MLEssentials 1.1

A toolkit to install essential machine learning libraries with one command.

MLEssentials

MLEssentials is a powerful Python package designed to support a broad range of machine learning tasks. It integrates essential libraries and tools for data preprocessing, model building, evaluation, and visualization. With MLEssentials, you can streamline your machine learning workflows and focus more on solving problems and less on managing dependencies.

Features

• Data Manipulation: Utilizes libraries such as numpy, pandas, polars, and pandasql for efficient data handling and manipulation.
• Model Building: Supports various model-building frameworks including scikit-learn, xgboost, lightgbm, catboost, and statsmodels.
• Visualization: Provides tools for creating plots and visualizations with matplotlib, seaborn, plotly, and pydot.
• Natural Language Processing: Incorporates nltk, spacy, and pattern for advanced text processing and analysis.
• Web and API Interactions: Includes fastapi, flask, selenium, and requests for web scraping and building web applications.
• Data Storage and Retrieval: Features SQLAlchemy, mysql-connector, and pyodbc for database connectivity and operations.
• Additional Utilities: Offers joblib, pydantic, openpyxl, pyarrow, networkx, and beautifulsoup for extended functionalities.

Installation

To install MLEssentials, use the following pip command:

pip install MLEssentials

Usage

Here’s a quick example of how to use MLEssentials in your machine learning project:

# Importing necessary libraries from MLEssentials
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

# Load and preprocess data
data = pd.read_csv('data.csv')
X = data.drop('target', axis=1)
y = data['target']

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Train a model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Evaluate the model
accuracy = model.score(X_test, y_test)
print(f"Model Accuracy: {accuracy:.2f}")

# Visualize results
plt.figure(figsize=(10, 6))
plt.plot(range(len(y_test)), y_test, label='True Values')
plt.plot(range(len(y_test)), model.predict(X_test), label='Predicted Values', linestyle='--')
plt.legend()
plt.show()

Contributing

I welcome contributions to MLEssentials! To contribute:

Fork the repository from GitHub (replace with your actual GitHub link). Create a new branch for your feature or bug fix. Make your changes and commit them with descriptive messages. Push your changes to your forked repository. Submit a pull request to the main repository. Please ensure your code adheres to our coding standards and passes all tests before submitting a pull request.

License

MLEssentials is licensed under the MIT License.