Python-Based Occupational Health Predictive Model - 12/08/2025 21:05 EDT
Budget: $750 – $1,500 USD
Project Title:
Predictive Model Development in Python to Estimate Future Occupational Diseases (with Role-Based and Location-Based Analysis)
Project Description:
We are seeking a freelance professional with strong experience in predictive modeling, machine learning, and statistical analysis in Python to develop a system that can estimate the likelihood of occupational diseases occurring within a company over the next five years, using our historical datasets.
The main goal is to identify patterns and trends that support the prevention of health risks and improve decision-making in occupational health and biomechanical risk management, considering key variables such as diagnosis, worker location, and job role.
Feasibility Evaluation (Required):
Before building the final model, the freelancer must conduct an initial assessment of the data provided (Excel files with historical records) to:
* Determine whether the available data is sufficient to build a reliable predictive model.
* If not, propose a more suitable analytical alternative, such as trend analysis, segmentation, or risk scoring.
This step is essential, as the quality, structure, and volume of the data will directly impact the model’s accuracy and practical value.
Project Scope:
1. Data cleaning and preprocessing of the provided historical datasets
2. Exploratory data analysis to identify key variables related to occupational diseases (diagnosis, location, job role)
3. Development of an advanced predictive model (regression, classification, and/or clustering) to:
* Predict potential occupational disease diagnoses
* Identify areas, sites, or processes with higher risk
* Associate potential outcomes with specific job roles or worker profiles
4. Model tuning and optimization using cross-validation, hyperparameter tuning, and performance metrics
5. Visualization of results through dashboards or clear reports
6. Model documentation and Python code commenting to ensure future maintenance and usability
**Expected Deliverables:**
* A trained and validated model using our datasets
* A comprehensive report including key findings, identified patterns, and performance metrics
* Segmented analysis by job role, diagnosis, and location
* Strategic recommendations to reduce the incidence of occupational diseases
* Well-documented and reusable Python code (Google Colab compatible)
* A feasibility report summarizing the viability of the proposed model and alternative strategies if needed
**Meetings and Adjustments Included:**
The project scope must include up to three virtual meetings (maximum one hour each) to review progress, discuss deliverables, and make adjustments if necessary.
**Deadline:**
Maximum of five calendar days from project acceptance.
**Technical Requirements:**
* Proficiency in Python and libraries such as pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn
* Experience in predictive models and clustering (K-means, Random Forest, XGBoost, etc.)
* Knowledge of correlation analysis, linear and multivariable regression
* Ability to deliver fully functional code via Google Colab
* Previous experience in occupational health, ergonomics, or workplace-related prediction projects (preferred)
* Fluency in English is required. Spanish proficiency is strongly preferred, as our internal team primarily communicates in Spanish
**Expected Impact:**
This study will allow us to anticipate occupational health risks, optimize resources, and design effective prevention strategies. The analysis will focus on predicting outcomes by job role and location, contributing to improved employee well-being and strategic planning across the organization.
**Notes for Applicants:**
* Please include examples of similar past work or Python notebooks (preferably in Google Colab)
* Clearly state your level of English and Spanish proficiency
* Experience in predictive projects applied to health, ergonomics, or workplace risk will be highly valued
Predictive Model Development in Python to Estimate Future Occupational Diseases (with Role-Based and Location-Based Analysis)
Project Description:
We are seeking a freelance professional with strong experience in predictive modeling, machine learning, and statistical analysis in Python to develop a system that can estimate the likelihood of occupational diseases occurring within a company over the next five years, using our historical datasets.
The main goal is to identify patterns and trends that support the prevention of health risks and improve decision-making in occupational health and biomechanical risk management, considering key variables such as diagnosis, worker location, and job role.
Feasibility Evaluation (Required):
Before building the final model, the freelancer must conduct an initial assessment of the data provided (Excel files with historical records) to:
* Determine whether the available data is sufficient to build a reliable predictive model.
* If not, propose a more suitable analytical alternative, such as trend analysis, segmentation, or risk scoring.
This step is essential, as the quality, structure, and volume of the data will directly impact the model’s accuracy and practical value.
Project Scope:
1. Data cleaning and preprocessing of the provided historical datasets
2. Exploratory data analysis to identify key variables related to occupational diseases (diagnosis, location, job role)
3. Development of an advanced predictive model (regression, classification, and/or clustering) to:
* Predict potential occupational disease diagnoses
* Identify areas, sites, or processes with higher risk
* Associate potential outcomes with specific job roles or worker profiles
4. Model tuning and optimization using cross-validation, hyperparameter tuning, and performance metrics
5. Visualization of results through dashboards or clear reports
6. Model documentation and Python code commenting to ensure future maintenance and usability
**Expected Deliverables:**
* A trained and validated model using our datasets
* A comprehensive report including key findings, identified patterns, and performance metrics
* Segmented analysis by job role, diagnosis, and location
* Strategic recommendations to reduce the incidence of occupational diseases
* Well-documented and reusable Python code (Google Colab compatible)
* A feasibility report summarizing the viability of the proposed model and alternative strategies if needed
**Meetings and Adjustments Included:**
The project scope must include up to three virtual meetings (maximum one hour each) to review progress, discuss deliverables, and make adjustments if necessary.
**Deadline:**
Maximum of five calendar days from project acceptance.
**Technical Requirements:**
* Proficiency in Python and libraries such as pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn
* Experience in predictive models and clustering (K-means, Random Forest, XGBoost, etc.)
* Knowledge of correlation analysis, linear and multivariable regression
* Ability to deliver fully functional code via Google Colab
* Previous experience in occupational health, ergonomics, or workplace-related prediction projects (preferred)
* Fluency in English is required. Spanish proficiency is strongly preferred, as our internal team primarily communicates in Spanish
**Expected Impact:**
This study will allow us to anticipate occupational health risks, optimize resources, and design effective prevention strategies. The analysis will focus on predicting outcomes by job role and location, contributing to improved employee well-being and strategic planning across the organization.
**Notes for Applicants:**
* Please include examples of similar past work or Python notebooks (preferably in Google Colab)
* Clearly state your level of English and Spanish proficiency
* Experience in predictive projects applied to health, ergonomics, or workplace risk will be highly valued