Python_project

Data-Driven Insights on Mortality Trends in the U.S.

A Python-based data analysis project exploring weekly death counts in the U.S., categorized by causes such as COVID-19, heart disease, cancer, and more. This project focuses on cleaning, transforming, and analyzing real-world mortality data to reveal meaningful patterns and trends using visual storytelling techniques.

Project Overview

This analysis leverages publicly available datasets to understand mortality trends across the United States, focusing on:

• Cause-specific death rates (COVID-19, heart disease, cancer, etc.)
• State-wise impact and jurisdictional comparisons
• Temporal trends from 2020 to 2021
• Identification of vulnerable populations

Through insightful visualizations and exploratory data analysis (EDA), the project delivers actionable insights into one of the most crucial public health topics.

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Technologies & Tools Used

• Python
• Pandas – Data cleaning & manipulation
• Matplotlib & Seaborn – Data visualization
• NumPy – Numerical operations
• Jupyter Notebook – Interactive analysis

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Dataset

The dataset used contains weekly provisional counts of deaths by select causes and jurisdiction in the U.S.
Source: CDC National Center for Health Statistics

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Key Visualizations

• Stacked Area Charts – To compare death trends over time
• Heatmaps – To analyze intensity by state and cause
• Pie Charts – To show percentage distribution of deaths by cause
• Correlation Matrices – To identify statistical relationships between variables

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Insights & Findings

• COVID-19 mortality spiked significantly during 2020–2021.
• Heart disease remains a consistently leading cause of death.
• Certain states were disproportionately affected across various causes.
• Visualizations help highlight how public health crises evolve over time.

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What I Learned

• Advanced data wrangling with Pandas
• Effective visual storytelling techniques
• Handling missing/incomplete data
• Crafting insights from complex real-world datasets

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Acknowledgments

Big thanks to my mentors, peers, and the online data science community for their support and guidance throughout this project.

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How to Run

1. Clone the repository:

   git clone https://github.com/your-username/mortality-trends-analysis.git
   cd mortality-trends-analysis

2. Install required libraries:

   pip install -r requirements.txt

3. Launch the Jupyter Notebook:

   jupyter notebook

4. Open and run mortality_trends_analysis.ipynb.

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Connect with Me

Feel free to reach out or connect with me on LinkedIn for feedback, collaboration, or just to say hi!

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License

This project is licensed under the MIT License - see the LICENSE file for details.