Churn Prediction Project

Overview

This project aims to predict customer churn using machine learning algorithms. 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:

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.
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.
Model Selection: The project selects the best-performing model based on the evaluation metrics and uses it to make predictions on new, unseen data.

Getting Started

Prerequisites

Python 3.11 or higher
Required libraries: pandas, numpy, scikit-learn, matplotlib, seaborn, PyYAML

Installation

Clone the repository: git clone https://github.com/your-username/Churn-Prediction.git
Install required libraries: pip install -r requirements.txt

Running the Project

Run the main script: python main.py
Use command-line arguments to customize the project:
- -c or --classifier: specify the classifier to use (default: AdaBoost)
- -t or --test-size: specify the test size for train-test split (default: 0.2)
- -p or --preprocessing-method: specify the preprocessing method to use (default: standardization)

Example Usage

python main.py: run the project with default settings
python main.py -c KNN -t 0.5 -p robust-scaling: run the project with KNN classifier, 50% test size, and robust scaling preprocessing