File Name	Description
Analyze_Telematics_syn.ipynb	Analyzes synthetic telematics data with a high number of zero values in AMT_Claim, a typical scenario in insurance where claims are rare. Discusses potential techniques like zero-inflated models (e.g., ZIP, ZINB, and Zero-Inflated Beta) and two-stage modeling to handle zero-inflated data. Outlier handling methods like Winsorization and robust regression are also considered.
ClaimYN_Prediction_TabNet_80result.ipynb	Implements a simple TabNet model to predict ClaimYN. After 50 epochs, achieves a best accuracy of 78.4% at epoch 46. The preprocessing steps include creating the ClaimYN label based on NB_Claim and AMT_Claim and three levels of preprocessing before training.
ClaimYN_Prediction_TabNet_Bad_Result.ipynb	Reattempts the ClaimYN prediction with more extensive preprocessing and feature engineering, including encoding categorical features and applying scaling. The model achieves an accuracy of 85%. The model evaluation shows the importance of using appropriate scaling and preprocessing for better results.
ClaimYN_Prediction_With_DL&ML.ipynb	Predicts ClaimYN with both deep learning and machine learning models. Includes steps like handling missing values, encoding, standardizing, balancing the dataset with SMOTE, and dropping irrelevant columns. The data is split into training, testing, and validation sets (70%-15%-15%) to ensure reliable evaluation.
Insurance_TabNet_AMT_Claim_Prediction.ipynb	Attempts a regression task to predict AMT_Claim using TabNet but notes unsatisfactory results, leading to abandoning the approach for this specific task.
NB_Claim_Prediction_FirstV_without_balance.ipynb	Predicts NB_Claim without addressing data imbalance, leading to overly optimistic results (100% accuracy) across all algorithms. This highlights the importance of handling imbalance to avoid misleading performance metrics.
NB_Claim_Prediction_Insurance.ipynb	Compares various models for NB_Claim prediction, with results showing high accuracy for Logistic Regression, XGBoost, TabNet, Random Forest, and LightGBM. After using SMOTE and hyperparameter tuning, the models achieve excellent performance, effectively handling class imbalance.
NB_Claim_Prediction_TabNet_Tuning.ipynb	Step-by-step guide for model training, including TabNet hyperparameter tuning with RandomizedSearchCV. Data preprocessing includes standardization, normalization, and feature engineering. The TabNet model is then trained and tuned to maximize accuracy and interpretability for insurance risk pricing.
Risk_Category_TabNet.ipynb	Predicts Risk_Category using TabNet. Sets up the Risk_Category label as a binary indicator based on claims and claim amount, achieving a test accuracy of 75.44% and F1 score of 75.25%.
Risk_Category_TabNet_best_version.ipynb	A refined version of Risk_Category prediction with PCA applied for dimensionality reduction before training the TabNet model. Achieves high accuracy (99.53%), showing improved performance due to feature reduction and optimal data processing.
insurance_classification_ensemble_model_claim_YN.ipynb	Utilizes both TabNet and DNN in an ensemble model for ClaimYN classification. Metrics for the ensemble model include 97.47% accuracy, with other metrics calculated for deeper insights into model performance (precision, recall, F1 score, AUC).
predict_ClaimYN_with_combined_models.ipynb	Uses SMOTE, feature engineering, and visualization for ClaimYN prediction. Evaluates TabNet, XGBoost, and ensemble models, with detailed performance metrics, showing the impact of ensemble approaches for robustness.
telematics_syn_Risk_Category.ipynb	Implements TabNet for predicting Risk_Category using synthetic telematics data, achieving near-perfect validation accuracy (99.98%) and F1 score (99.98%). The model is saved for future use, indicating it has performed well on this synthetic dataset. Validation Accuracy: 0.9998, Validation F1 Score: 0.9998, Successfully saved model at tabnet_model.zip.
improvements_in_telematics_syn_Risk_Category.ipynb	1. Better Organization: Created a class InsuranceRiskClassifier that encapsulates all the functionality, making the code more maintainable and reusable. 2. Added SVM Model: Included Support Vector Machine classifier alongside TabNet for comparison. 3. Improved Error Handling: Added logging to track the progress and potential issues. 4. Type Hints: Added type hints to make the code more readable and maintainable. 5. Modular Design: Split the functionality into smaller, focused methods that each handle a specific task. 6. Better Preprocessing: More robust feature preparation, added option to save preprocessor for future use, improved categorical encoding. 7. Model Evaluation: Added comprehensive evaluation metrics, side-by-side comparison of TabNet and SVM performance, detailed classification reports. 8. Memory Efficiency: Better handling of large datasets through proper data processing steps.