Develop a Predictive Linear Regression Model for Water Coagulant Dosage

We are seeking an experienced data scientist or machine learning engineer to develop a robust predictive model for calculating optimal coagulant dosage in drinking water treatment. The primary focus will be on implementing a linear regression model, but candidates with experience in other regression techniques are also encouraged to highlight their expertise. The project involves several key phases: 1. Data Analysis and Preprocessing: Understanding the existing water quality parameters and historical dosage data, including cleaning, transformation, and feature engineering. 2. Model Development: Building and training a linear regression model to predict coagulant dosage based on the identified water quality indicators. 3. Model Validation and Optimization: Rigorously testing the model's accuracy and performance using appropriate metrics (e.g., R-squared, mae, rmse) and fine-tuning parameters for optimal results. 4. Documentation: Providing clear documentation of the model, including code, methodology, and instructions for future use and maintenance. The goal is to create an accurate and reliable tool that will optimize the coagulant dosage process, leading to improved water quality, reduced chemical consumption, and enhanced operational efficiency in our drinking water treatment facility.