Update README.md

README.md

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 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.
 
-Developed these components:
+Project Pipeline:
 * Data Ingestion
 * Data Transformation
 * Model Trainer
 * Model Evaluation
 * 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.