Applied Process-Mining

@MashoodurRehman1

I am a skilled applied process-mining research engineer with extensive experience in event logs and process mining. With strong proficiency in Python and libraries like pandas, Polars, and PM4Py, I am well-equipped to meet the needs of your Applied Process-Mining project. My background includes implementing reproducible analytical workflows and working with financial services data, making me a strong fit for validating historical loan-application event logs. In previous projects, I constructed canonical analytical representations from raw event data and meticulously documented mappings among cases and events. I am familiar with preregistered analysis methodologies and have successfully managed holdout evaluations and sensitivity tests to prevent analytical discretion and ensure robust, reproducible results. I excel in ensuring all calculations and results can be traced back to source events and maintained through version-controlled environments. I am available to begin immediately and commit 15–20 hours weekly to meet your September timeline. I would like to discuss how my skills align with your specific needs and am happy to provide more detailed examples of my work.

@neerajdhameliya

Hello, Answers to your questions: 1. Example: Built event-based lead-scoring/automation systems tracking lifecycle stages, timestamps, rules, and outcomes. 2. Validation: Verified entity mappings, event order, timestamps, lifecycle transitions, duplicates, and missing events while documenting assumptions. 3. Tools: Strong Python/pandas, PostgreSQL, Git, Docker, and automated testing; familiar with PM4Py/XES workflows. 4. Leakage: Use chronological discovery/holdout splits, prevent future data from selection logic, version rules, and audit every change. 5. Availability: Mon–Fri, 9:30 AM–7 PM IST, 40 hrs/week. $15/hr; 90–120 hours through late September. I can deliver a reproducible Python pipeline covering lifecycle validation, process analysis, strict holdout controls, regression, bootstrap uncertainty, sensitivity checks, automated testing, and fully traceable, audit-ready outputs. Looking forward to your reply! Best, Niral

@ShaikhAneesa

Hi there, I have carefully reviewed the project requirements for the Applied Process-Mining research engineer position. I understand the need for implementing a time-bounded, preregistered comparative analysis of a historical loan-application event log. Let's chat and discuss it further. To handle your project, I will start with inspecting and validating the Loan Application event log, constructing a canonical analytical representation, and documenting mappings among cases, applications, offers, activities, resources, and timestamps. I will then implement the necessary discovery and holdout partitions, calculate specified transition intervals, waiting measures, and distinguish inter-event gaps accurately. The deliverables for this project will include a validated analytical dataset, a reproducible analysis pipeline, frozen-method implementation results, holdout and robustness findings, and a technical findings package. Before signing-off my bid, I would like to ask a question, i.e., how crucial is the integration of financial-services application workflows in this analysis? Warm Regards, Aneesa.

@ahmadayaz

With a background in process mining and Python analytics, I'm well-equipped to handle your project needs. I'll conduct a precise comparative analysis of the historical loan-application event log using frozen diagnostic selection methods. Deliverables will include a validated dataset, reproducible analysis pipeline, implementation of frozen-method rules, holdout results, and technical findings. I propose 15-20 hours/week, totaling 90-120 hours, to be completed by late September for an independent review ahead of the October presentation. Let's work together to create a robust analytical pipeline for your research presentation.

@DeveloperStation

Hi, The part that matters most here is the discipline, not the mining. On a recent contract we built a deterministic ingestion pipeline with a secure CI setup where the same inputs always reproduced the same outputs, with automated tests guarding the critical steps. Secure CI Pipeline & Deterministic Ingestion: reproducible data workflow One thing I'd pin down before any analysis: the exact lifecycle-event semantics you want treated as authoritative, since inter-event gaps versus supported active-work or waiting measures depend entirely on how the log's start and complete events are read. I'd document that mapping first, freeze it, then split discovery and holdout so no rule shifts after seeing results. Which log are you using, the BPI 2017 loan application set or a private one? Adil

@tangramua

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@mayr81

You’re requesting a reproducible, preregistered applied process-mining research pipeline, specifically a time-bounded comparative analysis over a historical loan-application event log, with frozen diagnostic selection rules and a concealed chronological holdout. I can implement the end-to-end computational workflow in Python (pandas/Polars), including XES ingestion via PM4Py, canonical case-level representation, lifecycle reconstruction, timestamp semantic validation, and audit-ready mapping tables (cases ↔ applications/offers ↔ activities/resources ↔ lifecycle events). I will build deterministic partitioning (discovery vs holdout), calculate interval- and variant-level measures (wait/active-work separation, gaps vs active work, durations, loops/rework/handoffs, rework/iteration and transition intervals), and implement the frozen wait-first and automation-first selection methods exactly as specified by the protocol. I will ensure each reported quantity is traceable to source events and executable code, with automated data-quality/calculation tests, controlled missingness handling, and leakage-sensitive evaluation. Outputs will include holdout results, robustness/sensitivity checks (including calendar-aware duration calculations and lifecycle ambiguity checks), uncertainty estimation, and a limitations/claim-boundary register suitable for early-October presentation.

