Update README.md

README.md

@@ -10,10 +10,72 @@ tags:
 pinned: false
 short_description: Streamlit template space
 ---
+# 🏦 Porto Seguro – Safe Driver Prediction
 
-# Welcome to Streamlit!
+This machine learning app predicts the probability that a driver will file an auto insurance claim.
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+## 📌 Problem Statement
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+Insurance companies need accurate risk estimation to price policies fairly.  
+In this Kaggle competition, the goal is to build a model that predicts whether a policyholder will file a claim in the next year.
+
+Better predictions help:
+
+- reduce costs for safe drivers
+- price high-risk drivers correctly
+- improve accessibility of insurance
+
+This is a **binary classification problem** with highly imbalanced data.  
+
+## 📊 Dataset Overview
+
+The dataset contains anonymized features related to:
+
+- driver information (`ind`)
+- regional data (`reg`)
+- car characteristics (`car`)
+- calculated features (`calc`)
+- binary and categorical variables
+
+Missing values are represented by **-1**.
+
+Target:
+- `target = 1` → claim filed
+- `target = 0` → no claim
+
+## ⚙️ Machine Learning Pipeline
+
+1. Data cleaning & handling missing values
+2. Feature selection
+3. Train-test split
+4. Model training
+5. Evaluation
+## 🤖 Model
+
+Algorithm used:
+
+- Logistic Regression / Random Forest / XGBoost *(pas aan naar jouw model)*
+
+The model outputs the **probability of a claim**.
+
+## 📏 Evaluation Metric
+
+Competition metric:
+
+**Normalized Gini Coefficient**
+
+Why Gini?
+
+It measures how well the model ranks high-risk drivers above low-risk drivers.
+
+## 🚀 Streamlit App
+
+The app allows users to:
+
+- Enter driver & vehicle features
+- Get real-time claim probability prediction
+
+### Output
+
+- Claim probability
+- Risk interpretation