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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.
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For #83 |
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ombhojane
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Oct 11, 2024
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ombhojane
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ombhojane
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perfect!
please move the codes in /examples dir
and have your own project dir name say /SnP500AI, in this dir have SnP500AI.py and csv file
Thanks for contribution and I'm looking to merge this PR
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hey @SIDDHARTH1-1CHAUHAN any update? Please do required small fixes, I'm looking to merge this PR |
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Yes sir , will do , was busy with something , extremely sorry
…On Mon, 14 Oct, 2024, 12:47 Om Bhojane, ***@***.***> wrote:
hey @SIDDHARTH1-1CHAUHAN <https://github.com/SIDDHARTH1-1CHAUHAN> any
update? Please do required small fixes, I'm looking to merge this PR
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Nvm, please do requested changes |
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#83 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.