https://github.com/krishnaura45/insurelead

🧪Predictive Modeling for Insurance Cross-Selling Response 🔥 Deep-ensemble approach
https://github.com/krishnaura45/insurelead

ann binary-classification blending boosting ensemble-learning insurance kaggle-competition ml stacking

Last synced: 5 months ago
JSON representation

🧪Predictive Modeling for Insurance Cross-Selling Response 🔥 Deep-ensemble approach

• Host: GitHub
• URL: https://github.com/krishnaura45/insurelead
• Owner: krishnaura45
• License: gpl-3.0
• Created: 2025-04-20T06:18:09.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-04-24T22:14:05.000Z (about 1 year ago)
• Last Synced: 2025-06-13T00:40:45.635Z (about 1 year ago)
• Topics: ann, binary-classification, blending, boosting, ensemble-learning, insurance, kaggle-competition, ml, stacking
• Language: Jupyter Notebook
• Homepage:
• Size: 269 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# InsureLead

Predicting Customer Responses to Insurance Offers Using ML

![Python](https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white)

![Scikit-Learn](https://img.shields.io/badge/Scikit--Learn-F7931E?style=for-the-badge&logo=scikit-learn&logoColor=white)

![Kaggle](https://img.shields.io/badge/Kaggle-20BEFF?style=for-the-badge&logo=kaggle&logoColor=white)

![ROC AUC Optimized](https://img.shields.io/badge/Optimized--for-ROC%20AUC-yellowgreen?style=for-the-badge)

![Optuna](https://img.shields.io/badge/Optuna-Tuning-blueviolet?style=for-the-badge)

![AUC Score](https://img.shields.io/badge/Best%20AUC-0.89690-2ECC71?style=for-the-badge)

![Rank](https://img.shields.io/badge/Rank-70%20of%202425-brightgreen?style=for-the-badge)

![Solo](https://img.shields.io/badge/Submission-Type%3A%20Solo-orange?style=for-the-badge)

### Project Duration: Jul 15, 2024 - Aug 1, 2024

---

## 🌟 Introduction

The objective is to predict which customers will respond positively to a vehicle insurance offer. This project is part of a binary classification challenge which was hosted on Kaggle. Submissions were evaluated using **Area Under the ROC Curve (AUC)**.

---

## 🥉 Top Approach

Explore full implementation here: 🔗 [PS4E7 - Stacking Boosters with ANN](https://github.com/krishnaura45/InsureLead/blob/main/ps4e7-stacking-boosters-and-ann.ipynb)

- 📊 **Data Integration & Inspection**

  - Combined official training dataset with original insurance dataset for feature enrichment.

    

- 🛠️ **Preprocessing Pipelines**

  - Utilized Scikit-learn pipelines and transformers with encoders: `StandardScaler`, `PowerTransformer`, `OneHotEncoder`, `OrdinalEncoder`.

    

- 🔍 **Feature Engineering & Selection**

  - Applied mutual information filtering to retain informative features.

    

- 🧰 **Modeling with Ensembles**

  - Trained and validated XGBoost, CatBoost, LightGBM classifiers using Stratified K-Fold CV.

  - Hyperparameter tuning with Optuna and visual exploration tools.

    

- 🏋️ **Submission Strategy**

  - Ensemble predictions via model averaging on test data.

---

## 📊 Results / Outcomes

- ✅ Public Leaderboard Scores: ranging from *0.50060* to *0.89727*

- 🏁 Best Private Score: ***0.89690**

- 🥇 Rank Achieved: Ranked 70 / 2425 participants and 2234 teams as a solo participant

![Score Progression Plot](https://github.com/user-attachments/assets/989ab79b-db3e-40ae-a7fb-ed3a040bba09)

---

## 🔗 References

- 📁 Kaggle Competition: Binary Classification of Insurance Cross Selling


- 📂 Original Dataset: Health Insurance Cross Sell Prediction Data

---

## 🛠️ Tech Stack

- Language: Python 🐍

- Libraries:

  - `pandas`, `polars`, `numpy` for data handling

  - `matplotlib`, `seaborn` for EDA and plotting

  - `scikit-learn`, `xgboost`, `catboost`, `lightgbm` for modeling

  - `optuna` for hyperparameter tuning

- Tools:

  - Jupyter Notebook / Kaggle Notebooks for experimentation

  - Custom pipelines and scoring functions for AUC optimization

 ---