-
-
Notifications
You must be signed in to change notification settings - Fork 38
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Create SnP500AI.py #92
base: main
Are you sure you want to change the base?
Create SnP500AI.py #92
Conversation
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.
For #83 |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
🎉 Thank you for your contribution! Your pull request has been submitted successfully. A maintainer will review it as soon as possible. We appreciate your support in making this project better
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
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
hey @SIDDHARTH1-1CHAUHAN any update? Please do required small fixes, I'm looking to merge this PR |
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
—
Reply to this email directly, view it on GitHub
<#92 (comment)>,
or unsubscribe
<https://github.com/notifications/unsubscribe-auth/BHUM4GE23YFOV6SRFLG5NNLZ3NVX7AVCNFSM6AAAAABPXMPJZ6VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDIMJQGIZTSMRVGQ>
.
You are receiving this because you were mentioned.Message ID:
***@***.***>
|
Nvm, please do requested changes |
#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.