In this project, we use a crash dataset to figure out the speed at a crash site

Pawang Rai - 20SW037
Zaid Soomro - 20SW003
Farheen Qazi - 20SW055

Accidents and Crashes are a important factor of society and result in the loss of human life This project aims to prevent those by finding out different findings on crash dataset

Pandas for data loading Numpy for data cleaning Seaborn for visualization Matplotlib for visualization

• DS&A_Final_Project.ipynb: This is the file of the actual project.
• ped_crashes_2.csv: This is the dataset that we have used

In this project, we have answered 3 business questions by visualization. The questions being

Q1 Can we identify the day of the week with the highest fatality rate in traffic accidents and use this information to allocate resources more effectively?

Q2 How does the average speed at crash sites correlate with the number of number of accidents, and can we use this information to target speed limit enforcement or road safety campaigns more effectively?

Q3 Do certain days with dark light conditions, especially on weekends, exhibit a higher frequency of accidents compared to weekdays, and how can this information inform traffic management strategies or public awareness campaigns?

Link of dataset: https://www.kaggle.com/datasets/syedasimalishah/auto-pedestrians-crashes