Spaces:
Runtime error
Runtime error
| # Importing required Libraries | |
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import os, pickle | |
| # Setting up page configuration and directory path | |
| st.set_page_config(page_title= "Customer Churn Prediction", page_icon="🛳️", layout="centered") | |
| DIRPATH = os.path.dirname(os.path.realpath(__file__)) | |
| # Setting background image | |
| import base64 | |
| def add_bg_from_local(image_file): | |
| with open(image_file, "rb") as image_file: | |
| encoded_string = base64.b64encode(image_file.read()) | |
| st.markdown( | |
| f""" | |
| <style> | |
| .stApp {{ | |
| background-image: url(data:image/{"jpg"};base64,{encoded_string.decode()}); | |
| background-size: cover | |
| }} | |
| </style> | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| add_bg_from_local('images/background.jpg') | |
| # Setting up logo | |
| left, mid, mid1, right = st.columns(4) | |
| with mid: | |
| st.image("images/logo.jpeg", use_column_width=True) | |
| # Setting up Sidebar | |
| social_acc = ['Data Field Description', 'EDA', 'About App'] | |
| social_acc_nav = st.sidebar.radio('**INFORMATION SECTION**', social_acc) | |
| if social_acc_nav == 'Data Field Description': | |
| st.sidebar.markdown("<h2 style=[],'text-align: center;'> Data Field Description </h2> ", unsafe_allow_html=True) | |
| st.sidebar.markdown(""" | |
| The table below gives a description on the variables required to make predictions. | |
| | Variable | Definition: | | |
| | :------------ |:--------------- | | |
| | FREQUENCE | number of times the client has made an income | | |
| | TENURE | duration in the network | | |
| | FREQUENCE_RECH| number of times the customer refilled | | |
| | MONTANT | top-up amount | | |
| | DATA_VOLUME | number of connections| | |
| | ORANGE | call to orange | | |
| | ARPU_SEGMENT | income over 90 days / 3 | | |
| | ON_NET | inter expresso call | | |
| | REGULARITY | number of times the client is active for 90 days | | |
| | FREQ_TOP_PACK | number of times client has activated the top pack packages| | |
| | REVENUE | monthly income of each client | | |
| """) | |
| elif social_acc_nav == 'EDA': | |
| st.sidebar.markdown("<h2 style=[],'text-align: center;'> Exploratory Data Analysis </h2> ", unsafe_allow_html=True) | |
| st.sidebar.markdown('''---''') | |
| st.sidebar.markdown(""" | |
| | About EDA| | |
| | :------------ | | |
| The exploratory data analysis of this project can be found in a Notebook in a github repository from the link below""" ) | |
| st.sidebar.markdown("[Open Repository ](https://github.com/Kyei-frank/Customer-Churn-Prediction--Expresso)") | |
| elif social_acc_nav == 'About App': | |
| st.sidebar.markdown("<h2 style=[],'text-align: center;'> Titanic Survival Prediction App </h2> ", unsafe_allow_html=True) | |
| st.sidebar.markdown('''---''') | |
| st.sidebar.markdown(""" | |
| | Brief Introduction| | |
| | :------------ | | |
| This projet is based on a Zindi challenge for an African telecommunications company (Expresso) | |
| that provides customers with airtime and mobile data bundles. The objective of this challenge | |
| is to develop a machine learning model to predict the likelihood of each customer “churning,” | |
| i.e. becoming inactive and not making any transactions for 90 days. This solution will help | |
| this telecom company to better serve their customers by understanding which customers are at risk of leaving""") | |
| st.sidebar.markdown("") | |
| st.sidebar.markdown("[ Visit Github Repository for more information](https://github.com/Kyei-frank/Customer-Churn-Prediction--Expresso)") | |
| # Loading Machine Learning Objects | |
| def load_saved_objects(file_path = 'ML_items'): | |
| # Function to load saved objects | |
| with open('ML_items', 'rb') as file: | |
| loaded_object = pickle.load(file) | |
| return loaded_object | |
| # Instantiating ML_items | |
| Loaded_object = load_saved_objects(file_path = 'ML_items') | |
| pipeline_of_my_app = Loaded_object["pipeline"] | |
| # Setting up variables for input data | |
| def setup(tmp_df_file): | |
