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app.py

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+# 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/Titanic-Project---Machine-Learning-from-Disaster)")
+
+# Loading Machine Learning Objects
+@st.cache_data
+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
+@st.cache_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 prediction
+    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)
+
+    
+    

requirements.txt

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+streamlit
+pandas==1.4.2
+numpy==1.21.5
+seaborn==0.11.2
+scikit-learn