Customer Churn Prediction: An End-to-End Python Project

Overview

This repository contains a Python-based end-to-end project focusing on predicting customer churn in a hypothetical scenario. The project covers the entire machine learning pipeline, from data generation to model evaluation and visualization.

Table of Contents

• Features
• Dependencies
• Code Structure
  • Data Generation
  • Data Preprocessing
  • Model Training
  • Model Evaluation
  • Feature Importance Visualization
• Usage
• Metrics

Features

The project simulates a customer churn prediction scenario with the following features:

• Age: The age of the customer (between 18 to 65).
• Gender: The gender of the customer (Male/Female).
• Tenure: The tenure of the customer with the service (in months, between 1 to 72).
• Monthly Charges: The amount the customer is charged monthly (between $20 to $100).
• Feedback: The customer's feedback about the service (Good/Average/Poor).

The target variable is Churn, indicating whether the customer churned (1) or not (0).

Dependencies

The following libraries are used:

• Matplotlib
• Pandas
• NumPy
• Scikit-learn
• Seaborn

Code Structure

Data Generation

The ChurnPredictionDataSpoofer class is responsible for generating a synthetic dataset. It creates a data frame with random data for the features and the target variable.

```python data_gen = ChurnPredictionDataSpoofer(n_samples=1000) data = data_gen.generate_data() ```

Data Preprocessing

The ChurnDataPreprocessing class handles preprocessing tasks such as encoding categorical variables and scaling numerical features.

```python preprocessor = ChurnDataPreprocessing(data) preprocessor.encode_categorical() preprocessor.scale_numerical() ```

Model Training

The ChurnModel class trains a Random Forest Classifier on the preprocessed data.

```python churn_model = ChurnModel(X_train, y_train) churn_model.train_model() ```

Model Evaluation

The model's performance is evaluated using the following metrics:

• Accuracy
• Precision
• Recall
• F1 Score

```python y_pred = churn_model.predict(X_test) metrics = churn_model.evaluate(y_test, y_pred) ```

Feature Importance Visualization

The ChurnDataVisualization class visualizes the feature importances using a bar plot.

```python viz = ChurnDataVisualization(churn_model.model, feature_names) viz.plot_feature_importance() ```

Usage

The entire pipeline can be executed in a Python environment with the required dependencies installed.

```python

Generate data, preprocess it, train model, evaluate, and visualize

```

Metrics

The example model metrics can be printed as follows:

```python print(f"Model Metrics: {metrics}") ```

The metrics are a dictionary containing the Accuracy, Precision, Recall, and F1 Score of the model.

This project serves as a comprehensive example of building a customer churn prediction model, right from data generation to feature importance visualization. Feel free to adapt and extend this code for your specific use-cases!