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Model Selection Repository

Overview

The "Model Selection" repository is dedicated to exploring and comparing different regression models for predicting continuous outcomes. This repository includes implementations and evaluations of five key regression models: Multiple Linear Regression, Polynomial Regression, Decision Tree Regression, Random Forest Regression, and Support Vector Regression (SVR). The primary goal is to demonstrate how each model works, evaluate their performance using R-squared values, and guide users in selecting the best model for a given dataset.

Models Included

1. Multiple Linear Regression: A regression technique that models the relationship between a dependent variable and multiple independent variables.
2. Polynomial Regression: An extension of linear regression, where the relationship between the independent variable and the dependent variable is modeled as an nth degree polynomial.
3. Decision Tree Regression: A model that uses a tree-like graph of decisions to model decisions and their possible consequences.
4. Random Forest Regression: An ensemble learning method that operates by constructing a multitude of decision trees at training time to output a more accurate prediction.
5. Support Vector Regression (SVR): A type of support vector machine that supports linear and non-linear regression.

Dataset

• The repository utilizes a generic dataset named Data.csv for model training and evaluation. Users can replace this dataset with their own to test different scenarios.

Key Features

• Model Implementation: Each regression model is implemented in a clear and understandable manner.
• Performance Evaluation: The models are evaluated based on their R-squared values, providing insights into their accuracy and fit.
• Comparison: The repository allows for easy comparison between different regression models on the same dataset.

Usage

• Clone the repository.
• Replace Data.csv with your dataset or use the existing one.
• Run each model script to train and evaluate on your data.
• Compare the R-squared values to determine the best model for your specific dataset.

Requirements

• Python 3.x
• Libraries: pandas, numpy, scikit-learn, matplotlib (for visualization)

Contributing

Contributions to this repository are welcome. Feel free to fork the repo, add improvements, and create a pull request with your changes.

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