https://github.com/yashksaini-coder/binary-classification-of-insurance-cross-selling
The objective is to predict which customers respond positively to an automobile insurance offer
https://github.com/yashksaini-coder/binary-classification-of-insurance-cross-selling
Last synced: 4 months ago
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The objective is to predict which customers respond positively to an automobile insurance offer
- Host: GitHub
- URL: https://github.com/yashksaini-coder/binary-classification-of-insurance-cross-selling
- Owner: yashksaini-coder
- License: mit
- Created: 2024-07-04T19:26:43.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-08-01T07:19:44.000Z (11 months ago)
- Last Synced: 2024-08-02T08:16:45.551Z (11 months ago)
- Language: HTML
- Size: 6.48 MB
- Stars: 7
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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# 🛠️ Binary Classification of Insurance Cross Selling - Kaggle Playground Series (Season 4, Episode 7) 🔮
## **Objective** 🎯
The objective of this competition is to predict which customers respond positively to an automobile insurance offer. Accurate predictions can help insurance companies efficiently target potential customers, improving their marketing strategies and customer engagement.
## ⚙️ Built With
- [Python](https://www.python.org/)
- [Pandas](https://pandas.pydata.org/)
- [NumPy](https://numpy.org/)
- [Matplotlib](https://matplotlib.org/)
- [Seaborn](https://seaborn.pydata.org/)
- [Scikit-learn](https://scikit-learn.org/stable/)
- [XGBoost](https://xgboost.readthedocs.io/en/latest/)
- [LightGBM](https://lightgbm.readthedocs.io/en/latest/)
## Dataset 📦
The dataset provided for this competition includes various features related to customer demographics, vehicle information, and previous insurance history. Participants will use this data to build and train binary classification models, aiming to accurately predict the likelihood of a customer responding positively to the insurance offer.
**Explore the [dataset](https://www.kaggle.com/competitions/playground-series-s4e7/data), develop your machine learning models, and submit your predictions through the Kaggle [competition](https://www.kaggle.com/competitions/playground-series-s4e7/overview) platform before the deadline.**
## Files 📄
- **train.csv**: Training dataset containing abalone measurements and their corresponding ages.
- **test.csv**: Test dataset for which you need to predict the abalone ages.
- **sample_submission.csv**: A sample submission file demonstrating the correct format for predictions.
## **Evaluation** 📊
Submissions will be evaluated based on the Area Under the ROC Curve (AUC-ROC) metric. This metric measures the ability of the model to distinguish between positive and negative responses, with higher scores indicating better performance.
## Installation & Usage 💻
Follow these simple steps to get started:
1. **Clone the Repository**:
```bash
git clone https://github.com/yashksaini-coder/Binary-Classification-of-Insurance-Cross-Selling
```
2. **Navigate to the Repository Directory**:
```bash
cd Binary-Classification-of-Insurance-Cross-Selling
```
3. **Install Dependencies**:
```bash
pip install -r requirements.txt
```
---
## 🌟Contributing Guide
You are welcome to contribute to this project! To ensure a smooth workflow, please follow these steps:
### 1. Fork the Project
- Go to the project repository on GitHub.
- Click on the [Fork]("https://github.com/yashksaini-coder/Binary-Classification-of-Insurance-Cross-Selling/fork") button in the upper right corner of the page.
- This will create a copy of the repository in your GitHub account.
### 2. Create Your Feature Branch
- Clone the forked repository to your local machine:
```bash
git clone https://github.com/your-username/Binary-Classification-of-Insurance-Cross-Selling.git
```
- Navigate to the project directory:
```bash
cd Binary-Classification-of-Insurance-Cross-Selling
```
- Create a new branch for your feature:
```bash
git checkout -b feature/AmazingFeature
```
### 3. Commit Your Changes
- Make your changes to the code.
- Stage your changes:
```bash
git add .
```
- Commit your changes with a descriptive message:
```bash
git commit -m 'Add some AmazingFeature'
```
### 4. Push to the Branch
- Push your changes to your forked repository:
```bash
git push origin feature/AmazingFeature
```
### 5. Open a Pull Request
- Go to the original repository on GitHub.
- Click on the "Compare & pull request" button.
- Ensure the base repository is the original repository and the base branch is the branch you want to merge into (e.g., `main` or `master`).
- Provide a descriptive title and detailed description of your changes.
- Click on the "Create pull request" button.
---
Thank you for your contributions! Your efforts help make this project better for everyone. If you have any questions or need assistance, feel free to open an issue or contact the maintainers.
---
## 📝 Conclusion
In this project, we worked on the Binary Classification of Insurance Cross Selling dataset. Our goal was to predict customer responses to an automobile insurance offer. We started by preprocessing the data and performing exploratory data analysis. Then, we trained XGBoost and LightGBM models to make predictions. We evaluated the models using the Area Under the ROC Curve (AUC-ROC) metric. Finally, we submitted our predictions to the Kaggle competition.
To visualize our results, we have two submission images. The first image, labeled as `XGBoost Model`, shows the performance of our XGBoost model. The second image, labeled as `LightGBM Model`, shows the performance of our LightGBM model. Both images have been resized for better viewing.
XGBoost Model
LightGBM Model
---
### License
Distributed under the MIT License. See [LICENSE](LICENSE) for more information.
---
# TODO 📝
- [X] Use LightGBM model
- [X] Add the project conclusion