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| 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('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 (CCPA)') | |
| # 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.png", 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('No. 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"DANGER! 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_prob = churn_probability[churn_index] | |
| with col1: | |
| st.success(f"This customer is a loyal (not churn) with a probability of {churn_prob * 100:.2f}% 😀") | |
| resized_not_churn_image = Image.open('NotChurn.png') | |
| 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 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)}") | |