Analyzing Suicide Mortality Trends: A Data-Driven Approach to Suicide Prevention

Capstone Project for M.S. Data Analytics Program

Melissa Stone Rogers, GitHub

Introduction

This is a professional project exaiming trends in suicide over time. Data has been gathered from Center for Disease Control using the Wide-ranging ONline Data for Epidemiologic Research(WONDER) system.

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How to Install and Run the Project

1. Create a new project repository in Github, copy the repository URL, then clone to your machine:

git clone project.url

2. Verify Python version of Python 3: Mac/Linux:

python3 --version

Windows:

py --version

If Python 3 is not installed, install it before proceeding.

3. Create your .venv and activate it. Mac/Linux:

python3.11 -m venv .venv
source .venv/bin/activate

Windows:

py -3.11 -m venv .venv
.venv\Scripts\activate

4. Install the required dependencies: Mac/Linux:

pip install jupyterlab pandas matplotlib scikit-learn numpy seaborn
python3 -m pip install -U scikit-learn

Windows:

py -m pip install jupyterlab pandas matplotlib scikit-learn numpy seaborn
py -m pip install --upgrade scikit-learn

5. Freeze Dependencies into requirements.txt Mac/Linux:

python3 -m pip freeze > requirements.txt

Windows:

py -m pip freeze > requirements.txt

6. Add .gitignore File Add .gitignore file to the root project folder if not created during repo set up.

touch .gitignore

Add the following lines to ignore unnecessary files:

• .venv/
• .vscode/
• .ipynb_checkpoints/

7. Initial Project Save Enable autosave in VS Code (File > Auto Save). Push changes to GitHub frequently to effectively track changes. Update the commit message to a meaningful note for your changes.

git add .
git commit -m "initial project set up"                         
git push origin main

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Exploratory Data Analysis

Create a Jupyter Notebooks file to begin EDA:

touch your_project.ipynb

To run Jupyter within VS Code, use the Jupyter extension. Go to the Extensions pane on the left sidebar (the icon looks like four squares), search for "Jupyter," and install the "Jupyter" extension provided by Microsoft. If already installed, check for available updates for this extension.

Open the newly create notebook and ceate/select the notebook kernel.

Outline Notebook with Markdown and Code Cells: The following markdown cells should be included, with code cells in between.

• Import Dependencies
• Data Acquisition
• Initial Data Inspection
• Initial Data Transformation and Feature Engineering, if needed
• Initial Descriptive Statistics
• Initial Data Distributions
• Initial Visualizations

Verify your dataset is saved in your project folder if loading in from a static file.

Complete the structured data exploration using the code structures noted within the eda.ipynb file.

Make notes as needed within your REAMME.md for furture reference.

Final EDA Commit Insure all final changes are committed to GitHub.

git add .
git commit -m "final eda"                         
git push origin main

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Model Building and Testing

Create another Jupyter Notebooks file to begin model buildling:

touch your_project.ipynb

Open the newly create notebook and ceate/select the notebook kernel.

Outline Notebook with Markdown and Code Cells: Outline your project with markdown cells with code cells in between. Note what algorithms you plan to use.

• Import and Read Data
• Train/Test Data Split
• Train and Evaluate Linear Regression Model
• Train and Evaluate Random Forest Regressor
• Train and Evaluate Decision Tree Classifier Model
• Train and Evaluate Random Forest Model
• Results

Model Execution and Review Execute and evaluate each model. A robust discussion of results can be found at the end of the modeling.ipynb file.

Final Modeling Commit Insure all final changes are committed to GitHub.

git add .
git commit -m "final modeling"                         
git push origin main

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Predictive Modeling and Analysis

Create another Jupyter Notebooks file to begin predictive modeling and anlysis:

touch your_project.ipynb

Open the newly create notebook and ceate/select the notebook kernel.

Outline Notebook with Markdown and Code Cells: Outline your project with markdown cells with code cells in between.

• Make Prediction
• Interpretation of Results

Predictive Model Execution and Review Execute and evaluate each model. A robust discussion of results can be found at the end of the predictive_analysis.ipynb file.

Final Preditive Commit Insure all final changes are committed to GitHub.

git add .
git commit -m "final predictive"                         
git push origin main

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