10.3 Effective Executive Summaries

    !pip install fpdf
    from fpdf import FPDF
    
    # Define a custom PDF class that inherits from FPDF
    class PDF(FPDF):
      def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.set_margins(25.4, 25.4, 25.4) # Set 1-inch margins (25.4 mm)
        
    # Define the header method
      def header(self):
        self.set_font('Arial', 'B', 12) # Set the font for the header
        self.cell(0, 10, 'Executive Summary', 0, 1, 'C') # Add the header text
    
    # Define a method to add chapter titles
      def chapter_title(self, title):
        self.set_font('Arial', 'B', 10) # Set the font for chapter titles
        self.cell(0, 10, title, 0, 1, 'L') # Add the chapter title
        self.ln(2) # Adjust the line break for single spacing
    
    # Define a method to add chapter body text
      def chapter_body(self, body):
        self.set_font('Arial', '', 10) # Set the font for the body text
        self.multi_cell(0, 5, body) # Add the body text with decreased line spacing
        self.ln(2) # Adjust the line break for single spacing
    
    # Define a function to create the executive summary
    def create_executive_summary(title, sections):
      pdf = PDF() # Create a PDF object
      pdf.add_page() # Add a page
      pdf.chapter_title(title) # Add the main title
      for section_title, section_body in sections.items(): # Loop through sections
        pdf.chapter_title(section_title) # Add section title
        pdf.chapter_body(section_body) # Add section body text
      pdf.output('executive_summary.pdf') # Generate and save the PDF to current directory 
    
    # Content of the executive summary
    title = 'Applying Data Science Techniques to Detect Diabetes'
    sections = {
      'Purpose of the Study': 'This research study aimed to analyze patient health data and physical characteristics to detect diabetes using a classification decision tree model. The primary goal is to identify key predictors of diabetes and develop an accurate model for early detection and diagnosis. The study utilized a dataset comprising health metrics (i.e., cholesterol, glucose, HDL cholesterol, systolic BP, diastolic BP) and physical characteristics (i.e., sex, age, height, weight, BMI, waist, hip) of patients. A classification decision tree model was employed to analyze the data, leveraging its ability to handle both numerical and categorical data, interpretability, and effectiveness in identifying significant predictors of diabetes.',
      'Findings': 'The study found that glucose levels, BMI, and waist circumference are the most significant predictors of diabetes using a classification decision tree model. This model achieved high accuracy (91%) in detecting diabetes, indicating its effectiveness in correctly identifying both diabetic and nondiabetic patients. While sex and age were included, they had less impact compared to the health metrics. The research highlights the potential of this model to improve early diabetes detection and suggests further refinement and integration into clinical practice, alongside public health initiatives, to manage obesity and glucose levels for diabetes prevention.',
      'Recommendation': 'Based on the findings of the study, we recommend integrating the classification decision tree model into clinical practice to enhance early detection and diagnosis of diabetes. By utilizing this model, health care providers can identify at-risk individuals more effectively, enabling timely intervention and management. Additionally, further research is encouraged to refine the model and explore the inclusion of other relevant health metrics and demographic factors to improve its predictive accuracy. The findings should also inform public health initiatives aimed at reducing obesity and managing glucose levels, thereby contributing to diabetes prevention and overall population health improvement.'
    }
    
    create_executive_summary(title, sections)
  

The resulting output will look like: