Customer churn prediction in banking sector using PCA with machine learning algorithms

Customer Churn Prediction in Banking Sector is the most research area in past few years. Nowadays there isvarious options available for customer in banking sector, where to put money. It is important to gain Customer satisfaction and to maintain bank-customer satisfaction relationship with customer. Generally, if a client stops mistreatment the services of an organization for an extended amount of your time (which varies looking on the merchandise or services of the company), such a client is taken into account churned. In this research paper, a method is proposed to to anticipate client stir in financial area using Machine Learning Algorithms with PCA. In this study Support Vector Machine (SVM), Random Forest Classifier (RFC), Linear Regression (LR), Principal Component Analysis (PCA), Feature selection technique are used to find most relevant features. The dataset is taken from Kaggle. After preprocessing of dataset, the dataset is cleaned from the noisy data. And the dataset is ready for the training process. Now the algorithms are used to train the model. In this using PCA with SVM, PCA with RFC, PCA with LR, Accuracy is measured using confusion matrix. As a result, applying PCA with SVM gives high accuracy as compared to others models.