Create SnP500AI.py

SnP500AI.py

@@ -0,0 +1,43 @@
+# SNP 500 USING explainableai
+
+import pandas as pd
+from explainableai import XAIWrapper
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
+
+# Load the mock data
+df = pd.read_csv('mock_snp500_data.csv', index_col=0)
+
+# Prepare features and target
+X = df.drop(columns=['S&P500'])  # Features (Open, High, Low, Close, Volume)
+y = df['S&P500']  # Target (S&P500 index)
+
+# Split data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Initialize the ExplainableAI wrapper
+xai = XAIWrapper()
+
+# Define the model
+model = RandomForestRegressor(n_estimators=100, random_state=42)
+
+# Fit the model and analyze
+xai.fit(model, X_train, y_train)
+results = xai.analyze(X_test, y_test)
+
+# Print LLM-powered explanation of the results
+print("LLM Explanation of the Results:")
+print(results['llm_explanation'])
+
+# Generate a detailed PDF report
+xai.generate_report('snp500_analysis_report.pdf')
+
+# Make an individual prediction with explanation (using a sample from the test set)
+new_data = X_test.iloc[0].to_dict()
+prediction, probabilities, explanation = xai.explain_prediction(new_data)
+
+print(f"Prediction for individual sample: {prediction}")
+print(f"Explanation for prediction: {explanation}")
+
+# Indicate where the report is stored
+print("Report generated as 'snp500_analysis_report.pdf'")