This is ML project

In this project, taken a student database in order to implement student performance predictor. Deployed this end to end ML application using CI CD pipelines and GitHub actions using ECR and EC2 instance.

Project Pipeline:

• Data Ingestion
• Data Transformation
• Model Trainer
• Model Deployment

1. Data Ingestion:

Data ingestion is the process in which unstructured data is extracted from one or multiple sources and then prepared for training machine learning models.

2. Data Transformation

Data transformation is the process of converting raw data into a format or structure that would be more suitable for model building.

3. Model Trainer

Model training in machine learning is the process in which a machine learning (ML) algorithm is created or selected and fed with sufficient training data so that it could learn and give accurate predictions in the future.

4. Model Deployment

Deployment is the method by which we integrate a machine learning model into production environment to make practical business decisions based on data.

Used different ML models like LinearRegression, CatBoostRegressor, KNeighborsRegressor, DecisionTreeRegressor , XGBRegressor, also different ensemble techniques like - AdaBoostRegressor, GradientBoostingRegressor, RandomForestRegressor. From these list of models, got the best model along with the best model score.

End to End Machine Learning Project deployment in AWS:

1. Docker Build checked
2. Github Workflow
3. Iam User In AWS

Configured EC2 as self-hosted runner, and did setup GitHub secrets:

• AWS_ACCESS_KEY_ID=
• AWS_SECRET_ACCESS_KEY=
• AWS_REGION =
• AWS_ECR_LOGIN_URI =
• ECR_REPOSITORY_NAME =

EC2 instance connection establshied with GitHub, and successfully completed Continuous Integration, Continuous Delivery and Continuous-Deployment

Run instance with Public IPv4 address