The problem Customer Churn Prediction solves

The problem that the customer churn prediction model using Artificial Neural Networks (ANN) solves is accurately identifying and predicting customers who are likely to churn or discontinue their relationship with a business or service. By leveraging ANN, the model can analyze various customer data points, patterns, and behavior to forecast which customers are at a higher risk of churning. This helps businesses proactively take preventive measures, such as targeted retention strategies or personalized interventions, to reduce customer churn and improve overall customer satisfaction and profitability.

Challenges I ran into

1. Data quality and availability: Obtaining high-quality and relevant data can be a challenge. Incomplete or inconsistent data, missing values, and data from various sources can affect the performance of the model.

2. Feature engineering: Identifying and selecting the most relevant features for the model can be challenging. It requires a deep understanding of the business domain and the factors that contribute to customer churn. Choosing the right set of features can significantly impact the accuracy of the predictions.

3. Model complexity and tuning: ANN models are complex and require careful tuning of hyperparameters, such as the number of layers, neurons, activation functions, and learning rates. Finding the optimal configuration can be time-consuming and may involve trial and error.