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import streamlit as st | |
import joblib | |
import pandas as pd | |
import numpy as np | |
from PIL import Image | |
import time | |
import matplotlib.pyplot as plt | |
import qrcode | |
from io import BytesIO | |
import csv | |
# Load the trained models and transformers | |
num_imputer = joblib.load('numerical_imputer.joblib') | |
cat_imputer = joblib.load('cat_imputer.joblib') | |
encoder = joblib.load('encoder.joblib') | |
scaler = joblib.load('scaler.joblib') | |
model1 = joblib.load('lr_model_vif_smote.joblib') | |
model2 = joblib.load('gb_model_vif_smote.joblib') | |
def preprocess_input(input_data): | |
input_df = pd.DataFrame(input_data, index=[0]) | |
cat_columns = [col for col in input_df.columns if input_df[col].dtype == 'object'] | |
num_columns = [col for col in input_df.columns if input_df[col].dtype != 'object'] | |
input_df_imputed_cat = cat_imputer.transform(input_df[cat_columns]) | |
input_df_imputed_num = num_imputer.transform(input_df[num_columns]) | |
input_encoded_df = pd.DataFrame(encoder.transform(input_df_imputed_cat).toarray(), | |
columns=encoder.get_feature_names_out(cat_columns)) | |
input_df_scaled = scaler.transform(input_df_imputed_num) | |
input_scaled_df = pd.DataFrame(input_df_scaled, columns=num_columns) | |
final_df = pd.concat([input_encoded_df, input_scaled_df], axis=1) | |
final_df = final_df.reindex(columns=original_feature_names, fill_value=0) | |
return final_df | |
original_feature_names = ['MONTANT', 'FREQUENCE_RECH', 'REVENUE', 'ARPU_SEGMENT', 'FREQUENCE', | |
'DATA_VOLUME', 'ON_NET', 'ORANGE', 'TIGO', 'ZONE1', 'ZONE2', 'REGULARITY', 'FREQ_TOP_PACK', | |
'REGION_DAKAR', 'REGION_DIOURBEL', 'REGION_FATICK', 'REGION_KAFFRINE', 'REGION_KAOLACK', | |
'REGION_KEDOUGOU', 'REGION_KOLDA', 'REGION_LOUGA', 'REGION_MATAM', 'REGION_SAINT-LOUIS', | |
'REGION_SEDHIOU', 'REGION_TAMBACOUNDA', 'REGION_THIES', 'REGION_ZIGUINCHOR', | |
'TENURE_Long-term', 'TENURE_Medium-term', 'TENURE_Mid-term', 'TENURE_Short-term', | |
'TENURE_Very short-term', 'TOP_PACK_data', 'TOP_PACK_international', 'TOP_PACK_messaging', | |
'TOP_PACK_other_services', 'TOP_PACK_social_media', 'TOP_PACK_value_added_services', | |
'TOP_PACK_voice'] | |
# Set up the Streamlit app | |
st.set_page_config(layout="wide") | |
# Main page - Churn Prediction | |
st.title('📞 EXPRESSO TELECOM CUSTOMER CHURN PREDICTION APP 📞') | |
# Main page - Churn Prediction | |
st.image("banner.png", use_column_width=True) | |
st.markdown("This app predicts whether a customer will leave your company ❌ or not 🎉. Enter the details of the customer on the left sidebar to see the result") | |
# How to use | |
st.title('How to Use') | |
st.markdown('1. Select your model of choice on the left sidebar.') | |
st.markdown('2. Adjust the input parameters based on customer details') | |
st.markdown('3. Click the "Predict" button to initiate the prediction.') | |
st.markdown('4. The app will simulate a prediction process with a progress bar.') | |
st.markdown('5. Once the prediction is complete, the results will be displayed below.') | |
import csv | |
import streamlit as st | |
# Add context text | |
st.sidebar.markdown('**Welcome!**') | |
st.sidebar.markdown('This is a work in progress, and we would love to hear your suggestions on how to improve the user experience. Please feel free to provide your feedback in the suggestion box below.') | |
# Create the sidebar with a text input field for suggestions | |
correction_text = st.sidebar.text_input('Enter your suggestion') | |
# Button to submit the suggestion | |
if st.sidebar.button('Submit'): | |
# Perform action on suggestion submission (e.g., save to a CSV file) | |
with open('suggestions.csv', 'a', newline='') as file: | |
writer = csv.writer(file) | |
writer.writerow([correction_text]) | |
st.sidebar.info('Suggestion submitted successfully') | |
# Define a dictionary of models with their names, actual models, and types | |
models = { | |
'Logistic Regression': {'model': model1, 'type': 'logistic_regression'}, | |
'Gradient Boosting': {'model': model2, 'type': 'gradient_boosting'} | |
} | |
# Allow the user to select a model from the sidebar | |
# Allow the user to select a model from the sidebar | |
st.sidebar.title('Select Model') | |
model_name = st.sidebar.selectbox('Choose a model', list(models.keys())) | |
