Regression-Ramban: Generic Regression Models Repository

Welcome to Regression-Ramban! This collection provides generic code templates for various regression models, ideal for data scientists and analysts looking to streamline their workflow.

Description

This repository features templates for the following regression models:

• Linear Regression
• Polynomial Regression
• Ridge Regression
• Lasso Regression
• Elastic Net Regression
• Support Vector Regression (SVR)
• Decision Tree Regression
• Random Forest Regression

These templates are designed to be easily adaptable. Simply replace the CSV file name, ensuring your dataset has the last column as the dependent variable and all other columns as features. Note that data preprocessing like encoding and handling missing values is not included in these templates.

Installation

1. Clone this repository:

   git clone https://github.com/chowdhary19/Regression-Ramban.git

2. Navigate to the project directory:

   cd Regression-Ramban

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Replace the CSV file name in the code templates with your dataset. Ensure your dataset follows these requirements:

   • The last column should be the dependent variable.
   • All other columns should be features.

2. Run the desired regression model script:

   python linear_regression.py

   Replace linear_regression.py with the script you want to run.

Example

# Example for Linear Regression

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score

# Load dataset
data = pd.read_csv('your_dataset.csv')

# Split into features and target
X = data.iloc[:, :-1]
y = data.iloc[:, -1]

# Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize and train model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

# Evaluate model
r2 = r2_score(y_test, y_pred)
print(f'R-Squared: {r2}')

Important Note

• Ensure your dataset is cleaned and preprocessed before using these templates. This includes encoding categorical variables and handling missing values.

Contributing

We welcome contributions! If you have suggestions or improvements, please fork the repository and submit a pull request.

References

For additional regression templates and legal documentation, visit scikit-learn's official documentation.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Created by Yuvraj Singh Chowdhary

This README file includes a reference to the scikit-learn official documentation, which is a well-known and reliable resource for regression templates and other machine learning tools.