oc-p04-credit-scoring/README.md

**Source of truth** : https://git.gregoiremureau.com/openclassroom-ai/oc-p04-credit-scoring

```bash
git clone https://git.gregoiremureau.com/openclassroom-ai/oc-p04-credit-scoring.git
git clone https://git.gregoiremureau.com/openclassroom-ai/oc-p04-credit-scoring.git
```

Dataset: see `fetch-data.sh` (excluded from git if large).

# Credit Scoring Project - Prêt à dépenser

📘 This project is also available in [French 🇫🇷](./README.fr.md)

## 📋 Project Context

This project was carried out for **"Prêt à dépenser"**, a financial company specializing in consumer credit lending. The company wants to set up a **credit scoring tool** to assess the probability that a customer will repay their loan, thereby automating the credit approval decision process.

## 🎯 Objectives

The main objectives of the project are:

1. **Develop a scoring model**: Create a robust predictive model capable of assessing the risk of payment default
2. **Optimize business cost**: Minimize financial losses by taking into account the real cost of false positives and false negatives
3. **Ensure interpretability**: Provide clear explanations of the model's decisions for customer relationship managers
4. **Handle class imbalance**: Effectively deal with the imbalanced data characteristic of the credit domain

## 📊 Data

The data comes from the Kaggle **"Home Credit Default Risk"** competition and includes:
- Customers' credit history
- Previous loan applications
- Credit card information
- Installment payment data
- POS (Point of Sale) and cash balances

**⚠️ Important note**: The raw data files are not included in this repository due to their large size. They can be downloaded here:
- **URL**: https://www.kaggle.com/competitions/home-credit-default-risk/data

The required files include:
- `application_train.csv` / `application_test.csv`
- `bureau.csv`
- `previous_application.csv`
- `credit_card_balance.csv`
- `installments_payments.csv`
- `POS_CASH_balance.csv`

## 📁 Project Structure

```
├── P4_01_notebook_analyse_exploratoire_feature_engineering.ipynb
├── P4_02_notebook_modelisation.ipynb
├── P4_03_presentation.pptx
├── explication_client_0.html
└── explication_client_2.html
```

### 1. `P4_01_notebook_analyse_exploratoire_feature_engineering.ipynb`

**Exploratory Data Analysis (EDA) and Feature Engineering Notebook**

This notebook contains the entire data preparation phase:

**Data exploration**:
- Loading and analysis of the various CSV files
- Study of the target variable distribution (class imbalance)
- Analysis of missing values and outliers
- Study of correlations between variables and with the target

**Data enrichment**:
- Intelligent merging of the various data sources (bureau, previous_application, etc.)
- Aggregation of information from multiple tables
- Creation of relevant business variables (ratios, averages, statistics)

**Feature Engineering**:
- Creation of new features based on business ratios
- Encoding of categorical variables
- Imputation of missing values
- Normalization and standardization of variables
- Specific preparation for models tolerant of missing values (LightGBM)

### 2. `P4_02_notebook_modelisation.ipynb`

**Modeling, Optimization and Interpretation Notebook**

This notebook presents the entire modeling process:

**Baseline modeling**:
- Logistic regression (baseline)
- Random Forest
- Evaluation with standard metrics (AUC-ROC, precision, recall)

**Advanced modeling**:
- Training of gradient boosting models: XGBoost, LightGBM, CatBoost
- Handling class imbalance (class_weight, scale_pos_weight)
- Hyperparameter optimization with GridSearchCV

**Business optimization**:
- Definition of a **business score** incorporating the cost of false positives and false negatives
- Determination of the **optimal decision threshold** to minimize financial losses
- Analysis of the cost-threshold curve

**Interpretability**:
- **Global interpretation**: Feature importance with SHAP (TreeExplainer)
- **Local interpretation**: Explanation of individual predictions with LIME
- Analysis of the impact of each variable on decisions

**Model simplification**:
- Selection of the most important features
- Reduction of the number of features while maintaining performance (AUC)

### 3. `P4_03_presentation.pptx`

**Defense Presentation**

This presentation summarizes:
- The business context and challenges of credit scoring
- The data preparation and enrichment methodology
- The comparison of the different models tested
- The choice of the final model and its business optimization
- The global and local interpretability approach (SHAP and LIME)
- Recommendations and improvement perspectives

### 4. `explication_client_0.html` and `explication_client_2.html`

**Local Prediction Interpretation Reports**

These interactive HTML reports illustrate concrete examples of explanations for individual customers:
- The model's predictions for each customer (default probability)
- SHAP and/or LIME explanations detailing why the model made a given decision
- The contribution of each variable to the final prediction for this specific customer
- Visualizations allowing customer relationship managers to understand and justify decisions
- Examples of acceptance and rejection cases with their justifications

These files serve as a demonstration of the local interpretability system that could be deployed in production to explain each credit decision.

## 🛠️ Technologies Used

- **Python**: Development and analysis
  - pandas, numpy: data manipulation
  - scikit-learn: baseline models, preprocessing, metrics
  - XGBoost, LightGBM, CatBoost: gradient boosting models
  - SHAP: global model interpretation
  - LIME: local prediction interpretation
  - matplotlib, seaborn, plotly: visualization
- **Jupyter Notebook**: development and documentation

## 📈 Methodology

1. **Exploration and understanding**: In-depth analysis of multi-source data
2. **Feature Engineering**: Creation of relevant business variables through aggregation and transformation
3. **Modeling**: Testing several algorithms with class imbalance handling
4. **Business optimization**: Definition and optimization of a business score incorporating real costs
5. **Interpretability**: Implementation of global (SHAP) and local (LIME) explanation tools
6. **Validation**: Evaluation on test data and verification of generalization

## 🎯 Expected Results

- ✅ Enriched and preprocessed dataset with new business features
- ✅ Scoring model optimized according to a business criterion (cost minimization)
- ✅ Optimal decision threshold determined
- ✅ Global and local interpretability reports
- ✅ Complete documentation for end users

## 💡 Key Points of the Project

### Handling class imbalance
The dataset shows a strong imbalance between good and bad payers. Several techniques were implemented:
- Use of `class_weight` and `scale_pos_weight` parameters
- Optimization based on appropriate metrics (AUC-ROC, recall, business score)
- Post-modeling decision threshold adjustment

### Custom business score
Rather than relying solely on classic metrics, a **business score** was defined taking into account:
- The cost of a false positive (wrongly rejecting a good customer → lost opportunity)
- The cost of a false negative (accepting a bad payer → significant financial loss)
- Optimization of the decision threshold to minimize total cost

### Interpretability
Two levels of interpretation were implemented:
- **Global (SHAP)**: Which variables are most important for the model in general?
- **Local (LIME)**: Why did the model make this decision for this specific customer?

This dual approach allows customer relationship managers to understand and explain decisions to credit applicants.

## 🎓 Skills Developed

- Preparation and enrichment of complex multi-source data
- Business-oriented feature engineering in the financial domain
- Modeling with class imbalance handling
- Optimization based on business criteria (cost/benefit)
- Interpretability of machine learning models (SHAP, LIME)
- Communication of technical results to business audiences

## 👤 Author

**Grégoire Mureau**

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*This project illustrates the complete data science approach applied to credit scoring: from data preparation to business optimization, including model interpretability.*