import streamlit as st import joblib import pandas as pd import numpy as np import plotly.graph_objects as go from PIL import Image import time import matplotlib.pyplot as plt from io import BytesIO # num_imputer = joblib.load('C:/Users/user/Desktop/Churn_Prediction_ML/models/numerical_imputer.joblib') # cat_imputer = joblib.load('C:/Users/user/Desktop/Churn_Prediction_ML/models/cat_imputer.joblib') # encoder = joblib.load('C:/Users/user/Desktop/Churn_Prediction_ML/models/encoder.joblib') # scaler = joblib.load('C:/Users/user/Desktop/Churn_Prediction_ML/models/scaler.joblib') # lr_model = joblib.load('C:/Users/user/Desktop/Churn_Prediction_ML/models/lr_smote_model.joblib') num_imputer = joblib.load('numerical_imputer.joblib') cat_imputer = joblib.load('cat_imputer.joblib') encoder = joblib.load('encoder.joblib') scaler = joblib.load('scaler.joblib') lr_model = joblib.load('lr_smote_model.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_VAS', 'TOP_PACK_data', 'TOP_PACK_international', 'TOP_PACK_messaging', 'TOP_PACK_other_services', 'TOP_PACK_social_media', 'TOP_PACK_voice'] # Set up the Streamlit app st.set_page_config(layout="wide") # Main page - Churn Prediction st.title('CUSTOMER CHURN PREDICTION APP ') # Main page - Churn Prediction st.markdown("Churn is a one of the biggest problem in the telecom industry. Research has shown that the average monthly churn rate among the top 4 wireless carriers in the US is 1.9% - 2%") st.image("bg.png", use_column_width=True) # How to use st.sidebar.image("welcome.jpg", use_column_width=True) st.sidebar.title("ENTER THE DETAILS OF THE CUSTOMER HERE") # Define a dictionary of models with their names, actual models, and types models = { 'Logistic Regression': {'Logistic Regression': lr_model, 'type': 'logistic_regression'}, #'ComplementNB': {'ComplementNB': cnb_model, 'type': 'Complement NB'} } # Allow the user to select a model from the sidebar model_name = st.sidebar.selectbox('Select Model', list(models.keys())) # Retrieve the selected model and its type from the dictionary model = models[model_name]['Logistic Regression'] 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)', ['DAKAR','DIOURBEL','FATICK','AFFRINE','KAOLACK', 'KEDOUGOU','KOLDA','LOUGA','MATAM','SAINT-LOUIS', 'SEDHIOU','TAMBACOUNDA','HIES','ZIGUINCHOR' ]), 'TENURE': st.sidebar.selectbox('Duration in the Network (TENURE)', ['Long-term','Medium-term','Mid-term','Short-term', 'Very short-term']), 'TOP_PACK': st.sidebar.selectbox('Most Active Pack (TOP_PACK)', ['VAS', 'data', 'international', 'messaging','other_services', 'social_media', '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 == "ComplementNB": 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("Wait, Results loading..."): # 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('CHURN PREDICTION RESULTS') col1, col2 = st.columns(2) if churn_labels[churn_index] == "Churn": churn_prob = churn_probability[churn_index] with col1: st.error(f"CHURN ALERT! This customer is likely to churn with a probability of {churn_prob * 100:.2f}% 😢") resized_churn_image = Image.open('Churn.jpeg') 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("ADVICE TO EXPRESSOR MANAGEMENT:\n" "- Identify Reasons for Churn\n" "- Offer Incentives\n" "- Showcase Improvements\n" "- Gather Feedback\n" "- Customer Surveys\n" "- Personalized Recommendations\n" "- Reestablish Trust\n" "- Follow-Up Communication\n" "- Reactivation Campaigns\n" "- Improve product or service offerings based on customer feedback\n" " SUMMARY NOTE\n" "- Remember that winning back churning customers takes time and persistence.\n" "- It\s crucial to genuinely address their concerns and provide value to rebuild their trust in your business\n" "- Regularly evaluate the effectiveness of your strategies and adjust them as needed based on customer responses and feedback\n") 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.jpeg') 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("ADVICE TO EXPRESSOR MANAGEMENT\n" "- Quality Products/Services\n" "- Personalized Experience\n" "- Loyalty Programs\n" "- Excellent Customer Service\n" "- Exclusive Content\n" "- Early Access\n" "- Personal Thank-You Notes\n" "- Surprise Gifts or Discounts\n" "- Feedback Opportunities\n" "- Community Engagement\n" "- Anniversary Celebrations\n" "- Refer-a-Friend Programs\n" "SUMMARY NOTE\n" "- Remember that the key to building lasting loyalty is consistency.\n" "- Continuously demonstrate your commitment to meeting customers needs and enhancing their experience.\n" "- Regularly assess the effectiveness of your loyalty initiatives and adapt them based on customer feedback and preferences.") 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 churn likelihood ({predicted_churn_prob:.2f}) compares to the " \ f"overall trend." \ f"The prediction indicates that the customer is not likely to churn. " \ f"The churn probability is lower than the no-churn probability." st.plotly_chart(fig) st.write(explanation) except Exception as e: st.error(f"An error occurred: {str(e)}")