From 2315a6d0824720bb576fc71889b31a4006b630c1 Mon Sep 17 00:00:00 2001
From: SIDDHARTH1-1CHAUHAN
 <166252056+SIDDHARTH1-1CHAUHAN@users.noreply.github.com>
Date: Fri, 11 Oct 2024 00:05:29 +0530
Subject: [PATCH] Create SnP500AI.py

The main.py script handles the training and analysis of a machine learning model for predicting S&P 500 index values using the ExplainableAI package.

It first loads the mock S&P 500 data generated from a CSV file (mock_snp500_data.csv) and splits the data into training and testing sets.
The script then initializes the XAIWrapper and fits a Random Forest Regressor model on the training data.
Once the model is trained, it analyzes the model's performance using ExplainableAI, providing insights such as feature importance and model behavior, and prints a LLM-powered explanation of the results.
Additionally, it generates a professional PDF report (snp500_analysis_report.pdf) summarizing the analysis.
Finally, it demonstrates explainability for individual predictions by selecting a sample from the test set, making a prediction, and providing an explanation for the prediction.
The script automates model training, analysis, and interpretability using advanced explainable AI techniques.
---
 SnP500AI.py | 43 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 43 insertions(+)
 create mode 100644 SnP500AI.py

diff --git a/SnP500AI.py b/SnP500AI.py
new file mode 100644
index 0000000..71e8fdb
--- /dev/null
+++ b/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'")