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mltrain

mltrain is a comprehensive machine learning package that provides easy-to-use implementations of popular algorithms. This toolkit is designed to help developers and researchers quickly prototype and deploy machine learning models. The package includes various supervised and unsupervised learning algorithms, each built from scratch for maximum flexibility and understanding.

Features

  • Linear Regression: Implementation of linear regression using gradient descent.
  • Logistic Regression: Includes logistic regression with binary classification only.
  • Decision Tree: A flexible decision tree implementation for Classification only.
  • Random Forest: An ensemble method using multiple decision trees for improved accuracy.
  • Support Vector Machine (SVM): Includes both linear and kernel-based SVMs with support for soft margins.
  • k-Nearest Neighbors (k-NN): A simple and intuitive classification algorithm.
  • Naive Bayes: Supports for Gaussian Naive Bayes for continuous Data.
  • KMeans Clustering: A clustering algorithm.
  • DBSCAN: A density-based clustering algorithm for discovering clusters of arbitrary shape.
  • PCA: Dimensionality Reduction Model to reduce dimensions of dataset.

Installation

You can install the package using pip:

pip install mltrain

Usage

Supervised Learning Models

Linear Regression

It is Built from scratch. It can be applied to Multiple Weights...

from mltrain.supervised.LinearRegression import LinearRegression

# Initialize the model
model = LinearRegression(learning_rate=0.01, epochs=1000)

# Train the model
model.train(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

Logistic Regression

Implemented only Binary Classification.

from mltrain.supervised.LogisticRegression import LogisticRegression

# Initialize the model
model = LogisticRegression(learning_rate=0.01, epochs=1000)

# Train the model
model.train(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

KNN Model

You can use for both Regression and Classification. Default is given below:-

from mltrain.supervised.KNN import KNN

# Initialize the model
model = KNN(k=3,distance='euclidean',purpose='classification')

# Train the model
model.train(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

Naive Bayes Model

Implemented only Gaussian Naive Bayes.

from mltrain.supervised.NaiveBayes import NaiveBayes

# Initialize the model
model = NaiveBayes()

# Train the model
model.train(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

Naive Bayes Model

Implemented only Gaussian Naive Bayes.

from mltrain.supervised.NaiveBayes import NaiveBayes

# Initialize the model
model = NaiveBayes()

# Train the model
model.train(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

Decision Tree

Implemented only for Classification Purpose only. Default Params are shown below:-

from mltrain.supervised.DecisionTree import DecisionTree

# Initialize the model
model = DecisionTree(max_depth=10, min_samples_split=2, criteria='gini')

# Train the model
model.train(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

Random Forest

Implemented only for Classification Purpose only. Default Params are shown below:-

from mltrain.supervised.RandomForest import RandomForest

# Initialize the model
model = RandomForest(n_trees=100, max_depth=10, min_samples_split=2, criteria='gini')

# Train the model
model.train(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

KernelSVM Model

Implemented only for Classification Purpose only. Default Params are shown below:-

from mltrain.supervised.KernelSVM import KernelSVM

# Initialize the model
model = KernelSVM(C=1.0, kernel_type='rbf', epochs=1000, learning_rate=0.01, gamma=0.5, coef0=0, degree=3)

# Train the model
model.train(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

Unsupervised Learning Models

K-Means Clustering Model

from mltrain.unsupervised.KMeans import KMeans

# Initialize the model
model = KMeans(k=3, epochs=100)

# Train the model which Returns Cluster Labels
cluster_labels = model.train(X_train, y_train)
print(cluster_labels)

DBSCAN Clustering Model

from mltrain.unsupervised.DBSCAN import DBSCAN

# Initialize the model
model = DBSCAN(epsilon=0.5, min_points=5)

# Train the model which Returns Cluster Labels
cluster_labels = model.train(X_train, y_train)
print(cluster_labels)

PCA

from mltrain.unsupervised.PCA import PCA

# Initiliaze the Model
pca = PCA(n_components=2)

# Fit the Data and plot Graph
X_pca = pca.train_transform(X,plot_graph=True)

# Printing PC values
print(X_pca)

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT License