Oluwapeluminitoto/Flu-cases-Analysis

Data analysis project focusing on monthly time-series trendsof 'flu' cases and treatment costs using a simulated Rwanda clinic database. Provides actionable SQl insights to help the financial planning department forecast peak demand and expenditure periods. Flu Season and Financial Forecasting Project **Overview** This project focuses on a critical business intelligence initiative for a Rwandan clinic's financial planning department. The goal was to provide a data-driven view of how the annual flu season impacts the clinic's finances to support accurate budget forecasting. The core task was to analyze time-series data to identify the peak months of flu activity and the corresponding total treatment costs. **Dataset Schema** The analysis utilized four distinct relational tables from the clinic's operational database: Table Name - Key Columns - Description Doctors - doctorid, speciality - Information about physicians. Patient_mapping - patientid, insurancetype - Patient demographic and administrative details. Wellness_activity - patientid, activity type - Records of patient wellness activities. Patient_records_fact - patientid, visit date, cost, diagnosis, doctor ID - The central fact table containing visit details, costs, and diagnoses. **Primary Analytical Task** Title: Analyzing Monthly Trends of Flu Cases and Treatment Costs Objectives: Filtering: Isolate records where the diagnosis column equals 'Flu'. Aggregation: Group the filtered data by Year and Month. Calculation: Summarize the total case count and the total cost for each month. Insight: Determine the months representing the absolute peak of the flu season in terms of both case volume and financial expenditure. 💻 SQL Implementation The analysis was performed by grouping records from the Patient_records_fact table and utilizing date manipulation functions. Query for Monthly Aggregation This query generated the time-series summary table for the financial planning department. SQL created another column for the month and year -- Extracts Year and Month for chronological grouping (using SQL server) alter table patient_records_fact add Visit_year int, vist_month int; update patient_records_fact set Visit_year = year(visit_date) update patient_r …