Customer churn analysis is a crucial process for businesses aiming to retain customers by identifying reasons for churn and predicting at-risk customers. This project leverages machine learning to develop a predictive model that can forecast customer churn based on behavioral and transactional data.
The primary objectives of this project include:
Analyzing customer churn patterns to understand key factors influencing customer retention.
Developing a machine learning-based model to predict customer churn.
Creating an interactive interface for users to test the model with real-time inputs.
Providing insights into actionable strategies for reducing churn.
The project follows a structured approach, which includes:
Data Collection: Utilizing a publicly available dataset containing customer demographic, behavioral, and service usage details.
Data Preprocessing: Handling missing values, encoding categorical variables, and feature scaling.
Exploratory Data Analysis (EDA): Understanding data distribution through visualization techniques.
Model Development: Implementing a Random Forest Classifier to predict churn.
Model Evaluation: Measuring model performance using accuracy, confusion matrix, and classification reports.
User Interface Development: Creating an interactive interface using Python’s ipywidgets for easy accessibility.
Pandas, NumPy (for data manipulation and preprocessing)
Matplotlib, Seaborn (for data visualization)
Scikit-learn (for model training and evaluation)
ipywidgets (for building an interactive user interface)
Random Forest and other algorithms for prediction
Before running this project, you need to have the following installed:
Python 3.x
Required Python libraries
To get started, follow these steps:
Clone the repository:
git clone https://github.com/andetazeqiri/Data-Analytics-Lifecycle-Customer-Churn-Prediction.git
Navigate to the project directory:
cd Data-Analytics-Lifecycle-Customer-Churn-Prediction
Create a virtual environment:
python -m venv venv
Install the required dependencies:
pip install -r requirements.txt
To execute the project, follow these steps:
Run the main data analytics script:
python dataanalytics.py
Run the bank customer churn analysis:
python bankcustomerchurn.py