ModelForge

Welcome to the ModelForge repository! This project contains a collection of machine learning models developed from scratch for various tasks. Each model is implemented to showcase different machine learning techniques and methodologies.

Overview

• ModelForge: A collection of machine learning models created for educational and practical purposes.
• Languages: Python
• Libraries: Numpy , pandas , scikit-learn

Models

K-Nearest Neighbors (KNN)

• Description: A classification algorithm that assigns a class based on the majority vote of its k-nearest neighbors.
• Key Features: Simple and effective for small to medium-sized datasets.
• Usage:

  from knn import KNN
  model = KNN(k=3)
  model.train(training_data, training_labels)
  predictions = model.predict(test_data)

Lasso Regression

• Description: A regression analysis method that performs L1 regularization to enhance prediction accuracy and interpretability.
• Key Features: Useful for feature selection and regularization.
• Usage:

  from lasso import Lasso
  model = Lasso(alpha=0.1)
  model.train(training_data, training_labels)
  predictions = model.predict(test_data)

Linear Regression

• Description: A linear approach to modeling the relationship between a dependent variable and one or more independent variables.
• Key Features: Simple and widely used for regression tasks.
• Usage:

  from linear_regression import LinearRegression
  model = LinearRegression()
  model.train(training_data, training_labels)
  predictions = model.predict(test_data)

Logistic Regression

• Description: A statistical model that uses a logistic function to model a binary dependent variable.
• Key Features: Ideal for binary classification problems. =- Usage:

  from logistic_regression import LogisticRegression
  model = LogisticRegression()
  model.train(training_data, training_labels)
  predictions = model.predict(test_data)

Support Vector Machine (SVM) Classifier

• Description: A supervised learning model that finds the hyperplane that best separates the classes in the feature space.
• Key Features: Effective in high-dimensional spaces and when the number of dimensions exceeds the number of samples.
• Usage:

  from svm_classifier import SVMClassifier
  model = SVMClassifier(kernel='linear')
  model.train(training_data, training_labels)
  predictions = model.predict(test_data)

Installation

1. Clone the repository:

   git clone https://github.com/tatuskarjaiwanth/ModelForge.git

2. Navigate to the repository directory:

   cd ModelForge

Usage

Import and use each model as demonstrated in the examples above.

Contributing

If you have suggestions for improvements or would like to contribute, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License.

Contact

For any questions or feedback, please contact me at [[email protected]].