_score", palette = 'winter', ax=ax[0]) |
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ax[0].set_title('Gender Distribution', fontsize=14, fontweight='bold',) |
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ax[0].set_xlabel("Gender", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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ax[0].tick_params(axis="x", labelsize= 9.5) |
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ax[0].legend(fontsize=10,title='Churn Classification', loc='upper right', labels=['Not Churn', 'Churn']) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+175), ha='center', va='center',fontsize = 10) |
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ax[0].set_ylim(0,13000) |
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sns.barplot(x = 'gender', y = 'churn_risk_score', data = df_eda, palette = 'winter',ax=ax[1]) |
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ax[1].set_xlabel("Gender", fontsize= 12) |
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ax[1].set_ylabel("% Churn", fontsize= 12) |
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ax[1].set_title('% Churn based on Gender', fontsize=14, fontweight='bold') |
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ax[1].set_ylim(0,0.7) |
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for p in ax[1].patches: |
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ax[1].annotate("%.2f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+0.02), ha='center', va='center',fontsize = 11) |
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st.pyplot(fig) |
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st.subheader('**EDA Feature Region Category**') |
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st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :') |
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st.markdown('- *Customer* paling banyak adalah *customer* yang berasal dari kota (44.8%)') |
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st.markdown('- *Customer* yang paling banyak *churn* adalah *customer* yang tinggal di kota') |
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st.markdown('- Akan tetapi jika dilihat dari persentase *churn* pada setiap kelas *region*, maka tidak ada perbedaan signifikan') |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(x='region_category', data=df_eda, palette='winter', ax=ax[0]) |
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ax[0].set_xlabel("region_category", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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fig.suptitle('Region Category Distribution', fontsize=18, fontweight='bold') |
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ax[0].set_ylim(0,16000) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+305), ha='center', va='center',fontsize = 10) |
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df_eda['region_category'].value_counts().plot(kind='pie', autopct='%1.1f%%', textprops = {"fontsize":12}) |
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ax[1].set_ylabel("% of Customer", fontsize= 10) |
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st.pyplot(fig) |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(data = df_eda, x = 'region_category', hue="churn_risk_score", palette = 'winter', ax=ax[0]) |
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ax[0].set_title('Region Category Distribution', fontsize=14, fontweight='bold',) |
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ax[0].set_xlabel("region_category", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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ax[0].tick_params(axis="x", labelsize= 9.5) |
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ax[0].legend(fontsize=10,title='Churn Classification', loc='upper right', labels=['Not Churn', 'Churn']) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+180), ha='center', va='center',fontsize = 10) |
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ax[0].set_ylim(0,10000) |
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sns.barplot(x = 'region_category', y = 'churn_risk_score', data = df_eda, palette = 'winter',ax=ax[1]) |
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ax[1].set_xlabel("region_category", fontsize= 12) |
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ax[1].set_ylabel("% Churn", fontsize= 12) |
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ax[1].set_title('% Churn based on Region Category', fontsize=14, fontweight='bold') |
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ax[1].set_ylim(0,0.7) |
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for p in ax[1].patches: |
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ax[1].annotate("%.2f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+0.03), ha='center', va='center',fontsize = 11) |
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st.pyplot(fig) |
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st.subheader('**EDA Feature Membership Category**') |
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st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :') |
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st.markdown('- *Customer* paling banyak adalah *customer* dengan *basic membership* (20.9%) dan *no membership* (20.8%)') |
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st.markdown('- *Customer* yang paling banyak *churn* adalah *customer* dengan *no membership*') |
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st.markdown('- Akan tetapi jika dilihat dari persentase *churn* pada setiap kelas *membership*, maka terdapat perbedaan yang signifikan') |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(x='membership_category', data=df_eda, palette='winter', ax=ax[0]) |
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ax[0].set_xlabel("membership_category", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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fig.suptitle('Membership Category Distribution', fontsize=18, fontweight='bold') |
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ax[0].tick_params(axis='x', rotation=90) |
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ax[0].set_ylim(0,8500) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+185), ha='center', va='center',fontsize = 10) |
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df_eda['membership_category'].value_counts().plot(kind='pie', autopct='%1.1f%%', textprops = {"fontsize":12}) |
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ax[1].set_ylabel("% of Customer", fontsize= 10) |
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st.pyplot(fig) |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(data = df_eda, x = 'membership_category', hue="churn_risk_score", palette = 'winter', ax=ax[0]) |
