Churn Modelling Project

This project aims to build and evaluate a deep learning model to predict customer churn using Keras and Scikit-Learn. The project includes data preprocessing, model training, evaluation, and visualization of the results.

Author: Kaíque Freire dos Santos
Date: 2024/06/26

Table of Contents

• Project Structure
• Installation
• Usage
• Script Overview
• Results
• Dependencies

Project Structure

├── .gitignore
├── LICENSE
├── README.md
├── dataset-variables/
│ ├── churn-modelling.pkl
│ ├── churn-modelling.zip
├── logs/
│ ├── info-preprocessing.log
│ ├── training.log
├── plots/
│ ├── pie-exited.png
│ ├── count-per-country.png
│ ├── count-per-gender.png
│ ├── count-per-product.png
│ ├── correlation-map-numerics.png
│ ├── plot-training-validation.png
│ ├── confusion-matrix.png
├── save_model/
│ ├── model.h5
│ ├── weights_model.weights.h5
├── .spyproject/
│ ├── config
├── kaggle.json
├── model.py
├── preprocessing.py

Installation

1. Clone the repository:

   git clone https://github.com/kaiquefreire05/py-churn-modelling.git

2. Navigate to the project directory:

   cd py-churn-modelling

4. Create and activate a virtual environment:

  python -m venv env
  source env/bin/activate # On Windows use env\Scripts\activate

6. Install the required dependencies:

  pip install -r requirements.txt

Usage

1. Preprocess the data:

python preprocessing.py

This script will load, clean, visualize, and transform the data, saving it for model training.

2. Train and evaluate the model:

 python model.py

This script will load the preprocessed data, define and train the deep learning model, generate performance charts, evaluate the model, and save the trained model and its weights.

Script Overview

Model Training and Evaluation (model.py)

This module handles the training and evaluation of a deep learning model using Keras and Scikit-Learn.

Main Components:

• LoggingCallback: Custom callback to log metrics during model training.
• create_model(): Defines and returns a deep learning model with the specified architecture.
• Main Execution:
• Loads training and testing data.
• Sets up the logging system.
• Defines and compiles the model.
• Trains the model with callbacks for early stopping, learning rate reduction, and logging.
• Generates accuracy and loss charts.
• Evaluates the model using accuracy metrics, classification report, and confusion matrix.
• Saves the trained model and its weights.

Data Preprocessing (preprocessing.py)

This module performs data preprocessing for the deep learning project.

Main Components:

• Data Loading: Loads data from a zip file into a DataFrame.
• Data Cleaning: Removes unnecessary columns.
• Data Visualization: Generates charts to understand data distribution.
• Data Transformation: Converts categorical columns using OneHot encoding and normalizes numerical data.
• Data Splitting: Splits the data into training and testing sets.
• Save Data: Saves the processed data using pickle.

Results

• Training/Validation Accuracy and Loss: Charts showing model performance during training.
• Confusion Matrix: Visual representation of model predictions vs. actual labels.
• Accuracy Score: Overall accuracy of the model.
• Classification Report: Detailed classification metrics.

Dependencies

• pandas
• numpy
• scikit-learn
• keras
• matplotlib
• seaborn
• logging
• zipfile
• pickle

Ensure all dependencies are installed using the provided requirements.txt.