# Machine Learning Classification Python Package
## Overview
This Python package provides a comprehensive solution for performing classification tasks using various popular machine learning algorithms. It allows you to read a dataset, preprocess it, train multiple classifiers, perform hyperparameter tuning, and visualize model performance. Additionally, it provides options for scaling data, saving trained models, and customizing the output display.
## Features
1. **Classification Algorithms**:
- Logistic Regression
- K-Nearest Neighbors
- Decision Tree
- Random Forest
- Gradient Boosting
- Support Vector Classifier
- Gaussian Naive Bayes
- Bernoulli Naive Bayes
2. **Advanced Functionality**:
- **Data Scaling**: Options for Min-Max Scaling or Standard Normalization.
- **Hyperparameter Tuning**: Option to perform Grid Search for finding the best model parameters.
- **Model Persistence**: Save and load trained models using `joblib`.
- **Visualization**: Option to plot confusion matrices and detailed classification reports.
- **Cross-Validation**: Evaluate models using cross-validation scores.
- **Configurable Outputs**: Options to control the display of confusion matrices and classification reports.
3. **Results**:
- Returns a DataFrame with model names, accuracy, F1-score, and optionally the best hyperparameters.
## Parameters
The package takes the following parameters as input:
- `dataset`: Path to the CSV or Excel dataset file or a pandas DataFrame.
- `output_column`: Name of the output column containing the target variable.
- `train_test_ratio`: Ratio in which the dataset is divided into train and test splits (must be between 0 and 1).
- `scaling_method` (optional): Method to scale the data ('minmax' or 'normalize').
- `perform_grid_search` (optional): Whether to perform grid search for hyperparameter tuning (default is `False`).
- `save_models` (optional): Whether to save trained models to disk (default is `False`).
- `show_confusion_matrix` (optional): Whether to display confusion matrix plots (default is `False`).
- `show_classification_report` (optional): Whether to print classification reports (default is `False`).
## Installation
Make sure you have Python installed on your system. You can install the package using pip:
```sh
pip install classifier_agent
```
## Usage
Here's an example of how to use the package:
```python
from classifier_agent import classifier_agent
dataset_path = "diabetes.csv"
output_column = "Outcome"
train_test_ratio = 0.25
scaling_method = 'minmax' # Choose 'minmax' or 'normalize'
perform_grid_search = True # Whether to perform grid search
save_models = True # Whether to save models
show_confusion_matrix = True # Whether to plot the confusion matrix
show_classification_report = True # Whether to print the classification report
results = classifier_agent(dataset_path, output_column, train_test_ratio, scaling_method, perform_grid_search, save_models, show_confusion_matrix, show_classification_report)
print(results)
```
## Example Output
The output is a DataFrame that looks like this:
| Classifier | Accuracy | F1-Score | Best Parameters |
|-------------------------|----------|----------|-----------------|
| KNeighborsClassifier | 0.78 | 0.76 | {'n_neighbors': 5, 'weights': 'uniform'} |
| LogisticRegression | 0.80 | 0.79 | {'C': 0.1, 'solver': 'liblinear'} |
| DecisionTreeClassifier | 0.72 | 0.70 | {'criterion': 'entropy', 'max_depth': 20} |
| RandomForestClassifier | 0.85 | 0.84 | {'n_estimators': 200, 'max_depth': 20} |
| GradientBoostingClassifier | 0.83 | 0.82 | {'n_estimators': 200, 'learning_rate': 0.1} |
| SVC | 0.81 | 0.80 | {'C': 1, 'kernel': 'rbf'} |
| GaussianNB | 0.75 | 0.73 | {} |
| BernoulliNB | 0.73 | 0.72 | {} |
## Notes
- The package is actively developed and may receive updates.
- The project is developed with Python version `3.10`.
- If you encounter any issues or have questions, feel free to contact me on [LinkedIn](https://www.linkedin.com/in/adnan-karol-aa1666179/).
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Publishing to PyPI
To publish this package to PyPI, follow these steps:
1. **Ensure Your Package is Ready:**
- Make sure your `setup.py` and `README.md` are correctly configured.
- Verify that your package is properly structured and tested.
2. **Create Distribution Archives:**
Run the following command to create distribution archives of your package:
```sh
python setup.py sdist bdist_wheel
```
3. **Install Twine:**
If you haven't already, install Twine, a utility for publishing packages to PyPI:
```sh
pip install twine
```
4. **Upload to PyPI:**
Use Twine to upload your package to PyPI:
```sh
twine upload dist/*
```
You will be prompted to enter your PyPI username and password.
5. **Verify Upload:**
After uploading, check your package on [PyPI](https://pypi.org/) to ensure it appears correctly.
For more detailed instructions, refer to the [PyPI documentation](https://packaging.python.org/tutorials/packaging-projects/).
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