README: Regression Models with Linear, Polynomial, and SVR
Overview
This project demonstrates the use of Linear Regression , Polynomial Regression , and Support Vector Regression (SVR)
to predict data trends. The models are built using Python libraries like scikit-learn, matplotlib, and numpy,
with visualizations to compare model performance. The project is ideal for predictive analytics, data analysis, and machine learning education.
Key Features
- Linear Regression : Predicts target values assuming a linear relationship with input features.
- Polynomial Regression : Extends linear regression to model non-linear relationships by using polynomial transformations.
- Support Vector Regression (SVR) : Models non-linear relationships using the Radial Basis Function (RBF) kernel.
Use Cases
- Predictive Analytics : Forecast sales, stock prices, and other trends.
- Data Analysis : Analyze relationships between variables.
- Machine Learning Education : Learn how to implement basic regression models.
- Business Intelligence : Build predictive tools for decision-making.
- Scientific Research : Analyze trends in scientific datasets.
Future Use in AI & ML
AWS SageMaker Integration:
- Scalable Training : Train models on large datasets using AWS SageMaker.
- Hyperparameter Tuning : Optimize model parameters with SageMaker's automatic tuning.
- Real-Time Inference : Deploy models for real-time predictions at scale.
Benefits:
- Handle large datasets and perform advanced ML tasks.
- Use SageMaker to automate model deployment and monitor performance.
How to Use
- Install Dependencies :
pip install numpy matplotlib pandas scikit-learn- Clone the Repo :
git clone https://github.com/yourusername/regression-analysis.git- Run the Script :
python regression_model.py- Deploy on AWS SageMaker (optional):
- Follow AWS documentation to deploy models for scalable training and inference.
Conclusion
This project provides foundational regression models that can be scaled using AWS SageMaker, making it a powerful tool for predictive analytics and real-time machine learning applications.