BANK CUSTOMER CHURN PREDICTION PROJECT

Problem: Predicting customer churn in the banking industry using machine learning algorithms.

Summary

I employed Exploratory Data Analysis (EDA) and various Model Classifications, including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Gradient Boosting (GB) to examine the dataset 'Bank Customer Churn Prediction' from the Kaggle website, which is labeled as 'Churn_Modelling.csv.'

(https://www.kaggle.com/datasets/shubhammeshram579/bank-customer-churn-prediction?resource=download ).

I attempted to explore the dataset comprehensively, examining various aspects and visualizing as much as possible to gain insights into the data. I employed four(4) Machine Learning Classification Algorithms.