Exploratory Data Analysis: Scatter Plots and Correlation Matrix for Diabetes, Obesity, and Inactivity Metrics

In today’s analysis, I begins with an introduction to the analysis and focuses on exploring relationships between diabetes, obesity, and inactivity rates. The data is loaded from an Excel file, and necessary libraries are imported.

Step 1: Uploading the Excel File
The analysis starts by prompting the user to upload the Excel file (cdc-diabetes-2018.xlsx) containing the data. This interactive step ensures that the data is accessible for analysis.

Step 2: Loading and Preprocessing Data
Data is loaded from multiple sheets (Diabetes, Obesity, Inactivity) of the Excel file into separate DataFrames.
Inconsistencies in column names are corrected.
The data is merged into a single DataFrame (merged_data) for analysis.
Rows with missing values are dropped to create a clean dataset (merged_data_clean).
Step 3: Generating Scatter Plots
Three scatter plots are created to visualize relationships between variables:
Diabetic Rate vs. Obese Rate
Diabetic Rate vs. Inactive Rate
Obese Rate vs. Inactive Rate
Insights from scatter plots include positive correlations between diabetic rates and obesity/inactivity rates.
Step 4: Generating the Correlation Matrix
A correlation matrix is calculated for the variables: Diabetic Rate, Obese Rate, and Inactive Rate.
The matrix is visualized as a heatmap, showing correlation coefficients.
Key insights:
Moderate positive correlation between Diabetic Rate and Obese Rate.
Moderate positive correlation between Diabetic Rate and Inactive Rate.
Strong positive correlation between Obese Rate and Inactive Rate.

Insights and Information:
The analysis focuses on health metrics, specifically diabetes, obesity, and inactivity rates, which are critical public health concerns.
The scatter plots visually demonstrate relationships between these metrics, highlighting potential risk factors.
The correlation matrix quantifies these relationships, providing valuable insights for public health research.
The positive correlations observed between diabetic rates and obesity/inactivity rates reinforce established medical knowledge.

 

cdc-diabetes-analysis-colab-eda