ML and EDA App Deployment

The provided files represent a comprehensive web application built with Streamlit, focusing on Telco Customer Churn Analysis and Prediction. Let me break down the components and functionality.

Application Structure

Main Components
Authentication System

• EDA (Exploratory Data Analysis) Dashboard
• Telco Churn Prediction Model

Authentication Module

The authentication system (authenticationapp.py) implements a secure login interface with:

• Username and password fields
• Social login options (Google, Facebook)
• “Welcome Back” greeting message
• Password visibility toggle[1]

EDA Dashboard

The EDA dashboard (edaapp.py) provides data analysis capabilities:

• File upload functionality supporting CSV and Excel formats
• Data caching for improved performance
• Interactive navigation sidebar
• Responsive layout with wide-screen configuration[1]

Telco Churn Prediction

The prediction system (telcochurnapp.py) incorporates:

Data Processing Pipeline

<span>preprocessor</span> <span>=</span> <span>ColumnTransformer</span><span>(</span>
    <span>transformers</span><span>=</span><span>[</span>
        <span>(</span><span>'</span><span>num</span><span>'</span><span>,</span> <span>numeric_transformer</span><span>,</span> <span>numeric_columns</span><span>),</span>
        <span>(</span><span>'</span><span>cat</span><span>'</span><span>,</span> <span>categorical_transformer</span><span>,</span> <span>categorical_columns</span><span>)</span>
    <span>])</span>
<span>preprocessor</span> <span>=</span> <span>ColumnTransformer</span><span>(</span>
    <span>transformers</span><span>=</span><span>[</span>
        <span>(</span><span>'</span><span>num</span><span>'</span><span>,</span> <span>numeric_transformer</span><span>,</span> <span>numeric_columns</span><span>),</span>
        <span>(</span><span>'</span><span>cat</span><span>'</span><span>,</span> <span>categorical_transformer</span><span>,</span> <span>categorical_columns</span><span>)</span>
    <span>])</span>
preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_columns),
        ('cat', categorical_transformer, categorical_columns)
    ])

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Machine Learning Models

• Random Forest Classifier
• Logistic Regression
• Gradient Boosting Classifier[3]

Key Features

• Automated data preprocessing
• Model performance evaluation
• Real-time prediction capabilities
• Data validation and error handling[3]

Technical Implementation

Data Processing

• Handles missing values using SimpleImputer
• Implements feature scaling with StandardScaler
• Performs one-hot encoding for categorical variables[3]

Model Training

<span>@st.cache_data</span>
<span>def</span> <span>train_models</span><span>(</span><span>_X</span><span>,</span> <span>y</span><span>):</span>
    <span>X_train</span><span>,</span> <span>X_test</span><span>,</span> <span>y_train</span><span>,</span> <span>y_test</span> <span>=</span> <span>train_test_split</span><span>(</span><span>X</span><span>,</span> <span>y</span><span>,</span> <span>test_size</span><span>=</span><span>0.2</span><span>,</span> <span>random_state</span><span>=</span><span>42</span><span>)</span>
    <span>models</span> <span>=</span> <span>{</span>
        <span>"</span><span>Random Forest</span><span>"</span><span>:</span> <span>RandomForestClassifier</span><span>(</span><span>random_state</span><span>=</span><span>42</span><span>),</span>
        <span>"</span><span>Logistic Regression</span><span>"</span><span>:</span> <span>LogisticRegression</span><span>(</span><span>random_state</span><span>=</span><span>42</span><span>),</span>
        <span>"</span><span>Gradient Boosting</span><span>"</span><span>:</span> <span>GradientBoostingClassifier</span><span>(</span><span>random_state</span><span>=</span><span>42</span><span>)</span>
    <span>}</span>
<span>@st.cache_data</span>
<span>def</span> <span>train_models</span><span>(</span><span>_X</span><span>,</span> <span>y</span><span>):</span>
    <span>X_train</span><span>,</span> <span>X_test</span><span>,</span> <span>y_train</span><span>,</span> <span>y_test</span> <span>=</span> <span>train_test_split</span><span>(</span><span>X</span><span>,</span> <span>y</span><span>,</span> <span>test_size</span><span>=</span><span>0.2</span><span>,</span> <span>random_state</span><span>=</span><span>42</span><span>)</span>
    <span>models</span> <span>=</span> <span>{</span>
        <span>"</span><span>Random Forest</span><span>"</span><span>:</span> <span>RandomForestClassifier</span><span>(</span><span>random_state</span><span>=</span><span>42</span><span>),</span>
        <span>"</span><span>Logistic Regression</span><span>"</span><span>:</span> <span>LogisticRegression</span><span>(</span><span>random_state</span><span>=</span><span>42</span><span>),</span>
        <span>"</span><span>Gradient Boosting</span><span>"</span><span>:</span> <span>GradientBoostingClassifier</span><span>(</span><span>random_state</span><span>=</span><span>42</span><span>)</span>
    <span>}</span>
@st.cache_data
def train_models(_X, y):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    models = {
        "Random Forest": RandomForestClassifier(random_state=42),
        "Logistic Regression": LogisticRegression(random_state=42),
        "Gradient Boosting": GradientBoostingClassifier(random_state=42)
    }

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The system employs model caching to optimize performance and provides comprehensive error handling throughout the application[3].

User Interface

The application features a clean, intuitive interface with:

• Wide-layout configuration
• Navigation sidebar
• Interactive data upload functionality
• Real-time model predictions[1][3]

This comprehensive system combines modern machine learning techniques with an accessible web interface, making it a powerful tool for telco churn analysis and prediction.

Appreciation
I highly recommend Azubi Africa for their comprehensive and effective programs. Read More articles about Azubi Africa here and take a few minutes to visit this link to learn more about Azubi Africa life-changing programs
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原文链接：ML and EDA App Deployment