📊 Customer Churn Prediction Model

A Machine Learning project that predicts whether a customer is likely to churn (leave the service) based on historical customer data. This project applies multiple classification algorithms and compares their performance to identify the best predictive model.

🚀 Project Objective

Customer churn is one of the biggest challenges for subscription-based businesses. The objective of this project is to:

Analyze customer behavior

Identify key churn indicators

Build predictive classification models

Compare model performance

Provide business insights for retention strategies

📁 Repository Structure 📦 Customer-Churn-Prediction ┣ 📜 Customer_churn_prediction_LogisticRegression_model.ipynb ┣ 📜 Customer_churn_prediction_Decision_Tree_model.ipynb ┣ 📜 Customer_churn_prediction_Random_Forest_model.ipynb ┣ 📜 customer_churn_prediction_dataset.csv ┗ 📜 README.md 🛠️ Tech Stack

Python

Pandas

NumPy

Matplotlib

Seaborn

Scikit-learn

📊 Machine Learning Models Implemented 1️⃣ Logistic Regression

Used for baseline binary classification

Applied class_weight='balanced' to handle imbalance

Evaluated using cross-validation

2️⃣ Decision Tree Classifier

Tree-based interpretable model

Visual understanding of decision paths

Compared performance with linear model

3️⃣ Random Forest Classifier

Ensemble learning method

Reduced overfitting compared to Decision Tree

Improved generalization performance

🔍 Data Preprocessing

Handled missing values

One-Hot Encoding for categorical variables

Feature scaling (where required)

Train-Test Split

Class imbalance handling

📈 Model Evaluation Metrics

Accuracy

Cross-validation score

Confusion Matrix

Precision

Recall

F1-Score

🧠 Key Learnings

Impact of class imbalance on model accuracy

Importance of cross-validation

Difference between linear and tree-based models

Model comparison and selection techniques

📌 Business Impact

This model can help businesses:

Identify high-risk customers

Take proactive retention actions

Reduce revenue loss

Improve customer lifetime value

🔮 Future Improvements

Hyperparameter tuning using GridSearchCV

SMOTE for advanced imbalance handling

Feature importance visualization

Model deployment using Streamlit or Flask

Adding ROC-AUC comparison chart

📎 How to Run the Project

Clone the repository:

git clone

Install dependencies:

pip install -r requirements.txt

Open Jupyter Notebook:

jupyter notebook

Run any model notebook.

👨‍💻 Author

Kaleem Ali Machine Learning Enthusiast | Data Science Learner