Merge pull request #87 from alo7lika/main

Traffic Accident Prediction

examples/Traffic Accident Prediction Model/README .md

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+# 🚦 Traffic Accident Prediction Model 🚗
+
+<p align="center">
+    <img src="https://raw.githubusercontent.com/alo7lika/explainableai/refs/heads/main/examples/Traffic%20Accident%20Prediction%20Model/TrafficVision%20-%20Accident%20Prediction%20Model.png" width="600" />
+</p>
+
+
+## 📚 Table of Contents
+1. [Introduction](#introduction)
+2. [Objective](#objective)
+3. [Data](#data)
+   - [Dataset Description](#dataset-description)
+   - [Data Preprocessing](#data-preprocessing)
+4. [Modeling](#modeling)
+   - [Model Selection](#model-selection)
+   - [Evaluation Metrics](#evaluation-metrics)
+5. [SHAP Analysis](#shap-analysis)
+6. [Results](#results)
+7. [Conclusion](#conclusion)
+8. [Future Work](#future-work)
+
+## 📝 Introduction
+The Traffic Accident Prediction Model aims to leverage machine learning techniques to predict the likelihood of traffic accidents based on various factors, ultimately enhancing road safety and supporting data-driven decision-making.
+
+## 🎯 Objective
+- Enhance road safety by predicting potential accident scenarios.
+- Analyze contributing factors such as weather and traffic conditions.
+- Provide insights for policymakers and urban planners.
+- Develop systems for real-time alerts to drivers.
+- Optimize resource allocation for emergency services.
+
+## 📊 Data
+
+### Dataset Description
+| Feature               | Description                                    |
+|-----------------------|------------------------------------------------|
+| `weather_conditions`  | Current weather conditions (sunny, rainy, etc.) |
+| `traffic_volume`      | Number of vehicles on the road                |
+| `time_of_day`        | Hour of the day when the observation is made  |
+| `location`            | Geographical area where the observation is made |
+| `accident_occurred`   | Target variable (1 if accident occurred, 0 otherwise) |
+
+### Data Preprocessing
+- **Handling Missing Values**: Filling or removing missing data.
+- **Encoding Categorical Variables**: Converting categorical features to numerical formats.
+- **Feature Scaling**: Normalizing data for better model performance.
+
+## 🤖 Modeling
+
+### Model Selection
+We utilized various machine learning algorithms, including:
+- Logistic Regression
+- Random Forest
+- Gradient Boosting
+
+### Evaluation Metrics
+| Metric                | Description                                    |
+|-----------------------|------------------------------------------------|
+| **Accuracy**          | Proportion of true results among the total cases |
+| **Precision**         | True positive results divided by all positive predictions |
+| **Recall**            | True positive results divided by all actual positives |
+| **F1 Score**          | Harmonic mean of precision and recall         |
+
+## 🔍 SHAP Analysis
+To interpret the model predictions, we employed SHAP (SHapley Additive exPlanations) values, providing insights into how features contribute to predictions.
+
+## 📈 Results
+- The model achieved an accuracy of X% on the test set.
+- Important features influencing predictions included weather conditions, traffic volume, and time of day.
+
+## 🏁 Conclusion
+The Traffic Accident Prediction Model successfully demonstrates the potential of machine learning in enhancing road safety. By analyzing contributing factors, the model provides valuable insights for policymakers and traffic management authorities.
+
+## 🔮 Future Work
+- Explore additional features such as road conditions and historical accident data.
+- Implement real-time data streaming for dynamic predictions.
+- Collaborate with local authorities to apply findings in real-world scenarios.
+