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ax[0].set_title('Membership Category Distribution', fontsize=14, fontweight='bold',) |
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ax[0].set_xlabel("membership_category", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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ax[0].tick_params(axis="x", labelsize= 9.5) |
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ax[0].legend(fontsize=10,title='Churn Classification', loc='upper right', labels=['Not Churn', 'Churn']) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+180), ha='center', va='center',fontsize = 10) |
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ax[0].tick_params(axis='x', rotation=90) |
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ax[0].set_ylim(0,8500) |
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sns.barplot(x = 'membership_category', y = 'churn_risk_score', data = df_eda, palette = 'winter',ax=ax[1]) |
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ax[1].set_xlabel("membership_category", fontsize= 12) |
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ax[1].set_ylabel("% Churn", fontsize= 12) |
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ax[1].set_title('% Churn based on Membership Category', fontsize=14, fontweight='bold') |
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ax[1].set_ylim(0,1.2) |
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ax[1].tick_params(axis='x', rotation=90) |
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for p in ax[1].patches: |
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ax[1].annotate("%.2f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+0.03), ha='center', va='center',fontsize = 11) |
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st.pyplot(fig) |
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st.subheader('**EDA Feature Joined Through Referral**') |
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st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :') |
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st.markdown('- *Customer* paling banyak adalah *customer* yang tidak menggunakan *referral* (50.2%). Akan tetapi tidak berbeda signifikan, hanya berbeda 0.4% dari *customer* yang menggunakan *referral*') |
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st.markdown('- *Customer* yang paling banyak *churn* adalah *customer* dengan *referral*') |
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st.markdown('- Akan tetapi jika dilihat dari persentase *churn* pada setiap kelas *joined through referral*, maka tidak terdapat perbedaan yang signifikan') |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(x='joined_through_referral', data=df_eda, palette='winter', ax=ax[0]) |
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ax[0].set_xlabel("joined_through_referral", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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fig.suptitle('Joined Through Referral Distribution', fontsize=18, fontweight='bold') |
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ax[0].set_ylim(0,17500) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+235), ha='center', va='center',fontsize = 10) |
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df_eda['joined_through_referral'].value_counts().plot(kind='pie', autopct='%1.1f%%', textprops = {"fontsize":12}) |
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ax[1].set_ylabel("% of Customer", fontsize= 10) |
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st.pyplot(fig) |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(data = df_eda, x = 'joined_through_referral', hue="churn_risk_score", palette = 'winter', ax=ax[0]) |
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ax[0].set_title('Joined Through Referral Distribution', fontsize=14, fontweight='bold',) |
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ax[0].set_xlabel("joined_through_referral", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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ax[0].tick_params(axis="x", labelsize= 9.5) |
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ax[0].legend(fontsize=10,title='Churn Classification', loc='upper right', labels=['Not Churn', 'Churn']) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+180), ha='center', va='center',fontsize = 10) |
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ax[0].set_ylim(0,12000) |
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sns.barplot(x = 'joined_through_referral', y = 'churn_risk_score', data = df_eda, palette = 'winter',ax=ax[1]) |
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ax[1].set_xlabel("joined_through_referral", fontsize= 12) |
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ax[1].set_ylabel("% Churn", fontsize= 12) |
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ax[1].set_title('% Churn based on Joined Through Referral', fontsize=14, fontweight='bold') |
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ax[1].set_ylim(0,0.8) |
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for p in ax[1].patches: |
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ax[1].annotate("%.2f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+0.03), ha='center', va='center',fontsize = 11) |
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st.pyplot(fig) |
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st.subheader('**EDA Feature Preferred Offer Types**') |
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st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :') |
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st.markdown('- *Customer* paling banyak adalah *customer* menggunakan kupon (33.7%)') |
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st.markdown('- *Customer* yang paling banyak *churn* adalah *customer* yang tidak menggunakan penawaran') |
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st.markdown('- Akan tetapi jika dilihat dari persentase *churn* pada setiap kelas *preferred offer types*, maka tidak terdapat perbedaan yang signifikan') |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(x='preferred_offer_types', data=df_eda, palette='winter', ax=ax[0]) |
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ax[0].set_xlabel("preferred_offer_types", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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fig.suptitle('Offer Types Distribution', fontsize=18, fontweight='bold') |
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ax[0].set_ylim(0,14000) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+235), ha='center', va='center',fontsize = 10) |
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df_eda['preferred_offer_types'].value_counts().plot(kind='pie', autopct='%1.1f%%', textprops = {"fontsize":10}) |
