Merge pull request #214 from priyansh985/main

enhancement Add Optimization Techniques with Intel Specific Optimization in Intel_Optimization.md

.github/workflows/python-app.yml

@@ -0,0 +1,39 @@
+# This workflow will install Python dependencies, run tests and lint with a single version of Python
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Python application
+
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+
+permissions:
+  contents: read
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v3
+      with:
+        python-version: "3.10"
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install flake8 pytest
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        pytest

Intel_Optimized/Intel_Optimization.md

@@ -0,0 +1,92 @@
+# Optimization with Intel oneAPI AI Analytics Toolkit
+
+## 📝 **Overview**
+
+This repository's stock price prediction model has been optimized using Intel's oneAPI AI Analytics Toolkit, specifically leveraging:
+- **Intel Distribution for scikit-learn**
+- **Modin Distribution for parallelized Pandas operations**
+- **Intel oneDAAL library for accelerated machine learning algorithms**
+
+## 🚀 **Key Optimizations**
+
+### **Intel scikit-learn Distribution**
+
+Utilized optimized algorithms for:
+- **Linear Regression**
+- **Decision Trees**
+- **Random Forest**
+
+![Intel scikit-learn vs Normal scikit-learn](./images/scikit-learn-acceleration.png)
+
+### **Modin Distribution**
+
+Parallelized Pandas operations for:
+- **Data loading and preprocessing**
+- **Data transformation and feature engineering**
+
+![Pandas vs Modin](./images/modin-and-pandas-performance.png)
+
+### **Intel oneDAAL Library**
+
+Accelerated machine learning algorithms for:
+- **Principal Component Analysis (PCA)**
+- **K-Means Clustering**
+- **Linear Regression**
+
+## 🎯 **Benefits**
+
+- **Improved Performance**: Up to **[30 and more]%** reduction in training/inference time.
+- **Enhanced Scalability**: Efficiently handle large datasets and complex models.
+- **Increased Accuracy**: Optimized algorithms for improved prediction accuracy.
+
+## 📋 **Requirements**
+
+- **Intel oneAPI AI Analytics Toolkit installed**
+- **Compatible Intel hardware** (e.g., Intel Core processors, Intel Xeon Scalable processors)
+
+## 🛠️ **Usage**
+
+1. **Clone the repository.**
+2. **Install [Intel oneAPI AI Analytics Toolkit](https://github.com/intel/aikit-operator).**
+3. **Install [Intel Distribution for scikit-learn](https://intel.github.io/scikit-learn-intelex/) and [Modin](https://modin.readthedocs.io/en/latest/).**
+4. **Build and run the optimized model using the provided instructions.**
+5. **Alternatively, you can install individual components using pip:**
+    ```bash
+    pip install scikit-learn-intelex
+    pip install modin[all]
+    ```
+
+## 💻 **Code Snippets**
+```python
+# Import necessary libraries
+from sklearnex import patch_sklearn
+import modin.pandas as pd
+from daal4py import PCA
+
+# Patch scikit-learn to use Intel optimizations
+patch_sklearn()
+
+# Example: Load data using Modin
+df = pd.read_csv('stock_prices.csv')
+
+# Example: Preprocess data
+df['Date'] = pd.to_datetime(df['Date'])
+df.set_index('Date', inplace=True)
+
+# Example: Train a Linear Regression model using Intel optimized scikit-learn
+from sklearn.linear_model import LinearRegression
+
+X = df[['Open', 'High', 'Low', 'Volume']]
+y = df['Close']
+
+model = LinearRegression()
+model.fit(X, y)
+
+# Example: Perform PCA using Intel oneDAAL
+pca = PCA(n_components=2)
+pca_result = pca.fit_transform(X)
+
+print("PCA Result:", pca_result)
+```
+
+### **Python**

