Build ACLED-Based Global Daily Panel Dataset (Research-Grade, Python)

I am conducting an academic research project using ACLED conflict data. I need a Python developer to: Task Use ACLED API to extract data: Date range: 2023-01-01 to 2024-03-31 Event types: Battles Explosions/Remote violence Violence against civilians Riots Protests Build a daily global panel dataset with: violent_events_global (count) fatalities_global (sum) acled_source_count date (continuous, no missing days) Add: weekday month time index Add intervention variables: post (after 2023-12-29) time_after Add exposure variable: exposure_phase (low/medium/peak) mapped to numeric G_t (Optional but preferred) Add GDELT article count per day Output Must provide: Plain text data/raw/[login to view URL] data/processed/[login to view URL] Requirements (VERY IMPORTANT) Must handle API pagination correctly Must ensure NO missing dates in panel Must NOT fill missing violence data with zero unless verified Must document all steps Validation Your output must pass this validation: Continuous daily date range Required columns present No missing values in outcome variables Proper aggregation Deliverables Python scripts CSV outputs short documentation

Project ID: 40412969