Road-Traffic-Severity-Classification-Project

This is a multiclass classification project to classify the severity of road accidents into three categories. this project is based on real-world data and the dataset is also highly imbalanced. Learn more about detailed descriptions and problems with tasks performed.

Description: This data set is collected from Addis Ababa Sub-city, Ethiopia police departments for master's research work. The data set has been prepared from manual records of road traffic accidents of the year 2017-20. All the sensitive information has been excluded during data encoding and finally, it has 32 features and 12316 instances of accident. Then it is preprocessed for identification of major causes of the accident by analyzing it using different machine learning classification algorithms.

Source of dataset: Link to the dataset

Problem Statement: The target feature is Accident_severity which is a multi-class variable. The task is to classify this variable based on the other 31 features step-by-step by going through each day's task. Your metric for evaluation will be f1-score.

Web application

Published article on Analytics Vidhya

Link: Click here

Collaboration with StackUp developer community:

Link to the campaign: Click Here

Web app screenshots:

Tasks and techniques used:

1. Exploratory data analysis

• Data analysis using dabl
• Exploratory data analysis using matplotlib and seaborn

2. Data preparation and pre-processing

• Missing Values Tretment using fillna method
• One Hot encoding using pandas get_dummies
• Feature selection using chi2 statistic and SelectKBest method
• PCA to reduce dimentinality
• Imbalance data tretment using SMOTENC technique

3. Modelling using sci-kit learn library

• Baseline model using RandomForest using default technique
• Tuned hyperparameters using n_estimators and max_depth parameters

4. Evaluation

• Evaluation metric was weighted f1_score
• Baseline model evaluation fl_score = 61%
• Final model evaluation f1_score = 88%

Acknowledgement: TMLC Academy

References:

1. Machine learning mastery
2. Feature engieering techniques