| "Setup the required elements like files, models, global variables, etc" | |
| pd.DataFrame( | |
| dict( | |
| FREQUENCE= [], | |
| TENURE= [], | |
| FREQUENCE_RECH= [], | |
| MONTANT= [], | |
| DATA_VOLUME= [], | |
| ORANGE= [], | |
| ARPU_SEGMENT= [], | |
| ON_NET= [], | |
| REGULARITY= [], | |
| FREQ_TOP_PACK= [], | |
| REVENUE= [], | |
| ) | |
| ).to_csv(tmp_df_file, index=False) | |
| # Setting up a file to save our input data | |
| tmp_df_file = os.path.join(DIRPATH, "tmp", "data.csv") | |
| setup(tmp_df_file) | |
| # setting Title for forms | |
| st.markdown("<h2 style=[],'text-align: right;'>....... Customer Churn Prediction ...... </h2> ", unsafe_allow_html=True) | |
| st.markdown("<h6 style=[],'text-align: center;'> Fill in the details below and click on SUBMIT button to make a prediction for a Client. </h6> ", unsafe_allow_html=True) | |
| # Creating columns for input data(forms) | |
| left_col, middle_col, right_col = st.columns(3) | |
| # Developing forms to collect input data | |
| with st.form(key="information", clear_on_submit=True): | |
| # Setting up input data for 1st column | |
| left_col.markdown(":blue[**CALLS & ACTIVITY DETAILS**]") | |
| ORANGE = left_col.number_input("Insert Number of calls to ORANGE") | |
| ON_NET = left_col.number_input("Insert Number of inter expresso calls") | |
| DATA_VOLUME = left_col.number_input("Insert Number of connections") | |
| REGULARITY = left_col.number_input("Insert number of times the client is active for 90 days") | |
| FREQ_TOP_PACK = left_col.number_input("Insert number of times client has activated the top pack packages") | |
| # Setting up input data for 2nd column | |
| middle_col.markdown(":blue[**TOP-UP & INCOME DETAILS**]") | |
| MONTANT = middle_col.number_input("Insert top-up amount") | |
| FREQUENCE_RECH = middle_col.number_input("Insert Number of times the customer refilled") | |
| REVENUE = middle_col.number_input("Insert monthly income of client") | |
| ARPU_SEGMENT = middle_col.number_input("Insert income over 90 days / 3") | |
| FREQUENCE = middle_col.number_input("Insert number of times client has made an income") | |
| # Setting up input data for 2nd column | |
| right_col.markdown(":blue[**TENURE DETAILS**]") | |
| TENURE = right_col.radio("What is Client's duration in the network?", ('D 3-6 month', | |
| 'E 6-9 month', 'F 9-12 month', 'G 12-15 month', 'H 15-18 month', | |
| 'I 18-21 month', 'J 21-24 month', 'K > 24 month',)) | |
| submitted = st.form_submit_button(label="Submit") | |
| # Setting up background operations after submitting forms | |
| if submitted: | |
| # Saving input data as csv after submission | |
| pd.read_csv(tmp_df_file).append( | |
| dict( | |
| FREQUENCE= FREQUENCE, | |
| TENURE= TENURE, | |
| FREQUENCE_RECH= FREQUENCE_RECH, | |
| MONTANT= MONTANT, | |
| DATA_VOLUME= DATA_VOLUME, | |
| ORANGE= ORANGE, | |
| ARPU_SEGMENT= ARPU_SEGMENT, | |
| ON_NET= ON_NET, | |
| REGULARITY= REGULARITY, | |
| FREQ_TOP_PACK= FREQ_TOP_PACK, | |
| REVENUE= REVENUE, | |
| ), | |
| ignore_index=True, | |
| ).to_csv(tmp_df_file, index=False) | |
| st.balloons() | |
| # Converting input data to a dataframe for predictions | |
| df = pd.read_csv(tmp_df_file) | |
| df= df.copy() | |
| # Making Predictions | |
| # Passing data to pipeline to make prediction | |
| pred_output = pipeline_of_my_app.predict(df) | |
| prob_output = np.max(pipeline_of_my_app.predict_proba(df)) | |
| # Interpleting prediction output for display | |
| X= pred_output[-1] | |
| if X == 1: | |
| explanation = 'Client will CHURN' | |
| else: | |
| explanation = 'Client will NOT CHURN' | |
| output = explanation | |
| # Displaying prediction results | |
| st.markdown('''---''') | |
| st.markdown("<h4 style=[],'text-align: center;'> Prediction Results </h4> ", unsafe_allow_html=True) | |
| st.success(f"Prediction: {output}") | |
| st.success(f"Confidence Probability: {prob_output}") | |
| st.markdown('''---''') | |
| # Making expander to view all records | |
| expander = st.expander("See all records") | |
| with expander: | |
| df = pd.read_csv(tmp_df_file) | |
| df['Survival']= pred_output | |
| st.dataframe(df) | |