ML Model Evaluator

A Python program that allows users to input data and automatically tests multiple machine learning models to find the one that predicts the best.

Features

• Load user data from a CSV file.
• Preprocess the data.
• Split the data into training and testing sets.
• Train multiple machine learning models.
• Evaluate models using common metrics.
• Report the best model.
• Visualize model performance with plots.

Models Included

Classification Models

• Logistic Regression
• Decision Tree
• Random Forest
• SVM
• K-Nearest Neighbors
• Gradient Boosting
• AdaBoost
• Extra Trees
• Naive Bayes

Regression Models

• Linear Regression
• Decision Tree
• Random Forest
• SVM
• K-Nearest Neighbors
• Gradient Boosting
• AdaBoost
• Extra Trees
• Ridge
• Lasso

Installation

1. You can install the ML Model Evaluator library via pip:

   pip install ML_evaluator
   

2. Install the required packages:

   pip install pandas numpy scikit-learn matplotlib seaborn

Usage

from ml_model_evaluator import ml_model_evaluator

# Replace 'file_path' and 'target_column' with appropriate values
file_path = "your_data.csv"
target_column = "target_column_name"

ml_model_evaluator(file_path, target_column)

Contributing

Feel free to fork this repository, make changes, and submit pull requests. All contributions are welcome!

License

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