from .main import dataExport


def detect_outliers(data):
    """Detect outliers in a dataset using statistical methods."""
    # Implement your outlier detection algorithm here
    pass

def impute_missing_values(data):
    """Impute missing values in a dataset using advanced techniques."""
    # Implement your advanced imputation techniques here
    pass

def feature_scaling(data):
    """Perform feature scaling on numerical features."""
    # Implement your custom feature scaling methods here
    pass

def text_preprocessing(text_data):
    """Preprocess text data for NLP tasks."""
    # Implement your unique text preprocessing techniques here
    pass

def anomaly_detection(data):
    """Detect anomalies in time-series data."""
    # Implement your anomaly detection algorithm here
    pass

def dimensionality_reduction(data):
    """Reduce dimensionality of data using advanced techniques."""
    # Implement your advanced dimensionality reduction methods here
    pass

def cluster_analysis(data):
    """Perform cluster analysis on a dataset."""
    # Implement your custom clustering algorithms here
    pass

def time_series_forecasting(data):
    """Forecast future values in a time-series dataset."""
    # Implement your time series forecasting algorithm here
    pass

def recommendation_system(data):
    """Build a recommendation system using collaborative filtering."""
    # Implement your recommendation system algorithm here
    pass

def network_analysis(graph_data):
    """Analyze networks and graphs for insights."""
    # Implement your network analysis algorithms here
    pass

def topic_modeling(text_data):
    """Extract topics from text data using topic modeling."""
    # Implement your topic modeling algorithm here
    pass

def graph_embedding(graph_data):
    """Embed graphs into lower-dimensional vector spaces."""
    # Implement your graph embedding algorithms here
    pass

def anomaly_explanation(anomaly_data):
    """Explain detected anomalies and their causes."""
    # Implement your anomaly explanation methods here
    pass

def time_series_segmentation(data):
    """Segment time-series data into meaningful patterns."""
    # Implement your time series segmentation algorithm here
    pass

def graph_clustering(graph_data):
    """Cluster nodes in a graph to identify communities."""
    # Implement your graph clustering algorithms here
    pass

def text_summarization(text_data):
    """Generate summaries from text data."""
    # Implement your text summarization algorithms here
    pass

def interpret_ml_models(model, data):
    """Interpret machine learning models to understand predictions."""
    # Implement your model interpretation techniques here
    pass

def feature_importance(data, target):
    """Calculate feature importance for predictive modeling."""
    # Implement your feature importance calculation methods here
    pass

def time_series_similarity(data):
    """Measure similarity between time-series data."""
    # Implement your time series similarity measurement methods here
    pass

def graph_similarity(graph_data):
    """Measure similarity between graphs."""
    # Implement your graph similarity measurement methods here
    pass