mymlcore 1.1.2

A high-performance, extensible machine learning library for Python. fastmlcore provides a rich suite of algorithms, preprocessing tools, metrics, and utilities for rapid prototyping and production-grade ML workflows. Designed for clarity, speed, and flexibility, it empowers researchers, engineers, and data scientists to build, evaluate, and deploy models with ease.

mymlcore

mymlcore is a simple, extensible machine learning library for Python.

Features

• LinearRegression, LogisticRegression, DecisionTree, KMeans, SVM, RandomForest, NaiveBayes, NeuralNetwork
• Preprocessing: StandardScaler, MinMaxScaler, OneHotEncoder, Imputer, PCA
• Metrics: mean_squared_error, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, log_loss, roc_auc_score
• Utilities: train_test_split, cross_val_score, grid_search

Installation

pip install mymlcore

Usage Example

from mymlcore import LinearRegression, mean_squared_error
import numpy as np

X = np.array([[2, 600], [3, 900], [6, 1700], [5, 1300], [4, 1050], [7, 1900]])
y = np.array([120000, 180000, 400000, 320000, 250000, 450000])

model = LinearRegression()
model.fit(X, y)
preds = model.predict(X)
print(mean_squared_error(y, preds))

License

MIT