This repository contains a machine learning model developed for predicting customer churn. The model is built using Python and popular libraries for data preprocessing, model training, and performance evaluation.
- Overview
- Getting Started
- Data Preprocessing
- Model Training and Evaluation
- Performance Visualization
- Choosing a Machine Learning Model
- Contributing
Customer churn prediction is a vital task for businesses looking to retain customers. In this repository, we have developed a machine learning model to predict customer churn. The model is trained on a dataset with various customer attributes and their churn status (whether they churned or not). The goal is to accurately predict which customers are at risk of churning.
To get started with this project, follow these steps:
-
Clone this repository to your local machine:
git clone https://github.com/yourusername/customer-churn-prediction.git
Data Preprocessing Data preprocessing is a crucial step in building a machine learning model. In this project, we perform several data preprocessing tasks, including:
Handling missing values Encoding categorical features Scaling features Feature selection The code for data preprocessing can be found in the provided Python script.
Model Training and Evaluation We train the customer churn prediction model using various machine learning algorithms, including Decision Trees, Random Forest, Logistic Regression, and K-Nearest Neighbors. Model training and evaluation are performed using the provided Python script.
Performance Visualization The performance of the trained model is visualized using various metrics, including accuracy, confusion matrix, classification report, and ROC curves. The code for performance visualization can be found in the provided Python script.
Choosing a Machine Learning Model You can choose from different machine learning algorithms to train the customer churn prediction model. The provided Python script includes a function to select the model of your choice.
Contributing Contributions are welcome! If you have any suggestions, improvements, or bug fixes, please open an issue or create a pull request. We appreciate your contributions to make this project better.