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ax[1].set_ylabel("% of Customer", fontsize= 10) |
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st.pyplot(fig) |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(data = df_eda, x = 'preferred_offer_types', hue="churn_risk_score", palette = 'winter', ax=ax[0]) |
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ax[0].set_title('Offer Types Distribution', fontsize=14, fontweight='bold',) |
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ax[0].set_xlabel("preferred_offer_types", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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ax[0].tick_params(axis="x", labelsize= 9.5) |
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ax[0].legend(fontsize=10,title='Churn Classification', loc='upper right', labels=['Not Churn', 'Churn']) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+180), ha='center', va='center',fontsize = 10) |
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ax[0].set_ylim(0,9000) |
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sns.barplot(x = 'preferred_offer_types', y = 'churn_risk_score', data = df_eda, palette = 'winter',ax=ax[1]) |
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ax[1].set_xlabel("preferred_offer_types", fontsize= 12) |
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ax[1].set_ylabel("% Churn", fontsize= 12) |
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ax[1].set_title('% Churn based on Offer Types', fontsize=14, fontweight='bold') |
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ax[1].set_ylim(0,0.8) |
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for p in ax[1].patches: |
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ax[1].annotate("%.2f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+0.03), ha='center', va='center',fontsize = 11) |
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st.pyplot(fig) |
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st.subheader('**EDA Feature Used Special Discount Types**') |
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st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :') |
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st.markdown('- *Customer* paling banyak adalah *customer* yang menggunakan diskon (55%)') |
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st.markdown('- *Customer* yang paling banyak *churn* adalah *customer* yang menggunakan diskon') |
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st.markdown('- Akan tetapi jika dilihat dari persentase *churn* pada setiap kelas *used special discount*, maka tidak terdapat perbedaan yang signifikan') |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(x='used_special_discount', data=df_eda, palette='winter', ax=ax[0]) |
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ax[0].set_xlabel("used_special_discount", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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fig.suptitle('Used Special Discount Distribution', fontsize=18, fontweight='bold') |
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ax[0].set_ylim(0,23000) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+335), ha='center', va='center',fontsize = 10) |
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df_eda['used_special_discount'].value_counts().plot(kind='pie', autopct='%1.1f%%', textprops = {"fontsize":10}) |
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ax[1].set_ylabel("% of Customer", fontsize= 10) |
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st.pyplot(fig) |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(data = df_eda, x = 'used_special_discount', hue="churn_risk_score", palette = 'winter', ax=ax[0]) |
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ax[0].set_title('Used Special Discount Distribution', fontsize=14, fontweight='bold',) |
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ax[0].set_xlabel("used_special_discount", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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ax[0].tick_params(axis="x", labelsize= 9.5) |
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ax[0].legend(fontsize=10,title='Churn Classification', loc='upper right', labels=['Not Churn', 'Churn']) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+180), ha='center', va='center',fontsize = 10) |
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ax[0].set_ylim(0,12000) |
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sns.barplot(x = 'used_special_discount', y = 'churn_risk_score', data = df_eda, palette = 'winter', ax=ax[1]) |
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ax[1].set_xlabel("used_special_discount", fontsize= 12) |
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ax[1].set_ylabel("% Churn", fontsize= 12) |
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ax[1].set_title('% Churn based on Used Special Discount', fontsize=14, fontweight='bold') |
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ax[1].set_ylim(0,0.8) |
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for p in ax[1].patches: |
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ax[1].annotate("%.2f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+0.03), ha='center', va='center',fontsize = 11) |
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st.pyplot(fig) |
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st.subheader('**EDA Feature Offer Application Preference**') |
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st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :') |
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st.markdown('- *Customer* paling banyak adalah *customer* yang menyukai tawaran (55.3%)') |
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st.markdown('- *Customer* yang paling banyak *churn* adalah *customer* yang menyukai tawaran') |
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st.markdown('- Akan tetapi jika dilihat dari persentase *churn* pada setiap kelas kelas tawaran, maka tidak terdapat perbedaan yang signifikan') |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(x='offer_application_preference', data=df_eda, palette='winter', ax=ax[0]) |
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ax[0].set_xlabel("offer_application_preference", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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fig.suptitle('Offer Aplication Preference Distribution', fontsize=18, fontweight='bold') |
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ax[0].set_ylim(0,23000) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+335), ha='center', va='center',fontsize = 10) |
