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Ndumbe
Ndumbe

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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

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

@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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Azubi Data Science

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