Everything-Data-Africa/Loan-Risk-Prediction-Challenge-DATAMONDAYVIEWS
Domaine:
socioeconomicType de record:
dataset
Créateur:
Eve
Hôte:
# loan-default-analysis
# Loan Risk Prediction Challenge – #DATAMONDAYVIEWS
Welcome to the **Everything Data #DATAMONDAYVIEWS Challenge**! This week's challenge involves exploring a loan dataset to derive insights, analyze risk factors, and build predictive models for loan defaults.
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## 📂 Dataset Access
The dataset for this challenge, **Loan Risk Prediction Dataset**, is available on Kaggle. Please download it from the following link:
- **Loan Risk Prediction Dataset on Kaggle**
*Note:* If you don’t have a Kaggle account, you’ll need to create one to access the dataset.
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## 📊 Challenge Objectives
### Beginners
- Conduct **Exploratory Data Analysis (EDA)** on the dataset.
- Identify insights on loan distribution, applicant demographics, default rates, and factors influencing loan approval.
### Advanced
- Perform EDA and **Feature Engineering** to improve the dataset for predictive modeling.
- Build a **classification model** to predict loan default risks using the dataset.
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## 🛠️ Tools
You are free to use any tool or programming language of your choice:
- **Excel**: For descriptive analysis and charts.
- **SQL**: For data manipulation and aggregations.
- **Python/R**: For data analysis, visualization, and machine learning models.
- **Data Visualization Tools**: Power BI, Tableau, etc.
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## 🗂️ Repository Structure
To keep submissions organized, please follow this directory structure:
- **/data**: Instructions to download the dataset from Kaggle.
- **/notebooks**: For Jupyter notebooks or analysis scripts.
- **/submissions**: Create a folder with your GitHub username for your final submission.
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## 📝 How to Participate
1. **Fork** this repository to your GitHub account.
2. **Clone** the forked repository to your local machine.
3. Download the dataset from Kaggle and place it in your local project folder.
4. Work on your analysis and, if applicable, your predictive model.
5. **Submit your work** as follows:
- **Beginners**: Post your visuali …