The use case of AI Course Recommender System is to provide personalized recommendation to the user based on their interest, course they can take and their current knowledge. This system will be able to recommend course based on user’s interest, current knowledge, analytical view of students’ performance in mathematics and recommends if a student can consider math subject for his/ her higher education. The recommended course will be based on the information of user’s profile, analysis of grades of students, visualization of patterns, prediction of grade in final test, and some rules that were set by their instructor. Using machine learning algorithms, we can train our model on a set of data and then predict the ratings for new items. This is all done in Python using numpy, pandas, matplotlib, scikit-learn and seaborn. kandi kit provides you with a fully deployable AI Course Recommender System. Source code included so that you can customize it for your requirement.

Kit Deployment Instructions

The entire solution is available as a package to download and install from the source code repository. Follow below instructions to download and deploy the solution. 1. Navigate to the repository by clicking the component under Kit Solution Source section 2. Clone the repository 3. Locate requirements.txt file to find all the dependencies 4. Run the command 'pip install -r requirements.txt' to install all the dependencies 5. Open the Jupyter Notebook from the package as entire solution is available in the form of Jupyter Notebook 6. Execute all the cells in Jupyter Notebook If there're any challenges while installing dependencies, run the command below to upgrade pip and try again. python -m pip install --upgrade pip

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Data Mining

Our solution integrates data from various sources, and we have used below libraries for exploring patterns in these data and understanding correlation between the features.

Machine learning

Below libraries and model collections helps to create the machine learning models for the core prediction of use case in our solution.