Update README.md

README.md

@@ -15,22 +15,22 @@ This repository implements a Machine Learning Operations (MLOps) pipeline for a
 
 ## Project Structure:
 
-	. Constant/init.py file
-	 	* it contains folder,file,variable names, port number and URL used inside project. 
-	. Artifact folder constains output of each steps like data ingestion, transformation, validation, model trainer. 
-	. Config folder is user defined, user manually write these files for performing specific operation.
-	. logs/logger.py class used to write Log at runtime. 
-	. Static and template folder, files are used for flask implementation.
-	. Components/DataIngestion
+-  Constant/init.py file
+	* it contains folder,file,variable names, port number and URL used inside project.
+- Artifact folder constains output of each steps like data ingestion, transformation, validation, model trainer.
+- Config folder is user defined, user manually write these files for performing specific operation.
+-  logs/logger.py class used to write Log at runtime. 
+- Static and template folder, files are used for flask implementation.
+- Components/DataIngestion
 		* it will fetch data from mongo db and USVisa.cv
 	 	* It will split the Data into train.csv and test.csv files14:36 25-03-2024
-	. Components/DataValidation 
+-  Components/DataValidation 
 		* it will read both train and test csv files and preform bellow operations.
 		* firstly it will read schema file for validation purpose.
 		* it checks the number of cloumns exists as per schema file.
 		* it will verify numerical and categorial columns as well. 
 		* it will create report.yaml file and write drift state in that file.
-	 . Components/DataTransformation
+- Components/DataTransformation
 		* if validation status is true then only transformation will start. 
 		* it will read the test and train csv files. 
 		* drop unneccesary columns mentioned in schema file 
@@ -39,12 +39,12 @@ This repository implements a Machine Learning Operations (MLOps) pipeline for a
 		* finally convert dataframe into numpy array which are used for model training. 
 		* "preprocessing.pkl" file is used for normalization of input data by user.
 
-	. Components/ModelTrainer
+- Components/ModelTrainer
 		* initiate_model_trainer method will read numpy array files for model training. 
 		* ModelFactory with is a inbuit library from ineuron used to find best model which are written in model.yaml located in config folder. it will also perform hypertunnig of given models. 
 		* It retruns f1, precision, recall score. 
 		* best model details will be stored in "model.pkl" file inside trained_model folder, which we later copy into model folder.
-	. Components/ModelEvaluation
+-  Components/ModelEvaluation
 		* it will read model which is already uploaded inside S3 bucket and perform model evaluation
 
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