# Retrieve the selected model and its type from the dictionary | |
model = models[model_name]['model'] | |
model_type = models[model_name]['type'] | |
# Collect input from the user | |
st.sidebar.title('Enter Customer Details') | |
input_features = { | |
'MONTANT': st.sidebar.number_input('Top-up Amount (MONTANT)'), | |
'FREQUENCE_RECH': st.sidebar.number_input('Number of Times the Customer Refilled (FREQUENCE_RECH)'), | |
'REVENUE': st.sidebar.number_input('Monthly income of the client (REVENUE)'), | |
'ARPU_SEGMENT': st.sidebar.number_input('Income over 90 days / 3 (ARPU_SEGMENT)'), | |
'FREQUENCE': st.sidebar.number_input('Number of times the client has made an income (FREQUENCE)'), | |
'DATA_VOLUME': st.sidebar.number_input('Number of Connections (DATA_VOLUME)'), | |
'ON_NET': st.sidebar.number_input('Inter Expresso Call (ON_NET)'), | |
'ORANGE': st.sidebar.number_input('Call to Orange (ORANGE)'), | |
'TIGO': st.sidebar.number_input('Call to Tigo (TIGO)'), | |
'ZONE1': st.sidebar.number_input('Call to Zone 1 (ZONE1)'), | |
'ZONE2': st.sidebar.number_input('Call to Zone 2 (ZONE2)'), | |
'REGULARITY': st.sidebar.number_input('Number of Times the Client is Active for 90 Days (REGULARITY)'), | |
'FREQ_TOP_PACK': st.sidebar.number_input('Number of Times the Client has Activated the Top Packs (FREQ_TOP_PACK)'), | |
'REGION': st.sidebar.selectbox('Location of Each Client (REGION)', ['SAINT-LOUIS', 'THIES', 'LOUGA', 'MATAM', 'FATICK', 'KAOLACK', | |
'DIOURBEL', 'TAMBACOUNDA', 'ZIGUINCHOR', 'KOLDA', 'KAFFRINE', 'SEDHIOU', | |
'KEDOUGOU']), | |
'TENURE': st.sidebar.selectbox('Duration in the Network (TENURE)', ['Short-term', 'Mid-term', 'Medium-term', 'Very short-term']), | |
'TOP_PACK': st.sidebar.selectbox('Most Active Pack (TOP_PACK)', ['data', 'international', 'messaging', 'social_media', | |
'value_added_services', 'voice']) | |
} | |
# Input validation | |
valid_input = True | |
error_messages = [] | |
# Validate numeric inputs | |
numeric_ranges = { | |
'MONTANT': [0, 1000000], | |
'FREQUENCE_RECH': [0, 100], | |
'REVENUE': [0, 1000000], | |
'ARPU_SEGMENT': [0, 100000], | |
'FREQUENCE': [0, 100], | |
'DATA_VOLUME': [0, 100000], | |
'ON_NET': [0, 100000], | |
'ORANGE': [0, 100000], | |
'TIGO': [0, 100000], | |
'ZONE1': [0, 100000], | |
'ZONE2': [0, 100000], | |
'REGULARITY': [0, 100], | |
'FREQ_TOP_PACK': [0, 100] | |
} | |
for feature, value in input_features.items(): | |
range_min, range_max = numeric_ranges.get(feature, [None, None]) | |
if range_min is not None and range_max is not None: | |
if not range_min <= value <= range_max: | |
valid_input = False | |
error_messages.append(f"{feature} should be between {range_min} and {range_max}.") | |
#Churn Prediction | |
def predict_churn(input_data, model): | |
# Preprocess the input data | |
preprocessed_data = preprocess_input(input_data) | |
# Calculate churn probabilities using the model | |
probabilities = model.predict_proba(preprocessed_data) | |
# Determine churn labels based on the model type | |
if model_type == "logistic_regression": | |
churn_labels = ["No Churn", "Churn"] | |
elif model_type == "gradient_boosting": | |
churn_labels = ["Churn", "No Churn"] | |
# Extract churn probability for the first sample | |
churn_probability = probabilities[0] | |
# Create a dictionary mapping churn labels to their indices | |
churn_indices = {label: idx for idx, label in enumerate(churn_labels)} | |
# Determine the index with the highest churn probability | |
churn_index = np.argmax(churn_probability) | |
# Return churn labels, churn probabilities, churn indices, and churn index | |
return churn_labels, churn_probability, churn_indices, churn_index | |
# Predict churn based on user input | |
if st.sidebar.button('Predict Churn'): | |
try: | |
with st.spinner("Predicting..."): | |
# Simulate a long-running process | |
progress_bar = st.progress(0) | |
step = 20 # A big step will reduce the execution time | |
for i in range(0, 100, step): | |
time.sleep(0.1) | |
progress_bar.progress(i + step) | |
#churn_labels, churn_probability = predict_churn(input_features, model) # Pass model1 or model2 based on the selected model | |
churn_labels, churn_probability, churn_indices, churn_index = predict_churn(input_features, model) | |
st.subheader('Main Results') | |
col1, col2 = st.columns(2) | |
if churn_labels[churn_index] == "Churn": | |
