The aim of this project is to recommend courses to prospective students in an e-learning platform.

## **Problem Statement**

Designing a course recommender system that will recommend courses to prospective candidates in an e-learning platform.

Client is an e-learning based platform with courses on various topics including Web Development, Data Science, Mobile Development, Cloud Services etc.

The Client has a user base of 10000 users and offers more than 100 courses.

## **Data**

The data consists of

1 -> Users Information.

2 -> Courses Information.

3 -> User – Course Interaction Activity.

## **Feature Building and Feature Store**

### **Requirements**

Download data stored in AWS and Create necessary features.

Design features and create a feature store.

### **Architecture**

![](/images/dcv22.drawio.png)

### **Explanation**

Downloaded data from AWS bucket using Boto3.

Created a Mongo DB Database Collection to store courses data.

Extracted, cleaned, and validated users and interactions data to create features in correct format.

Used terraform to create Infrastructure required to store the Features in AWS ( Redshift and Dynamo DB ).

Created  a feature store using Feast Feature Store and the AWS Infrastructure created.

Sync the feature store registries in AWS S3.

Building Docker File for the entire pipeline.

## **Model Training and Evaluation**

### **Requirements**

Create a model for recommending courses to students based on their interests and history of recorded activities.

Cater to new users with no recorded activities.

Scaling up as required.

### **Architecture**

![](/images/modelTrainingHLDv3.drawio.png)

### **Explanation**

Ingestion of features from feast feature store.

Split of Data into Train Validation Test sets.

Model Training using a hybrid recommender system.

Evaluation of the model and generating metrics.

Finding the best model whenever model is trained.

Upload of best model, metrics, artifacts to AWS S3 bucket.

Using a scheduler (Airflow) to schedule the pipeline.

Using Grafana and Prometheus to monitor statistics.

Building Docker file for the entire process.

#### **All code was written using Python and the mentioned technologies / frameworks following OOPs Principles and guidelines.**



##### **Check out Prototype Version Code Down Here**

***https://github.com/bsb4018/bsb_rec_sys_mti***


Projects

Course Recommender System