Cleaned Dataset: Preprocessed and annotated data ready for analysis and model training. Custom ML/DL Models: Tailored models including ANN, CNN, RNN, SVM, Decision Trees, Random Forests, GBM, GANs, LSTM, and more. Predictive Insights: Models designed to provide actionable predictions and valuable insights based on the data. Comprehensive Codebase: Complete set of documented scripts and code for reproducibility and further development. Includes all necessary preprocessing, training, and deployment code.

Data Preprocessing: Handle missing values, encode categorical variables, and scale numerical features. Exploratory Data Analysis (EDA): Visualize relationships between features and churn status to identify influential factors. Model Building: Experiment with machine learning algorithms like logistic regression, decision trees, and random forests. Model Evaluation: Assess model performance using metrics like accuracy, precision, recall, and F1-score. Results and Visualization: Visualize model performance using ROC curves, precision-recall curves, and confusion matrices. Create interpretable visualizations to explain churn drivers.