Author: Tiago Russomanno
version test( development)
This project is a test implementation of a Streamlit app designed to predict the severity of road accidents in France. The primary objective is to leverage historical data to develop a predictive model capable of estimating the severity of accidents. This project encompasses all stages of a Data Science project lifecycle, providing an opportunity to explore data cleaning, feature extraction, and model training. Project Overview
Objective: Predict the severity of road accidents in France.
Data Source: Historical data on road accidents.
Methodology: The project involves multiple stages:
Data Cleaning: Study and application of methods to clean the dataset, ensuring high-quality input for the predictive model.
Feature Extraction: Extraction of relevant characteristics from historical data to estimate accident severity.
Scoring of Risk Zones: Utilizing model results to score risk zones based on meteorological information, geographical location (GPS coordinates), satellite images, etc.
Model Training: Development of a predictive model using machine learning techniques.
Model Comparison: Comparison of the trained model's predictions with historical data.