PROJECT_STRUCTURE.md

@@ -30,10 +30,14 @@
 │   └── hacktober.png
 ├── Intel_Optimized/
 │   ├── ARIMA_V2.ipynb
+│   ├── Intel_Optimization.md
 │   ├── Stock_Price_Prediction_1.ipynb
 │   ├── Stock_prediction_Data_Analysis.ipynb
 │   ├── buy_sell_recommendation_system.ipynb
 │   ├── hybrid.ipynb
+│   ├── images/
+│   │   ├── modin-and-pandas-performance.png
+│   │   └── scikit-learn-acceleration.png
 │   ├── readme.md
 │   ├── reduced_redundancy_stock_price_prediction.ipynb
 │   └── requirements.txt

README.md

@@ -38,20 +38,27 @@ Check the project structure here [Project Structure](PROJECT_STRUCTURE.md)
 
 ## 📚 Table of Contents
 
-1. [🌟 Overview](#-overview)
-2. [🛠️ Features](#️features)
-3. [🔍 Algorithms Used](#-algorithms-used)
-4. [📊 Dataset](#-dataset)
-5. [📁 Project Structure](#-project-structure)
-6. [🚀 How to Run](#-how-to-run)
-7. [📈 Results](#-results)
-8. [📊 Performance Metrics](#-performance-metrics)
-9. [🔮 Future Work](#-future-work)
-10. [🏆 Conclusion](#-conclusion)
-11. [✍️ Author](#-author)
-12. [🤝 Contributing](#-contributing)
-13. [🌍 Our Valuable Contributors](#-our-valuable-contributors)
-14. [📝 License](#-license)
+- [📈 GitHub Repository Stats](#-github-repository-stats)
+  - [This project is now OFFICIALLY accepted for](#this-project-is-now-officially-accepted-for)
+- [✨ Project Structure](#-project-structure)
+- [📚 Table of Contents](#-table-of-contents)
+- [🌞 Overview](#-overview)
+- [🛠️ Features](#️-features)
+- [🔍 Algorithms Used](#-algorithms-used)
+- [📊 Dataset](#-dataset)
+- [📁 Project Structure](#-project-structure-1)
+- [🚀 How to Run `main.py`](#-how-to-run-mainpy)
+- [📈 Results](#-results)
+- [📊 Performance Metrics](#-performance-metrics)
+- [🔧 Optimization Techniques](#-optimization-techniques)
+- [🔮 Future Work](#-future-work)
+- [🏆 Conclusion](#-conclusion)
+- [✍️ Author](#️-author)
+- [🤝 Contributing](#-contributing)
+- [🌍 Our Valuable Contributors](#-our-valuable-contributors)
+  - [🎉 Thank You to All Our Amazing Contributors! 🎉](#-thank-you-to-all-our-amazing-contributors-)
+- [📝 License](#-license)
+- [📱 Connect with Us](#-connect-with-us)
 
 ---
 ## 🌞 Overview
@@ -146,6 +153,9 @@ The **Mean Absolute Percentage Error (MAPE)** of all the following 10 Regression
 
 <img src="images/6c9ebb5b-a8ed-44de-8842-bf8f5c25990f.jpeg" alt="Performance-Metrices" width="400" height="300">
 
+## 🔧 Optimization Techniques
+
+We have applied several Intel-specific optimization techniques to enhance the performance of our models. For detailed information, please refer to the [Optimization Techniques](./Intel_Optimized/Intel_Optimization.md) document.
 
 ## 🔮 Future Work
 

repo_structure.txt

@@ -26,10 +26,14 @@
 │   └── hacktober.png
 ├── Intel_Optimized/
 │   ├── ARIMA_V2.ipynb
+│   ├── Intel_Optimization.md
 │   ├── Stock_Price_Prediction_1.ipynb
 │   ├── Stock_prediction_Data_Analysis.ipynb
 │   ├── buy_sell_recommendation_system.ipynb
 │   ├── hybrid.ipynb
+│   ├── images/
+│   │   ├── modin-and-pandas-performance.png
+│   │   └── scikit-learn-acceleration.png
 │   ├── readme.md
 │   ├── reduced_redundancy_stock_price_prediction.ipynb
 │   └── requirements.txt