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df_eda['offer_application_preference'].value_counts().plot(kind='pie', autopct='%1.1f%%', textprops = {"fontsize":10}) |
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ax[1].set_ylabel("% of Customer", fontsize= 10) |
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st.pyplot(fig) |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(data = df_eda, x = 'offer_application_preference', hue="churn_risk_score", palette = 'winter', ax=ax[0]) |
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ax[0].set_title('Offer Aplication Preference Distribution', fontsize=14, fontweight='bold',) |
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ax[0].set_xlabel("offer_application_preference", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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ax[0].tick_params(axis="x", labelsize= 9.5) |
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ax[0].legend(fontsize=10,title='Churn Classification', loc='upper right', labels=['Not Churn', 'Churn']) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+180), ha='center', va='center',fontsize = 10) |
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ax[0].set_ylim(0,12000) |
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sns.barplot(x = 'offer_application_preference', y = 'churn_risk_score', data = df_eda, palette = 'winter',ax=ax[1]) |
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ax[1].set_xlabel("offer_application_preference", fontsize= 12) |
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ax[1].set_ylabel("% Churn", fontsize= 12) |
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ax[1].set_title('% Churn based on Offer Aplication Preference', fontsize=14, fontweight='bold') |
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ax[1].set_ylim(0,0.8) |
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for p in ax[1].patches: |
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ax[1].annotate("%.2f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+0.03), ha='center', va='center',fontsize = 11) |
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st.pyplot(fig) |
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st.subheader('**EDA Feature Past Complaint**') |
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st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :') |
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st.markdown('- *Customer* paling banyak adalah *customer* yang tidak *complaint* (50.3%). Akan tetapi tidak berbeda signifikan, hanya berbeda 0.6% dari *customer* yang *complaint*') |
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st.markdown('- *Customer* yang paling banyak *churn* adalah *customer* yang *complaint*') |
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st.markdown('- Akan tetapi jika dilihat dari persentase *churn* pada setiap kelas *past complaint*, maka tidak terdapat perbedaan yang signifikan') |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(x='past_complaint', data=df_eda, palette='winter', ax=ax[0]) |
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ax[0].set_xlabel("past_complaint", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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fig.suptitle('Past Complaint Distribution', fontsize=18, fontweight='bold') |
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ax[0].set_ylim(0,23000) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+335), ha='center', va='center',fontsize = 10) |
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df_eda['past_complaint'].value_counts().plot(kind='pie', autopct='%1.1f%%', textprops = {"fontsize":10}) |
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ax[1].set_ylabel("% of Customer", fontsize= 10) |
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st.pyplot(fig) |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(data = df_eda, x = 'past_complaint', hue="churn_risk_score", palette = 'winter', ax=ax[0]) |
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ax[0].set_title('Past Complaint Distribution', fontsize=14, fontweight='bold',) |
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ax[0].set_xlabel("past_complaint", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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ax[0].tick_params(axis="x", labelsize= 9.5) |
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ax[0].legend(fontsize=10,title='Churn Classification', loc='upper right', labels=['Not Churn', 'Churn']) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+180), ha='center', va='center',fontsize = 10) |
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ax[0].set_ylim(0,14000) |
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sns.barplot(x = 'past_complaint', y = 'churn_risk_score', data = df_eda, palette = 'winter', ax=ax[1]) |
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ax[1].set_xlabel("past_complaint", fontsize= 12) |
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ax[1].set_ylabel("% Churn", fontsize= 12) |
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ax[1].set_title('% Churn based on Past Complaint', fontsize=14, fontweight='bold') |
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ax[1].set_ylim(0,0.8) |
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for p in ax[1].patches: |
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ax[1].annotate("%.2f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+0.03), ha='center', va='center',fontsize = 11) |
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st.pyplot(fig) |
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st.subheader('**EDA Feature Complaint Status**') |
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st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :') |
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st.markdown('- *Customer* paling banyak adalah *customer* yang *complaint* nya tidak dapat diaplikasikan/direalisasikan (50.3%)') |
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st.markdown('- *Customer* yang paling banyak *churn* adalah *customer* yang *complaint* nya tidak dapat diaplikasikan/direalisasikan') |
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st.markdown('- Akan tetapi jika dilihat dari persentase *churn* pada setiap kelas *complaint status*, maka tidak terdapat perbedaan yang signifikan') |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(x='complaint_status', data=df_eda, palette='winter', ax=ax[0]) |
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ax[0].set_xlabel("complaint_status", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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fig.suptitle('Complaint Status Distribution', fontsize=18, fontweight='bold') |
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ax[0].set_ylim(0,23000) |
