# 🧠 Universal ML Model Explorer Pro

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> One-line ML pipeline that preprocesses, trains, compares, and visualizes the best model — automatically.


Automatically train, evaluate, compare, and visualize multiple machine learning models — all with one command.

## 🚀 Features

* Auto detection: Classification or Regression
* Auto preprocessing: Scaling, Encoding, Imputation, PCA
* Parallel model training on all cores
* SHAP interpretability plots
* Beautiful visual reports (Confusion Matrix, ROC, Residuals, etc.)
* CLI + Notebook compatible

## 📦 Installation

```bash
pip install -r requirements.txt
```

## 🧪 CLI Usage

```bash
python main.py path/to/dataset.csv target_column_name
```

### Optional flags:

* `--output_dir`: Folder to save results (default: `results`)
* `--pca_components`: Apply PCA on numeric features
* `--no_shap`: Disable SHAP plot (faster)

## 🧬 Python Usage

```python
from yourlib import run_pipeline_in_notebook

run_pipeline_in_notebook(
    dataset_path="data.csv",
    target_column="target",
    pca_components=5,
    no_shap=False
)
```

## 📂 Output

* `best_model.pkl`: Trained model
* Plots: Confusion Matrix, ROC, Residuals, SHAP
* `model_report.txt`: Full model comparison

## 🛠️ Supported Models

* Linear, Tree-based, Ensemble (RF, GB, AdaBoost, XGBoost), KNN, SVM, Stacking
* Auto selection of best based on Accuracy / R²

## Run this in your terminal to install all dependencies

```cmd
pip install pandas numpy matplotlib seaborn scikit-learn xgboost shap joblib rich
```

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