The Monroe County Car Crash Data Analysis project is a comprehensive Python-based analysis of car crash data in Monroe County. Using libraries such as pandas for data manipulation, matplotlib and seaborn for data visualization, and scipy for statistical analysis, this project explores patterns, correlations, and trends to uncover the underlying factors contributing to car crashes.
This project aims to analyze car crash data from Monroe County to identify key insights into accident frequencies, causes, and distributions. Through statistical analysis and data visualization, it offers a detailed view of various factors influencing car crash occurrences, providing valuable information for enhancing road safety and informing policy decisions.
- Python 3
- pandas
- matplotlib
- seaborn
- scipy
Ensure you have Python installed on your system. This project was developed using Python 3.8, but it should be compatible with other Python 3 versions.
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Clone the repository to your local machine:
git clone https://github.com/joshmoy/ADS_1_Statistics_And_Trends.git
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Install the required Python libraries:
pip install pandas matplotlib seaborn scipy
Navigate to the project directory and run the analysis script:
cd ADS_1_Statistics_And_Trends
python ADS_assignment.ipynbThis project generates several key visualizations to support the analysis, including:
- Accidents During the Week vs. Weekend: Illustrates the distribution of accidents between weekdays and weekends.
- Distribution of Collision Types in Monroe County: Shows the various types of collisions and their frequencies.
- Top 10 Primary Causes of Accidents: Highlights the main factors contributing to accidents in the county.
- Trend of Car Accidents Over the Years: Analyzes the annual fluctuation in the number of car accidents.
- Distribution of Car Crashes by Hour of the Day: Demonstrates when accidents are most likely to occur within a 24-hour period.
Joshua Torgbor Obodai
This project is licensed under the MIT License - see the LICENSE file for details.
- Thanks to JACKSON DIVAKAR R for providing the dataset on Kaggle.
- Appreciation to the Python community for the excellent data analysis libraries.