pycodeml 0.0.20

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# PyCodeML
**PyCodeML** is a Python package designed to automate the training, evaluation, tuning, and selection of the best-performing machine learning models for regression, classification, and clustering tasks. It simplifies the process of model training, comparison, tuning, and deployment.

## ✅ Features
- Supports **Regression**, **Classification**, and **Clustering** tasks  
- Evaluates multiple models and selects the best one  
- **Hyperparameter tuning** support for optimized performance  
- Saves and loads trained models for future use  
- Simple and intuitive API for fast prototyping and deployment  

## 📦 Installation

pip install PyCodeML

💻 Usage

1️⃣ Train and Save the Best Regression Model

import pandas as pd
from PyCodeML.regressor import RegressorTrainer  # For regression tasks

df = pd.read_csv("data.csv")

trainer = RegressorTrainer(df, "target", data_sample_percent=100)
best_model = trainer.train_and_get_best_model()

trainer.save_best_model("best_regression_model.pkl")

2️⃣ Train and Save the Best Classification Model

import pandas as pd
from PyCodeML.classifire import ClassifierTrainer  # For classification tasks

df = pd.read_csv("classification_data.csv")

trainer = ClassifierTrainer(df, "label", data_sample_percent=100)
best_model = trainer.train_and_get_best_model()

trainer.save_best_model("best_classifier_model.pkl")

3️⃣ Tune the Best Regression Model

from PyCodeML.tunner import RegressorTuner

tuner = RegressorTuner(
    dataset=df,
    target_column="target",
    model_name="Random Forest"  # Must match one of the supported models
)

tuned_model, score = tuner.tune()

4️⃣ Tune the Best Classification Model

from PyCodeML.tunner import ClassifierTuner

tuner = ClassifierTuner(
    dataset=df,
    target_column="label",
    model_name="Random Forest Classifier"
)

tuned_model = tuner.tune()

5️⃣ Train and Save the Best Clustering Model

import pandas as pd
from PyCodeML.clustering import ClusteringTrainer

df = pd.read_csv("unsupervised_data.csv")

trainer = ClusteringTrainer(df, n_clusters=3, data_sample_percent=100)
best_model = trainer.train_and_get_best_model()

trainer.save_best_model("best_clustering_model.pkl")

6️⃣ Load and Use the Saved Model

import pandas as pd
from PyCodeML.utils import load_model

model = load_model("best_model.pkl")

new_data = pd.read_csv("new_data.csv")
predictions = model.predict(new_data)

print("Predicted Values:", predictions)

7️⃣ Label Data Using Clustering

labeled_df = trainer.label_data()
print(labeled_df.head())

📊 Supported Models

Regression

• Linear Regression
• Decision Tree Regressor
• Random Forest Regressor
• Support Vector Regressor (SVR)
• Gradient Boosting Regressor
• Ridge Regression
• Lasso Regression
• Elastic Net

Classification

• Logistic Regression
• Random Forest Classifier
• Support Vector Machine (SVM)
• Decision Tree Classifier
• Gradient Boosting Classifier
• K-Nearest Neighbors (KNN)

Clustering

• KMeans
• DBSCAN
• Agglomerative Clustering

🤝 Contributing

Contributions are welcome! If you'd like to improve this package, feel free to fork the repository and submit a pull request.

🔗 GitHub

https://github.com/Nachiket858/PyCodeML