Integrating Scikit-learn Machine Learning models into the Microsoft .NET

While being part of a team working on designing and developing a lead scoring system prototype, I faced the challenge of integrating machine learning models into the target environment built around the Microsoft .NET ecosystem. Technically, I implemented the lead scoring predictive model using the Scikit-learn machine learning built-in algorithm for regression, more precisely Logistic Regression. Considering the phases of initial data analysis, data preprocessing, exploratory data analysis (EDA), and the data preparation for the model building itself, I used the Jupyter Notebook environment powered by Anaconda distribution for Python scientific computing. Previously, I have investigated and touched Python within Flask as a micro web framework written in this programming language. However, I aimed to integrate or deploy the machine learning model written in Python into the .NET ecosystem, using the C# programming language and Visual Studio IDE.