churn_prob = churn_probability[churn_index] | |
with col1: | |
st.error(f"Beware!!! This customer is likely to churn with a probability of {churn_prob * 100:.2f}% 😢") | |
resized_churn_image = Image.open('Churn.png') | |
resized_churn_image = resized_churn_image.resize((350, 300)) # Adjust the width and height as desired | |
st.image(resized_churn_image) | |
# Add suggestions for retaining churned customers in the 'Churn' group | |
with col2: | |
st.info("Suggestions for retaining churned customers in this customer group:\n" | |
"- Offer personalized discounts or promotions\n" | |
"- Provide exceptional customer service\n" | |
"- Introduce loyalty programs\n" | |
"- Send targeted re-engagement emails\n" | |
"- Provide a dedicated account manager\n" | |
"- Offer extended trial periods\n" | |
"- Conduct exit surveys to understand reasons for churn\n" | |
"- Implement a customer win-back campaign\n" | |
"- Provide incentives for referrals\n" | |
"- Improve product or service offerings based on customer feedback") | |
else: | |
#churn_index = churn_indices["No Churn"] | |
churn_prob = churn_probability[churn_index] | |
with col1: | |
st.success(f"This customer is not likely to churn with a probability of {churn_prob * 100:.2f}% 😀") | |
resized_not_churn_image = Image.open('NotChurn.jpg') | |
resized_not_churn_image = resized_not_churn_image.resize((350, 300)) # Adjust the width and height as desired | |
st.image(resized_not_churn_image) | |
# Add suggestions for retaining churned customers in the 'Churn' group | |
with col2: | |
st.info("Suggestions for retaining non-churned customers in this customer group:\n" | |
"- Provide personalized product recommendations\n" | |
"- Offer exclusive features or upgrades\n" | |
"- Implement proactive customer support\n" | |
"- Conduct customer satisfaction surveys\n" | |
"- Recognize and reward loyal customers\n" | |
"- Organize customer appreciation events\n" | |
"- Offer early access to new features or products\n" | |
"- Provide educational resources or tutorials\n" | |
"- Implement a customer loyalty program\n" | |
"- Offer flexible billing or pricing options") | |
st.subheader('Churn Probability') | |
# Create a donut chart to display probabilities | |
fig = go.Figure(data=[go.Pie( | |
labels=churn_labels, | |
values=churn_probability, | |
hole=0.5, | |
textinfo='label+percent', | |
marker=dict(colors=['#FFA07A', '#6495ED', '#FFD700', '#32CD32', '#FF69B4', '#8B008B']))]) | |
fig.update_traces( | |
hoverinfo='label+percent', | |
textfont_size=12, | |
textposition='inside', | |
texttemplate='%{label}: %{percent:.2f}%' | |
) | |
fig.update_layout( | |
title='Churn Probability', | |
title_x=0.5, | |
showlegend=False, | |
width=500, | |
height=500 | |
) | |
st.plotly_chart(fig, use_container_width=True) | |
# Calculate the average churn rate (replace with your actual value) | |
st.subheader('Customer Churn Probability Comparison') | |
average_churn_rate = 19 | |
# Convert the overall churn rate to churn probability | |
main_data_churn_probability = average_churn_rate / 100 | |
# Retrieve the predicted churn probability for the selected customer | |
predicted_churn_prob = churn_probability[churn_index] | |
if churn_labels[churn_index] == "Churn": | |
churn_prob = churn_probability[churn_index] | |
# Create a bar chart comparing the churn probability with the average churn rate | |
labels = ['Churn Probability', 'Average Churn Probability'] | |
values = [predicted_churn_prob, main_data_churn_probability] | |
fig = go.Figure(data=[go.Bar(x=labels, y=values)]) | |
fig.update_layout( | |
xaxis_title='Churn Probability', | |
yaxis_title='Probability', | |
title='Comparison with Average Churn Rate', | |
yaxis=dict(range=[0, 1]) # Set the y-axis limits between 0 and 1 | |
) | |
# Add explanations | |
if predicted_churn_prob > main_data_churn_probability: | |
churn_comparison = "higher" | |
elif predicted_churn_prob < main_data_churn_probability: | |
churn_comparison = "lower" | |
else: | |
churn_comparison = "equal" | |
explanation = f"This bar chart compares the churn probability of the selected customer " \ | |
f"with the average churn rate of all customers. It provides insights into how the " \ | |
f"individual customer's churn likelihood ({predicted_churn_prob:.2f}) compares to the " \ | |