@Aaleen110

The hardest part of this job is faithfully executing a written protocol on data that is not yet fully understood, so I will build a Python pipeline to load the historical loan-application event log using Pandas, and then implement the three diagnostic selection methods as functions, each taking the event log as input. I will build the data ingestion and cleaning first, so that the data is ready for analysis, then I will implement the three selection methods, and finally the evaluation on the holdout data, because the protocol dictates this order. I will pick the side of treating the event log as a single source of truth for the analysis, as the brief implies a need to distinguish established facts from hypotheses. What is the exact format and granularity of the "concealed chronological holdout" data and how should it be accessed for evaluation? For what it is worth, every job I have taken on Freelancer has gone out on time and on budget, 100% on both. I need the event log data file to start.

@ImranAli01

Hi, I got that you are looking for an applied process-mining research engineer to implement a time-bounded, preregistered comparative analysis of a historical loan-application event log. This is what I can help you with, let's chat. My approach is to meticulously inspect and validate the Loan Application event log, construct a case-level analytical representation, and implement the frozen wait-first and automation-first comparison methods using Python, specifically leveraging pandas for data manipulation. By ensuring the separation of discovery and holdout evidence, I will maintain the integrity of the analysis. The deliverables will include a validated analytical dataset, a reproducible analysis pipeline with version-controlled source code, and the implementation of frozen-methods with audit trails for transparency. As final deliverables, you will receive a validated analytical dataset, documented entity and event mappings, a reproducible analysis pipeline, frozen-method implementation outputs, holdout and robustness results, and a technical findings package. One thing I'd like to confirm before we start: Are there any specific calendar effects or lifecycle ambiguities that need to be addressed? Looking forward to discussing this project further. Regards, Imran

@SkillCrafts

I understand you need a rigorous, data-driven comparison of diagnostic methods applied to a time-bounded event log, mirroring the analytical depth required for a preregistered study. My experience with similar event log analysis projects, focusing on reproducible pipelines and objective evaluation of findings against holdout data, aligns perfectly with your requirements. My approach will involve utilizing Python with libraries like `pm4py` for process discovery and analysis. I’ll implement a robust ETL process to clean and structure the loan-application event log, ensuring temporal integrity. The three diagnostic selection methods will be implemented as distinct, parameterized functions. I will then execute these methods on the training data, followed by a blinded evaluation against the chronological holdout set, quantifying performance metrics to objectively distinguish established insights from hypotheses. Could you clarify the specific nature of the "concealed chronological holdout" and the expected format for presenting the "clearly distinguished" findings? I'm keen to discuss how my technical plan can be tailored to meet these specific output requirements and ensure a successful, reproducible analysis.

@demivision

Hi there, Thank you for sharing such a thorough and thoughtfully designed project brief. I’m excited by the opportunity to contribute to your applied process-mining analysis and help ensure your practitioner research presentation is supported by a robust, transparent, and fully reproducible analytical pipeline. I bring extensive experience working with event log and process-mining datasets—including projects analyzing operational processes in both financial services and healthcare. In a recent assignment, I reconstructed loan origination event lifecycles, mapped granular activities to business entities, and implemented case-level analytical features for downstream modeling. Validating event and lifecycle semantics involved cross-verifying timestamp consistency, reconciling activity-resource mappings, and confirming event ordering directly with SME feedback and data-quality scripts, ensuring analytical measures reflected true business process dynamics. To prevent leakage and undocumented discretion, I adhere to preregistered protocols, implement automated tests for all critical partition and calculation steps, and document every transformation and exclusion. I also ensure that observed associations are clearly separated from causal claims, with all uncertainty estimates and simulation assumptions transparently reported.

@Microlent

Hello, I am Dr. Rajesh Rolen, PhD in Computer Science & Engineering, with experience of over 20+ years in Data Visualization, Python As a preferred freelancer in the top 1%, I have done 400+ projects here on freelancer.com, I have 4.9 ratings out of 5 on average, which showcases my quality of work and timely delivery. Key Highlights: - Free Hosting Support on the Cloud or any desired platform. - Free 3 months of post-delivery support to ensure that our client doesn’t face any challenges after the launch of the project. - Free Dedicated tester on projects to ensure quality delivery, so clients don’t need to act as a tester. - 10+ Years experience UI/UX team to ensure intuitive UI. Portfolio: https://www.freelancer.com/u/Microlent Please open the chat and send me a message, so we can have a more detailed discussion about the project to give you the project timeline and cost. Thank you for considering my services. I look forward to engaging in a productive conversation and understanding how I can be of assistance in bringing your project to life. Regards Rajesh Rolen

@Tanmoy236

Good day! I can implement this as a reproducible, protocol-driven process-mining analysis rather than a conventional optimisation exercise. My approach would prioritise faithful execution of the frozen methodology, careful validation of event and lifecycle semantics, and a clear separation between what the historical event data demonstrates and what remains observational or uncertain. Using Python with pandas/Polars and PM4Py where appropriate, I can construct the canonical case-level representation, validate entity mappings and timestamps, implement discovery and chronological holdout partitions, and calculate the specified interval, duration, rework, handoff and variant measures. I will maintain strict separation between discovery and holdout data to prevent leakage and avoid undocumented analytical discretion after results are observed. I can deliver a version-controlled, reproducible pipeline with deterministic configuration, automated data-quality and calculation tests, frozen method outputs, robustness analyses, uncertainty estimates and audit-ready tables and charts. My focus will be ensuring every reported result is traceable to source events and executable code, with clear documentation of exclusions, limitations and claim boundaries for independent review and the final presentation.

