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🚀 Customer Churn Exploratory Data Analysis

📋 Description

This project performs exploratory data analysis (EDA) in Python to identify patterns related to customer churn and propose strategies to reduce cancellations. It uses a fictitious dataset and libraries such as Pandas and Plotly for data manipulation and visualization.

🛠️ Technologies Used

  • Python 3
  • Pandas — data manipulation and cleaning
  • Plotly — interactive and static visualizations
  • Kaleido — exporting Plotly charts as images

⚙️ How It Works

  • 📥 Loads customer dataset from a CSV file.
  • 🧹 Cleans and preprocesses data by removing irrelevant columns and handling missing values.
  • 📊 Generates color-coded histograms to visualize the relationship between features and customer churn.
  • 🔍 Applies filtering conditions to simulate scenarios aimed at reducing churn.
  • 🖼️ Saves charts as PNG images in pre-resolution and post-resolution directories for before/after analysis.

✅ Setup Instructions

📌 Prerequisites

  • Python 3 installed on your system

🧱 Environment Setup

  1. Create and activate a Python virtual environment:
python3 -m venv venv
source venv/bin/activate    # Linux / MacOS
venv\Scripts\activate       # Windows
  1. Install dependencies
pip install -r requirements.txt

▶️ Running the Analysis

Run the main Python script or open the Jupyter Notebook to execute the analysis and generate charts:

python main.py

📊 Example Charts

Customer Age vs Churn (Before Resolution)

Age vs Churn - Before

Customer Age vs Churn (After Resolution)

Age vs Churn - After

⚠️ Important Notes

  • The project generates PNG images of graphs in the pre-resolution and post-resolution folders.
  • For best interactive experience, use Jupyter Notebook.
  • Kaleido is required to export Plotly charts as images.