ML and EDA App Deployment

This Streamlit application offers a complete solution for Telco customer churn analysis and prediction. Let's explore its key features and functionality.

Application Components:

The application comprises three main modules: an authentication system, an Exploratory Data Analysis (EDA) dashboard, and a Telco churn prediction model.

1. Secure Authentication:

The authentication module (authenticationapp.py) provides a robust login system featuring:

• Username and password-based login.
• Integration with Google and Facebook for social login.
• A welcoming message upon successful login.
• An option to show/hide passwords.

2. Interactive EDA Dashboard:

The EDA dashboard (edaapp.py) facilitates comprehensive data exploration:

• Supports CSV and Excel file uploads.
• Utilizes data caching for faster performance.
• Includes an intuitive navigation sidebar.
• Adapts seamlessly to various screen sizes.

3. Telco Churn Prediction Engine:

The prediction module (telcochurnapp.py) incorporates a sophisticated data processing pipeline and multiple machine learning models:

Data Processing:

The pipeline handles data preprocessing steps including:

• Missing value imputation using SimpleImputer.
• Feature scaling with StandardScaler.
• One-hot encoding for categorical features.

Machine Learning Models:

The application trains and utilizes several models:

• Random Forest Classifier
• Logistic Regression
• Gradient Boosting Classifier

The system automatically evaluates model performance and provides real-time predictions, incorporating robust error handling.

Technical Details:

Model training leverages train_test_split for data partitioning and employs model caching (@st.cache_data) for efficiency. The code snippet below illustrates the model training process:

<code class="language-python">@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)
    }
    # ... (rest of the training and evaluation logic)</code>

User Experience:

The application boasts a user-friendly interface:

• A wide-layout design for optimal viewing.
• A convenient navigation sidebar.
• Intuitive file upload functionality.
• Real-time prediction display.

This application effectively combines advanced machine learning techniques with a streamlined user interface, providing a powerful tool for analyzing and predicting telco customer churn.

Acknowledgements:

The author expresses gratitude to Azubi Africa for their impactful training programs. For more information on Azubi Africa and their initiatives, please visit [link to Azubi Africa].

Tags: Azubi Data Science

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