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
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Tags
Azubi Data Science
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