This project as a Data Science ML Project I have performed:

• Data Preparation: Load and inspect the datasets.
• Exploratory Data Analysis (EDA): Understand the data, handle missing values, and visualize key relationships.
• Feature Engineering: Create new features if necessary and prepare data for modeling.
• Model Selection: Choose a variety of models to evaluate.
• Model Training and Evaluation: Train the models using the churn-80 dataset and evaluate using cross-validation.
• Model Testing: Use the churn-20 dataset to test the best performing model.
• Results Interpretation: Interpret the results and identify key factors influencing churn.