Freelance Machine Learning Engineer/Programmer Required for Predictive Model Development Project Overview: We are seeking a skilled freelance Machine Learning Engineer or Programmer to develop a robust machine learning model capable of accurately predicting occurrences of patterns from a predefined set of 780 available patterns, based on a dataset comprising 6840 actual occurrences. The dataset will be provided in an Excel file. The model must enable end-users to download a self-contained folder including the model, all necessary programs, libraries, and dependencies, ensuring seamless deployment on their local systems without additional installations. Key Functional Requirements: • The model shall generate predictions for pattern occurrences with high accuracy. • Upon downloading and extracting the folder, the end-user will open the provided Excel file, navigate to the "Predicted vs Actual" page, and click a "Predict" button to initiate the prediction process. • The system will output the predicted value, allowing the user to input the actual value adjacent to it for immediate comparison and verification of model performance. • All predicted versus actual data pairs must be stored persistently within the system to facilitate periodic re-training and model refinement. Deliverables: • A fully functional machine learning model integrated with an Excel-based interface for prediction and data entry. • A downloadable folder containing the model, supporting scripts, required libraries (e.g., Python-based with packages such as scikit-learn, pandas, and any others deemed necessary), and clear installation/usage instructions. • Documentation outlining the model architecture, training process, evaluation metrics, and re-training procedures. • Source code for the model and Excel integration (e.g., via VBA or external scripting). Implementation Considerations • Dependency Management: The source code must be accompanied by a clear list of dependencies (e.g., Python version, required libraries like pandas, scikit-learn) and instructions for setting them up, possibly using a requirements.txt file or a virtual environment. • Portability: The downloadable folder should include all necessary files (model, scripts, libraries, and instructions) to ensure the system works out-of-the-box on the user’s computer without requiring additional installations. • User Experience: The Excel integration should be intuitive, with a clear "Predict" button and fields for entering actual values. The system should handle errors gracefully (e.g., missing input data) and provide feedback to the user. • Retraining: The source code should include logic to save predicted versus actual data pairs and periodically retrain the model, potentially triggered by a separate button or script. • Qualifications and Skills: • Proven experience in machine learning model development, particularly in pattern recognition or predictive analytics. • Proficiency in programming languages such as Python, with expertise in relevant libraries (e.g., TensorFlow, Keras, PyTorch, scikit-learn). • Familiarity with integrating machine learning models into user-friendly interfaces, including Excel automation. • Strong understanding of data handling, model evaluation, and retraining mechanisms. • Ability to ensure model portability and dependency management (e.g., using virtual environments or containerization). • Excellent problem-solving skills and attention to detail. Project Timeline and Compensation: This is a fixed-price project with an expected duration of 2-4 weeks, depending on complexity. Compensation will be competitive and commensurate with experience; please include your proposed rate in your application. Application Instructions: Interested candidates should submit a proposal including: • A brief overview of relevant experience and past projects. • Your approach to this specific project including proposed technologies and methodology. • Estimated timeline and fee. • References or portfolio links. We look forward to collaborating with a professional who can deliver a high-quality, reliable solution.