"""
AdvancedDataAnalyzer
Generated by Eden via recursive self-improvement
2025-11-01 02:35:44.921865
"""

import pandas as pd
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

def advanced_data_analyzer(data_path):
    """
    Analyzes a given dataset to identify patterns or anomalies.
    
    Parameters:
        data_path (str): The file path of the dataset in CSV format.
        
    Returns:
        A summary report containing insights from the analysis.
    """
    # Load the dataset
    df = pd.read_csv(data_path)
    
    # Perform initial exploratory data analysis
    print("Initial Data Summary:")
    print(df.describe())
    
    # Identify numerical and categorical columns for clustering
    numerical_features = df.select_dtypes(include='number').columns.tolist()
    categorical_features = df.select_dtypes(include='object').columns.tolist()
    
    if len(numerical_features) > 0:
        # Apply KMeans clustering to the numerical data
        kmeans = KMeans(n_clusters=3, random_state=42)
        clusters = kmeans.fit_predict(df[numerical_features])
        
        # Plotting cluster distribution for the first two numerical features
        plt.scatter(df[numerical_features[0]], df[numerical_features[1]], c=clusters, cmap='viridis')
        plt.title("KMeans Clustering of Numerical Features")
        plt.xlabel(numerical_features[0])
        plt.ylabel(numerical_features[1])
        plt.show()
        
    if len(categorical_features) > 0:
        # Display frequency distribution for categorical features
        print("\nCategorical Feature Frequency Distribution:")
        for cat in categorical_features:
            value_counts = df[cat].value_counts(normalize=True)
            print(f"{cat}: {value_counts}")
    
    # Generate a summary report based on the analysis findings
    report = "Data Analysis Report:\n"
    if len(numerical_features) > 0:
        report += f"Key numerical patterns observed: Clusters created and visualized for the first two numerical features."
    if len(categorical_features) > 0:
        report += f"Categorical insights: Frequency distribution of categorical features as percentage of total observations."
    
    return report

# Example usage
result = advanced_data_analyzer('path/to/your/dataset.csv')
print(result)