This project focuses on customer churn analysis, aiming to understand and predict the major factors that lead to customer churn. Customer churn, also known as customer attrition, occurs when customers cease doing business with a company. Analyzing and mitigating customer churn is crucial for maintaining a healthy customer base and ensuring business growth.

The project includes the following key components:

Exploratory Data Analysis (EDA): We performed an in-depth exploratory data analysis to gain insights into the dataset. This phase involved data cleaning, visualization, and statistical analysis to understand the distribution of variables and their relationships.

Machine Learning Models: To predict and understand the major factors influencing customer churn, we applied several machine learning algorithms, including:

Logistic Regression K-Nearest Neighbors (KNN) Decision Tree XGBoost These models were used to analyze and predict customer churn based on various features and attributes.

Feature Importance: We investigated the importance of different features in influencing customer churn. This helped us identify which factors have the most significant impact on customer retention.