ChurnANNalyzer is a customer churn prediction project that utilizes Artificial Neural Network (ANN) algorithm. The project aims to analyze and predict customer churn in a given dataset.

Key Features:

Handling Imbalanced Data: The project implements techniques to handle imbalanced data, ensuring accurate predictions even when the churn class is underrepresented.

One-Hot Encoding: The dataset is preprocessed using one-hot encoding, which converts categorical variables into binary vectors for improved model performance.

Exploratory Data Analysis (EDA): The project includes an exploratory data analysis phase, providing insights into the dataset, identifying patterns, and understanding the factors that influence customer churn.

ANN Model Building: An Artificial Neural Network model is constructed to predict customer churn. This model is trained using the preprocessed dataset and achieves an impressive accuracy of 79.5%.

Kagg;e Notebook : https://www.kaggle.com/code/muhammadmuzammil196/customer-churn-prediction-using-ann