Data-Driven-Insights-into-Alcohol-Related-Mortality

The Fatal Sip: Alcohol-Related Mortality Uncovered

Overview

This project analyzes alcohol-specific deaths and causes of death data to uncover trends, generate insights, and build predictive models. The project workflow includes data preprocessing, exploratory data analysis (EDA), visualization, and modeling.

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Visit the GitHub Pages Site

You can view the live project results and additional resources here:

🔗 Trends and Future Prediction 🔗 Exploring through Visualizations

What You’ll Find

• Interactive Visualizations: Explore the graphs and insights generated from the data analysis.
• Documentation: Read the full project overview and methodology.
• Downloadable Outputs: Access processed data or model results.

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📂 Project File Structure

The repository is organized into the following folders and files:

Alcohol-Mortality-Analysis/
├── code/                     # All R scripts for the project
│   ├── dataPreprocess.R    # Script for cleaning and preprocessing the data
│   ├── dataAnalysis.R      # Statistical and predictive modeling analysis
│   ├── Visualisations.R    # Script for generating visualizations
│   ├── modeling.R          # Script for building predictive model
│   ├── runScripts.R          # Script for running all the above scripts
├── data/                     # Data used for the project
├── notebooks/                # Jupyter or Google Colab notebooks
│   ├── Alcohol_Mortality_Notebook.ipynb  # Main notebook for running the project
├── README.md                 # Project overview and details
├── LICENSE                   # License for the project

How to Run the Project

Option 1: Using R Scripts 🖥️

1. Setup Environment:
   • Ensure you have R installed on your machine.
   • Install the required R packages by running:

     if (!require("openxlsx")) install.packages("openxlsx")
     if (!require("plyr")) install.packages("plyr")
     if (!require("reshape2")) install.packages("reshape2")
     if (!require("randomForest")) install.packages("randomForest")
     if (!require("ggplot2")) install.packages("ggplot2")
     if (!require("dplyr")) install.packages("dplyr")
     if (!require("tidyr")) install.packages("tidyr")
     

2. Run the Project:
   • Execute the master script:

     source("code/runScripts.R")
     

3. Outputs:
   • Generated plots and processed datasets will be displayed in the console

   Option 2: Using Jupyter Notebooks 🐍📒

4. Setup the Environment:
   • Ensure you have Python and Jupyter Notebook installed. You can install Jupyter via pip:

     pip install notebook
     

5. Run the Notebooks:
   • Navigate to the notebooks/ folder and open Alcohol_Mortality_Notebook.ipynb:

     jupyter notebook notebooks/Alcohol_Mortality_Notebook.ipynb
     

   • Execute each cell in the notebook to run the entire workflow.
6. View Results:
   • Outputs such as visualizations and processed data will be in the output console.

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Dependencies

The following R packages are required for this project:

• openxlsx
• plyr
• reshape2
• randomForest
• ggplot2
• dplyr
• tidyr

Install any missing packages using:

install.packages("<package_name>")

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License

This project is licensed under the MIT License.