[FEATURE] Optimize Stock Price Prediction Code for Intel Hardware Using AI Analytics Toolkit

[priyansh985]

Is this a unique feature?

• I have checked "open" AND "closed" issues and this is not a duplicate

Is your feature request related to a problem/unavailable functionality? Please describe.

Yes, the current stock price prediction code is not fully optimized for Intel hardware, leading to slower performance, especially with large datasets during data preprocessing and model training. We need to utilize the Intel AI Analytics Toolkit, which offers enhanced parallelism and optimizations through Modin pandas and Intel-optimized scikit-learn. The absence of these optimizations is causing inefficiencies in terms of computation speed and resource utilization.

Proposed Solution

To optimize the stock price prediction code for Intel hardware, we propose using the Intel AI Analytics Toolkit, which includes Modin for faster data manipulation and Intel-optimized scikit-learn for efficient model training. By replacing standard pandas operations with Modin, the code will leverage parallel processing, speeding up computations on large datasets. Intel-optimized scikit-learn will further accelerate the machine learning workflows by utilizing Intel’s low-level optimizations. Performance improvements will be measured through benchmarks, and profiling tools like Intel VTune can be used to analyze resource utilization. You can also explore Intel's detailed documentation on AI optimizations for more insights.

Screenshots

No response

Do you want to work on this issue?

Yes

If "yes" to above, please explain how you would technically implement this (issue will not be assigned if this is skipped)

For the implementation, we will first install the necessary libraries from the Intel AI Analytics Toolkit, such as Modin and Intel-optimized scikit-learn. Once installed, we will update the code by replacing the standard pandas with Modin (import modin.pandas as pd), allowing for parallel data processing, which significantly speeds up operations on large datasets. No changes are needed for scikit-learn since the Intel-optimized version will automatically enhance the performance of machine learning models like RandomForestRegressor. After these modifications, we’ll benchmark the performance to ensure the optimizations are effectively improving the computation time.

[github-actions]

Ensure the issue is not similar or previously being worked on.Thanks for your time

[priyansh985]

hello sir { rohitinu6 }
sir i raised this issue because i think i can make the model and data preprocessing using intel ai Analytics tookit this project is part of my hacktoberfest and gccoc ext open source contributor by implementing this features the model will perform upto 50 times faster than traditional libraries in intel hardware like eg: cpu and gpu kindly please grant me this issue