Deep Learning Churn Prediction 🚀

Overview

This project leverages Keras and TensorFlow to build a deep learning model that predicts customer churn. By transforming raw customer data into actionable insights, it helps businesses proactively improve retention strategies.

Key Features

• Data Preprocessing:
  Cleans, encodes, and scales data for optimal performance. 🧹

• Exploratory Analysis:
  Visualizes trends and patterns with insightful graphs. 📊

• Neural Network Model:
  Implements a multi-layer architecture with dropout for regularization. 🤖

• Model Evaluation:
  Assesses performance using metrics like accuracy and confusion matrices. 📈

Technologies Used

-Python

-Jupyter Notebook

-Pandas & NumPy for data manipulation

-Matplotlib & Seaborn for visualization

-TensorFlow & Keras for model development

-Scikit-Learn for evaluation and data splitting

Setup

1. Clone the Repository:

   git clone https://github.com/yourusername/deep-learning-churn-prediction.git
   cd deep-learning-churn-prediction
   
   

2. Create & Activate a Virtual Environment:

   python -m venv venv
   venv\Scripts\activate
   
   

3. Install Dependencies:

   pip install -r requirements.txt
   
   

4. Launch the Notebook:

   jupyter notebook churn.ipynb
   
   

Project Structure

.
├── churn.ipynb         # Main Notebook with project code
├── data/               # Dataset directory
├── requirements.txt    # Required Python packages
└── README.md           # Project documentation