End to END Machine Learning Workflow using github actions(CI/CD), Github Self-Hosted(app runner) and AWS-ECR(for the docker image) and EC2 to deploy my machine learnig Flask Application/Rest endpoint into the Ec2 instance
Approach of the Project:
Data Ingestion :
1) In Data Ingestion phase the data is first read as csv.
2) Then the data is split into training and testing and saved as csv file.
Data Transformation :
1) In this phase a ColumnTransformer Pipeline is created.
2) For Numeric Variables first SimpleImputer is applied with strategy median ,
then Standard Scaling is performed on numeric data.
for Categorical Variables SimpleImputer is applied with most frequent strategy,
3) then ordinal encoding performed , after this data is scaled with Standard Scaler.
4) This preprocessor is saved as pickle file.
Model Training :
1) In this phase base model is tested . The best model found was catboost regressor.
2) After this hyperparameter tuning is performed on catboost and knn model.
3) This model is saved as pickle file.
Prediction Pipeline :
1) This pipeline converts given data into dataframe and has various functions to load
pickle files and predict the final results in python.
Flask App/REST Endpoint creation :
2) Flask app is created with User Interface to predict the gemstone prices inside a Web Application.
React
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Django
Laravel
Facebook
Microsoft
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Alibaba
D3
Tencent