@carlosd178

Hello, As a result of a detailed review of your protocol and acceptance criteria, I understand this is a reproducibility-focused research implementation, not a dashboard or process-optimisation exercise. I’m available to start immediately and can commit 15–20 hours/week through late September at $20/hour. For comparable event-sequence work, I reconstruct canonical case/event tables first and validate case IDs, activity/lifecycle transitions, timestamp ordering, duplicate events, missingness and impossible durations before calculating process measures. For your analysis, I would encode the preregistered rules as versioned configuration and tested functions, then freeze discovery/chronological-holdout assignments before evaluation. Wait-first, automation-first and Flow-First computations would share the same validated source representation while maintaining separate selection logic and audit trails. I’d use deterministic pipelines and tests for mappings, intervals, loops, handoffs, variants, partitions and selection calculations. Every exhibit would be generated from authoritative outputs, with associations, causal claims and simulated effects explicitly separated. One question: is the event log already available as XES, or will the canonical lifecycle need to be reconstructed from CSV/database extracts? Best regards, Carlos.

@AZTURK1

Hi there, I've built reproducible pipelines for longitudinal operational data, and this preregistered process-mining design with chronological holdouts is exactly where strict separation between discovery and evaluation matters. I'll implement the three frozen diagnostic methods faithfully with a clean audit trail. What I'll do: ✅ Build the canonical case-level representation from the Loan Application event log, including lifecycle reconstruction and entity mapping ✅ Implement wait-first, automation-first, and Flow-First computational methods exactly as preregistered with full audit trails ✅ Run holdout evaluation, robustness tests, and uncertainty estimates with calendar-aware duration calculations ✅ Back up all intermediate datasets and version every pipeline stage before modifying anything ✅ Python (pandas), PM4Py, and event log analysis ✅ Reproducible workflows with automated data-quality and calculation tests ✅ Chronological validation and holdout evaluation design ✅ Regression, bootstrapping, and uncertainty estimation ✅ Microsoft® Certified: MCSA | MCSE | MCT ✅ 300+ projects delivered, 280+ five-star reviews I'm available around the clock and respond fast, so you won't be waiting during the push to your October presentation. Is the event log in XES format or raw tabular data, and is the lifecycle event mapping already defined or derived from the data? I can deliver in 5 days at $20 USD/hr and I'm ready to start immediately.

@HamparProd

Hi, Your emphasis on frozen analytical rules, chronological holdout separation and traceability is exactly how I would structure this engagement. I work in Python with pandas/Polars, statistical modelling, reproducible data pipelines, Git and automated testing. For this project I would use PM4Py/XES tooling where useful, while keeping the authoritative transformations and analytical outputs reproducible through documented Python code. I would first validate case IDs, activities, lifecycle transitions, timestamps, resources and application/offer relationships before calculating durations or waiting measures. This avoids treating inter-event gaps as active work or organisational waiting without evidence. Discovery/holdout assignment would be deterministic and frozen before holdout evaluation. Method parameters, exposure definitions, exclusions and selection rules would live in version-controlled configuration, with tests protecting partitions and calculations from leakage or undocumented changes. Outputs would include traceable analytical tables, robustness results, uncertainty estimates, presentation exhibits and a reproducibility handoff.

@miguelcen64

Hello, The key part of this project is **turning a historical loan-application event log into a reproducible, preregistered analysis without leaking holdout evidence**. I can help you handle this accurately and efficiently without overcomplicating the process. I have hands-on experience with **Python, Pandas, and Data Analysis**, including cleaning longitudinal datasets, building audit-ready tables, and documenting calculation logic. For your project, I would focus on **validating event and lifecycle semantics**, **constructing the canonical case-level representation**, and **implementing the frozen discovery/holdout pipeline with traceable outputs**, while making sure the final result is **reproducible, testable, and clear about what the data can and cannot support**. I can start **immediately** and expect to complete this within **18 weeks at 15-20 hours per week**. One detail I'd like to confirm before starting: **will the event log be provided in XES, CSV, or another format, and is the preregistered protocol already frozen?** Best regards, Miguel

@abdf2010

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@dataspro

Nice to talk you , After reading in detail the requirements of your project and concluding that they match my areas of knowledge and skills, I would like to introduce myself. My name is Anthony Muñoz and I am the lead engineer for DS Pro IT agency. I have worked for over 10 years in Backend and software development and have successfully done multiple jobs. It will be a pleasure to work together to make your project a reality. Please feel free to contact me. I´m looking forward to working with you. I really appreciate your time and remain attentive to any request or question. Greetings