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ax[0].tick_params(axis='x', rotation=90) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+335), ha='center', va='center',fontsize = 10) |
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df_eda['complaint_status'].value_counts().plot(kind='pie', autopct='%1.1f%%', textprops = {"fontsize":10}) |
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ax[1].set_ylabel("% of Customer", fontsize= 10) |
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st.pyplot(fig) |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(data = df_eda, x = 'complaint_status', hue="churn_risk_score", palette = 'winter', ax=ax[0]) |
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ax[0].set_title('Complaint Status Distribution', fontsize=14, fontweight='bold',) |
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ax[0].set_xlabel("complaint_status", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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ax[0].tick_params(axis="x", labelsize= 9.5) |
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ax[0].legend(fontsize=10,title='Churn Classification', loc='upper right', labels=['Not Churn', 'Churn']) |
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for p in ax[0].patches: |
|
ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+180), ha='center', va='center',fontsize = 10) |
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ax[0].tick_params(axis='x', rotation=90) |
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ax[0].set_ylim(0,12000) |
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sns.barplot(x = 'complaint_status', y = 'churn_risk_score', data = df_eda, palette = 'winter',ax=ax[1]) |
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ax[1].set_xlabel("complaint_status", fontsize= 12) |
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ax[1].set_ylabel("% Churn", fontsize= 12) |
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ax[1].set_title('% Churn based on Complaint Status', fontsize=14, fontweight='bold') |
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ax[1].set_ylim(0,0.8) |
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ax[1].tick_params(axis='x', rotation=90) |
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for p in ax[1].patches: |
|
ax[1].annotate("%.2f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+0.03), ha='center', va='center',fontsize = 11) |
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st.pyplot(fig) |
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st.subheader('**EDA Feature Feedback**') |
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st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :') |
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st.markdown('- *Customer* paling banyak adalah *customer* yang memberikan *feedback* buruk (*too many ads*, *poor product quality*) dan yang tidak memberikan *feedback*. 3 kelas tersebut memiliki persentase proporsi 17%') |
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st.markdown('- *Customer* yang paling banyak *churn* adalah *customer* yang memberikan *feedback poor product quality*') |
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st.markdown('- Akan tetapi jika dilihat dari persentase *churn* pada setiap kelas *feedback*, maka terdapat perbedaan yang signifikan') |
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fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(x='feedback', data=df_eda, palette='winter', ax=ax[0]) |
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ax[0].set_xlabel("feedback", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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fig.suptitle('Feedback Distribution', fontsize=18, fontweight='bold') |
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ax[0].set_ylim(0,7000) |
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ax[0].tick_params(axis='x', rotation=90) |
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for p in ax[0].patches: |
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ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+135), ha='center', va='center',fontsize = 10) |
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df_eda['feedback'].value_counts().plot(kind='pie', autopct='%1.1f%%', textprops = {"fontsize":10}) |
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ax[1].set_ylabel("% of Customer", fontsize= 10) |
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st.pyplot(fig) |
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|
|
fig, ax =plt.subplots(1,2,figsize=(15,6)) |
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sns.countplot(data = df_eda, x = 'feedback', hue="churn_risk_score", palette = 'winter', ax=ax[0]) |
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ax[0].set_title('Feedback Distribution', fontsize=14, fontweight='bold',) |
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ax[0].set_xlabel("feedback", fontsize= 12) |
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ax[0].set_ylabel("# of Customer", fontsize= 12) |
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ax[0].tick_params(axis="x", labelsize= 9.5) |
|
ax[0].legend(fontsize=10,title='Churn Classification', loc='upper right', labels=['Not Churn', 'Churn']) |
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for p in ax[0].patches: |
|
ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+60), ha='center', va='center',fontsize = 6) |
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ax[0].tick_params(axis='x', rotation=90) |
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ax[0].set_ylim(0,5000) |
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|
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|
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sns.barplot(x = 'feedback', y = 'churn_risk_score', data = df_eda, palette = 'winter',ax=ax[1]) |
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ax[1].set_xlabel("feedback", fontsize= 12) |
|
ax[1].set_ylabel("% Churn", fontsize= 12) |
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ax[1].set_title('% Churn based on Feedback', fontsize=14, fontweight='bold') |
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ax[1].set_ylim(0,0.8) |
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ax[1].tick_params(axis='x', rotation=90) |
|
for p in ax[1].patches: |
|
ax[1].annotate("%.2f"%(p.get_height()), (p.get_x() + p.get_width() / 2, |
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p.get_height()+0.03), ha='center', va='center',fontsize = 11) |
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st.pyplot(fig) |
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if __name__ == '__main__': |
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run() |
