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

@ZohaibRoy

⭐⭐⭐⭐⭐ Extract and Analyze ACLED Conflict Data Using Python ❇️ Hi My Friend, I hope you are doing well. I reviewed your project requirements and see you are looking for a Python developer to extract ACLED conflict data. You don’t need to look any further; Zohaib is here to help you! My team has successfully completed over 50 similar projects focused on data extraction and analysis. I will efficiently use the ACLED API to gather the required data and create your desired datasets. ➡️ Why Me? I can easily handle your project as I have 5 years of experience in Python development, specializing in data extraction, analysis, and API integration. My expertise includes working with CSV files, data processing, and ensuring data integrity. I also have a strong grip on data validation techniques and documentation practices. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I look forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ Python Programming ✅ API Integration ✅ Data Extraction ✅ Data Analysis ✅ CSV File Handling ✅ Data Validation ✅ Data Processing ✅ Error Handling ✅ Documentation ✅ Time Series Analysis ✅ Data Visualization ✅ Database Management Waiting for your response! Best Regards, Zohaib

@Tanmoy236

Hey! I specialize in Python data engineering and conflict/event datasets with 9+ years working on large-scale API extraction, time-series construction, and research-grade panel datasets. Here’s how I can help: * Build robust ACLED API pipeline with correct pagination handling * Construct validated daily global panel with strict date continuity rules * Aggregate violent events, fatalities, and source counts accurately * Add time, intervention, and exposure variables with reproducible logic I’ll also ensure the pipeline is fully documented so your workflow can be replicated step-by-step, including validation checks for missing dates and aggregation integrity. Could you clarify whether you already have ACLED API credentials set up and if the GDELT integration is required in the initial MVP or as a second phase?

@muneeb6767

I will build a Python pipeline that extracts ACLED data via API with correct pagination, filters by your event types and date range, and constructs a continuous daily global panel. The dataset will include validated aggregations, no missing dates, properly handled outcomes, and added variables (weekday, month, time index, post, time_after, exposure phase). You’ll receive clean, documented scripts plus both raw and processed CSV files ready for analysis and validation.

@UzeeTech

As a seasoned Full-Stack Developer with over 7 years of experience, I can confidently assure you that I am the most qualified candidate to tackle your ACLED research project. Python is one of my core specializations, and I have employed it in various scenarios, including similar data extraction and manipulation tasks. I'm well-equipped to utilize the ACLED API effectively, ensuring that your date range and event type specifications are met. You can rely on me to build a robust daily global panel dataset for your research, complete with the critical variables you require such as weekday, month, and time index. Furthermore, my thorough understanding of the project's importance guarantees that I won't be a 'fill in the blanks with zeros' kind of developer unless it has been properly verified. I understand that your analysis depends on accurate and comprehensive data, and as such, I will adhere strictly to best practices during every step of the process. Regarding deliverables, rest assured that you'll receive high-quality outputs promptly. As an organized programmer, meticulousness is ingrained in my work approach; thus, handling API pagination correctly and ensuring no missing dates in the panel will be a given. Lastly, I find it important to mention that just like you require a developer who thinks beyond code -- someone who can provide technical guidance where required -- I am ready to offer precise recommendations should any unique issue arise during our collaboration.

@Web1devloper

With over a decade of experience in the realm of data analysis and data science, I bring a wealth of knowledge and expertise to the table. My Python fluency and familiarity with APIs makes me an ideal candidate for your project. I have a proven track record of not only accurately extracting and aggregating complex datasets, but also transforming them into meaningful insights - precisely what your research is after. In addition to the technical skills you require, my proficiency in Power BI, Tableau, Looker, and other visualization tools will ensure that your global daily panel dataset is presented in a way that is easily understandable and comprehensible. Furthermore, having worked extensively with GDELT article counts before, I can incorporate this optional component efficiently to provide you a comprehensive dataset that aligns with your objectives. Most importantly, I understand the rigor required in academic research projects. Rigorous documentation, accurate validation, and proper aggregation are values I hold dear in my work approach. I guarantee meticulous handling of ACLED API pagination to avoid any missing dates in your panel dataset. Finally and most critically, I will provide an intuitive plain text documentation for better understanding.

@johnedwardl

Hi, I’ll develop a robust Python solution to extract and process ACLED conflict data for your research project. My experience with APIs and data manipulation ensures efficient handling of pagination and the creation of a complete global panel dataset. The output will include the required daily counts, intervention variables, and exposure phases, while strictly adhering to your guidelines on data integrity. I've successfully managed similar projects, ensuring no missing dates and validating dataset accuracy. My approach will involve using Pandas for data processing, ensuring all outputs meet your specified criteria, and documenting each step for clarity. Could you clarify if there are any specific formats you prefer for the documentation? I’m ready to start immediately and ensure timely delivery of scripts and CSV outputs. Thank you.

@zamanh20

Hello, I can handle this project and deliver a clean, research-ready dataset with full accuracy. I have experience working with APIs, data extraction, and building structured panel datasets in Python. I will properly handle pagination, ensure a continuous daily date range with no missing days, and carefully aggregate events and fatalities without making incorrect assumptions about missing values. I can implement all required variables, including time features (weekday, month, time index), intervention variables (post, time_after), and exposure mapping. I’m also comfortable integrating additional sources like GDELT if needed. You will receive well-organized Python scripts, raw and processed CSV files, and clear documentation of each step so everything is reproducible. I’m ready to start immediately and can ensure reliable, high-quality results.

@toqeer74

The critical challenge of handling API pagination for ACLED data extraction will be pivotal in achieving a continuous daily panel dataset, spanning from January 1, 2023, to March 31, 2024. Utilizing advanced techniques in Python, I will ensure seamless integration of event types along with accurate aggregation while guaranteeing no missing dates are introduced into the dataset. I can deliver this initial functionality within 10 days, complete with essential scripts and two CSV outputs, all thoroughly documented to guide your research process. When can we start? I can have something to show you within 24 hours.

@SmartCodeDev

Hello, I can build your ACLED data pipeline in Python with clean, validated outputs for your research. I will integrate the ACLED API with proper pagination handling to extract all required events within the date range. I will construct a continuous daily global panel with no missing dates and correct aggregation for events, fatalities, and source counts. Outcome variables will be carefully validated and not defaulted to zero unless logically confirmed. I will add weekday, month, and a continuous time index along with post and time_after intervention variables. Exposure phases will be mapped to numeric values for G_t as specified. I can also integrate GDELT daily article counts if you want the extended dataset. Outputs will include raw and processed CSV files along with clean, well-structured Python scripts. I will provide short documentation explaining each step and validation checks. Ready to start and deliver accurate, research-grade data.

@KolmanOuassou

Hi, I am a data analyst/statistician and Economist with more than 6 years of experience. I can do your project, Please take time to check my profile and then you decide to contact me.