## Experiment Set 1 - Dummy Classifier

**Description:** In this set, a dummy classifier is utilized, where a decision tree is constructed based on level 1 preprocessed features. Level 1 preprocessing refers to minimal preprocessing required for the algorithm to function.

## Experiment Set 2 - XGBoost Without FE

**Description:** This set employs the XGBoost algorithm without any feature engineering (FE) techniques.

## Experiment Set 3 - XGBoost With FE

**Description:** XGBoost is utilized in this set with feature engineering techniques applied to enhance model performance.

## Experiment Set 4 - XGBoost With FE & Optuna

**Description:** XGBoost is employed with feature engineering techniques, and Optuna is used for hyperparameter optimization.

## Experiment Set 5 - LightGBM Without FE

**Description:** LightGBM is utilized in this set without any feature engineering.

## Experiment Set 6 - LightGBM With FE

**Description:** LightGBM is employed with feature engineering techniques to improve model performance.

## Experiment Set 7 - LightGBM With FE & Optuna

**Description:** LightGBM is used with feature engineering techniques, and Optuna is employed for hyperparameter tuning.

## Experiment Set 8 - LightGBM With custom loss function (focal loss)

**Description:** This set utilizes LightGBM with a custom loss function, specifically the focal loss function, to address class imbalance or prioritize hard-to-classify samples.

## Experiment Set 9 - Catboost With FE

**Description:** CatBoost algorithm is employed with feature engineering techniques.

## Experiment Set 10 - Catboost With FE & Optuna

**Description:** CatBoost algorithm is utilized with feature engineering techniques, and Optuna is employed for hyperparameter optimization.

## Experiment Set 11 - Pycaret

**Description:** This set utilizes the Pycaret library, which automates the machine learning workflow, including data preprocessing, model selection, and evaluation.

## Experiment Set 12 - Autogluon

**Description:** Autogluon is utilized in this set, which is an automated machine learning library designed for easy-to-use and efficient model selection and hyperparameter tuning.

## Experiment Set 13 - AutoXGB

**Description:** AutoXGB employs automated methods, likely through techniques such as AutoML, for training and optimizing an XGBoost model.

## Experiment Set 14 - AutoLGBM

**Description:** AutoLGBM utilizes automated techniques, possibly through AutoML, for training and optimizing a LightGBM model.

## Experiment Set 15 - Advanced LightGBM

**Description:** This set employs advanced techniques or configurations with LightGBM to enhance model performance further.

## Experiment Set 16 - XGB Stacked

**Description:** In this set, an ensemble approach is used where XGBoost models are stacked to improve overall predictive performance.

## Experiment Set 17 - Neural Network

**Description:** This set utilizes a neural network-based approach for modeling, which can handle complex relationships in the data.

## Experiment Set 18 

**Description:** *(Fill in the description of Experiment Set 18)*

## Experiment Set 19 

**Description:** *(Fill in the description of Experiment Set 19)*

## Experiment Set 20 - XGB with NN probabilities

**Description:** This set combines the predictions of an XGBoost model with probabilities obtained from a neural network model, likely for ensemble or boosting purposes.