Project Overview This project involves two distinct analyses: one focusing on petrol prices worldwide and the other on traffic accidents in Pakistan. The aim is to extract meaningful insights and patterns from both datasets.
Traffic Accidents in Pakistan (2008-2019) The dataset contains information about traffic accidents in Pakistan over an 11-year period, from 2008 to 2019. It includes the following columns: Place Year Total Injuries Total Killed Fatal Accidents Non-Fatal Accidents
The analysis addresses the following questions: In which province have the most accidents occurred? In which province have the least accidents occurred? In which province were the most people killed? In which province were the least people killed? In which province were the most people injured? In which province were the least people injured? In which year did the most accidents occur? In which year did the least accidents occur?
Petrol Prices Worldwide The dataset includes information about petrol prices worldwide, such as: World share of petrol consumption by country Price per gallon Price per liter Corresponding price in Pakistan Rupee (Conversion Rate 1 USD = 211.5 PKR)
The analysis aims to extract useful data patterns from the given dataset.
Methodology Traffic Accidents Analysis Data Cleaning: Handling missing values and data inconsistencies. Exploratory Data Analysis (EDA): Answering the specified questions.
Petrol Prices Analysis Exploratory Data Analysis (EDA): Visualizing petrol prices across different countries. Insights Extraction: Identifying useful patterns and unique information from the dataset.
Results Traffic Accidents Analysis Identified provinces with the most and least accidents, fatalities, and injuries. Determined the years with the highest and lowest number of accidents. Petrol Prices Analysis Visualized global petrol price patterns. Converted petrol price in Pakistan to Pakistani Rupees and compared with USD.
Conclusion This project provided valuable insights into traffic accidents in Pakistan and petrol prices worldwide. The analysis helped in understanding accident trends and exploring global petrol price patterns.
Tools and Technologies Python pandas numpy matplotlib seaborn Jupyter Notebook