🚀 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

2. 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)

Customer Age vs Churn (After Resolution)

⚠️ 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.