Customer Churn Prediction Using Machine Learning

🔍 Step-by-Step Journey

• Data Preparation: Clean and transform structured datasets for analysis
• Exploratory Data Analysis (EDA): Discover hidden trends and customer behavior patterns
• Feature Engineering: Create impactful variables such as account balance, tenure, and product usage
• Model Development: Train and compare algorithms like Logistic Regression, Random Forest, and XGBoost
• Performance Evaluation: Measure success with metrics including Accuracy, Precision, Recall, F1-Score, and ROC-AUC
• Visualization: Use Seaborn and Matplotlib to illustrate churn dynamics and customer segments
• Business Insights: Translate predictions into actionable strategies for customer loyalty and retention

🛠️ Tech Stack

• Programming Language: Python
• Data Handling: Pandas, NumPy
• Visualization Tools: Matplotlib, Seaborn
• Machine Learning Frameworks: Scikit-learn, XGBoost