Diabetes Prediction Dashboard - Psyliq Internship

Key Problem Statements and Solutions:

• Data Cleansing:
  • Implemented robust data cleansing strategies, addressing missing values and outliers to ensure data accuracy and reliability.
• Basic Visualization:
  • Created a simple yet informative bar chart to visually represent the gender distribution within the dataset.
• DAX Introduction:
  • Utilized Data Analysis Expressions (DAX) to perform advanced calculations and manipulations in Power BI, enhancing the capabilities of the dashboard.
• Calculated Columns:
  • Implemented calculated columns in Power BI to derive new insights and support complex calculations based on existing data.
• Filtering Data:
  • Demonstrated the process of filtering data to focus on specific age groups, catering to the user's need for targeted information.
• Joins:
  • Differentiated between inner join and left join, strategically employing each in Power BI to combine relevant data from disparate tables.
• Data Modeling:
  • Emphasized the critical role of data modeling in Power BI, showcasing its impact on visualization accuracy and overall dashboard effectiveness.
• Conclusion: The Diabetes Prediction Dashboard project not only involved addressing specific problem statements but also showcased my proficiency in Power BI development, data visualization. The project's outcomes contribute to a comprehensive understanding of diabetes trends and support informed decision-making in healthcare and employee wellness.