{"id":15157786,"url":"https://github.com/mohammad95labbaf/churn-prediction","last_synced_at":"2026-01-23T16:36:04.450Z","repository":{"id":252918618,"uuid":"841862114","full_name":"mohammad95labbaf/Churn-Prediction","owner":"mohammad95labbaf","description":"This project aims to predict customer churn using machine learning algorithms. 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The goal is to identify customers who are likely to stop using a service or product, allowing businesses to take proactive measures to retain them. The project includes the following key components:\n\n* **Data Preprocessing**: The project starts by loading and preprocessing a dataset containing customer information and behavior. This involves handling missing values, encoding categorical variables, and scaling numerical features.\n* **Model Evaluation**: The project evaluates the performance of different machine learning algorithms, including Decision Trees, Random Forests, Support Vector Machines, and more. The models are trained and tested using a variety of metrics, including accuracy, precision, recall, and F1-score.\n* **Model Selection**: The project selects the best-performing model based on the evaluation metrics and uses it to make predictions on new, unseen data.\n\n**Getting Started**\n-------------------\n\n### Prerequisites\n\n* Python 3.11 or higher\n* Required libraries: `pandas`, `numpy`, `scikit-learn`, `matplotlib`, `seaborn`, `PyYAML`\n\n### Installation\n\n1. Clone the repository: `git clone https://github.com/your-username/Churn-Prediction.git`\n2. Install required libraries: `pip install -r requirements.txt`\n\n### Running the Project\n\n1. Run the main script: `python main.py`\n2. Use command-line arguments to customize the project:\n\t* `-c` or `--classifier`: specify the classifier to use (default: `AdaBoost`)\n\t* `-t` or `--test-size`: specify the test size for train-test split (default: `0.2`)\n\t* `-p` or `--preprocessing-method`: specify the preprocessing method to use (default: `standardization`)\n\n**Example Usage**\n-----------------\n\n* `python main.py`: run the project with default settings\n* `python main.py -c KNN -t 0.5 -p robust-scaling`: run the project with KNN classifier, 50% test size, and robust scaling preprocessing\n\n**Project Structure**\n---------------------\n\n* `main.py`: main entry point for the project\n* `config.py`: configuration file for the project\n* `dataset.py`: data loading and preprocessing module\n* `classification.py`: classification module\n* `plot_confusion_matrix.py`: confusion matrix plotting module\n* `requirements.txt`: required libraries for the project\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmohammad95labbaf%2Fchurn-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmohammad95labbaf%2Fchurn-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmohammad95labbaf%2Fchurn-prediction/lists"}