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) ]) 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) } 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 Tags Azubi Data Science
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)
])
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)
}
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
Tags
Azubi Data Science
