Can you develop a model of machine learning that can predict customers who will leave the company?
The aim is to estimate whether a bank's customers leave the bank or not. The event that defines the customer abandonment is the closing of the customer's bank account. Details about the dataset:
It consists of 10000 observations and 12 variables. Independent variables contain information about customers. Dependent variable refers to customer abandonment status. Result; The model created as a result of LightGBM hyperparameter optimization became the model with the maxium Accuracy Score.
• Developed and implemented machine learning models for predictive analytics. Cleaned, and preprocessed large datasets for machine learning projects. Utilized libraries such as TensorFlow, scikit-learn, pandas, and Seaborn. • Developed a churn prediction model for a bank, achieving an accuracy of 89% Link • Employed various Classification, and Gradient Boosting techniques were used with the final model fine-tuned to optimize performance. The project demonstrated effective predictive capabilities for identifying high-risk customers. Conducted data analysis and visualization to support decision-making processes. • Gained hands-on experience with Natural Language Processing (NLP) techniques.