f"overall trend. The 'Churn Probability' represents the likelihood of churn " \ | |
f"for the selected customer, while the 'Average Churn Rate' represents the average " \ | |
f"churn rate across all customers ({main_data_churn_probability:.2f}).\n\n" \ | |
f"The customer's churn rate is {churn_comparison} than the average churn rate." | |
st.plotly_chart(fig) | |
st.write(explanation) | |
else: | |
# Create a bar chart comparing the no-churn probability with the average churn rate | |
labels = ['No-Churn Probability', 'Average Churn Probability'] | |
values = [1 - predicted_churn_prob, main_data_churn_probability] | |
fig = go.Figure(data=[go.Bar(x=labels, y=values)]) | |
fig.update_layout( | |
xaxis_title='Churn Probability', | |
yaxis_title='Probability', | |
title='Comparison with Average Churn Rate', | |
yaxis=dict(range=[0, 1]) # Set the y-axis limits between 0 and 1 | |
) | |
explanation = f"This bar chart compares the churn probability of the selected customer " \ | |
f"with the average churn rate of all customers. It provides insights into how the " \ | |
f"individual customer's likelihood of churn ({1 - predicted_churn_prob:.2f}) compares to the " \ | |
f"overall trend. A lower churn probability indicates that the customer is less likely to churn. " \ | |
f"The chart shows that the churn probability ({1 - predicted_churn_prob:.2f}) is lower than the " \ | |
f"average churn probability ({main_data_churn_probability:.2f}), suggesting that the customer " \ | |
f"is predicted to stay with the company. Keep in mind that the prediction is based on the " \ | |
f"available data and the applied model, and there might still be some uncertainty in the result." | |
st.plotly_chart(fig) | |
st.write(explanation) | |
# Visualize Feature Importance | |
st.subheader('Feature Importance') | |
if hasattr(model, 'coef_'): # Check if the model has attribute 'coef_' to determine importance type | |
feature_importances = model.coef_[0] | |
importance_type = 'Coef' | |
elif hasattr(model, 'feature_importances_'): | |
feature_importances = model.feature_importances_ | |
importance_type = 'Importance' | |
else: | |
st.write('Feature importance is not available for this model.') | |
# If importance information is available, create a DataFrame and sort it | |
if hasattr(model, 'coef_') or hasattr(model, 'feature_importances_'): | |
importance_df = pd.DataFrame({'Feature': original_feature_names, importance_type: feature_importances}) | |
importance_df = importance_df.sort_values(importance_type, ascending=False) | |
# Determine color for each bar based on positive or negative importance | |
colors = ['green' if importance > 0 else 'red' for importance in importance_df[importance_type]] | |
# Create a horizontal bar chart using Plotly | |
fig = go.Figure(go.Bar( | |
x=importance_df[importance_type], | |
y=importance_df['Feature'], | |
orientation='h', | |
marker=dict(color=colors), | |
text=importance_df[importance_type].apply(lambda x: f'{x:.2f}'), | |
textposition='inside')) | |
# Configure the layout of the bar chart | |
fig.update_layout( | |
title='Feature Importance', | |
xaxis_title='Importance', | |
yaxis_title='Feature', | |
bargap=0.1, | |
width=600, | |
height=800) | |
# Display the bar chart using Plotly chart in Streamlit | |
st.plotly_chart(fig) | |
# Explanation of feature importance | |
importance_explanation = f"The feature importance plot shows the relative importance of each feature " \ | |
f"for predicting churn. The importance is calculated based on the " \ | |
f"{importance_type} value of each feature in the model. " \ | |
f"A higher {importance_type} value indicates a stronger influence " \ | |
f"of the corresponding feature on the prediction of churn.\n\n" \ | |
f"For logistic regression, positive {importance_type} values indicate " \ | |
f"features that positively contribute to predicting churn, " \ | |
f"while negative {importance_type} values indicate features that " \ | |
f"negatively contribute to predicting churn.\n\n" \ | |
f"For gradient boosting, higher {importance_type} values " \ | |
f"indicate features that have a greater importance in predicting churn.\n\n" \ | |
f"Please note that the feature importance values may vary depending on the model " \ | |
f"and the data used for training." | |
st.write(importance_explanation) | |
except Exception as e: | |
st.error(f"An error occurred: {str(e)}") | |