📊 Churn Analysis Project

    🚀 Project Overview

Ever wondered why some customers leave a service while others stay loyal? This Customer Churn Analysis project dives deep into customer data to predict churn, uncover patterns, and provide actionable insights for businesses. By leveraging Python, machine learning, and data visualization, this project transforms raw data into strategic decisions.

      🗂 Dataset Description

The dataset contains detailed customer information, including demographics, services, and usage patterns. Key features include:

Feature Description customerID Unique customer identifier gender Male / Female SeniorCitizen 0 (No) / 1 (Yes) tenure Number of months the customer has stayed MonthlyCharges Monthly payment amount TotalCharges Total amount paid Churn Target variable (Yes / No)

Source: Replace with dataset source, e.g., Kaggle.

       🛠 Tools & Libraries

This project is built with Python and popular data science libraries:

pandas & numpy – Data manipulation & analysis

matplotlib & seaborn – Data visualization

scikit-learn – Machine learning

Jupyter Notebook – Interactive development

        🔍 Project Workflow

Exploratory Data Analysis (EDA)

Analyze distributions and patterns

Handle missing or inconsistent data

Visualize correlations between features and churn

Data Preprocessing

Encode categorical variables

Normalize numeric features

Split dataset into training and testing sets

Modeling

Apply algorithms: Logistic Regression, Random Forest, XGBoost

Train and evaluate models

Compare model performance metrics

Evaluation & Insights

Accuracy, Precision, Recall, F1-Score, ROC-AUC

Identify top factors affecting churn: tenure, contract type, monthly charges

Provide actionable business recommendations

               📈 Visual Insights

Here are some example visualizations from the project:

Churn Distribution by Gender & Seniority

Monthly Charges vs. Churn Rate

Feature Correlation Heatmap 💡 Key Findings

Customers with short tenure and high monthly charges are more likely to churn.

Contract type significantly influences retention.

Targeted strategies like loyalty programs or discounts can reduce churn.

            📂 Project Structure

churn-analysis/ │ ├── data/ # Dataset files (CSV) ├── notebooks/ # Jupyter notebooks ├── images/ # Visualizations & graphs ├── requirements.txt # Python dependencies ├── README.md # Project documentation └── churn_analysis.ipynb # Main notebook

            ⚡ How to Run

Clone the repository:

git clone https://github.com/birukd1/churn-analysis.git cd churn-analysis

Install dependencies:

pip install -r requirements.txt

Open the notebook and run:

jupyter notebook

Explore the analysis, models, and visualizations.

         🏆 Achievements

Built predictive machine learning models to identify churn

Generated actionable insights for customer retention strategies

Practiced full data science workflow from EDA → Modeling → Evaluation → Insights

               📌 References

Kaggle datasets

scikit-learn documentation: https://scikit-learn.org

Seaborn documentation: https://seaborn.pydata.org